Formatie verpleegkundigen

Beoordeeld: 16-10-2025

Uitgangsvraag

Wat is de benodigde formatie van IC-verpleegkundigen op de IC?

Aanbeveling

De formatie verpleegkundigen is minimaal 3,5 fte per operationeel bed. Minimaal 90% van deze 3,5 fte per bed bestaat uit gespecialiseerde IC-verpleegkundigen. Maximaal 10% van deze 3,5 fte per bed mag bestaan uit anders opgeleide verpleegkundigen (zoals MC-verpleegkundigen, CCU-verpleegkundigen of verpleegkundigen met een BAZ-certificaat). Evalueer een aanpassing in formatie per operationeel bed volgens de PDSA-cyclus op patiënt en medewerker niveau.

Naast patiëntgebonden taken zijn de volgende taken meegenomen in de minimale formatie:

klinisch onderwijs;
bij- en nascholing;
Zorgkwaliteitsbeleid.

De minimaal beoogde formatie per operationeel bed kan stijgen van 3,5 naar 4,2 fte wanneer er aanvullende taken zijn. Deze stijging in formatie kan ook gerealiseerd worden door de inzet van andere gespecialiseerde verpleegkundigen.

Voor de volgende aanvullende taken is extra formatie nodig:

reanimatieteam;
IC-practitioners;
regiefuncties;
nazorg aan patiënten en hun familie;
interdisciplinair en verpleegkundig wetenschappelijk onderzoek;
MICU-transport;
regio- en/ of ziekenhuisactiviteiten;
Informatie technologie.

De IC-verpleegkundige:bed ratio’s zijn 1:1,5 voor de dagdienst, 1:1,75 voor de avonddienst en 1:2 voor de nachtdienst.

ga hierbij uit van het aantal operationele bedden per verpleegkundige;
ongeacht de grootte van de IC zijn in elke dienst tenminste 2 IC-verpleegkundigen exclusief beschikbaar voor de IC.

Overweeg om de IC-verpleegkundige:bed ratio aan te passen naar een andere span of control die beter aansluit bij de populatie.

De ratio kan met maximaal 0,5 worden aangepast, wat resulteert in een variatie van 1:1 tot maximaal 1:2,5. Bij een verhoging van de ratio dient de vermindering van IC-verpleegkundigen te worden gecompenseerd door verpleegkundigen met een andere specialisatie die aansluit bij de zorgbehoefte van de populatie.

Indien de ratio wordt verhoogd, dient aangetoond te worden dat dit verantwoord is voor zowel patiënten als personeel.

Dit vereist een grondige onderbouwing en evaluatie volgens de PDSA-cyclus. Neem hierbij minimaal de volgende factoren mee:

patiënt gerelateerde factoren en uitkomsten: ziekte-ernst, zorgzwaarte, mortaliteit, patiënttevredenheid etc.;
medewerker gerelateerde factoren: werkplezier, werkdruk, verzuim, functiedifferentiatie, etc.

Overwegingen

(Samenvatting) resultaten

Een literatuuronderzoek onderzocht de impact van verschillende verpleegkundige-tot-patiënt-ratio’s op de IC. In totaal werden 23 observationele studies opgenomen, waarvan 4 vergelijkende studies die verschillende ratio’s evalueerden. Twee studies gericht op nachtdiensten bij electieve oesophagus- en leverchirurgie vonden een hogere mortaliteit en meer complicaties bij een ratio van 1:3 of 1:4 vergeleken met 1:1 of 1:2 (Amaravadi, 2000; Dimick, 2001). Een derde studie vond geen verschil in mortaliteit, maar wel in complicaties bij een ratio van 1:3 of 1:4 bij electieve aortachirurgie (Pronovost, 2001). De vierde studie rapporteerde een kortere opnameduur bij een ratio van 1:1 en een langere opnameduur bij 1:2, met tussenliggende waarden voor 1:2,5 en 1:3 (Blot, 2011). Deze studie suggereert mogelijk positieve resultaten voor een ratio van 1:2,5 of 1:3 vergeleken met 1:2. Een Nederlandse studie daarentegen vond een langere opnameduur bij een hogere ratio, waarbij in Nederland een maximale ratio van 1:2 geldt op de IC (Verburg, 2018). Een studie van Kim uit 2022 vond geen significant verschil in opnameduur bij ratio’s tussen 1:0,5 en 1:2.

De resultaten van de studie van Blot (2011) laten een significant verschil in opnameduur zien tussen de groepen met verschillende verpleegkundige-tot-patiënt ratio’s die werden onderzocht om de impact op VAP (Ventilator Associated Pneumonia) te beoordelen.

De hypothese is dat een hogere verpleegkundige bezetting leidt tot minder VAP-gevallen, gezien de vele en tijdsintensieve verpleegkundige interventies die nodig zijn voor VAP-preventie. Blot (2011) rapporteert de laagste VAP-incidentie bij een verpleegkundige-patiënt ratio van 1:1.

Andere observationele studies ondersteunen deze bevinding. Stone (2007) en Jansson (2019) tonen aan dat VAP minder vaak voorkomt bij een hogere verpleegkundige beschikbaarheid. Jansson beschrijft een significant minder voorkomen van VAP bij een ratio van 1:0,9-1,0 vergeleken met 1:1,2.

In de 19 niet-vergelijkende observationele studies uit Tabel 5 wordt de beschikbaarheid van IC-verpleegkundigen op diverse manieren onderzocht per unit, bed, patiënt of uur. Van deze 19 studies rapporteren er 11 over mortaliteit (Stone, 2007; Cho, 2008; Graf, 2010; Checkley, 2014; Talsma, 2014; West, 2014; Neuraz, 2015; Kim, 2018; Jansson, 2020; Duclos, 2023; Zhou, 2023). Vier studies (Stone, 2007; Graf, 2010; Talsma, 2014; Jansson, 2020) vonden geen verschil in mortaliteit bij verschillende verpleegkundige-patiënt ratio’s, terwijl zeven studies lagere mortaliteit rapporteerden bij hogere verpleegkundige bezetting (Cho, 2008; Checkley, 2014; West, 2014; Neuraz, 2015; Kim, 2018; Duclos, 2023; Zhou, 2023). Geen enkele studie meldde hogere mortaliteit bij meer verpleegkundigen.

De gerapporteerde ratio's en de beoordeling van onderbezetting variëren per studie. De hoogste ratio werd gevonden in de studie van Zhou (2023) in China, waar overdag 2-3 patiënten per verpleegkundige werden verzorgd en 3-5 in de avond/nacht. Bovenop deze ratio was er ook een hoofdverpleegkundige beschikbaar. Deze studie beschreef echter dat elke extra patiënt de mortaliteitskans verdubbelde. Drie grote Franse studies (Neuraz, 2015; Faisy, 2016; Duclos, 2023) laten zien dat een suboptimale bezetting, gedefinieerd als een ratio hoger dan 1:2,5 voor verpleegkundigen en 1:4 voor zorgassistenten, leidt tot een verhoogde kans op onverwacht overlijden (RR: 1,35) en ernstige complicaties zoals extubatie, hartstilstand, heropname en re-intubatie. In een systematisch review naar het effect van beschikbaarheid van verpleegkundigen op verschillende uitkomsten blijkt dat binnen de acute zorg voldoende bezetting leidt tot significante verbetering van de volgende uitkomsten: opname duur, maagulcus, gastritis, gastro-intestinale bloedingen, acuut myocard infarct, vrijheidsbeperkende middelen, failure to rescue, pneumonie, sepsis, urineweginfectie, decubitus, infecties, mortaliteit/30 dagen mortaliteit, en shock (Twigg, 2019). Een optimale, minimale of maximale ratio kan niet eenduidig worden bepaald gezien de variatie in context, de rol van IC-verpleegkundigen, opleidingsniveau, ervaring en patiëntpopulatie.

Zowel uit studies (Neuraz, 2015; Faisy, 2016; Zhou, 2023) als uit de praktijk blijkt dat de verpleegkundige-per-bed ratio hoger is dan de verpleegkundige-per-patiënt ratio, gezien de niet bezette bedden voor acute opnames. Zoals beschreven in module 7, is het essentieel dat 10-20% van de bedden niet bezet is maar wel operationeel voor acute opnames. De taakgroep formatie adviseerde om uit te gaan van het aantal patiënten per verpleegkundige, de werkgroep is van mening dat dit moeilijk uitvoerbaar is omdat er altijd beschikbaarheid van bedden moet zijn voor spoedopnames. Daarom is het advies om bij de planning van personeel uit te gaan van het werkelijke aantal operationele bedden. Ten overvloede: een operationeel bed is een bed waar de benodigde apparatuur aanwezig is en het benodigde personeel, ongeacht of het bed op dat moment bezet is door een patiënt. Zonder patiënt is het nog steeds een operationeel bed, zonder verpleegkundige is het geen operationeel bed meer. Het aantal operationele bedden kan variëren gedurende het jaar en gedurende de week. Gebruik voor het bepalen van de formatie het gemiddelde aantal operationele bedden.

Zorgzwaarte

Naast ratio per bed of per patiënt is het interessant om te kijken naar zorgzwaarte. Dit kan helpen de verpleegkundige werkdruk te kwantificeren en zo de zorgbehoefte en de daarmee samenhangende personeelsbehoefte te bepalen. Lee (2017) toont aan dat de overlevingskans stijgt bij een TISS-score van 40 en de mortaliteit toeneemt bij een score boven 52. Ook in Nederland is aangetoond dat de NAS (nursing activities score) geassocieerd is met mortaliteit (Hoogendoorn, 2020). Hoewel Hoogendoorn (2020) de NAS als beste meetinstrument identificeert uit 34 verschillende systemen, blijft de betrouwbaarheid en nauwkeurigheid suboptimaal en zijn bepaalde items verouderd. De NAS blijkt niet geassocieerd met ervaren werklast, die wel samenhangt met ernst van de ziekte van de patiënt en opleidingsniveau van de verpleegkundige (Margadant, 2021).

Bij het veranderen van de ratio is het belangrijk om te evalueren welke impact dit heeft op het uitvoeren van verpleegkundige taken. Enerzijds kan voldoende personele bezetting leiden tot het volledig uitvoeren van verpleegkundige taken terwijl onderbezetting juist kan leiden tot het missen van taken, welke impact kunnen hebben op zorgkwaliteit en patiënt uitkomsten. Alanzi (2023) toont aan dat lagere personele bezetting leidt tot meer gemiste taken en meer onbedoelde schade. Recent is de Missed Intensive Nursing Care Scale (MINCS) gevalideerd, wat mogelijk een geschikt instrument is om gemiste verpleegkundige taken op de IC te identificeren (Yang, 2024).

De taakgroep formatie heeft uitgangspunten voor de taken van IC-verpleegkundigen beschreven (Taakgroep formatie 2022). Er is een onderscheid gemaakt tussen primaire, secundaire en overige taken.

Primaire zorgtaken betreffen niet-uitstelbare patiëntgebonden zorgtaken, zowel voor patiënten die zijn opgenomen op de IC als daarbuiten.

Secundaire zorgtaken omvatten niet-patiëntgebonden activiteiten zoals:

klinisch onderwijs;
bij- en nascholing;
zorgkwaliteitsbeleid.

Voor de volgende taken is extra formatie nodig, zoals:

IC-practitioners;
regiefunctie;
nazorg aan patiënten en hun familie;
interdisciplinair en verpleegkundig wetenschappelijk onderzoek;
MICU-transport;
regio- en/ of ziekenhuisactiviteiten;
informatie technologie.

Op de IC worden soms patiënten opgenomen die niet volledig voldoen aan de definitie van een IC-patiënt (module Definitie van de intensive care patiënt). Voor deze patiënten kan de zorg, waar passend, worden verleend door niet-IC-gespecialiseerde verpleegkundigen. De werkgroep stelt dat dit maximaal 10% kan zijn. Denk hierbij aan een PACU-verpleegkundige voor een PACU-patiënt, een MC-verpleegkundige voor een MC-patiënt, een CCU-verpleegkundige voor een CCU-patiënt, of een verpleegkundige met een BAZ-certificaat voor laag-complexe patiënten. Dit betekent dat maximaal 10% van de benodigde formatie per operationeel IC-bed kan worden ingevuld door een andere gespecialiseerde verpleegkundige. Hierdoor ontstaat er ruimte binnen de IC-formatie, die kan worden benut voor de verdere ontwikkeling van de IC-verpleegkundige professie. Denk hierbij aan het creëren van regiefuncties, het opleiden van IC-practitioners en het stimuleren van onderwijs en onderzoek binnen de IC. Voor de zelfstandige uitvoering van zorgtaken binnen de IC-context lijkt een BAZ-certificaat een minimale vereiste. De verpleegkundige met een BAZ-certificaat kan daarna ook de opleiding vervolgen tot IC-verpleegkundige.

Waarden en voorkeuren van patiënten (en evt. hun verzorgers)

De patiënt verdient adequate (verpleegkundige) zorg op een IC, verleend door daartoe bevoegde en bekwame zorgprofessionals. Dit hangt samen met de beschikbare formatie, waarbij de vastgestelde uitgangspunten in een ratio verpleegkundige:operationeel bed te allen tijde gewaarborgd moet zijn. Belangrijk is dat er aandacht is voor het behoud van werkplezier in de organisatie. Daarnaast is het essentieel dat er voldoende formatie is voor nazorg aan patiënten en hun naasten.

Kosten (middelenbeslag)

Een hogere verpleegkundige-patiënt ratio leidt tot meer tijd voor de patiënten en mogelijk betere zorg. Dit leidt echter tot meer kosten. Een te lage ratio verpleegkundige-patiënt ratio leidt tot meer complicaties en meer burn-out. Op korte termijn lijkt dit wellicht goedkoper, maar op lange termijn kan dit leiden tot een toename in uitstroom of uitval, waardoor de kosten toenemen.

Uit de data van de NICE, uit de enquête die de werkgroep heeft gedaan onder ongeveer een kwart van de IC afdelingen in Nederland en uit visitatiegegevens blijkt dat de formatie verpleegkundigen op dit moment ook al 3,5 – 4,2 fte per IC bed is, conform eerdere aanbeveling.

Rationale van de aanbeveling: weging van argumenten voor en tegen de interventies

Op basis van de beschikbare literatuur is er geen bewijs dat een verandering van de IC-verpleegkundige-patiënt ratio de zorg verbetert. Diverse studies tonen zelfs aan dat de kwaliteit van zorg achteruitgaat of dat er geen significante verschillen zijn bij toename van het aantal patiënten per verpleegkundige. Daarom wordt de eerder geformuleerde norm in de blauwdruk van de NVIC en de aanbeveling van de taakgroep formatie overgenomen.

Indien er vanuit de praktijk behoefte is om deze ratio aan te passen naar een grotere span of control, vereist dit een grondige evaluatie op basis van ziekte van de patiënt, zorgzwaarte, werklast en functiedifferentiatie inclusief effectuitkomsten op patiënt- en teamniveau. De ratio kan hiermee met maximaal 0.5 worden aangepast, resulterend in een variatie van 1:1 tot maximaal 1:2,5. Bij een verhoging van de ratio dient de vermindering van IC-verpleegkundigen te worden opgevangen door verpleegkundigen met een andere specialisatie passend bij de populatie of een IC-verpleegkundige i.o. Voor primaire en secundaire zorgtaken is minimaal 3,5 fte gediplomeerd IC-verpleegkundige per operationeel bed beschikbaar waarbij minimaal twee dagen per jaar gereserveerd zijn voor gefaciliteerd onderwijs of congresbezoek buiten de instelling.

Voor de ontwikkeling van het IC-verpleegkundige domein en het behouden van IC- verpleegkundigen voor het vakgebied, is het essentieel dat zij de mogelijkheid krijgen om onderwijs buiten hun instelling te volgen. Facilitering in middelen en tijd is hierbij van essentieel belang. De opgedane kennis kan desgewenst gedeeld worden met het hele team in klinisch onderwijs. Tevens kunnen IC verpleegkundig onderzoekers en IC-practitioners een belangrijke bijdrage leveren aan de ontwikkeling van de IC zorg. Voor deze overige taken is extra formatie nodig. Richtinggevend kunnen dan de cijfers uit een eerdere leidraad zijn van 4,2 fte IC-verpleegkundige per operationeel bed, waarbij deze stijging ook kan worden gerealiseerd door de inzet van andere gespecialiseerd verpleegkundigen. Deze stijging in formatie is eveneens wenselijk om te kunnen voldoen aan opschalingseisen en pandemische weerbaarheid.

