key: cord-1025009-0bo9qmk9 authors: Bellou, Vanesa; Tzoulaki, Ioanna; van Smeden, Maarten; Moons, Karel G. M.; Evangelou, Evangelos; Belbasis, Lazaros title: Prognostic factors for adverse outcomes in patients with COVID-19: a field-wide systematic review and meta-analysis date: 2021-06-25 journal: Eur Respir J DOI: 10.1183/13993003.02964-2020 sha: 9cf6916e11e42429dad5d87ca1a648538d7de9fe doc_id: 1025009 cord_uid: 0bo9qmk9 INTRODUCTION: The individual prognostic factors for COVID-19 are unclear. For this reason, we aimed to present a state-of-the-art systematic review and meta-analysis on the prognostic factors for adverse outcomes in COVID-19 patients. METHODS: We systematically reviewed PubMed from January 1, 2020 to July 26, 2020 to identify non-overlapping studies examining the association of any prognostic factor with any adverse outcome in patients with COVID-19. Random-effects meta-analysis was performed, and between-study heterogeneity was quantified using I(2) metric. Presence of small-study effects was assessed by applying the Egger's regression test. RESULTS: We identified 428 eligible articles, which were used in a total of 263 meta-analyses examining the association of 91 unique prognostic factors with 11 outcomes. Angiotensin-converting enzyme inhibitors, obstructive sleep apnea, pharyngalgia, history of venous thromboembolism, sex, coronary heart disease, cancer, chronic liver disease, chronic obstructive pulmonary disease, dementia, any immunosuppressive medication, peripheral arterial disease, rheumatological disease and smoking were associated with at least one outcome and had >1000 events, p-value <0.005, I(2) <50%, 95% prediction interval excluding the null value, and absence of small-study effects in the respective meta-analysis. The risk of bias assessment using the Quality In Prognosis Studies tool indicated high risk of bias in 302 of 428 articles for study participation, 389 articles for adjustment for other prognostic factors, and 396 articles for statistical analysis and reporting. CONCLUSIONS: Our findings could be used for prognostic model building and guide patients’ selection for randomised clinical trials. In December 2019, a cluster of pneumonia cases was reported in Wuhan, China, and subsequent epidemiological tracking identified a novel coronavirus (Severe Acute Respiratory Syndrome-Coronavirus-2, SARS-CoV-2) as the cause. 1 The SARS-CoV-2 has spread across all continents since then and caused a public health crisis. 2 As of February 2021, there have been more than 100 million confirmed cases with coronavirus disease 2019 (COVID-19) and more than 2 million deaths according to World Health Organization. 3 The ongoing public health emergency necessitates the discovery of reliable prognostic factors to guide clinical decision making and treatment plan tailored to the patient characteristics. These prognostic factors could also improve the design and analysis of future clinical trials and suggest novel insights on molecular pathways of the disease. 4, 5 There is a growing body of literature on COVID-19 patients, examining prognostic features of the disease. However, clinical decision-making and research guidance is often based on narrative reviews or low-quality studies. 6 There is yet no published effort to critically and systematically summarize the epidemiological evidence on the entire prognostic factor landscape for multiple adverse outcomes in COVID-19 patients. Our study aims to fill this gap by conducting the first comprehensive systematic review, critical appraisal and (in case of sufficient data) meta-analysis of all prognostic factors in patients with COVID-19 by applying state-of-the-art approaches. guideline and the latest guidance of the Prognosis Methods Group of the Cochrane Collaboration to design and report our meta-analysis. 7, 8 We designed the research question and data extraction of this systematic review according to the modified checklist for critical appraisal and data extraction for systematic reviews of prognostic factor research (CHARMS-PF). 5, 9 A protocol for this systematic review was published on Open Science Foundation (osf.io/382wj). We systematically searched PubMed from January 1, 2020 to July 26, 2020 to identify all studies examining any prognostic factor of any adverse outcome in patients with COVID-19. Our search algorithm was based on LitCovid 10, 11 and was the following: We additionally performed a reference screening of the eligible articles to identify additional potentially eligible articles. We considered as eligible observational studies or clinical trials that included patients with SARS-CoV-2 infection (defined by real-time polymerase chain reaction testing or standardized clinical/radiological criteria) and examined the effect of any prognostic factor on any outcome. We considered eligible any outcome that was relevant to patients if its definition and measurement was standardized or explicitly defined. We excluded outcomes measured at the time of diagnosis or hospital admission, and prognostic factors measured after diagnosis or hospital admission, to ensure temporality between prognostic factor and outcome. Prognostic factor was defined as "any measure that, among people with a given health condition, is associated with a subsequent clinical outcome." 12 We considered all types of prognostic factors including demographic and anthropometric individual characteristics, biomarkers, symptoms, clinical signs, medical history and comorbid diseases, medications, and findings in chest imaging. Table 1 shows a detailed description of the PICOTS (Population, Index prognostic factor, Comparator prognostic factor, Outcome, Timing, Setting) system. 