De COVID-19 pandemie roept wereldwijd diverse vragen op bij zorgverleners, onderzoekers en patiënten. Patiënten met cardiovasculaire risicofactoren en/of cardiovasculaire aandoeningen zijn een kwetsbare groep op het gebied van infectieziekten. Dit is onder meer te verklaren door de effecten van de diverse cardiovasculaire risicofactoren op de immunologische functie en de betrokkenheid van het immuunsysteem bij het ontstaan van cardiovasculaire aandoeningen. Kennis over het risico op een ernstig ziektebeloop en overlijden bij deze groep patiënten is van belang voor een passende response op het gebied van voorzorgsmaatregelen, monitoring en therapie. Het tempo waarin de pandemie zich voltrekt, de globale verschillen in opname criteria, het ontbreken van voldoende testcapaciteiten en het ontbreken van systematische datacollectie zijn van invloed op de beschikbaarheid en kwaliteit van wetenschappelijke literatuur die inzicht kan verschaffen in de prevalentie van cardiovasculaire risicofactoren en cardiovasculaire aandoeningen bij COVID-19 patiënten. Gegeven het hiervoor genoemde is het zinvol om met regelmaat een kritische selectie te maken van beschikbare literatuur over de voorspellende waarde van cardiovasculaire risicofactoren en cardiovasculaire aandoeningen op het ziektebeloop en mortaliteit van COVID-19 patiënten en door middel van passende analyse modellen de data te doorgronden.

Mortality predicted by prognostic factors measured during hospital stay

Hospital admission predicted by prognostic factors measured during hospital stay

Hospital admission predicted by prognostic factors measured during hospital stay

Hospital admission was only reported in the study of Petrilli (2020). Because the model was unvalidated and was included in only one study, the results regarding prognostic factors for hospital admission will not be discussed.

Level of evidence of the literature

The level of evidence was assessed according to the GRADE methodology (GRADE: Grading Recommendations Assessment, Development and Evaluation, http://www.gradeworkinggroup.org/).

Mortality predicted by prognostic factors

Body Mass Index (BMI)

Starting with a high level of evidence for prognostic studies, the level of evidence regarding the outcome measure mortality was downgraded by 1 level because of risk of bias (some patients were still under treatment at the end of the study) and by 1 level because of indirectness (studies are not validated) and 1 level for imprecision (wide confidence intervals, one included in the confidence interval) to ‘very low’.

Smoking

Starting with a high level of evidence for prognostic studies, the level of evidence regarding the outcome measure mortality was downgraded by 1 level because of risk of bias (some patients were still under treatment at the end of the study) and by 1 level because of indirectness (studies are not validated ) and 1 level for imprecision (wide confidence intervals, one included in the confidence interval) to ‘very low’.

Hypertension

Starting with a high level of evidence for prognostic studies, the level of evidence regarding the outcome measure mortality was downgraded by 1 level because of risk of bias (some patients were still under treatment at the end of the study) and by 1 level because of indirectness (studies are not validated) and 1 level for imprecision (wide confidence intervals, too many prognostic factors included in relation to number of events) to ‘very low’.

Diabetes

Starting with a high level of evidence for prognostic studies, the level of evidence regarding the outcome measure mortality was downgraded by 1 level because of risk of bias (some patients were still under treatment at the end of the study) and by 1 level because of indirectness (studies are not validated ) ) and 1 level for imprecision (wide confidence intervals and one included in the confidence interval) to ‘very low’.

Coronary artery disease or congestive heart failure

Starting with a high level of evidence for prognostic studies, the level of evidence regarding the outcome measure mortality was downgraded by 1 level because of risk of bias (some patients were still under treatment at the end of the study), by 1 level because of indirectness (studies are not validated ) and by 1 level because of inconsistency of results to ‘very low’.

Cerebrovascular disease

Starting with a high level of evidence for prognostic studies, the level of evidence regarding the outcome measure mortality was downgraded by 1 level because of risk of bias (some patients were still under treatment at the end of the study), by 1 levels because of indirectness (studies are not validated ), by 1 level because of inconsistency of results and by 1 level because of number of included patients (imprecision) to ‘very low’.

Heart failure

Starting with a high level of evidence for prognostic studies, the level of evidence regarding the outcome measure mortality was downgraded by 1 level because of risk of bias (some patients were still under treatment at the end of the study), by 1 levels because of indirectness (studies are not validated), by 1 level because of inconsistency of results and by 1 level because of number of included patients (imprecision) to ‘very low’.