Onderbouwing

Achtergrond

De kwaliteit van de IC zorg hangt onder andere af van de tijd die de IC-verpleegkundige kan besteden aan de zorg voor de IC-patiënt. Kritiek zieke patiënten op de IC hebben bij uitstek veel zorg nodig. Deze complexe en specifieke zorg, zoals monitoring en orgaanondersteuning, vraagt om gespecialiseerde IC-verpleegkundige zorg. De complexiteit maakt dat IC-verpleegkundigen een beperkt aantal patiënten per dienst kunnen verzorgen. Er zijn veel studies verricht naar het verband tussen de inzet van verpleegkundigen op een afdeling en de kwaliteit van zorg. De inzet van meer en hoger opgeleide verpleegkundigen verbetert doorgaans de uitkomsten (Dall’ora, 2022; Almenyan, 2021; Bruyneel, 2024). Het is wel lastig gebleken om de optimale verpleegkundige:patiënt ratio vast te stellen. In de blauwdruk van 2021 is door de NVIC een formatie eis vastgelegd. Eind 2022 heeft de NVIC Taakgroep Formatie op verzoek van het bestuur van de NVIC een advies uitgebracht over de formatie eisen en geconcludeerd dat een grote herziening van de eisen niet nodig was. De aanbeveling bleef een verpleegkundige formatie van 3,5-4,2 fte IC-verpleegkundige per IC bed, gebaseerd op een IC-verpleegkundige:bed-verhouding van 1:1,5 in de dagdienst, 1:1,75 in de avond dienst en 1:2 in de nachtdienst. Het vraagstuk rondom inzetratio’s blijft actueel; enerzijds of het passend is voor de huidige situatie met vergrijzing en tekorten en anderzijds vraagt de ontwikkeling van het vak, in relatie tot werkdruk en kwaliteit van zorg, om een borging van beschikbare tijd voor onderwijs, scholing en wetenschappelijk onderzoek. Ten aanzien van dit vraagstuk werd de literatuur verkend met betrekking tot de relatie tussen de verpleegkundige:patiënt of bed ratio en de mortaliteit (IC en ziekenhuis), opnameduur (IC en ziekenhuis), morbiditeit en heropname op de IC en patiënttevredenheid.

Conclusies / Summary of Findings

Very low GRADE

The evidence is very uncertain for the effect of nurse-to-patient ratio X compared with nurse-to-patient ratio Y on ICU and hospital mortality in adult ICU patients.

Sources: Amaravadi, 2000; Dimick, 2001; Pronovost, 2001

Very low GRADE

The evidence is very uncertain for the effect of nurse-to-patient ratio X compared with nurse-to-patient ratio Y on ICU and hospital length of stay in adult ICU patients.

Sources: Amaravadi, 2000; Dimick, 2001; Pronovost, 2001; Blot, 2011

Very low GRADE

The evidence is very uncertain for the effect of nurse-to-patient ratio X compared with nurse-to-patient ratio Y on morbidity in adult ICU patients.

Sources: Amaravadi, 2000; Dimick, 2001; Pronovost, 2001; Blot, 2011

No GRADE

No evidence was found for the effect of nurse-to-patient ratio X compared with nurse-to-patient ratio Y on ICU readmission in adult ICU patients.

No GRADE

No evidence was found for the effect of nurse-to-patient ratio X compared with nurse-to-patient ratio Y on patient satisfaction in adult ICU patients.

Samenvatting literatuur

Description of studies

Four observational studies that compared the effect of different nurse-to-patient ratios were included in the literature analysis. Relevant study characteristics are presented in Table 1. The results of the 19 non-comparative observational studies are described in Table 5.

Table 1. Study characteristics

Study	Design, country	Type of ICU patients	Patients, N, age, %M	NPR measure	Intervention	Comparison	Outcomes reported
Amaravadi, 2000	Retrospective cohort study of 35 (staffing data for 32) hospitals, Maryland, US	Patients admitted to ICU after esophageal resection	366 (353) patients (225/128), mean age 60/63 years, % male 79/77	Nighttime nurse-to-patient ratio	1 nurse cares for 1 or 2 patients: NNPR ≥ 1:2	1 nurse cares for 3 or more patients: NNPR < 1:2	Hospital mortality, length of hospital stay, postoperative complications
Dimick, 2001	Retrospective cohort study of 35 (staffing data for 33) hospitals, Maryland, US	Patients admitted to ICU after hepatic resection	569 (556) patients (316/240), mean age 56/57 years, % male 51/55	Nighttime nurse-to-patient ratio	More nurses: NPR 1:1 or 1:2	Fewer nurses: NPR 1:3 or 1:4	Hospital mortality, length of hospital stay, postoperative pulmonary complications
Pronovost, 2001	Retrospective cohort study of 46 (staffing data for 38) hospitals, Maryland, US	Patients admitted to ICU after abdominal aortic surgery	2,128/478 patients, mean age 68/68 years, 69/66% men	ICU nurse staffing during the day	More nurses: NPR 1:1 or 1:2	Fewer nurses: NPR 1:3 or 1:4	Inpatient mortality, length of ICU and hospital stay, postoperative complications
Blot, 2011	Prospective cohort study, 27 ICU’s in 9 European countries	All patients who were admitted to the ICU for treatment of pneumonia or received invasive mechanical ventilation for more than 48 hours, irrespective of the admission diagnosis	1,658 patients (1,066/592), median age 59/69 years, %male 64/61	Routine staffing levels; nurse-to-patient ratio that is standard in a particular ICU	patient-to-nurse ratio ≤ 2:1	patient-to-nurse ratio > 2:1	Length of ICU and hospital stay, VAP

^{ICU = intensive care unit, (N)NPR = (nighttime) nurse-to-patient ratio, , VAP = ventilator-associated pneumonia}

Results

Mortality

Amaravadi (2000) reported the unadjusted hospital mortality rate. The unadjusted mortality rate was 5.6% in the group with an NNPR of ≥ 1:2 compared with 15% in the group with an NPR of < 1:2. The odds ratio (OR) reported in the multivariate analysis was 0.7 (95% CI 0.3 to 2.0) after adjusting for other univariate predictors of mortality, which is considered clinically relevant in favor of the group with an NNPR of ≥ 1:2 (more nurses).

Dimick (2001) reported the unadjusted in-hospital mortality rate. The mortality rate was 2.5% in the group with more nurses compared with 7.1% in the group with fewer nurses. The unadjusted OR was 0.34 (95% CI 0.15 to 0.49). The for demographic factors, comorbid disease, severity of illness, type of procedure, hospital volume, and surgeon volume adjusted OR was 0.49 (95% CI 0.18 to 1.29), which is clinically relevant in favor of the group of patients with more nurses.

Pronovost (2001) reported the inpatient mortality rate. The mortality rate was 7% (95%CI 6.0 to 8.1) in the group with an NPR of 1:1 or 1:2 compared with 8% (95%CI 6.0 to 11.2) in the group with an NPR of 1:3 or 1:4. The difference of 1% is not considered clinically relevant.

Blot (2011) did not report the outcome mortality.

Length of stay: ICU

Pronovost (2001) reported median length of ICU stay in days. The median length of stay in the ICU was 2 days (range 0 to 118) for the group with an NPR of 1:1 or 1:2 compared with 3 days (range 0 to 112) for the group with an NPR of 1:3 or 1:4. The difference of 1 day is clinically relevant in favor of the group with an NPR of 1:1 or 1:2 (more nurses).

Blot (2011) reported the median length of ICU stay in days. The median length of stay in the ICU was 12 days (IQR 6 to 22) for the group with a patient-to-nurse ratio of ≤ 2:1 compared with 11 days (IQR 6 to 20) for the group with a patient-to-nurse ratio of > 2:1. The difference of 1 day is clinically relevant in favor of the group with a PNR of > 2:1 (fewer nurses).

Amaravadi (2000) and Dimick (2001) did not report the outcome ICU length of stay.

Length of stay: hospital

Amaravadi (2000) reported median length of hospital in days. Median length of stay in the hospital was 9 days (IQR 1.8 to 13) for the group with an NNPR of ≥ 1:2 compared with 15 days (IQR 11 to 27) for the group with an NNPR of < 1:2. The difference of 6 days is clinically relevant in favor of the group with an NNPR of ≥ 1:2 (more nurses).

Dimick (2001) reported median length of hospital stay in days. For patients with more ICU nurses the median length of hospital stay was 7 days (IQR 6 to 10 days) and for patients with fewer ICU nurses the median length of hospital stay was 8 days (IQR 6 to 12 days). This difference is not considered clinically relevant.

Pronovost (2001) reported median length of hospital stay in days. Median length of stay in the hospital was 8 days (range 0 to 171) for the group with an NPR of 1:1 or 1:2 compared with 8 days (range 0 to 130) for the group with an NPR of 1:3 or 1:4. This difference is not clinically relevant.

Blot (2011) reported the median length of hospital stay in days. Median length of stay in the hospital was 22 days (IQR 12 to 42) for the group with a patient-to-nurse ratio of ≤ 2:1 compared with 17 days (IQR 9 to 31) for the group with a patient-to-nurse ratio of > 2:1. The difference of 5 days is clinically relevant in favor of the group with a PNR of >2:1 (fewer nurses).

Morbidity

Amaravadi (2000) reported multivariate associations of postoperative complications and nighttime nurse-to-patient-ratios, see Table 2. Patients with an NNPR < 1:2 had an increased risk of reintubation, pneumonia and septicemia.

Table 2. Associations between postoperative complications and NNPR

Complication	NNPR ≥ 1:2	NNPR < 1:2	OR (95%CI)
Pneumonia	8%	16%	2.4 (1.2 to 4.7)
Reintubation	12%	25%	2.5 (1.4 to 4.5)
Aspiration	22%	25%	1.2 (0.7 to 2.0)
Septicemia	1.8%	6.2%	3.7 (1.1 to 12.5)
Postoperative infection	4%	5.5%	1.4 (0.5 to 3.8)
Myocardial infarction	0.9%	0.8%	0.9 (0.08 to 9.7)
Cardiac arrest	0%	0.8%	1.2 (0.6 to 2.2)
Surgical complications	8%	17%	1.9 (0.9 to 3.8)
Acute renal failure	2.7%	5.5%	2.1 (0.7 to 6.4)

Dimick (2001) reported univariate associations of nighttime nurse staffing and postoperative pulmonary complications, see Table 3. In the multivariate analysis however, only reintubation remained significantly associated with fewer nurses at night.

Table 3. Postoperative complications associated with nighttime nurse staffing

Complication	More nurses (NPR 1:1 or 1:2), n=316	Fewer nurses (NPR 1:3 or 1:4), n=240	OR (95%CI)
Pneumonia	2.8%	4.2%	1.4 (0.6 to 3.5)
Reintubation	1.9%	10.8%	5.7 (2.4 to 13.7)
Pulmonary failure	1.6%	5.8%	3.6 (1.3 to 10.1)
Aspiration	12.0%	7.5%	0.62 (0.4 to 1.1)
Septicemia	2.7%	5.4%	NR
Postoperative infection	2.9%	3.0%	NR
Cardiac arrest	0.6%	0.8%	NR
Myocardial infarction	6.6%	1.2%	NR
Acute renal failure	14.6%	4.2%	NR

Pronovost (2001) reported crude and adjusted relative risks for several medical and surgical complications, see Table 4. In the multivariate analysis adjusted for patient characteristics and hospital and surgeon volume, having fewer nurses was associated with an increased risk for any complication, any medical complication, pulmonary insufficiency after procedure, and reintubation.

Table 4. postoperative complications

Complication	Hospitals with fewer ICU nurses	Hospitals with more ICU nurses	Crude RR (95% CI)	Adjusted RR (95% CI)¹
Any complication	47%	34%	1.4 (1.2 to 1.5)	1.7 (1.3 to 2.4)
Medical complications
Any medical complication	43%	28%	1.5 (1.4 to 1.7)	2.1 (1.5 to 2.9)
Pulmonary insufficiency after procedure	24%	9%	2.6 (2.1 to 3.2)	4.5 (2.9 to 6.9)
Reintubation	21%	13%	1.5 (1.3 to 1.8)	1.6 (1.1 to 2.5)
Cardiac complications after procedure	15%	10%	1.4 (1.1 to 1.7)	1.3 (0.8 to 1.8)
Acute renal failure	6%	4%	1.3 (0.8 to 1.9)	1.6 (0.9 to 2.7)
Septicemia	4%	3%	1.4 (0.8 to 2.1)	1.9 (0.9 to 3.9)
Acute myocardial infarction	4%	3%	1.2 (0.8 to 2.4)	1.5 (0.9 to 2.2)
Cardiac arrest	2%	1%	1.4 (0.6 to 3.0)	1.7 (0.7 to 4.7)
Surgical complications
Any surgical complication	10%	11%	0.9 (1.6 to 1.4)	0.7 (0.4 to 1.5)
Surgical complications after procedure	8%	9%	0.9 (0.6 to 1.2)	1.0 (0.6 to 1.4)
Surgical E codes	1%	0%	2.2 (0.4 to 10.5)	Insufficient data
Reoperation for bleeding	2%	3%	0.8 (0.4 to 1.6)	1.2 (0.4 to 3.5)

_{¹adjusted for patient characteristics, hospital volume and surgeon volume}

Blot (2011) reported the outcome ventilator-associated pneumonia (VAP). VAP developed in 393 of the 1,658 patients (23.7%) during their ICU stay; 220 of the patients with VAP had late-onset VAP (13.3%). In the group of patients with a patient-to-nurse ratio ≤ 2:1 262 patients (24.6%) developed VAP compared with 131 patients (22.1%) with a patient-to-nurse ratio > 2:1. This difference is not clinically relevant.

ICU readmission

No results could be reported because none of the included studies reported the outcome ICU readmission.

Patient satisfaction

No results could be reported because none of the included studies reported the outcome patient satisfaction.

Level of evidence of the literature

The level of evidence regarding the outcome measure mortality started at low (observational studies) was downgraded to very low because of study limitations (risk of bias).

The level of evidence regarding the outcome measure length of stay started at low (observational studies) was downgraded to very low because of conflicting results (inconsistency).

The level of evidence regarding the outcome measure morbidity started at low (observational studies) was downgraded to very low because of study limitations (risk of bias).

The level of evidence regarding the outcome measure ICU readmission could not be determined because none of the included studies reported the outcome measure.

The level of evidence regarding the outcome measure patient satisfaction could not be determined because none of the included studies reported the outcome measure.

Table 5. Results of the non-comparative observational studies

First author, year

Definition variable ‘nurse-patient ratio’ / nurse-workload ratio’

Analysis, confounders

Results

Stone, 2007

Observational study, with patient outcome data collected using the National Nosocomial Infection Surveillance system protocols and Medicare files.

Elderly Medicare ICU patients (>65 years); 15,846 patients in 51 adult intensive care units in 31 hospitals; United States.

Staffing: registered nurse hours per patient day, in quartiles (higher quartiles = more RN hours per patient day)

Multivariate logistic regressions were constructed for each outcome. Robust variance estimators (Huber–White) were calculated and analyses were clustered at the hospital level to allow for an arbitrary variance– covariance matrix, adjusted odds ratios (OR) and 95% confidence intervals (CI) were examined.

All models are adjusted for a comprehensive set of (1) patient characteristics, including severity of illness, comorbidities, demographics, and socioeconomic status, and (2) setting characteristics, including hospital size and teaching status and ICU type and case-mix.

30-day mortality (n=15,846)

The average 30-day mortality rate was 22% (3,185 of 15,846)

Adjusted OR (95% CI)

Second quartile: 0.89 (0.77–1.02)

Third quartile: 0.81 (0.69–0.95)

Fourth quartile: 0.89 (0.76–1.05)

VAP (n=5,462)

Overall rate: VAP 1.5% (81 of 5,462)

Adjusted OR (95% CI)

Second quartile: 0.71 (0.43–1.19)

Third quartile: 0.68 (0.39–1.21)

Fourth quartile: 0.21 (0.08–0.53)

Cho, 2008

Observational study, retrospective;

Using survey and administrative databases, this study included 27,372 ICU patients discharged from 42

tertiary and 194 secondary hospitals; Korea.

Staffing of RNs was quantified as the ratio of average daily census (ADC) to the total number of full-time equivalent (FTE) RNs in ICUs, termed the ADC/RN ratio, by dividing ADC by the number of fte RNs.

The RN staffing included not only staff nurses but also head nurses who would have no direct responsibility for patient care.