5, 9, 13 2.3 Data Extraction Data extraction was performed independently by two researchers (VB, LB) following the CHARMS checklist. 5, 9 From each article, we extracted the first author, the year and the journal of publication, the geographic region and the involved hospital, the recruitment period, the examined prognostic factors, and the examined outcomes and their definition. For each prognostic factor, we extracted the reported measure of association (i.e., odds ratio, risk ratio, or hazard ratio) and the level of comparison. We extracted both univariable and multivariable prognostic factor's effect estimates, whenever available. 5, 8 2.4 The presence of bias in the eligible studies was assessed independently by two researchers (VB, LB) using the Quality In Prognosis Studies (QUIPS) tool. QUIPS assesses 6 domains: participation, attrition, prognostic factor measurement, adjustment for other prognostic factors, outcome measurement and analysis and reporting. 5, 14 In each one domain apart from adjustment for other prognostic factors, risk of bias was assessed as low, moderate, or high based on a prespecified set of questions. In the absence of a reported prognostic effect size, we calculated the univariable odds ratio (OR) and its standard error from the reported 2 × 2 contingency table. When zero counts occurred in a cell of the contingency table, we applied the Haldane-Anscombe correction. 15 For associations examined in at least 5 studies, we estimated the summary effect estimate and its 95% confidence interval (CI) applying the DerSimonian and Laird random-effects model, because methodological heterogeneity was expected between the eligible studies. 16, 17 To minimize the effect of different outcome definitions on betweenstudy heterogeneity, we combined only studies using the same outcome definition. A statistically significant effect was claimed at P-value <0.05. Also, between-study heterogeneity was quantified by the I 2 statistic. 18, 19 Values greater than 50% and 75% were judged as large and very large heterogeneity, respectively. We estimated the 95% prediction interval, which further accounts the uncertainty for the effect that would be expected in a new study addressing the same association. 16 We assessed whether there was evidence for small-study effects with the Egger's regression asymmetry test. 20 The presence of small-study effects was based on a statistically significant Egger's test at P-value <0.10 combined by a more conservative effect in the largest study of the meta-analysis compared to the effect in the random-effects metaanalysis. We performed three sensitivity analyses to explore potential sources of betweenstudy heterogeneity by excluding (a) studies presenting hazard ratios, (b) studies with a sample smaller than 100 COVID-19 patients, and (c) studies including only individuals with specific comorbidities. Then, we estimated the Spearman correlation coefficient to examine the correlation of effect estimate, I 2 statistic and P-value in the main analysis and the sensitivity analyses. We also examined which associations presented a highly significant effect at P-value <0.005, absence of large or very large between-study heterogeneity (i.e., I 2 <50%), 95% prediction interval excluding the null value, and more than 1000 events. The rationale for the use of a strict P-value threshold is based on current recommendations to avoid falsepositive findings. [21] [22] [23] Statistical analysis was performed on R Statistical Software version 3.6.3 (Foundation for Statistical Computing, Vienna, Austria) and the packages "metafor" and "metareg" were used for all analyses. Additional details on the eligibility criteria, the data extraction process, and the risk of bias assessment are presented in the supplementary material. We screened a total of 36,661 articles, and we identified 428 eligible articles that were published between January 1, 2020 and July 26, 2020 and were included in a metaanalysis (Figure 1) . The majority of the included studies were from China (n=106), USA (n=98), Italy (n=65), Spain (n=34), France (n=26) and UK (n=20). Risk of bias assessment 424 of 428 articles (99%) that participated in at least one meta-analysis were graded as having high risk of bias in at least one domain (Figure 2 ). To specify on domains, high risk of bias was present in 302 articles (71%) for study participation, one article for study attrition (0.2%), 255 articles (60%) for prognostic factor measurement, 19 articles (4%) for outcome measurement, and 396 articles (93%) for statistical analysis and reporting. Also, 389 articles (91%) did not present a prognostic factor's effect estimate that was adjusted for other prognostic factors. The detailed assessment of the eligible articles per domain is presented in Supplementary Table 1. We performed a total of 263 meta-analyses focused on risk for mortality (n=89 metaanalyses), ICU admission (n=48 meta-analyses), hospital admission (n=50 metaanalyses), two composite outcomes (n=24 meta-analyses), invasive mechanical ventilation (n=23 meta-analyses), acute kidney injury (n=9 meta-analyses), venous thromboembolism (n=6 meta-analyses), pulmonary embolism (n=5 meta-analyses), acute respiratory distress syndrome (n=5 meta-analyses), and deep venous thrombosis (n=4 meta-analyses). Overall, we evaluated 91 unique prognostic factors which were categorized into seven categories: biomarkers (n=16 meta-analyses, 11 unique factors), comorbidities (n=120 meta-analyses, 30 The median number of studies per meta-analysis was 9 (interquartile range [IQR], 6 -14) , the median number of events per meta-analysis was 