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

Which independent prognostic factors (cardiovascular risk factors or cardiovascular disease) strongly predict a poor outcome of COVID-19 infection, independent of other factors?

P:        All proven COVID-19 patients

I:         Presence of one of the following prognostic factors: cardiovascular risk factors such as smoking, obesity, hypercholesterolemia, hypertension, diabetes (insulin resistance, non-alcoholic steatohepatitis), cardiovascular disease, cardiovascular history (arrhythmias, coronary artery disease, heart failure, valvular heart disease)

C:        absence of the prognostic factors

O:       mortality (crucial), IC-admission (crucial), hospital admission, length of stay, thromboembolic complications (pulmonary embolism, stroke, transient ischemic attack)

T:        admission to hospital, admission to ICU, during hospital stay, at home

S:        in-hospital, pre-hospital

Confounder: age

Relevant outcome measures

Mortality and IC-admission were considered as critical outcome measures for decision making and the other outcomes as important outcomes for decision making.

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

A priori, the working group did not define minimal clinically relevant differences for the outcome measures.

Prognostic research: Study design and hierarchy

For this research question, it is aimed to investigate multiple cardiovascular factors that might predict disease severity and mortality in patients with COVID-19. To investigate whether factors are predictive, a longitudinal relation between candidate prognostic factors (measured at T0) and outcome (measured at T1) has to be investigated. Often, prognostic factors are correlated with other factors (Foroutan, 2020). To describe the effect of single prognostic factors, these should be measured in relation to its confounders. Mostly, important confounders can be predefined. Only models including all predefined confounders and measuring longitudinal associations between candidate predictors and outcome can be taken into account. When multiple factors are investigated, multivariable models predicting outcome are developed and will be used as a tool for clinical decision making.

When reviewing literature, there is a hierarchy in quality of individual studies. Preferably, models for clinical decision making show the efficacy of using multivariable models in healthcare. In this way, they are most helpful to decide which factors should be used in clinical practice. Differences in clinical decision making according to the developed model and the effect on patient related outcome should be measured after sufficient follow-up periods (Moons, 2008). If such studies are not available, studies validating the effects of developed prognostic multivariable models in other samples of the target population (external validation) are preferred as there is more confidence in the results of these studies compared to not externally validated studies. Most samples do not completely reflect the characteristics of the total population, resulting in deviated associations, possibly having consequences for conclusions. Studies validating prognostic multivariable models internally (e.g. bootstrapping or cross validation) or studies reporting unvalidated prognostic multivariable models can be used to answer the research question as well, but downgrading the level of evidence is obvious due to risk of bias and/or indirectness as it is not clear whether models perform sufficient in target populations. Due to the low confidence in the results of externally unvalidated models, specific results regarding factors of interest reported in such models will be described only if factors have been used in at least two multivariable models/studies. Univariable prognostic models (or multivariable prognostic models not taking confounders into account) cannot be graded as confidence in these models is too low.

Search and select (Methods)

The databases Medline (via OVID) and Embase (via Embase.com) were searched with relevant search terms until 9-6-2020. The systematic literature search resulted in 567 hits. See search strategy for detail. No studies investigating the impact of a multivariable cardiovascular prognostic model on healthcare regarding mortality, IC-admission, hospital admission, length of stay and/or thromboembolic complications were found. Studies developing and/or validating a multivariable prognostic model were selected based on the following criteria: systematic reviews, randomized controlled trials (RCTs) and observational studies (cohort studies) assessing the longitudinal relation between cardiovascular risk factors (smoking, obesity, hypercholesterolemia, hypertension, diabetes), cardiovascular disease and cardiovascular history (measured at hospital admission/during hospital stay), with mortality, IC-admission, hospital admission, length of stay, thromboembolic complications (measured at endpoint) in proven COVID-19 patients. Age was considered as confounder that had to be included in the multivariable models.

45 studies were initially selected based on title and abstract screening. After reading the full text, 37 studies were excluded (see the table with reasons for exclusion under the tab Methods), and 8 studies were included.

Some systematic reviews evaluated the association between cerebrovascular, cardiovascular disease, diabetes mellitus or hypertension and poor outcome in patients with COVID-19, for example Huang (2020), Li (2020), Lippi (2020), Pranata (2020a), Pranata (2020b) and Zheng (2020). These systematic reviews were not included, because associations were calculated without taking any confounders into account.

Results

Eight 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 table.