Data were treated as having a two-tiered structure to use multilevel analysis. The first tier was the hospital level, in which the variables of hospital and ICU characteristics were aggregated. The second tier was the patient level where patient characteristics were measured.

Using the patient as the unit of analysis, all variables of the two levels were included simultaneously into the regression model. This multilevel modeling allowed simultaneous examination of the effects of nurse staffing, ICU, hospital, and patient characteristics on mortality. Tertiary and secondary hospitals were analyzed separately under the assumption that they treated groups of patients with a different level of illness severity, clinical features, and ICU utilization patterns, including admission and discharge policies.

Patient characteristics: mortality, age, gender, source of payment, primary diagnosis, and comorbid disease were used for risk adjustment.

Mortality

ADC/RN ratio, Adjusted OR (95%CI)

Tertiary hospitals: 0.54 (0.22-1.33)

Secondary hospitals: 1.43 (1.16-1.77)

This OR of 1.43 indicates that every additional patient per RN (i.e., an increase of 0.233 in the ADC/RN ratio) was associated with a 9% increase in the odds of death (OR = 1.09, 95%CI = 1.04-1.14). Two and three additional patients would be accompanied by 18% and 29% increases in mortality, respectively.

Graf, 2010

Observational study; data were collected prospectively on a cross-sectional (one-day) basis in a representative random sample of German hospitals. The final data set comprised information on 454 ICUs and 310 hospitals.

Nurse staffing (number of patients a nurse was responsible for)

For the analysis of a potential association between structural characteristics or associated processes of the ICU with the outcome (in-hospital mortality) of patients with severe sepsis or septic shock, multiple hypotheses testing was performed.

“For all patients with severe sepsis and septic shock we tested the hypothesis whether structural characteristics or associated processes of the ICU are related to outcomes,

i.e., in-hospital mortality. We neither found any significant association with nurse staffing, physician presence, size of hospital or ICU, nor with diagnostic measures or applied therapeutic interventions, after correction for multiple hypothesis testing.”

Checkley, 2014

Observational study; 69 ICU’s participating in the United States Critical Illness and Injury Trials Group Critical Illness Outcomes Study (USCIITG-CIOS) were surveyed; United States

bed-to-nurse ratio

The multivariable linear regression model included the following variables: average APACHE II score, ICU type, case volume, bed capacity, 24-hour intensivist coverage, bed-to-nurse ratio, trainee-to-bed ratio, ICU organization (open vs. closed), computerized order entry, daily plan of care review, multidisciplinary rounding, and use of protocols guiding management of electrolytes, mobility, codes, neuroprotection, delirium, and transfusions.

Annual ICU Mortality

Bed-to-nurse ratio (per 1:1 unit increase)

% difference in annual ICU mortality (95% CI):

The adjusted annual ICU mortality was lower among ICUs that had a lower bed-to-nurse ratio

(1.8% lower when the ratio decreased from 2:1 to 1.5:1; 95% CI 0.25%–3.4%).

Talsma, 2014

Observational study; A 3-year (2003-2005) multisite study was designed to include all acute care cases that met the inclusion criteria of the AHRQ Patient Safety Indicator (PSI) FTR; Southeast Michigan, United States

Nurse staffing data included total nursing direct HPPD,

RN HPPD, and RN staffing mix: the proportion of registered nurses (RNs) on the unit.

HPPD = Nursing Hours Per Patient Day

Multilevel analyses were used to take into account the hierarchical structure of the database: patients clustered on nursing units, which are clustered within hospitals. Because the

outcome variable (FTR rate) was defined as dichotomous, a

multilevel logistic model was used. We were interested in examining the effect of nurse staffing measures (unit characteristics) on FTR rate, controlling for patient demographic and clinical

conditions (patient characteristics), and other unit characteristics.

Patient mortality because of complications (FTR, failure to rescue)

OR (95% Wald CI)

HPPD: 1.015 (0.935, 1.102)

RN_HPPD: 1.031 (0.942, 1.130)

RN_MIX: 1.037 (0.976, 1.101)

The findings for the ICU discharges showed no significant associations between increased HPPD, RN_HPPD, and RN-mix and reduced FTR.

West, 2014

Observational study; Data for the six months before and after March 1998, which had been collected prospectively, were merged onto organizational data on 65 ICUs surveyed by the Audit Commission.

The matched dataset contained information only on ICUs in England.

Number of nurses per bed: This variable counts the

number of full-time equivalent nurses on the permanent staff of the ICU on one specific date (the date of the Audit Commission survey). The question on the survey asked for separate information on registered nurses and health care assistants. The variable used in these analyses is a count of the registered nurses at different grades who were in post on the census date. It is important to note that this is not the number of nursing staff available for duty when any particular patient is admitted.

Two separate variables: the number of direct care nurses and the number of supernumerary nurses.

Multilevel logistic regression was used to perform all the analyses.

Risk adjustment based on ICNARC score (physiology model, including blood pressure, respiratory rate, oxygenation, and acid base disturbance, along with a range of other factors known to be associated with mortality, including age, past medical history, and source of admission to an ICU).

ICU mortality

Number of direct care nurses per bed, OR from multilevel logistic regression models [95%CI]

Model 1: 0.90 [0.84,0.97]

Model 2*: 0.90 [0.83,0.97]

Model 3*: 0.90 [0.83,0.97]

Mortality in acute hospital

Model 1: 0.92 [0.87,0.98]

Model 2*: 0.92 [0.86,0.98]

Model 3*: 0.92 [0.86,0.98]

*Further, as we believe that the effect of staffing might depend on the severity of a patient’s illness; in the second column we add an interaction between the number of nurses and predicted log odds of mortality, while in

column 3 we add an interaction between the number of consultants and predicted log odds of mortality.

The most significant findings are that, controlling for patient characteristics and the workload of the unit, higher numbers of nurses per bed on the unit’s establishment and higher numbers of consultants per bed were both

associated with higher survival rates.

Neuraz, 2015

Multicenter longitudinal study using routinely collected hospital data, January to December 2013; 8 ICU’s from 4 university hospitals in Lyon, France; 5,718 inpatient stays.

Patient-to-nurse (P/N) ratio by shift in five categories:

(2:1 meaning two patients for one nurse).

To control for potential confounding variables, patients’ characteristics were a priori selected as clinically important covariates. The proportion of surgical cases versus medical cases was used to adjust on the type of patient case-mix admitted to ICU.

The final multivariate model included the following variables: P/N, P/P (patients/physician) and residents-to-physicians ratios, patient turnover, number of LSP, proportion of men, proportion of surgical cases, SAPSII, and number of comorbidities.

Mortality:

The primary outcome was mortality at time of ICU discharge by shift, excluding patients for whom a DFLST (decision to not forego life sustaining therapy) was made.

The fully adjusted model, taking into

account both staffing and workload levels, showed an increased risk of mortality, with the highest values for P/P and P/N. The ICU risk of death increased by a factor of 3.5 (1.3–9.1) when the number of patients was above 2.5 per nurse.

Faisy, 2016

Prospective, observational, dynamic cohort study; January 2006 to December 2013; a 20-bed adult medical intensive care unit of a tertiary teaching hospital in France

Bed-to-nurse ratio

Negative binomial regression for over-dispersed count outcome variables was then used to model the rate of severe adverse events because of the spread of severe adverse events over time. In the univariate and multivariate analyses, covariates were adjusted by the bed-to-nurse ratio, which reflects nursing workload and intensive care unit activity (Massey et al., 2009), thereby limiting confounding factors. Bed-to-nurse ratio was preferred to patient-to-nurse ratio because of the monthly changes in nurse staff and bed availability. In addition, an offset has been included in the model (volume of intensive care unit activity on the basis of the number of billable journeys) because the higher the activity the higher the risk of adverse events.

Severe adverse events:

Incidence rate ratio (95% CI)

Univariate: 1.28 (0.99–1.66)

Multivariate: 1.36* (1.05–1.75)

*Indicates the estimated incidence rate ratio for a one-unit increase in the bed-to-nurse ratio. Thus, if the bed-to-nurse ratio was to increase by one point, the monthly rate for severe adverse events would be expected to increase by a factor of 1.36, i.e., 36%

Lee, 2017

Retrospective analysis of prospectively collected data

Adult patients admitted to two multi-disciplinary Intensive Care Units; Hong Kong

Workload/nurse ratio:

Nursing workload (TISS-score) / average number of bedside nurses

Pearson’s r was used to test for co-linearity between TISS and workload-to-staffing ratio.

The lower 90% confidence interval crosses zero when the workload/staffing ratio is 40. This indicates that there is more than 95% probability that survival to hospital discharge is more likely to occur when the maximum workload to-nurse ratio is 52.

Outcome: Survival

Comparison: Workload/staffing < 40 vs. 40 or higher

APACHE III score of 60

OR 2.28, 95% CI 1.07–4.80

APACHE III score of 70-130

No sign difference between < 40 vs. 40 or

higher

APACHE III >130

OR 0.24, 95% CI 0.09–1.01

Kim, 2018

Observational;

Retrospective database study

Patients admitted with cardiovascular (CV) disease;

Study data were obtained from National Health Insurance Service-Senior (NHIS-Senior) claim database from 2002 to 2013 which was released by the Korean National Health Insurance Service (KNHIS).

Nurse staffing*:

nurse staffing grades were based on the nurse-to-bed ratio.

The highest nurse staffing grade was grade 1 (beds/ nurse ratio <0.5), with the lowest nurse staffing grade being grade 9 (beds/nurse ratio ≥2.0). Level of nurse staffing was categorized into 4 groups in each year: grade 1 to 2, grade 3 to 4, grade 5 to 6, and grade 7 to 9.

Cox proportional hazards models were used to investigate the association between nurse staffing and mortality; adjusted for all confounders.

Tertiary hospital; per level of nurse staffing*

30-day mortality after discharge

Grade 1–2: HR 1.000 (ref)

Grade 3–4: HR 1.038 (SE 0.120); p=.755

Grade 5–6: HR 1.382 (SE 0.323); p=.316

Grade 7–9: HR 0.967 (SE 0.106); p=.752

In-hospital 30-day mortality

Grade 1–2: HR 1.000 (ref)

Grade 3–4: HR 1.127 (SE 0.124); p=.333

Grade 5–6: HR 1.171 (SE 0.358); p=.658

Grade 7–9: HR 0.998 (SE 0.112); p=.982

General hospital; per level of nurse staffing

30-day mortality after discharge

Grade 1–2: HR 1.000 (ref)

Grade 3–4: HR 1.367 (SE 0.159); p=0.049

Grade 5–6: HR 1.353 (SE 0.180); p=0.093

Grade 7–9: HR 1.499 (SE 0.156); p= 0.010

In-hospital 30-day mortality

Grade 1–2: HR 1.000 (ref)

Grade 3–4: HR 1.277 (SE 0.160); p=.126

Grade 5–6 : HR 1.233 (SE 0.183); p=.250

Grade 7–9 : HR 1.377 (SE 0.157); p=.042

Verburg, 2018

Observational study, retrospective; data from the Dutch National Intensive Care Evaluation (NICE) registry.

78,822 admissions, 38 ICU’s; the Netherlands

Full-time equivalent ICU nurses

Mixed effects regression models; examined the association between ICU characteristics available in the NICE registry and ICU LoS, after correcting for patient characteristics.

ICU length of stay

Models including a single ICU characteristic

Full-time equivalent ICU nurses, coefficient (95%CI): -0.017 (−0.021 to −0.013)

Final model including multiple ICU characteristics

Full-time equivalent ICU nurses, coefficient (95%CI): −0.030 (−0.034 to −0.025)

The coefficients represent the change in log transformed intensive care unit length of stay associated with the characteristic.

We found that the ICU LoS increased as the number of ICU nurses decreased.

Jansson, 2019

prospective, observational cohort study

consecutive adult patients who were admitted to the mixed medical-surgical ICU and received invasive ventilation over 48 hours were recruited and monitored daily for the development of VAP until ICU discharge or death

900-bed tertiary-level teaching hospital; adult, closed, mixed medical-surgical ICU with 22 beds (four 1-bed rooms, three 2-bed rooms, four 3-bed rooms), Finland

Daily N/P ratio:

dividing the total number of nurses by the total number of patients for each calendar day.

ICNSS = Intensive Care Nursing Scoring System à nurse workload

Receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to determine the associations between nurse staffing and workload with VAP and mortality

N/P ratio

Lowest

Patients without VAP: 1.0 (1.0-1.1)

Patients with VAP: 1.0 (0.9-1.0)

P= 0.006*

AUC: 0.3 (0.2-0.4)

Median

Patients without VAP: 1.2 (1.2-1.3)

Patients with VAP: 1.2 (1.2-1.3)

Survivor: 36.0 (33.0-39.0)

Non-survivor: 38.0 (34.0-41.8)

P=0.09

AUC: 0.6 (0.5-0.8)

Median

Survivor: 30.0 (28.1-32.0)

Non-survivor: 31.0 (30.0-34.0)

P=0.03*

AUC: 0.7 (0.5-0.8)

Kim, 2019

Observational retrospective study; using NHI claim data on patient and hospital characteristics for 2140 patients undergoing craniotomy or percutaneous angioplasty from January to December 2009; Korea

The NHI claim data quantified nurse staffing levels using the nursing grade, which is based on the nurse-to-bed ratios in general wards and the ICUs; Nurse-to-bed ratio converted to nurse-to-patient ratio using an occupancy rate of 86%.

ICU nurse staffing level:

- major adherence

- adherence

- violation

Logistic regression applied with a generalized estimation model in order to adjust clustered data was used to analyze the associations between the nurse staffing level and survival after cardiac arrest. The same analysis was performed for the hospital type. Hospitals and general hospitals were categorized into one group, with tertiary hospitals classified separately.

Patient survival

Patients who were cared for in tertiary hospitals with major adherence ICUs nurse staffing were 2.35-fold more likely to survive than those in tertiary hospitals with adherence nurse staffing (95% CI = 1.27–4.36). The patient survival rate after cardiac arrest did not differ significantly between violation nurse staffing and adherence nurse staffing in general wards in tertiary hospitals.

Jansson, 2020

Cross-sectional study in a single tertiary-level teaching hospital during

2008–2017; 900-bed tertiary-level teaching hospital in Finland.

All admissions were identified from the hospital database. Patients were eligible for inclusion if they were adults (≥18 years), were admitted to the ICU between 1 January 2008 and 31 December 2017 (N = 13,720) and had complete data sets regarding nurse staffing and nursing workload. Because our focus was on high-risk critically ill patients, patients with low-risk elective surgery (e.g. cardiac surgery or neurosurgery) were excluded to reduce case mix heterogeneity. In total, 10,230 patients met the inclusion criteria and were included for further analysis

The level of nurse staffing was recorded by collecting the total number of nurses and patients throughout each calendar day (i.e. morning, evening and night shifts). The daily N/P ratio was determined by dividing the total number of nurses by the total number of patients for each calendar day. Only the daily lowest N/P ratios for each calendar day were considered. The daily ICNSS index was determined by dividing the sum of nurses needed by the sum of available nurses during each day. Only the daily highest indexes for each calendar day were considered.

Additionally, multivariable linear regression models were used to get adjusted results between MOF (no/yes), hospital mortality (no/yes) and a subgroup of MOF patients (early- vs. late-stage MOF) for TISS scores, ICNSS scores, N/P ratios and ICNSS indexes. Age, gender, APACHE II scores, admission type (emergency/elective), surgery (no/yes) and N/P ratios were used as adjustable variables, except for the N/P ratios for the models of the N/P ratio itself and the ICNSS index. The results for the Student's t test and linear regression model are presented as the difference between means with a 95% confidence interval (95% CI)

Shifts were categorized as understaffed (yes/no) if they had N/P ratios <1 and ICNSS indexes >1 and a shift's adjusted impact on MOF and hospital mortality was calculated using a multivariable logistic regression model. Age, gender, APACHE II score, admission type (emergency/elective) and surgery (no/yes) were used as adjustable variables. The results of the logistic regression model are presented as an odds ratio (OR) with a 95% CI.

Multiple organ failure (MOF):

In the subgroup analysis, the mean daily lowest N/P ratio prior to MOF was lower in patients with late-stage than those with early-stage MOF. In addition, the mean daily highest ICNSS

index was higher in patients with late-stage MOF. The proportion of N/P ratio <1 and ICNSS index >1 was significantly more common in

patients with MOF than in those without. In the subgroup analysis, the proportion of N/P ratio <1 and ICNSS index >1 was significantly

more common in patients with late-stage than those with early-stage MOF. The proportion of understaffing did not differ between survivors and non-survivors.