1195 (IQR, 514 -2937) and the median number of COVID-19 participants per meta-analysis was 5131 (IQR, 2394 -13,395). One hundred and fifty-seven of 263 meta-analyses (60%) included more than 1000 events. One hundred and forty-nine of 263 meta-analyses (57%) presented a statistically significant effect at P-value <0.05, whereas 119 of them had a P-value <0.005. One hundred and thirty-seven of 263 meta-analyses (52%) presented large or very large between-study heterogeneity (I 2 >50%). Forty-eight of 263 meta-analyses (18%) presented a 95% prediction interval that excluded the null value. Also, 18 of 263 metaanalyses (7%) presented evidence for small-study effects. The results of the metaanalyses for the 263 associations are available in Supplementary Table 2 . A forest plot of the 29 associations with more than 1000 events, P-value <0.005, I 2 <50%, and no evidence of small-study effects is presented in Figure 3 . Of the 149 statistically significant meta-analyses at P-value <0.05, 16 meta-analyses included more than 1000 events, had a P-value <0.005 in random-effects model, I 2 <50%, 95% prediction interval excluding the null value and absence of small-study effects. These meta-analyses examined the effect of obstructive sleep apnea ( (Figure 4 ). We repeated 57 meta-analyses after excluding the studies that reported hazard ratios (HR) without information on 2 × 2 contingency A total of 103 meta-analyses were repeated after the exclusion of studies including only COVID-19 patients with a specific comorbid disorder (Supplementary Table 5) . Overall, we observed high correlation of summary effect estimate (rho = 0.99, P <0.05), P-value (rho = 0.98, P-value <0.05), and I 2 statistic (rho = 0.98, P-value <0.05) before and after the exclusion of these studies. We conducted a comprehensive systematic review, and meta-analysis to present an overview of the prognostic factors associated with any adverse outcome in patients diagnosed with COVID-19. We applied state-of-the-art approaches to combine the data from more than 420 studies by following the relevant methodological guidance. In our research effort, we considered more than 260 associations covering a wide range of predictors for multiple outcomes in COVID-19 patients. More than half of these associations presented a nominally significant effect, and only 16 of them provided strong evidence in terms of sample size, statistical significance, consistency, and lack of small-study effects. Below, we discuss the findings of our research effort with a focus on the most credible predictors, and the potential biases in these associations. Our risk of bias assessment indicated that almost all the eligible studies included in our systematic review presented high risk of bias in at least one domain of QUIPS tool. Most articles had high risk of bias in the domains of participation and statistical analysis and presented several statistical pitfalls, including absence of a regression technique or inappropriate modelling strategy and handling of missing data, and poor reporting of the time horizon of prediction. In accordance with the systematic review of prognostic and diagnostic models for COVID-19, 24 our systematic review of individual prognostic factors also indicated poor reporting of the length of follow-up, exclusion of participants who still had the disease at the end of the study period and inappropriate statistical methods. Our meta-analyses focused on unadjusted effect estimates, indicating that the prognostic effect of some prognostic factors is likely to become smaller if they were included in a multivariable prediction model. 5 We could not combine adjusted effect estimates, because they were only scarcely reported in the literature. Whenever adjusted effect estimates were presented, selection of covariates was not consistent among different studies, whereas the selection of covariates in the multivariable model either was not clearly described in the eligible studies or was inappropriately based on the results of univariable modelling. An additional observation of our systematic review, corroborating the poor research practices in this field, was the substantial use of overlapping populations to examine the same or correlated prognostic factors and/or outcomes in different articles. [25] [26] [27] Age and sex constitute known prognostic factors for many chronic diseases. Our analysis confirms that age is a prognostic factor related to hospitalization, and mortality, with a linear dose-response association with mortality. Although large between-study heterogeneity was observed in these meta-analyses, 95% prediction intervals excluded the null value. Also, sex was identified as a prognostic factor for ICU admission, acute kidney injury, invasive mechanical ventilation, and composite outcome (defined as ICU admission, and death) in male compared to female patients. In these associations small or moderate between-study heterogeneity was observed, but 95% prediction intervals included the null value for acute kidney injury, and composite outcome. The strong association of age and sex with adverse outcomes in COVID-19 patients indicates that they should be considered in multivariable prognostic models as covariates. Symptoms and clinical signs associated with adverse events in COVID-19 patients could be used as "red flags" for patients requiring enhanced monitoring or treatment. 