N/P ratio < 1

4,612 of 8,204 (56.3%) patients without MOF; 1,578 of 2,026 (77.9%) patients with MOF.

Adjusted OR=2.59 (2.29 to 2.92).

In-hospital mortality

The AUC values for the mean daily lowest N/P ratios for in-hospital mortality were 0.51 (95% CI 0.47–0.54) in patients with early-stage MOF and 0.46 (95% CI 0.38–0.54) in patients with late-stage MOF respectively.

Ding, 2022

Observational;

Retrospective database study;

The data in this study were collected between January 1, 2019, and December 31, 2019.. The data source was the National Clinical Improvement System ((https://ncisdc.medidata.cn/login.jsp), collected by the China-National Critical Care Quality Control Center (China-NCCQC), which is the official national department that regulates ICU quality control in China.

patient-to-bed ratio (calculated by the total number of ICU patients divided by the number of beds in the ICU),

physician-to-bed ratio (calculated by the total number of ICU physicians divided by the number of beds in the ICU),

nurse-to-bed ratio (calculated by the total number of ICU nurses divided by the number of beds in the ICU),

patient-to-physician ratio (calculated by the total number of ICU patients divided by the number of ICU physicians),

patient-to-nurse ratio (calculated by the total number of ICU patients divided by the number of nurse).

Poisson regression analysis (generalized linear model for count data)

VAP incidence rate (β (95% CI), p-value)

Nurse-to-bed ratio: -0.146 (-0.229, -0.063), 0.0006

Patient-to-nurse ratio: -0.015 (-0.019, -0.011), <0.0001

VAP mortality (β (95% CI), p-value)

Nurse-to-bed ratio: 0.038 (-0.17, 0.246), 0.7186

Patient-to-nurse ratio: -0.002 (-0.014,0.009), 0.6918

Structural factors associated with lower ICU VAP incidence rate included patient-to-bed ratio (β=−0.002 (−0.004,−0.001), p=0.0126), nurse-to-bed ratio (β=−0.146 (−0.229,−0.063), p=0.0006), patient-to-nurse ratio (β=−0.015 (−0.019, −0.011), p<0.0001).

Kim, 2022

Retrospective cohort study design using the National Health Insurance Sampling (NHIS) cohort data from 2014 to 2015, Korea.

A total of 13,135 ICU patients were included.

Nurse staffing level was classified as nine grades in the ICU at the time of this study.

The level of nurse staffing by nurse-to-bed ratio in the ICU used data that was provided in insurance claims. If the nurse-to-bed ratio was less than 0.5, it was classified as 1st grade, 1 ~ 5th grade (5 grade: ≥1.00), or 1 ~ 9th grade (9 grade, ≥2.00) for ICU, tertiary hospitals, hospitals, and general hospitals, respectively. Since the nurse staffing level entered in the claim data was based on the nurse-to-bed ratio, the nurse-to-patients ratio was calculated based on the total number of in-patients and nurses in each hospital.

Next, we classified nurse staffing level into eight grades based on the

current nurse-to-patient ratio.

The generalized estimating equation (GEE) model was used to evaluate the association between nurse staffing level and LOS; GEE model with a gamma distribution and log-link function because the hospital's LOS is right-skewed. In the fully adjusted

model, all variables were entered simultaneously.

Length of stay:

Per nurse staffing level (M±SD)

Significant differences in the LOS according to the nurse staffing grade were observed in ICUs, with a longer LOS in nurse staffing grade 6 (mean [M]: 17.08, standard deviation [SD]: 18.37) and grade 7 (M: 17.70, SD: 21.30) institutions. Depending on the hospital type, LOS was found to be longest in a hospital (M: 19.35, SD: 18.49) and shortest in a tertiary hospital (M: 16.37, SD: 16.28).

Associations between nurse staffing level and length of stay, RR (95%CI):

Level 1 0.919 (0.844 to 1.001)

Level 2 0.906 (0.871 to 0.942)

Level 3 0.913 (0.881 to 0.946)

Level 4 0.947 (0.907 to 0.995)

Level 5 1.012 (0.951 to 1.076)

Level 6 1.115 (1.057 to 1.176)

Level 7 –

Level 8 1.031 (0.959 to 1.109)

Level 9 1.009 (0.931 to 1.094)

In general, higher nurse staffing levels were associated with shorter LOS. In the ICU, the level of nurse staffing in grades 4 and above resulted in reduced LOS compared to grade 7; however, only grades 2 to 4 were statistically significant. Nurse staffing level grades 8 and 9 were associated with a higher LOS compared to grade 7; however, this result was not statistically significant

Duclos, 2023

Retrospective multicenter observational study, Lyon, France; 8

academic ICUs over 6 years (43,479 ICU patients) between January 1, 2011 and December 31, 2016.

The patient-to-nurse ratio and the patient-to-assistant nurse ratio were defined as the number of patients per nurse and per assistant nurse by shift, respectively. According to the French law that recommends five ICU patients per two nurses and four ICU patients per one assistant nurse, the patient-to-nurse and patient-to–assistant nurse ratios were categorized as suboptimal when not complying with this guideline (i.e., more than five patients for two nurses and more than four patients for one assistant nurse, respectively) and as optimal otherwise.

Mortality assessment was systematically adjusted for patient characteristics (age, sex, admission context, SAPS II, and comorbidities), nursing team members’ workload (patient turnover, number of LSPs per patient, and proportion of isolated patients), time periods (year, quarter, and weekend), and staffing (experience length of nursing team members and patient-to-staffing ratios).

To identify the determinants of ICU mortality per shift and account for the clustering effect of patients within the ICU (i.e., patients treated and outcomes within a particular ICU tended to be more similar than those in another ICU), we computed multivariate modified Poisson regression (with a robust standard error estimation) and applied a small sample correction factor to take into account the low number of clusters

(19). The potential confounders described above were a priori entered in the model. We tested and included any significant interactions between variables in the model. The results were presented as adjusted relative risks (RRs) with their corresponding 95% confidence intervals (95% CIs). We plotted shifts with at least one death without DFLST according to nurse-to-nurse familiarity in an unadjusted and adjusted model. We estimated predicted probabilities with their 95% CIs from modified Poisson regression models with a robust error variance.

ICU mortality:

There were 3,101 shifts (9%) during which at least one death without a DFLST occurred during the ICU stay, including 2,902 shifts (8%) with one death. The risk of shift with death increased in the case of suboptimal patient-to-nurse ratio (RR=1.35; 95%CI 1.02 to 1.77; P = 0.035).

Shifts with death within 12 hours:

There were 731 shifts (2%) during which at least one patient death occurred within 12 hours of their ICU admission. The risk of shift with death increased in the case of suboptimal patient-to-nurse ratio (RR, 1.84; 95% CI, 1.43–2.38; P < 0.001) and suboptimal ratios for both nurses and assistant nurses (RR, 3.16; 95% CI, 1.94–5.14; P < 0.001)

Zhou, 2023

Retrospective study of single-center ICUs in China; 1,341 consecutive septic patients admitted to the emergency ICU, general ICU, or cardiovascular ICU in a tertiary teaching hospital.

In our hospital, during day time (08:00 to 16:59hr), the ICU team comprise three to four attending intensivists, two to three residents (critical care or other specialty fellows), and the average patient to nurse ratio (P/N ratio) is 2–3:1. In the other two time periods (17:00 to 23:59hr and 00:00 to 07:59hr), there is one senior intensivist, one resident, and the P/N ratio is 3–5:1. Imaging technical platform and surgical operating room are 24-hour available. Admissions may occur at any time of the day and night. This organization was maintained during the study period.

The potential confounders affecting the association with in-hospital mortality included admission/ discharge time and weekend admission, P/N ratio, compliance with SSC 1 hour, severity of illness, age, gender, Charlson index, mechanical ventilation, and shock. The causal relationships between the potential confounders were considered seriously before multivariate logistic regression models. Multivariate models were fit using covariates found to be clinically relevant or significant in univariate analysis. Missing values of variables were imputed by multiple imputations.

In-hospital mortality:

When the admission time was removed from the model, a significant association between P/N ratio and in-hospital mortality was found

in four models in which different disease severity scores were adjusted.

Logistic regression models: Odds Ratio for In-Hospital Mortality by Patient to Nurse Ratio After Adjusting the Severity of the Illness:

Zoeken en selecteren

A systematic review of the literature was performed to answer the following question:

What is the effect of nurse-to-patient ratio X versus nurse-to-patient ratio Y in the ICU?

P (Population):	Adult ICU patients (>17 years)
I (Intervention):	Nurse-to-patient ratio X
C (Control):	Nurse-to-patient ratio Y
O (Outcome):	Mortality (ICU, hospital), length of (ICU, hospital) stay, morbidity, ICU readmission, patient satisfaction

Relevant outcome measures

The guideline development group considered mortality as a critical outcome measure for decision making; and length of stay, morbidity, ICU readmission, patient satisfaction as an important outcome measure for decision making.

A priori, the working group did not define the outcome measures listed above but used the definitions used in the studies.

The working group defined the following values as minimal clinically (patient) important difference:

mortality (ICU, hospital): 3% difference (absolute);
length of stay (ICU, hospital): ICU 1 day, hospital 3 days;
morbidity: RR <0.8 or >1.25;
ICU readmission: RR <0.8 or >1.25;
patient satisfaction: 10% difference.

Search and select

The databases Medline (via OVID), Embase (via Embase.com) and Cinahl were searched with relevant search terms until 25 October 2023. The search was combined with the search for the module about the intensivist-to-patient ratio. The detailed search strategy is depicted under the tab Methods. The systematic literature search resulted in 2,021 hits. Studies were selected based on the following criteria: Systematic review, RCT or observational study comparing the effect of different nurse-to patient ratios on adults patients (>17 years) in the ICU, reporting at least one of the outcomes specified in the PICO, published after 2000. Initially, 42 studies were selected based on title and abstract screening. After reading the full text, 20 studies were excluded (see the table with reasons for exclusion under the tab Methods). Four individual studies were included in the analysis of the literature and the results of 18 studies were described in a table.

Results

Four comparative studies were included in the analysis of the literature. Important study characteristics and results are summarized in the evidence tables. The assessment of the risk of bias is summarized in the risk of bias tables.

Referenties

Alanzi, K.F., Lapkin, S., Molloy, L. & Sim. (2023). Healthcare-associated infections in adult intensive care units: A multisource study examining nurses' safety attitudes, quality of care, missed care, and nurse staffing. Intensive crit care nurs. 78:103480.
Almenyan, A. A., Albuduh, A., & Al-Abbas, F. (2021). Effect of nursing workload in intensive care units. Cureus, 13(1).
Amaravadi RK, Dimick JB, Pronovost PJ, Lipsett PA. ICU nurse-to-patient ratio is associated with complications and resource use after esophagectomy. Intensive Care Med. 2000 Dec;26(12):1857-62. doi: 10.1007/s001340000720. PMID: 11271096.
Bruyneel, A., Bouckaert, N., Pirson, M., Sermeus, W., & Van den Heede, K. (2024). Unfinished nursing care in intensive care units and the mediating role of the association between nurse working environment, and quality of care and nurses’ wellbeing. Intensive and Critical Care Nursing, 81, 103596.
Checkley W, Martin GS, Brown SM, Chang SY, Dabbagh O, Fremont RD, Girard TD, Rice TW, Howell MD, Johnson SB, O'Brien J, Park PK, Pastores SM, Patil NT, Pietropaoli AP, Putman M, Rotello L, Siner J, Sajid S, Murphy DJ, Sevransky JE; United States Critical Illness and Injury Trials Group Critical Illness Outcomes Study Investigators. Structure, process, and annual ICU mortality across 69 centers: United States Critical Illness and Injury Trials Group Critical Illness Outcomes Study. Crit Care Med. 2014 Feb;42(2):344-56. doi:10.1097/CCM.0b013e3182a275d7. PMID: 24145833; PMCID: PMC4035482.
Cho SH, Hwang JH, Kim J. Nurse staffing and patient mortality in intensive care units. Nurs Res. 2008 Sep-Oct;57(5):322-30. doi: 10.1097/01.NNR.0000313498.17777.71. PMID:18794716.
Dall'Ora, C., Saville, C., Rubbo, B., Turner, L., Jones, J., & Griffiths, P. (2022). Nurse staffing levels and patient outcomes: a systematic review of longitudinal studies. International Journal of Nursing Studies, 134, 104311.
Ding X, Ma X, Gao S, Su L, Shan G, Hu Y, Chen J, Ma D, Zhang F, Zhu W, Sun G, Meng X, Ma L, Zhou X, Liu D, Du B; China National Critical Care Quality Control Center Group. Effect of ICU quality control indicators on VAP incidence rate and mortality: a retrospective study of 1267 hospitals in China. Crit Care. 2022 Dec 29;26(1):405. doi: 10.1186/s13054-022-04285-6. PMID: 36581952; PMCID: PMC9798551.
Dimick JB, Swoboda SM, Pronovost PJ, Lipsett PA. Effect of nurse-to-patient ratio in the intensive care unit on pulmonary complications and resource use after hepatectomy. Am J Crit Care. 2001 Nov;10(6):376-82. PMID: 11688604.
Duclos A, Payet C, Baboi L, Allaouchiche B, Argaud L, Aubrun F, Bohé J, Dailler F, Fellahi JL, Lehot JJ, Piriou V, Rimmelé T, Terragrossa D, Polazzi S, Guérin C. Nurse-to-Nurse Familiarity and Mortality in the Critically Ill: A Multicenter Observational Study. Am J Respir Crit Care Med. 2023 Apr 15;207(8):1022-1029. doi: 10.1164/rccm.202204-0696OC. PMID: 36219472.
Faisy C, Davagnar C, Ladiray D, Djadi-Prat J, Esvan M, Lenain E, Durieux P, Leforestier JF, Marlet C, Seijo M, Guillou A. Nurse workload and inexperienced medical staff members are associated with seasonal peaks in severe adverse events in the adult medical intensive care unit: A seven-year prospective study. Int J Nurs Stud. 2016 Oct;62:60-70. doi:10.1016/j.ijnurstu.2016.07.013. Epub 2016 Jul 16. PMID: 27455207.
Graf J, Reinhold A, Brunkhorst FM, Ragaller M, Reinhart K, Loeffler M, Engel C; German Competence Network Sepsis (SepNet). Variability of structures in German intensive care units--a representative, nationwide analysis. Wien Klin Wochenschr. 2010 Oct;122(1920):572-8. doi: 10.1007/s00508-010-1452-8. Epub 2010 Sep 27. PMID: 20865456.
Hoogendoorn M, Margadant CC, Brinkman S, Haringman, J., Spijkstra, J.J., de Keizer, N.F. (2020). Workload scoring systmes in the intensive care and their ability to quantify the need for nursing time: a systematic literature review.
Jansson M, Ohtonen P, Syrjälä H, Ala-Kokko T. The proportion of understaffing and increased nursing workload are associated with multiple organ failure: A cross-sectional study. J Adv Nurs. 2020 Aug;76(8):2113-2124. doi:10.1111/jan.14410. Epub 2020 Jun 2. PMID:32488895.
Jansson MM, Syrjälä HP, Ala-Kokko TI. Association of nurse staffing and nursing workload with ventilator-associated pneumonia and mortality: a prospective, single-center cohort study. J Hosp Infect. 2019 Mar;101(3):257-263. doi: 10.1016/j.jhin.2018.12.001. Epub 2018 Dec 7. Erratum in: J Hosp Infect. 2020 Dec;106(4):839-840. PMID: 30529704.
Kim S, Kim TH. The association between nurse staffing level and length of stay in general ward and intensive care unit in Korea. Appl Nurs Res. 2022 Feb;63:151558. doi:10.1016/j.apnr.2021.151558. Epub 2022 Jan 4. Erratum in: Appl Nurs Res. 2022 Dec;68:151625. PMID: 35034705.
Kim Y, Kim J, Shin SA. Relationship between the legal nurse staffing standard and patient survival after perioperative cardiac arrest: A cross-sectional analysis of Korean administrative data. Int J Nurs Stud. 2019 Jan;89:104-111. doi:10.1016/j.ijnurstu.2018.09.012. Epub 2018 Oct 1. PMID: 30359876.
Lee A, Cheung YSL, Joynt GM, Leung CCH, Wong WT, Gomersall CD. Are high nurse workload/staffing ratios associated with decreased survival in critically ill patients? A cohort study. Ann Intensive Care. 2017 Dec;7(1):46. doi:10.1186/s13613-017-0269-2. Epub 2017 May 2. PMID: 28466462; PMCID: PMC5413463.
Margadant, C.C., Hoogendoorn, M.E., Bosman, R.J., Spijkstra, J.J., Brinkman, S. & de Keizer, N.F. (2021) Validation of the Nursing Activities Score (NAS) using time- and motion measurements in Dutch intensive care units. Neth J Crit care, 29 (1), 22-27.
Neuraz A, Guérin C, Payet C, Polazzi S, Aubrun F, Dailler F, Lehot JJ, Piriou V, Neidecker J, Rimmelé T, Schott AM, Duclos A. Patient Mortality Is Associated With Staff Resources and Workload in the ICU: A Multicenter Observational Study. Crit Care Med. 2015 Aug;43(8):1587-94. doi: 10.1097/CCM.0000000000001015. PMID: 25867907.
Pronovost PJ, Dang D, Dorman T, Lipsett PA, Garrett E, Jenckes M, Bass EB. Intensive care unit nurse staffing and the risk for complications after abdominal aortic surgery. Eff Clin Pract. 2001 Sep-Oct;4(5):199-206. PMID: 11685977.
Stone PW, Mooney-Kane C, Larson EL, Horan T, Glance LG, Zwanziger J, Dick AW. Nurse working conditions and patient safety outcomes. Med Care. 2007 Jun;45(6):571-8. doi:10.1097/MLR.0b013e3180383667. PMID: 17515785.
Taakgroepformatie: Deintensive care ook in de toekomst formatief op orde. 2022. www.NVIC.nl./overige documenten/taakgroepformatie.
Talsma A, Jones K, Guo Y, Wilson D, Campbell DA. The relationship between nurse staffing and failure to rescue: where does it matter most? J Patient Saf. 2014 Sep;10(3):133-9. doi:10.1097/PTS.0b013e31829954e2. PMID: 23860195.
Tarnow-Mordi WO, Hau C, Warden A, Shearer AJ. Hospital mortality in relation to staff workload: a 4-year study in an adult intensive-care unit. Lancet. 2000 Jul 15;356(9225):1859. doi: 10.1016/s0140-6736(00)02478-8. PMID: 10963195.
Verburg IWM, Holman R, Dongelmans D, de Jonge E, de Keizer NF. Is patient length of stay associated with intensive care unit characteristics? J Crit Care. 2018 Feb;43:114-121. doi:10.1016/j.jcrc.2017.08.014. Epub 2017 Aug 10. PMID:28865340.
West E, Barron DN, Harrison D, Rafferty AM, Rowan K, Sanderson C. Nurse staffing, medical staffing and mortality in Intensive Care: An observational study. Int J Nurs Stud. 2014 May;51(5):781-94. doi: 10.1016/j.ijnurstu.2014.02.007. Epub 2014 Feb 27. PMID: 24636667.
Yang, L., Zhou, W., Gao, Y., Wu, T., Zhang, H. & Gan X., (2023). Development and validation of the missed intensive nursing care scale. BMC Nursing 23:165.
Zhou X, Weng J, Xu Z, Yang J, Lin J, Hou R, Zhou Z, Wang L, Wang Z, Chen C. Effect of Admission and Discharge Times on Hospital Mortality in Patients With Sepsis. Crit Care Med. 2023 Mar 1;51(3):e81-e89. doi:10.1097/CCM.0000000000005767. Epub 2022 Dec 27. PMID:36728869.

Evidence tabellen

Study reference

Study characteristics

Patient characteristics

Intervention (I)

Comparison / control (C)

Follow-up

Outcome measures and effect size

Comments

Amaravadi, 2000

Type of study: retrospective cohort

Setting and country: Non-federal acute care hospitals (n=35) in Maryland, USA

Funding and conflicts of interest: NR

Inclusion criteria:

All adult patients discharged from Maryland hospitals from 1994 to 1998 with a primary procedure code for esophageal resection

Exclusion criteria:

N total at baseline:

I: 225 (in 9 hospitals)

C: 128 (in 23 hospitals)

Important prognostic factors:

Age, mean (SD):

I: 60 (12)

C: 63 (12)

Sex:

I: 79% M

C: 70% M

Groups comparable at baseline?

“There was no significant difference between the two groups except for a greater incidence of peripheral vascular disease in patients with a NNPR < 1:2”

Night-time nurse to patient ratio (NNPR) > 1:2 (nurse cared for one or two patients), obtained from ICU survey data

NNPR < 1:2 (nurse cared for three or more patients)

Length of follow-up:

Until discharge (patient data was obtained from discharge data)

Incomplete outcome data:

Unit survey data (used to determine the NNPR) was available for 32 of the 35 centres; 353 of 366 patients (96%)

Mortality

Unadjusted hospital mortality rate

I: 5.6%

C: 15%

Multivariate analysis

OR = 0.7 (95% CI 0.3 to 2.0)

Length of (ICU) stay

Median hospital LOS

I: 9 days (IQR 1.8 to 13)

C: 15 days (IQR 11 to 27)

Morbidity

Postoperative complications, %I/C, OR (95% CI)

Pneumonia: 8/16, OR = 2.4 (1.2 to 4.7)

Reintubation: 12/25, OR = 2.5 (1.4 to 4.5)

Aspiration: 22/25, OR = 1.2 (0.7 to 2.0)

Septicemia: 1.8/6.2, OR = 3.7 (1.1 to 12.5)

Postoperative infection: 4/5.5, OR = 1.4 (0.5 to 3.8)

Myocardial infarction: 0.9/0.8, OR = 0.9 (0.08 to 9.7)

Cardiac arrest: 0/0.8, OR = 1.2 (0.6 to 2.2)

Surgical complications: 8/17 OR = 1.9 (0.9 to 3.8)

Acute renal failure: 2.7/5.5, OR = 2.1 (0.7 to 6.4))

Readmission to ICU

Patient satisfaction

Authors’ conclusion: “Decreased nurse staffing (nurse to patient ratio less than 1 to 2) is associated with postoperative complications, increased LOS and increased health care cost.”

Dimick, 2001

Type of study:

retrospective cohort

Setting and country: Acute-care hospitals (n=33) in Maryland, USA

Funding and conflicts of interest:

Inclusion criteria:

Adults undergoing hepatic resection between 1994 and 1998

Exclusion criteria:

N total at baseline:

Intervention: 316 (at 8 hospitals)

Control: 240 (at 25 hospitals)

Important prognostic factors:

Age, mean (SD):

I: 56 (15)

C: 57 (16)

Sex:

I: 51% M

C: 55% M

Groups comparable at baseline?

“Compared with patients in hospitals with more ICU nurses, patients in hospitals with fewer ICU nurses had a higher percentage of urgent or emergent surgery, were more often nonwhite, more often had a hepatic lobectomy, and had fewer myocardial infarctions as comorbid history. Each of these factors was considered in the adjusted analyses.”

More nurses

NPR = 1 : 1 or 1 : 2 (High), at night)

Fewer nurses

NPR = 1 : 3 or 1 : 4 (Low) , at night

Length of follow-up:

Until discharge (patient data was obtained from discharge data)

Incomplete outcome data:

ICU survey data were available for 33 of 35 centers performing hepatic resection, providing information pertinent to the care of 556 (98%) of 569 patients.

Mortality

I: 2.5%

C: .7.1%

Unadjusted OR = 0.34 (95% CI 0.15 to 0.49)

Adjusted OR = 0.49 (95% CI 0.18 to 1.29)

Length of (ICU) stay

Median (IQR)

I: 7 days (6 to 10)

C: 8 days (6 to 12)

Morbidity

Pneumonia I/C, OR (95% CI): 2.8/4.2, 1.4 (0.6 to 3.5)

Reintubation: 1.9/10.8, 5.7 (2.4 to 13.7)

Pulmonary failure: 1.6/5.8, 3.6 (1.3 to 10.1)

Aspiration: 12.0/7.5,

Readmission to ICU

Patient satisfaction

Authors’ conclusion:

“Fewer nurses at night is associated with increased risk for specific pulmonary complications and with increased resource use in patients undergoing hepatectomy.”

Pronovost, 2001

Type of study: retrospective cohort

Setting and country: All non-federal acute care hospitals in Maryland

Funding and conflicts of interest: NR

Inclusion criteria:

We obtained information on all patients 30 years of age or older who were discharged from a Maryland hospital between January 1994 and December 1996 with a principal procedure code for abdominal aortic surgery (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 3844 for resection of abdominal aorta with replacement and ICD-9-CM code 3925 for aortoiliac–femoral bypass).

Exclusion criteria:

We excluded 9 patients who were

younger than 30 years of age, all of whom had had an

injury to a blood vessel (ICD-9-CM code 902).

N total at baseline:

I: 2,128

C: 478

Important prognostic factors:

Age (SD) in years:

I: 68 (10)

C: 68 (10)

Sex:

I: 69% M

C: 66% M

Groups comparable at baseline?

“Demographic characteristics and severity of illness did not differ between patients in either nurse staffing model. Mild diabetes mellitus was the only comorbid disease that occurred significantly more often in patients in ICUs with fewer nurses”

Hospitals with more ICU nurses (in which each

nurse cared for one or two patients), during the day.

We considered nurse-to-patient ratios of 1:1 or

1:2 as “more ICU nurses”

obtained from ICU survey data

Hospitals with fewer ICU nurses (in which each nurse cared for three or four patients), during the day

We considered nurse-to-patient ratios of 1:3 or 1:4 as

“fewer ICU nurses.”

obtained from ICU survey data

Length of follow-up:

Until discharge (patient data was obtained from discharge data)

Incomplete outcome data:

Patient and ICU nurse staffing data were available for 38 of the 46 hospitals in this study.

Mortality

Inpatient mortality rate (95% CI)

I: 7% (6.0%–8.1%)

C: 8% (6.0%–11.2%)

OR: 0.82 (0.57–1.18)

Length of (ICU) stay

Hospital length of stay, median (range), d

I: 8 (0–171)

C: 8 (0–130)

ICU length of stay, median (range), d

I: 2 (0–118)

C: 3 (0–112)

Morbidity

Adjusted for patient characteristics and hospital and surgeon volume.

Any complication % I/C: 34% / 47%

RR (95% CI), crude; adjusted 1.4 (1.2–1.5); 1.7 (1.3–2.4)

Any medical complication % I/C: 28% / 43%

RR (95% CI), crude; adjusted 1.5 (1.4–1.7); 2.1 (1.5–2.9)

Pulmonary insufficiency after procedure % I/C: 9% / 24%

RR (95% CI), crude; adjusted 2.6 (2.1–3.2); 4.5 (2.9–6.9)

Reintubation % I/C: 13% / 21%

RR (95% CI), crude; adjusted 1.5 (1.3–1.8); 1.6 (1.1–2.5)

Cardiac complications after procedure % I/C: 10% / 15%

RR (95% CI), crude; adjusted 1.4 (1.1–1.7); 1.3 (0.8–1.8)

Acute renal failure % I/C: 4% / 6%

RR (95% CI), crude; adjusted 1.3 (0.8–1.9); 1.6 (0.9–2.7)

Septicemia % I/C: 3% / 4%

RR (95% CI), crude; adjusted 1.4 (0.8–2.1); 1.9 (0.9–3.9)

Acute myocardial infarction % I/C: 3% / 4%

RR (95% CI), crude; adjusted 1.5 (0.8–2.4); 1.5 (0.9–2.2)

Cardiac arrest % I/C: 1% / 2%

RR (95% CI), crude; adjusted 1.4 (0.6–3.0); 1.7 (0.7–4.7)

Any surgical complication % I/C: 11% / 10%

RR (95% CI), crude; adjusted 0.9 (0.6–1.4); 0.7 (0.4–1.5)

Surgical complications after procedure % I/C: 9% / 8% R

R (95% CI), crude; adjusted 0.9 (0.6. –1.2); 1.0 (0.6–1.4)

Surgical E codes % I/C: 0% / 1%

RR (95% CI), crude; adjusted 2.2 (0.4–10.5); N/A (insufficient data)

Reoperation for bleeding % I/C: 3% / 2%

RR (95% CI), crude; adjusted 0.8 (0.4–1.6); 1.2 (0.4–3.5)

Readmission to ICU

Patient satisfaction

Authors’ conclusion:

“Having fewer ICU nurses per patient is associated with increased risk for respiratory-related complications after abdominal aortic surgery.”

Blot, 2011

Type of study:

Prospective observational study

Setting and country: 27 ICUs in 9 European countries: Belgium, France, Germany, Greece, Italy, Ireland, Portugal, Spain, and Turkey.

Funding and conflicts of interest: Dr Blot was supported by a grant from the European Society of Intensive Care Medicine and iMDsoft Patient Safety Research Award 2008. The study was supported, in part, by Generalitat de Catalunya grant SGR 05/920, by CIBER Enfermedades Respiratorias (CIBERES), and by Carlos III Health Institute grants PI05/2410 and AI/07/90031.

Inclusion criteria:

All patients who were admitted to the ICU for treatment of pneumonia or received invasive mechanical ventilation for more than 48 hours, irrespective of the admission diagnosis, were included in the initial cohort.

Exclusion criteria:

Because the focus of the study reported here was prevention of VAP, data on patients with a clinical diagnosis of community-acquired pneumonia, non–ventilator-associated hospital-acquired pneumonia, or very early VAP (due to aspiration and developing within 48 hours after intubation), were excluded from the analysis. Data on patients from 6 ICUs that did not provide data on nurse staffing levels were also excluded from the analysis.

N total at baseline:

I: 1,066

C: 592

Important prognostic factors:

Age median (IQR):

I: 59 (41-71)

C: 69 (57-77)

Sex:

I: 64% M

C: 61% M

Groups comparable at baseline?

Important differences in patients’ characteristics are considered, adjusted for in analyses.

Patient to nurse ratio ≤ 2:1

Routine staffing levels for all available ICU beds were considered, irrespective of bed occupancy. Routine staffing level is defined as the patient to nurse ratio that is standard in a particular ICU. As such, unit-based standard nurse staffing levels were used irrespective of acute shortages of staff and number of patients present. Daily bed occupancy levels were not taken into account because this cohort consisted solely of patients who received mechanical ventilation. Hence, actual day-to-day patient to nurse ratios were not available for the analysis. For units with variable staffing levels (eg, 1 to 1 during day shifts and 2 to 1 during night shifts), the highest patient to nurse ratio in a 24-hour period was considered.

Patient to nurse ratio > 2:1

Length of follow-up:

Loss-to-follow-up & incomplete data: NR

Mortality

Length of (ICU) stay

ICU stay, median

(25th-75th percentile), d

I: 12 (6-22)

C: 11 (6-20)

Hospital stay, median

(25th-75th percentile), d

I: 22 (12-42)

C: 17 (9-31)

Morbidity

ventilator-associated pneumonia (VAP): N (%)

I: 262 (24.6)

C: 131 (22.1)

Readmission to ICU

Patient satisfaction

Authors’ conclusion:

“In this cohort of patients treated with mechanical ventilation, a patient to nurse ratio of 1 to 1 appeared to be associated with a lower risk for VAP. After adjustment for confounding covariates, however, the difference was no longer significant. Although higher staffing levels may be beneficial for other outcomes, the effect of trauma, general disease severity, and duration of mechanical ventilation are more important risk factors for VAP. Our data indicate that efforts to reduce the number of days at risk should be a priority in the prevention of VAP. Thus, our results underscore the value of a proactive extubation policy with a “sedation vacation” as recommended in current guidelines. Further research is necessary to evaluate the relationship between higher staffing levels (patient to nurse ratio <2 to 1) and compliance rates with distinct evidence-based strategies to prevent VAP. In our study, the actual patient to nurse ratio should be taken into account (actual number of patients per nurse each day).”

Risk of bias

Author, year

Selection of participants

Was selection of exposed and non-exposed cohorts drawn from the same population?

Exposure

Can we be confident in the assessment of exposure?

Outcome of interest

Can we be confident that the outcome of interest was not present at start of study?

Confounding-assessment

Can we be confident in the assessment of confounding factors?

Confounding-analysis

Did the study match exposed and unexposed for all variables that are associated with the outcome of interest or did the statistical analysis adjust for these confounding variables?

Assessment of outcome

Can we be confident in the assessment of outcome?

Follow up

Was the follow up of cohorts adequate? In particular, was outcome data complete or imputed?

Co-interventions

Were co-interventions similar between groups?

Overall Risk of bias

Definitely yes, probably yes, probably no, definitely no

Low, Some concerns, High

Amaravadi, 2000

Probably yes

Reason:

Exposed and unexposed drawn for same administrative database of patients

Probably no

Reason:

Exposure data obtained via a survey that was reviewed independently by five intensive care physicians to ensure content validity.

Exposure data on hospital level, not individual patients.

Probably yes

Reason:

Outcomes mortality, LOS, ICU readmission cannot be present beforehand; not sure about complications

Probably yes /unclear

Reason:

Information obtained from patient hospital discharge data

Probably yes

Reason:

Models adjusted for age, sex, nature of admission, type of operation, comorbid disease, hospital volume and surgeon volume.

Probably yes

Reason:

Outcome data obtained from patient discharge data

Probably no

Reason:

Follow-up was adequate for all outcomes but no information on missing data

Probably no/ unclear

Reason:

For the outcome hospital mortality: no information on care patients received outside of the ICU

High (all outcomes)

Dimick, 2001

Probably yes

Reason:

Exposed and unexposed drawn for same administrative database of patients

Probably no

Reason:

Exposure information obtained from ICU survey.

Exposure data on hospital level, not individual patients.

Probably yes

Reason:

Outcomes mortality, LOS, ICU readmission cannot be present beforehand; not sure about complications

Probably yes /unclear

Reason:

Information obtained from patient hospital discharge data

Probably yes

Reason:

Adjustments in models were made for age, sex, nature of admission, type of operation, comorbid conditions, and hospital and surgeon volume.

Probably yes

Reason:

Outcome data obtained from patient discharge data

Probably no

Reason:

Follow-up was adequate for all outcomes but no information on missing data

Probably no/ unclear

Reason:

For the outcome hospital mortality: no information on care patients received outside of the ICU

High (all outcomes)

Pronovost, 2001

Probably yes

Reason:

Exposed and unexposed drawn for same administrative database of patients

Probably no

Reason:

Exposure information obtained from ICU survey.

Exposure data on hospital level, not individual patients.

Probably no

Reason:

Outcomes mortality, LOS, ICU readmission cannot be present beforehand.

For outcome complications: “the discharge diagnosis codes we used do not distinguish between complications and comorbid conditions, the medical diagnoses listed here are for acute problems and are therefore more likely to represent complications than comorbid conditions”

Probably yes /unclear

Reason:

Information obtained from patient hospital discharge data

Probably yes

Reason:

Covariates: number of hospital beds, volume of aortic surgery performed by hospital and surgeon, age, sex, race (white/ nonwhite), comorbidity (each disease separately), severity of illness, nature of admission.

Reported RRs adjusted for patient characteristics and hospital and surgeon volume.

Probably yes

Reason:

Outcome data obtained from patient discharge data

Probably no

Reason:

Follow-up was adequate for all outcomes but no information on missing data

Probably no/ unclear

Reason:

For the outcome hospital mortality: no information on care patients received outside of the ICU

High (all outcomes)

Blot, 2011

Probably yes

Reason:

Cohort of consecutive patients; exposed and unexposed from same database

Probably no

Reason:

Exposure was based routine staffing level; unit-based standard nurse staffing levels were used irrespective of acute shortages of staff and number of patients present.

Probably yes

Reason:

For the outcome VAP selection criteria were used to exclude patients that already had a similar/related diagnosis

Probably yes

Reason:

Information obtained from prospectively recorded patient data. Data recorded by investigators on study sites op paper forms; sent to central study site and put into electric database, checked for inconsistensies

Probably yes

Reason:

Variables considered were age, SAPS II, underlying diseases, admission diagnosis, and patient to nurse ratio.

Unclear

Reason:

Outcome VAP, criteria defined but may still be subjective, recorded by investigators on site, no information on whether they were blind to exposure (nurse staffing level)

Probably no

Reason:

Follow-up was adequate for all outcomes but no information on missing data

Unclear

Reason:

No information

High (all outcomes)

Table of excluded studies

Reference	Reason for exclusion
Bray K, Wren I, Baldwin A, St Ledger U, Gibson V, Goodman S, Walsh D. Standards for nurse staffing in critical care units determined by: The British Association of Critical Care Nurses, The Critical Care Networks National Nurse Leads, Royal College of Nursing Critical Care and In-flight Forum. Nurs Crit Care. 2010 May-Jun;15(3):109-11. doi: 10.1111/j.1478-5153.2010.00392.x. PMID: 20500648.	Review without systematic literature search
Chamberlain D, Pollock W, Fulbrook P; ACCCN Workforce Standards Development Group. ACCCN Workforce Standards for Intensive Care Nursing: Systematic and evidence review, development, and appraisal. Aust Crit Care. 2018 Sep;31(5):292-302. doi: 10.1016/j.aucc.2017.08.007. Epub 2017 Dec 12. PMID: 29246795.	Results of the literature analysis are not systematically presented
Dall'Ora C, Saville C, Rubbo B, Turner L, Jones J, Griffiths P. Nurse staffing levels and patient outcomes: A systematic review of longitudinal studies. Int J Nurs Stud. 2022 Oct;134:104311. doi: 10.1016/j.ijnurstu.2022.104311. Epub 2022 Jun 16. PMID: 35780608.	Systematic review not restricted to ICU settings, no relevant individual studies
Dodek PM, Norena M, Wong H, Keenan S, Martin C. Assessing the Influence of Intensive Care Unit Organizational Factors on Outcomes in Canada: Is There Residual Confounding? J Intensive Care Med. 2015 Oct;30(7):413-9. doi: 10.1177/0885066614521973. Epub 2014 Feb 7. PMID: 24509494.	Wrong comparison (does not describe nurse-to-patient ratio or workload)
Durbin CG Jr. Team model: advocating for the optimal method of care delivery in the intensive care unit. Crit Care Med. 2006 Mar;34(3 Suppl):S12-7. doi: 10.1097/01.CCM.0000199985.72497.D1. PMID: 16477198.	Non-exhaustive, selective literature search
Falk AC. Nurse staffing levels in critical care: The impact of patient characteristics. Nurs Crit Care. 2023 Mar;28(2):281-287. doi: 10.1111/nicc.12826. Epub 2022 Jul 27. PMID: 35896444.	Wrong comparison (two units with a different NPR)
Gershengorn HB, Garland A. Who Should Be at the Bedside 24/7: Doctors, Families, Nurses? Semin Respir Crit Care Med. 2016 Feb;37(1):107-18. doi: 10.1055/s-0035-1570350. Epub 2016 Jan 28. PMID: 26820278.	Review without systematic literature search
Halm M. The Influence of Appropriate Staffing and Healthy Work Environments on Patient and Nurse Outcomes. Am J Crit Care. 2019 Mar;28(2):152-156. doi: 10.4037/ajcc2019938. PMID: 30824521.	No systematic literature search conducted, not restricted to ICU settings
Heenan S. Examining potential relationships between nurse staffing and clinical incidents in ICUs. Aust Crit Care. 2018;31(2):136-7.	Wrong publication type (abstract)
Kane RL, Shamliyan TA, Mueller C, Duval S, Wilt TJ. The association of registered nurse staffing levels and patient outcomes: systematic review and meta-analysis. Med Care. 2007 Dec;45(12):1195-204. doi: 10.1097/MLR.0b013e3181468ca3. PMID: 18007170.	Systematic review not restricted to ICU settings, no risk of bias assessment; relevant individual studies already included
McGahan M, Kucharski G, Coyer F; Winner ACCCN Best Nursing Review Paper 2011 sponsored by Elsevier. Nurse staffing levels and the incidence of mortality and morbidity in the adult intensive care unit: a literature review. Aust Crit Care. 2012 May;25(2):64-77. doi: 10.1016/j.aucc.2012.03.003. Epub 2012 Apr 18. PMID: 22515951.	No meta-analysis, relevant individual studies already included
Minnick AF, Mion LC. Nurse labor data: the collection and interpretation of nurse-to-patient ratios. J Nurs Adm. 2009 Sep;39(9):377-81. doi: 10.1097/NNA.0b013e3181b3b656. Erratum in: J Nurs Adm. 2009 Nov;39(11):464. PMID: 19745633; PMCID: PMC2879153.	Not restricted to ICU, wrong study aim: to compare the degree of completeness and the agreement between two approaches (nurse survey and nurse to patient ratio staffing plans) to obtain patient-to-nurse ratios
Numata Y, Schulzer M, van der Wal R, Globerman J, Semeniuk P, Balka E, Fitzgerald JM. Nurse staffing levels and hospital mortality in critical care settings: literature review and meta-analysis. J Adv Nurs. 2006 Aug;55(4):435-48. doi: 10.1111/j.1365-2648.2006.03941.x. PMID: 16866839.	Systematic review: Relevant individual studies included separately in current analysis
Olley R, Edwards I, Avery M, Cooper H. Systematic review of the evidence related to mandated nurse staffing ratios in acute hospitals. Aust Health Rev. 2019 Jul;43(3):288-293. doi: 10.1071/AH16252. PMID: 29661270.	Wrong analysis (qualitative); wrong outcome
Penoyer DA. Nurse staffing and patient outcomes in critical care: a concise review. Crit Care Med. 2010 Jul;38(7):1521-8; quiz 1529. doi: 10.1097/CCM.0b013e3181e47888. PMID: 20473146.	No meta-analysis, relevant individual studies already included
Pitkäaho T, Partanen P, Miettinen MH, Vehviläinen-Julkunen K. The relationship between nurse staffing and length of stay in acute-care: a one-year time-series data. J Nurs Manag. 2016 Jul;24(5):571-9. doi: 10.1111/jonm.12359. Epub 2016 Feb 1. PMID: 26833964.	Wrong setting (acute care instead of ICU)
Rae PJL, Pearce S, Greaves PJ, Dall'Ora C, Griffiths P, Endacott R. Outcomes sensitive to critical care nurse staffing levels: A systematic review. Intensive Crit Care Nurs. 2021 Dec;67:103110. doi: 10.1016/j.iccn.2021.103110. Epub 2021 Jul 9. PMID: 34247936.	Systematic review, no relevant outcome data reported; relevant individual studies already included
Shuldham C, Parkin C, Firouzi A, Roughton M, Lau-Walker M. The relationship between nurse staffing and patient outcomes: a case study. Int J Nurs Stud. 2009 Jul;46(7):986-92. doi: 10.1016/j.ijnurstu.2008.06.004. PMID: 18675419.	Wrong population (also included pediatric ICU’s)
Shekelle PG. Nurse-patient ratios as a patient safety strategy: a systematic review. Ann Intern Med. 2013 Mar 5;158(5 Pt 2):404-9. doi: 10.7326/0003-4819-158-5-201303051-00007. PMID: 23460097.	Systematic review, only pooled OR reported, no information on individual studies; relevant individual studies already included
Tarnow-Mordi WO, Hau C, Warden A, Shearer AJ. Hospital mortality in relation to staff workload: a 4-year study in an adult intensive-care unit. Lancet. 2000 Jul 15;356(9225):185-9. doi: 10.1016/s0140-6736(00)02478-8. PMID: 10963195.	Wrong comparison (occupancy/ nursing requirement per shift, no nurse-to-patient-ratio)

Verantwoording

Beoordelingsdatum en geldigheid

Laatst beoordeeld : 16-10-2025

Initiatief en autorisatie

Initiatief:

Nederlandse Vereniging voor Intensive Care

Geautoriseerd door:

Nederlandse Internisten Vereniging
Nederlandse Vereniging van Artsen voor Longziekten en Tuberculose
Nederlandse Vereniging voor Anesthesiologie
Nederlandse Vereniging voor Cardiologie
Nederlandse Vereniging voor Heelkunde
Nederlandse Vereniging voor Neurologie
Verpleegkundigen en Verzorgenden Nederland
Nederlandse Vereniging voor Intensive Care
Stichting Family and patient Centered Intensive Care en IC Connect
Nederlandse Associatie Physician Assistants

Algemene gegevens

Voor meer details over de gebruikte richtlijnmethodologie verwijzen wij u naar de Werkwijze. Relevante informatie voor de ontwikkeling/herziening van deze leidraad is hieronder weergegeven.

De ontwikkeling/herziening van deze module werd ondersteund door het Kennisinstituut van de Federatie Medisch Specialisten (www.demedischspecialist.nl/kennisinstituut) en werd gefinancierd uit de Stichting Kwaliteitsgelden Medisch Specialisten (SKMS). De financier heeft geen enkele invloed gehad op de inhoud van de module.

Samenstelling werkgroep

Voor het ontwikkelen van de module is in 2023 een multidisciplinaire werkgroep ingesteld, bestaande uit vertegenwoordigers van alle relevante specialismen (zie hiervoor de Samenstelling van de werkgroep) die betrokken zijn bij de zorg voor patiënten die zijn opgenomen op de Intensive Care.

Werkgroep

Dr. I.A. (Iwan) Meynaar (voorzitter), internist-intensivist, HagaZiekenhuis, NVIC

Drs. B. (Ben) de Jong, internist-intensivist, Saxenburgh Medisch Centrum, NVIC

Dr. M. (Marieke) Zegers, Associate Professor, Radboudumc, NVIC

Drs. T.C. (Corien) Veenstra, longarts-intensivist, UMCG, NVIC

Dr. J. (Jasper) van Bommel, anesthesioloog-intensivist, Erasmus MC, NVIC

Dr. P. (Peter) van Vliet, neuroloog-intensivist, Haaglanden Medisch Centrum, NVN/NVIC

Dr. G.J. (Jan) Zijlstra, longarts-intensivist, Amsterdam UMC, NVALT

Dr. M.A.M. (Miriam) Moviat, internist-intensivist, Jeroen Bosch Ziekenhuis, NIV

Dr. M.V. (Mark) Koning, anesthesioloog-intensivist, Rijnstate Ziekenhuis, NVA

Drs. R.W.L. (Rens) van de Weyer, cardioloog-intensivist, Elkerliek Ziekenhuis, NVVC

Drs. J.M.R. (Joost) Meijer, chirurg-intensivist, Noordwest Ziekenhuisgroep, NVvH

Drs. L. (Lea) van Duijvenbode-den Dekker, IC verpleegkundige, Amphia Ziekenhuis, V&VN-IC

Dr. W. (Willemke) Stilma (vanaf maart 2024), Hoofddocent en postdoc onderzoeker, Hogeschool van Amsterdam, V&VN-IC

Dr. P.J.T. (Paul) Rood (tot maart 2024), bestuurder V&VN-IC, senior onderzoeker HAN University of applied sciences & Ziekenhuis Rijnstate

Dr. M.M.C. (Margo) van Mol, Assistant Professor, Erasmus MC, FCIC/IC-Connect

Klankbordgroep

Mevr. J.E. (Janine) de Kleijn, MSc, Physician Assistant, Catharina Ziekenhuis, NAPA

Dr. J.M. (Joep) Droogh, intensivist, UMCG, NVIC (namens de transportcommissie)

Dr. D.J. (David) van Westerloo, intensivist, LUMC, NVIC (namens de LHIC)

Drs. C.J.G.M. (Crétien) Jacobs, anesthesioloog-intensivist, Elkerliek Ziekenhuis, NVIC (namens de werkgroep beroepsprofiel intensivisten)

Drs. C. (Coby) Heij, anesthesioloog-intensivist, Spaarne Gasthuis, NVIC (namens de commissie beroepsbelangen intensivisten)

Drs. J. (Jacco) Rozendaal, Verpleegkundig Specialist IC/MC, St. Antonius Ziekenhuis, V&VN-VS

Met dank aan

Dr J. J. Spijkstra, intensivist, AmsterdamUMC (namens de taakgroep formatie)

Drs. A. (Arianne) Doorduin-Schmeets, Unithoofd Intensive Care, Jeroen Bosch Ziekenhuis, ’s-Hertogenbosch (namens het LHIC)

Dhr. F. (Frank) van der Zee, IC verpleegkundige/Avond-nacht-weekend Hoofd, Frisius MC locatie Leeuwarden, Leeuwarden (namens LNICV)

Mw. I. (Iepie) Plagge van der Vliet, manager intensive care en medium care, Martini ziekenhuis, Groningen (namens LHIC)

Dhr. D.R. (Dick) Streefkerk, hoofd IC, Alrijne ziekenhuis, Leiderdorp, namens LHIC

Drs. T. (Toine) Klarenbeek, Intensive Care Verpleegkundige/Klinisch epidemioloog, Maxima medisch centrum, Veldhoven (namens LNICV)

Mevr, L. (Lisette) Epping - Tijdhof, Adviseur Kwaliteit en Veiligheid / niet-praktiserende IC verpleegkundige, Medisch Spectrum Twente, Enschede (namens V&VN IC expertise kwaliteit en veiligheid)

Dhr. R. (Renze) Jongstra, IC-verpleegkundige volwassenen en kinderen, Circulation Practitioner (namens bestuur V&VN-IC)

Met ondersteuning van

Drs. F.M. (Femke) Janssen, adviseur, Kennisinstituut van de Federatie Medisch Specialisten

Dr. S.N. (Stefanie) Hofstede, senior adviseur, Kennisinstituut van de Federatie Medisch Specialisten

Belangenverklaringen

Een overzicht van de belangen van werkgroepleden en het oordeel over het omgaan met eventuele belangen vindt u in onderstaande tabel. De ondertekende belangenverklaringen zijn op te vragen bij het secretariaat van het Kennisinstituut van de Federatie Medisch Specialisten via secretariaat@kennisinstituut.nl.

Werkgroeplid	Functie	Nevenfuncties	Gemelde belangen	Ondernomen actie
Meynaar, voorzitter	Intensivist, HagaZiekenhuis	Bestuurslid Nederlandse Vereniging voor Intensive Care, onbetaald behoudens een onkostenvergoeding, onderzoeker	Leren van Juiste Diagnoses, door ZonMw gesubsidieerd onderzoek 160.000 euro (inmiddels afgerond 2021-2023) ZonMw Leren van Juiste Diagnoses	Geen restricties
De Jong	Internist-intensivist Saxenburgh Medisch Centrum	- Cliëntenraad Prinses Maxima Centrum (onkostenvergoeding) - Clinical research committee Prinses Maxima Centrum (onkostenvergoeding) - Incidentele waarneming op intensive care afdelingen in de regio Zwolle	Eenmalige deelname binnen adviesraad voor Paion t.a.v. positionering van giapreza binnen intensive care geneeskunde	Geen restricties
Veenstra	Intensivist UMCG	Instructeur NVIC bronchoscopie cursus (onbetaald) Lid NVIC commissie pulmonale diagnostiek en interventies (onbetaald) Lid NVIC commissie simulatie (onbetaald) FCCS instructeur (vergoeding wordt overgemaakt naar het UMCG) Medisch visiteur externe kwaliteitsvisitaties NVIC (vergoeding wordt overgemaakt naar het UMCG) Lid Zinnige Zorg traject Zorginstituut VTE (NVIC afgevaardigde, onbetaald) Secretaris sectie IC, NVALT (onbetaald) Lid sectie pulmonale interventies, NVALT (onbetaald)	Geen	Geen restricties
Zijlstra	Longarts-Intensivist, Dijklander Ziekenhuis, betaald	Geen	Geen	Geen restricties
Van Bommel	Intensivist, Erasmus Medisch Centrum Rotterdam. Werkzaam als staflid op de Intensive Care Volwassenen (betaald).	Geen	Geen	Geen restricties
Meijer	Chirurg-intensivist bij de Noordwest Ziekenhuisgroep (1,0 FTE)	Lid toelatingscommissie binnen Noordwestziekenhuisgroep (onbetaald) Instructeur bij stichting ALSG voor de cursussen ATLS, MedicALS en MRMI (betaald, d.w.z.het ziekenhuis krijgt mijn vergoeding). Lid van de GIC (gemeenschappelijke intensivisten commissie), namens de Nederlandse Vereniging voor Heelkunde. Bestuurslid stichting “ For Wis(h)dom Foundation (onbetaald) zie forwishdom.org. Een goede doelen stichting die een bijdrage levert naar de behandeling van zeldzame ziektes.	Geen	Geen restricties
Van Duijvenbode – den Dekker	IC verpleegkundige, Amphia Ziekenhuis	Docent Erasmus MC Academie Bestuurslid V&VN IC Commissielid NKIC	Geen	Geen restricties
Van Mol	Assistant Professor Erasmus MC, Intensive Care Volwassenen	Bestuurslid Stichting FCIC (onbezoldigd) Bestuurslid V&VN-IC (onbezoldigd) Commissielid N&AHP bij ESICM (onbezoldigd)	1. ZonMw - hoofdonderzoeker van de ICNaVen-studie, ontwikkelen digitale ondersteuning in IC-nazorg voor naasten van een IC-patiënt. Dit is een multicenter studie (nationale en internationale samenwerking) waarbij eerst de behoeften en prioritering wordt verkend en vervolgens een daarop aangepaste interventie wordt ontwikkeld. (Projectleider) 2. ZonMw - hoofdonderzoeker van de DIPIC-studie, een implementatiestudie voor een digitaal dagboek op de IC, als opmaat naar persoonsgerichte zorg. Dit is een multicenter studie in een multi-methods benadering, om het gebruik van een digitaal dagboek op de IC te stimuleren. (Projectleider) 3. ZonMw - Ik ben mede-onderzoeker bij het ontwikkelen van een PGO-IC(na)zorg. Hierbij wordt in co-creatie met verschillende stakholders en Quli een digitale omgeving specifiek ingericht op de voormalig IC-patiënt. Stichting FCIC is penvoerder. (Projectleider)	Geen restricties
Zegers	Associate Professor Radboudumc	Geen	1. Zorginstituut - Evaluatie van IC Nazorg (Projectleider) 2. ZonMw/NWO- Evaluatie van de kosten-effectivieti van IC-zorg (Projectleider) 3. NFU-Zire (Projectleider) 4. ZonMw - Safety 2 (Projectleider)	Geen restricties
Koning	Anesthesioloog-intensivist, Rijnstate Ziekenhuis, Arnhem	Geen	Geen	Geen restricties
Moviat	Intensivist Jeroen Bosch ziekenhuis	FCCS instructeur	Geen	Geen restricties
Rood (tot 11-03-2024)	Senior onderzoeker - Projectleider, HAN University of applied sciences	Vicevoorzitter, V&VN-IC, beroepsvereniging van IC verpleegkundigen	Ja, NWO Raak SIA NWO RAAK SIA Familieparticipatie op de IC	Geen restricties
Stilma (vanaf 11-03-2024)	Hogeschool hoofddocent bij cluster verpleegkunde, Hogeschool van Amsterdam, Amsterdam (0,8 FTE)	Bestuurslid V&VN-IC Betrokken bij: De Duurzame Verpleegkundige en De Groene IC	1. NWO - NWO docentenbeurs - promotietraject (Projectleider) 2. KIEM-MV - Circulaire kansen beademingszorg (Projectleider)	Geen restricties
Van Vliet	Intensivist / Haaglanden Medisch Centrum	Bestuursvoorzitter MuzIC (onbetaald) Docent RTG: docent voor de practitioner opleiding, specifiek gericht op uitstroomprofiel 'neural practitioner' (betaald) Docent opleiding IC-verpleegkundigen LUMC: docent voor de onderwerpen 'neuro-IC' (betaald) ATLS instructeur (onbetaald) Docent bij de Hogeschool Utrecht bij de PA-opleiding (betaald)	Geen	Geen restricties
Weyer	Cardioloog-intensivist Elkerliek ziekenhuis Helmond	FCCS instructeur	Geen	Geen restricties
De Kleijn	Physician assistant Intensive care Catharina ziekenhuis Eindhoven	Commissielid NVIC richtlijnontwikkeling lid NAPA vakgroep intensive care	Geen	Geen restricties
Droogh	Intensivist, UMCG	Voorzitter commissie transport NVIC, onbetaald Hoofd MICU UMCG	Geen	Geen restricties
Van Westeloo	Intensivist LUMC	MICU Zuidwest Nederland Eurocross	Circadiaan onderzoek Philips	Geen restricties
Jacobs	Intensivist Elkerliek Ziekenhuis Helmond	Geen	Geen	Geen restricties
Heij	Intensivist, Spaarne Gasthuis	Bestuurslid NVIC, onkostenvergoeding Voorzitter cie Beroepsbelangen NVIC Lid ledenraad LAD, onkostenvergoeding	Geen	Geen restricties
Rozendaal	Verpleegkundig Specialist IC/MC St. Antoniusziekenhuis	Docent respiratie en beademing, St. Antoniusacademie (parttime)	Geen	Geen restricties
Janssen	Adviseur Kennisinstituut FMS	Promovendus UMCU	Geen	Geen restricties
Hofstede	Senior adviseur Kennisinstituut FMS	Geen	Geen	Geen restricties

Inbreng patiëntenperspectief

Er werd aandacht besteed aan het patiëntenperspectief door een afgevaardigde patiëntenvereniging in de werkgroep (FCIC/IC-Connect). De verkregen input is meegenomen bij het opstellen van de uitgangsvragen, de keuze voor de uitkomstmaten en bij het opstellen van de overwegingen. De conceptleidraad is tevens voor commentaar voorgelegd aan de FCIC/IC-Connect en de eventueel aangeleverde commentaren zijn bekeken en verwerkt.

Kwalitatieve raming van mogelijke financiële gevolgen in het kader van de Wkkgz

Bij de leidraad voerde de werkgroep conform de Wet kwaliteit, klachten en geschillen zorg (Wkkgz) een kwalitatieve raming uit om te beoordelen of de aanbevelingen mogelijk leiden tot substantiële financiële gevolgen. Bij het uitvoeren van deze beoordeling is de richtlijnmodule op verschillende domeinen getoetst (zie het stroomschema bij Werkwijze).

Module	Uitkomst raming	Toelichting
Formatie verpleegkundigen	geen financiële gevolgen	De norm van 3,5 fte is onveranderd gebleven. Nieuw is dat maximaal 10% ook uit andere opgeleide verpleegkundigen mag bestaan. Dit zal naar verwachting een geringe, niet substantiële kostenbesparing opleveren.

Werkwijze

Achtergrond voor de herziening

In 2006 is de eerste kwaliteitsstandaard over de organisatie van de intensive care gepubliceerd en in werking getreden (NVA, 2006). In 2016 werd een herziene kwaliteitsstandaard gepubliceerd door het Zorginstituut Nederland (2016). Deze kwaliteitsstandaard werd vanuit de NVIC aangevuld met de zogenaamde blauwdruk (NVIC, 2021). Daaruit werd een visitatie normenkader ontwikkeld, wat deel uit maakt van de feitelijke handhaving en controle op de kwaliteit door de NVIC (NVIC, 2022).

De kwaliteitsstandaard uit 2016 had een looptijd van vijf jaar en moest na vijf jaar worden geëvalueerd en herzien. Door de COVID-19 pandemie kon de evaluatie pas in 2022 plaatsvinden. De NVIC benoemde een werkgroep die de evaluatie uitvoerde door middel van een enquête die werd gevolgd door interviews (NVIC, 2023). Het Kennisinstituut van de Federatie Medisch Specialisten ondersteunde deze evaluatie. In 2023 is gestart met de herziening van de kwaliteitsstandaard. Gezien de organisatorische aard van de uitgangsvragen, wordt de herziene versie een leidraad genoemd. Dit sluit aan bij de beschreven definities in het rapport Medisch Specialistische Richtlijnen 3.0.

Tijdens de voorbereidende fase voor deze herziening inventariseerde de werkgroep middels de evaluatie van kwaliteitsstandaard en een invitational conference de knelpunten met betrekking tot de organisatie van intensive care afdelingen. Op basis van de uitkomsten van de knelpuntenanalyse zijn door de werkgroep concept-uitgangsvragen opgesteld en definitief vastgesteld.

Aan de start van het proces is met de werkgroep besproken hoe de uitgangsvragen onderbouwd kunnen worden. De werkgroep heeft gekozen voor een combinatie van uitgangsvragen met en zonder literatuursearch. Dit vanwege het organisatorische karakter van de leidraad en specifieke situaties die alleen in Nederland van toepassing zijn. Een uitgebreide beschrijving van de strategie voor zoeken en selecteren van literatuur is te vinden onder ‘Zoeken en selecteren’ onder Onderbouwing. De beoordeling van de kracht van het wetenschappelijke bewijs wordt hieronder toegelicht. Daar waar de literatuur geen antwoord leverde, werd gebruik gemaakt van expert opinie.

Relevante conceptmodules zijn vóór de commentaar- en autorisatiefase eerst nog langs partijen uit de klankbordgroep gestuurd voor input. Binnen de NVIC en de V&VN betrof het de beroepsbelangen commissie, de visitatiecommissie NKIC, de richtlijncommissie, de werkgroep beroepsprofiel, het landelijk netwerk van ICs, de transportcommissie van de NVIC, de V&VN-IC, de V&VN-VS en de besturen van NVIC en V&VN. Buiten de NVIC betrof het, VPned (de vereniging voor practitioners) en NAPA (Nederlandse Associatie Physician Assistants), de NICE (Nationale Intensive Care Evaluatie), en de LHIC (landelijke IC hoofden overleg).

De conceptleidraadmodule werd aan de betrokken (wetenschappelijke) verenigingen en (patiënt) organisaties voorgelegd ter commentaar. De commentaren werden verzameld en besproken met de werkgroep. Tijdens de commentaarfase heeft tevens een Webinar plaatsgevonden (d.d. 07-01-2025). Naar aanleiding van de commentaren werd de conceptleidraadmodule aangepast en definitief vastgesteld door de werkgroep. De definitieve leidraadmodule werd aan de deelnemende (wetenschappelijke) verenigingen en (patiënt) organisaties voorgelegd voor autorisatie en door hen geautoriseerd dan wel geaccordeerd.

Voor meer details over de gebruikte richtlijnmethodologie verwijzen wij u naar de Werkwijze.

Zoekverantwoording

Algemene informatie

Cluster/richtlijn: NVIC – herziening leidraad organisatie van de intensive care
Uitgangsvraag/modules: Wat is de benodigde formatie intensivisten, verpleegkundigen op de IC
Database(s): Embase.com, Ovid/Medline, Cinahl	Datum: 25-10-2023
Periode: vanaf 2000	Talen: geen restrictie

Zoekopbrengst

	EMBASE	OVID/MEDLINE	CINAHL	Ontdubbeld
SR	103	70	85	128
RCT	297	190	545	844
Observationele studies	715	593	382	995
Totaal	1115	853	1012	*2021

^{*in Rayyan}

Zoekstrategie

Embase.com

No.	Query	Results
#1	'intensive care'/de OR 'intensive care unit'/exp OR 'artificial feeding'/exp OR 'artificial ventilation'/exp OR 'early goal-directed therapy'/exp OR 'sepsis'/exp OR 'acute respiratory failure'/exp OR 'respiratory tract intubation'/exp OR (((intensive OR critical OR medium) NEAR/2 care):ti,ab,kw) OR 'critically ill':ti,ab,kw OR 'acutely ill':ti,ab,kw OR weaning:ti,kw OR (((mechanical* OR artificial) NEAR/2 ventilat*):ti,ab,kw)	1164410
#2	(((optim* OR intensivist* OR physician* OR nurse* OR specialist* OR workforce OR requirement* OR necessit* OR demand* OR obligation* OR personnel) NEAR/3 staffing):ti,ab,kw) OR 'workforce optimiz*':ti,ab,kw	4618
#3	((intensivist* OR physician* OR nurs* OR staff* OR specialist* OR workforce OR workload) NEAR/4 ratio*):ti,ab,kw	7291
#4	'personnel management'/exp OR 'intensivist model*':ti,ab,kw	101164
#5	#2 OR #3 OR #4	109681
#6	#1 AND #5	4792
#7	#6 AND [2000-2023]/py NOT ('conference abstract'/it OR 'editorial'/it OR 'letter'/it OR 'note'/it) NOT (('animal'/exp OR 'animal experiment'/exp OR 'animal model'/exp OR 'nonhuman'/exp) NOT 'human'/exp) NOT (('adolescent'/exp OR 'child'/exp OR adolescent:ti,ab,kw OR child:ti,ab,kw OR schoolchild:ti,ab,kw OR infant:ti,ab,kw OR girl:ti,ab,kw OR boy:ti,ab,kw OR teen:ti,ab,kw OR teens:ti,ab,kw OR teenager:ti,ab,kw OR youth:ti,ab,kw OR pediatr:ti,ab,kw OR paediatr:ti,ab,kw OR puber:ti,ab,kw) NOT ('adult'/exp OR 'aged'/exp OR 'middle aged'/exp OR adult:ti,ab,kw OR man:ti,ab,kw OR men:ti,ab,kw OR woman:ti,ab,kw OR women:ti,ab,kw))	2355
#8	'meta analysis'/exp OR 'meta analysis (topic)'/exp OR metaanaly:ti,ab OR 'meta analy':ti,ab OR metanaly:ti,ab OR 'systematic review'/de OR 'cochrane database of systematic reviews'/jt OR prisma:ti,ab OR prospero:ti,ab OR (((systemati OR scoping OR umbrella OR 'structured literature') NEAR/3 (review* OR overview)):ti,ab) OR ((systemic NEAR/1 review):ti,ab) OR (((systemati OR literature OR database* OR 'data base') NEAR/10 search):ti,ab) OR (((structured OR comprehensive* OR systemic) NEAR/3 search):ti,ab) OR (((literature NEAR/3 review):ti,ab) AND (search:ti,ab OR database:ti,ab OR 'data base':ti,ab)) OR (('data extraction':ti,ab OR 'data source':ti,ab) AND 'study selection':ti,ab) OR ('search strategy':ti,ab AND 'selection criteria':ti,ab) OR ('data source':ti,ab AND 'data synthesis':ti,ab) OR medline:ab OR pubmed:ab OR embase:ab OR cochrane:ab OR (((critical OR rapid) NEAR/2 (review* OR overview* OR synthes)):ti) OR ((((critical OR rapid) NEAR/3 (review OR overview* OR synthes)):ab) AND (search:ab OR database:ab OR 'data base':ab)) OR metasynthes:ti,ab OR 'meta synthes':ti,ab	969137
#9	'clinical trial'/exp OR 'randomization'/exp OR 'single blind procedure'/exp OR 'double blind procedure'/exp OR 'crossover procedure'/exp OR 'placebo'/exp OR 'prospective study'/exp OR rct:ab,ti OR random:ab,ti OR 'single blind':ab,ti OR 'randomised controlled trial':ab,ti OR 'randomized controlled trial'/exp OR placebo:ab,ti	3891996
#10	'major clinical study'/de OR 'clinical study'/de OR 'case control study'/de OR 'family study'/de OR 'longitudinal study'/de OR 'retrospective study'/de OR 'prospective study'/de OR 'comparative study'/de OR 'cohort analysis'/de OR ((cohort NEAR/1 (study OR studies)):ab,ti) OR (('case control' NEAR/1 (study OR studies)):ab,ti) OR (('follow up' NEAR/1 (study OR studies)):ab,ti) OR (observational NEAR/1 (study OR studies)) OR ((epidemiologic NEAR/1 (study OR studies)):ab,ti) OR (('cross sectional' NEAR/1 (study OR studies)):ab,ti)	7880253
#11	'case control study'/de OR 'comparative study'/exp OR 'control group'/de OR 'controlled study'/de OR 'controlled clinical trial'/de OR 'crossover procedure'/de OR 'double blind procedure'/de OR 'phase 2 clinical trial'/de OR 'phase 3 clinical trial'/de OR 'phase 4 clinical trial'/de OR 'pretest posttest design'/de OR 'pretest posttest control group design'/de OR 'quasi experimental study'/de OR 'single blind procedure'/de OR 'triple blind procedure'/de OR (((control OR controlled) NEAR/6 trial):ti,ab,kw) OR (((control OR controlled) NEAR/6 (study OR studies)):ti,ab,kw) OR (((control OR controlled) NEAR/1 active):ti,ab,kw) OR 'open label':ti,ab,kw OR (((double OR two OR three OR multi OR trial) NEAR/1 (arm OR arms)):ti,ab,kw) OR ((allocat NEAR/10 (arm OR arms)):ti,ab,kw) OR placebo:ti,ab,kw OR 'sham-control':ti,ab,kw OR (((single OR double OR triple OR assessor) NEAR/1 (blind* OR masked)):ti,ab,kw) OR nonrandom:ti,ab,kw OR 'non-random':ti,ab,kw OR 'quasi-experiment':ti,ab,kw OR crossover:ti,ab,kw OR 'cross over':ti,ab,kw OR 'parallel group':ti,ab,kw OR 'factorial trial':ti,ab,kw OR ((phase NEAR/5 (study OR trial)):ti,ab,kw) OR ((case* NEAR/6 (matched OR control)):ti,ab,kw) OR ((match NEAR/6 (pair OR pairs OR cohort* OR control* OR group* OR healthy OR age OR sex OR gender OR patient* OR subject* OR participant)):ti,ab,kw) OR ((propensity NEAR/6 (scor OR match)):ti,ab,kw) OR versus:ti OR vs:ti OR compar:ti OR ((compar* NEAR/1 study):ti,ab,kw) OR (('major clinical study'/de OR 'clinical study'/de OR 'cohort analysis'/de OR 'observational study'/de OR 'cross-sectional study'/de OR 'multicenter study'/de OR 'correlational study'/de OR 'follow up'/de OR cohort:ti,ab,kw OR 'follow up':ti,ab,kw OR followup:ti,ab,kw OR longitudinal:ti,ab,kw OR prospective:ti,ab,kw OR retrospective:ti,ab,kw OR observational:ti,ab,kw OR 'cross sectional':ti,ab,kw OR cross?ectional:ti,ab,kw OR multicent:ti,ab,kw OR 'multi-cent':ti,ab,kw OR consecutive:ti,ab,kw) AND (group:ti,ab,kw OR groups:ti,ab,kw OR subgroup:ti,ab,kw OR versus:ti,ab,kw OR vs:ti,ab,kw OR compar:ti,ab,kw OR 'odds ratio':ab OR 'relative odds':ab OR 'risk ratio':ab OR 'relative risk*':ab OR 'rate ratio':ab OR aor:ab OR arr:ab OR rrr:ab OR ((('or' OR 'rr') NEAR/6 ci):ab)))	14491257
#12	#7 AND #8 SR	103
#13	#7 AND #9 NOT #12 Clinical trials	297
#14	#7 AND (#10 OR #11) NOT #12 NOT #13 Observationeel	715
#15	#12 OR #13 OR #14	1115
#16	'intensivist physician-to-patient ratios and mortality in the intensive care unit'	1
#17	'patient mortality is associated with staff resources and workload in the icu: a multicenter observational study'	1
#18	'physician staffing patterns' AND pronovost	1
#19	'the effect of nurse-to-patient ratios on nurse-sensitive patient outcomes in acute specialist units'	1
#20	#16 OR #17 OR #18 OR #19	4
#21	#15 AND #20 sleutelartikelen gevonden	4

Ovid/Medline

#	Searches	Results
1	Critical Care/ or Critical Illness/ or Early Goal-Directed Therapy/ or exp Intensive Care Units/ or exp Sepsis/ or exp Respiratory Distress Syndrome/ or exp Respiration, Artificial/ or exp Intubation, Intratracheal/ or Ventilator Weaning/ or weaning.ti,ab,kf. or ((intensive or critical) adj2 care).ti,ab,kf. or critically ill.ti,ab,kf. or acutely ill.ti,ab,kf. or (mechanicalor artificial adj2 ventilat).ti,ab,kf. or intubat*.ti,ab,kf.	628796
2	"Personnel Staffing and Scheduling"/ or intensivist model.ti,ab,kf. or ((intensivist or physician* or nurs* or staff* or specialist* or workforce or workload) adj4 ratio).ti,ab,kf. or ((optim or intensivist* or physician* or nurse* or specialist* or workforce or requirement* or necessit* or demand* or obligation* or personnel) adj3 staffing).ti,ab,kf. or 'workforce optimiz*'.ti,ab,kf.	24838
3	1 and 2	2337
4	limit 3 to yr="2000 -Current"	1917
5	4 not ((exp animals/ or exp models, animal/) not humans/) not (letter/ or comment/ or editorial/) not ((Adolescent/ or Child/ or Infant/ or adolescen.ti,ab,kf. or child.ti,ab,kf. or schoolchild.ti,ab,kf. or infant.ti,ab,kf. or girl.ti,ab,kf. or boy.ti,ab,kf. or teen.ti,ab,kf. or teens.ti,ab,kf. or teenager.ti,ab,kf. or youth.ti,ab,kf. or pediatr.ti,ab,kf. or paediatr.ti,ab,kf. or puber.ti,ab,kf.) not (Adult/ or adult.ti,ab,kf. or man.ti,ab,kf. or men.ti,ab,kf. or woman.ti,ab,kf. or women.ti,ab,kf.))	1499
6	meta-analysis/ or meta-analysis as topic/ or (metaanaly* or meta-analy* or metanaly).ti,ab,kf. or systematic review/ or cochrane.jw. or (prisma or prospero).ti,ab,kf. or ((systemati or scoping or umbrella or "structured literature") adj3 (review* or overview)).ti,ab,kf. or (systemic adj1 review).ti,ab,kf. or ((systemati or literature or database* or data-base) adj10 search).ti,ab,kf. or ((structured or comprehensive* or systemic) adj3 search).ti,ab,kf. or ((literature adj3 review) and (search or database* or data-base)).ti,ab,kf. or (("data extraction" or "data source") and "study selection").ti,ab,kf. or ("search strategy" and "selection criteria").ti,ab,kf. or ("data source" and "data synthesis").ti,ab,kf. or (medline or pubmed or embase or cochrane).ab. or ((critical or rapid) adj2 (review or overview* or synthes)).ti. or (((critical or rapid) adj3 (review or overview* or synthes)) and (search or database* or data-base)).ab. or (metasynthes or meta-synthes*).ti,ab,kf.	701576
7	exp clinical trial/ or randomized controlled trial/ or exp clinical trials as topic/ or randomized controlled trials as topic/ or Random Allocation/ or Double-Blind Method/ or Single-Blind Method/ or (clinical trial, phase i or clinical trial, phase ii or clinical trial, phase iii or clinical trial, phase iv or controlled clinical trial or randomized controlled trial or multicenter study or clinical trial).pt. or random.ti,ab. or (clinic adj trial).tw. or ((singl or doubl* or treb* or tripl) adj (blind$3 or mask$3)).tw. or Placebos/ or placebo.tw.	2646742
8	Epidemiologic studies/ or case control studies/ or exp cohort studies/ or Controlled Before-After Studies/ or Case control.tw. or cohort.tw. or Cohort analy$.tw. or (Follow up adj (study or studies)).tw. or (observational adj (study or studies)).tw. or Longitudinal.tw. or Retrospective.tw. or prospective.tw. or consecutive*.tw. or Cross sectional.tw. or Cross-sectional studies/ or historically controlled study/ or interrupted time series analysis/ [Onder exp cohort studies vallen ook longitudinale, prospectieve en retrospectieve studies]	4561054
9	Case-control Studies/ or clinical trial, phase ii/ or clinical trial, phase iii/ or clinical trial, phase iv/ or comparative study/ or control groups/ or controlled before-after studies/ or controlled clinical trial/ or double-blind method/ or historically controlled study/ or matched-pair analysis/ or single-blind method/ or (((control or controlled) adj6 (study or studies or trial)) or (compar* adj (study or studies)) or ((control or controlled) adj1 active) or "open label" or ((double or two or three or multi or trial) adj (arm or arms)) or (allocat adj10 (arm or arms)) or placebo* or "sham-control" or ((single or double or triple or assessor) adj1 (blind or masked)) or nonrandom* or "non-random" or "quasi-experiment" or "parallel group" or "factorial trial" or "pretest posttest" or (phase adj5 (study or trial)) or (case adj6 (matched or control)) or (match adj6 (pair or pairs or cohort* or control* or group* or healthy or age or sex or gender or patient* or subject* or participant)) or (propensity adj6 (scor or match))).ti,ab,kf. or (confounding adj6 adjust).ti,ab. or (versus or vs or compar).ti. or ((exp cohort studies/ or epidemiologic studies/ or multicenter study/ or observational study/ or seroepidemiologic studies/ or (cohort or 'follow up' or followup or longitudinal* or prospective* or retrospective* or observational* or multicent* or 'multi-cent' or consecutive).ti,ab,kf.) and ((group or groups or subgroup* or versus or vs or compar).ti,ab,kf. or ('odds ratio' or 'relative odds' or 'risk ratio' or 'relative risk' or aor or arr or rrr).ab. or (("OR" or "RR") adj6 CI).ab.))	5538148
10	5 and 6 SR	70
11	(5 and 7) not 10 Clinical trials	190
12	(5 and (8 or 9)) not 10 not 11 OBS	593
13	10 or 11 or 12	853

Cinahl

#	Query	Results
S1	(MH "Intensive Care Units+") OR (MH "Critical Care Nursing+") OR (MH "Critical Care+") OR TI ("critical care" OR "intensive care) OR AB ("critical care" OR "intensive care)	117,581
S2	(MH "Personnel Staffing and Scheduling+") OR TI ((((optim* OR intensivist* OR physician* OR nurse* OR specialist* OR workforce OR requirement* OR necessit* OR demand* OR obligation* OR personnel) N3 staffing) OR "workforce optimiz" OR ((intensivist OR physician* OR nurs* OR staff* OR specialist* OR workforce OR workload) N4 ratio) OR "intensivist model") OR AB ((((optim* OR intensivist* OR physician* OR nurse* OR specialist* OR workforce OR requirement* OR necessit* OR demand* OR obligation* OR personnel) N3 staffing) OR "workforce optimiz" OR ((intensivist OR physician* OR nurs* OR staff* OR specialist* OR workforce OR workload) N4 ratio) OR "intensivist model")	39,468
S3	S1 AND S2	2,133
S4	S3 NOT ((MH ("Adolescence" OR "Child+") OR TI (adolescen* OR child* OR schoolchild* OR infant* OR girl* OR boy* OR teen OR teens OR teenager* OR youth* OR pediatr* OR paediatr* OR puber) OR AB (adolescen OR child* OR schoolchild* OR infant* OR girl* OR boy* OR teen OR teens OR teenager* OR youth* OR pediatr* OR paediatr* OR puber)) NOT (MH ("Adult+") OR TI (adult OR man OR men OR woman OR women) OR AB (adult* OR man OR men OR woman OR women)))	1,872
S5	(MH "Meta Analysis") or TX (meta-analy* or metanaly* or metaanaly* or meta analy) or TX (systematic N5 review) or (evidence N5 review) or (methodol N5 review) or (quantitativ N5 review) or TX (systematic N5 overview) or (evidence N5 overview) or (methodol N5 overview) or (quantitativ N5 overview) or TX (systematic N5 survey) or (evidence N5 survey) or (methodol N5 survey) or (quantitativ N5 survey) or TX (systematic N5 overview) or (evidence N5 overview) or (methodol N5 overview) or (quantitativ N5 overview) or TX (pool N2 data) or (combined N2 data) or (combining N2 data) or (pool* N2 trials) or (combined N2 trials) or (combining N2 trials) or (pool* N2 studies) or (combined N2 studies) or (combining N2 studies) or (pool* N2 results) or (combined N2 results) or (combining N2 results)	319,039
S6	(MH "Clinical Trials+") OR (PT (Clinical trial)) OR (MH "Random Assignment") OR (MH "Quantitative Studies") OR (TX ((clini* N1 trial) OR (singl N1 blind) OR (singl N1 mask) OR (doubl N1 blind) OR (doubl N1 mask) OR (tripl N1 blind) OR (tripl N1 mask) OR (random N1 allocat) OR placebo OR ((waitlist* OR (wait* and list)) and (control OR group)) OR "treatment as usual" OR tau OR (control* N3 (trial* OR study OR studies OR group*)) OR randomized OR randomised))	1,985,979
S7	(MH "Case Control Studies+") OR (MH "Case Studies") OR (MH "Cross Sectional Studies") OR (MH "Prospective Studies+") OR (MH "Retrospective Panel Studies") OR (MH "Correlational Studies") OR TI "case control" OR TI “case referent” OR AB “case referent” OR TI “case stud” OR AB “case stud” OR TI “case series” OR AB “case series” OR TI cohort OR AB cohort* OR TI “cross sectional” OR AB “cross sectional” OR TI “follow up” OR AB “follow up” OR TI longitudinal OR AB longitudinal OR TI retrospective* OR AB retrospective* OR TI prospective* OR AB prospective* OR TI observational OR AB observational OR TI “Controlled before and after” OR AB “Controlled before and after” OR TI “Interrupted time series” OR AB “Interrupted time series” OR TI Correlational OR AB Correlational	1,570,792
S8	S4 AND S5 SR	85
S9	S4 AND S6 NOT S8 Clinical trials	545
S10	S4 AND S7 NOT S8 NOT S9 OBS	382
S1	S8 OR S9 OR S10	1012

Richtlijnendatabase

Leidraad Organisatie van Intensive Care in Nederland

Leidraad Organisatie van Intensive Care in Nederland

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