28 Dyspnea was a predictor of hospitalization, oxygen saturation was a predictor of mortality, and both associations presented large between-study heterogeneity with 95% prediction intervals excluding the null value. Also, pharyngalgia was a predictor of hospitalization with absence of large between-study heterogeneity and 95% prediction interval excluding the null value. Additionally, smoking could be considered a prognostic factor of clinical deterioration because available evidence showed a consistent association of smoking with risk of mortality. Almost half of the meta-analyses examined comorbid diseases as prognostic factors of COVID-19 outcomes. SARS-CoV-2 causes systemic inflammation and multi-organ damage outside the respiratory system, therefore patients with pre-existing chronic disorders of these organs may be more prone to organ insufficiency. 29 Future studies should include a detailed description of the recruitment process and sample selection, apply an appropriate regression modelling technique for the statistical analysis and present prognostic effects adjusted at least for age and sex. The time horizon for the prediction should be clearly reported and hazard ratios should be the effect size metric of choice, because odds ratios and risk ratios are sensitive to changes based on the time horizon of the prediction contributing to between-study heterogeneity. Also, selective reporting of findings should be avoided to minimize the effect of publication bias in the reported associations. Based on a recently published comprehensive systematic review of COVID-19 related prediction models, the selection of predictors for model building is more appropriate to be based on previous literature than on a purely data driven approach. 24 Considering that our meta-analyses synthesized unadjusted prognostic effect estimates, our findings do highlight the prognostic factors that could constitute good predictors for adverse events in patients with COVID-19. Our findings could be used as a pool for candidate predictors in future efforts to develop a new prediction model or update existing ones for COVID-19 patients. The major strength of our study is that it provides an overall mapping, and statistical synthesis of all published studies examining prognostic factors for multiple health outcomes in COVID-19 patients. We especially focused on including only studies with non-overlapping populations in each meta-analysis. This was achieved through detailed scrutinization of the methodology of eligible articles to capture the period of recruitment, and the hospital setting of each study. Furthermore, we enhanced our systematic review and meta-analysis by a risk of bias assessment using QUIPS tool to identify the domains that could introduce bias to the prognostic factor's effect estimates. However, our study has some limitations. We observed high risk of bias in at least one domain in almost all the studies, and this fact could lead to biased prognostic factor's effect estimates in the meta-analyses. Furthermore, half of the meta-analyses presented large between-study heterogeneity, but sources of heterogeneity could not be adequately Incomplete reporting did not allow us to examine the effect of these characteristics by subgroup analyses, sensitivity analyses or meta-regression in the summary effect of the meta-analyses. Moreover, a meta-analysis was not feasible for outcomes that were not defined in a standardized manner across different studies, such as progression of COVID-19, acute myocardial injury, and acute liver injury. Our article systematically identified and assessed all the published studies examining prognostic factors for adverse outcomes in patients with COVID-19 published until the end of July 2020. We made an exhaustive and comprehensive effort to assess the quality of the studies and to exclude all possible duplicate studies and studies including overlapping population to avoid inflation of the reported summary effects. Our findings could facilitate the selection of candidate predictors for development or update of multivariable prognostic models. VB is supported by PhD scholarship funded by the Greek State Scholarships Foundation. No funding body has influenced data collection, analysis, or interpretation. Not needed. . Sankey diagram presenting the 29 statistically significant associations at Pvalue <0.005 that had more than 1000 events, I 2 <50%, absence of small-study effects. The thickness of each line connecting a prognostic factor with an outcome depends on the number of studies examining this association. We excluded studies that examined the diagnostic factors distinguishing COVID-19 pneumonia and community-acquired pneumonia; the prognostic factors for infections by other coronaviruses; and the predictors of a positive result in testing for SARS-COV-2 in patients suspected for COVID-19. Moreover, we excluded studies that performed propensity score matching, because this approach is appropriate for causal inference rather than prediction. We excluded case-series including less than 20 COVID-19 patients, and studies published in languages other than English. If the handling of a prognostic factor (i.e., dichotomous, categorical, or continuous) was not reported, we did not extract this association. For dichotomized continuous prognostic factors, we extracted the cut-off value. Also, when a measure of association was not reported, for binary factors, we extracted the number of individuals with the factor present in both patients with and without the outcome. For studies conducted in the same geographic region, we compared hospitals and recruitment periods to ensure that fully independent populations will be included in each meta-analysis. When multiple studies with potentially overlapping samples examined the same association, we kept the study that had the largest sample size for the corresponding meta-analysis. We also assessed whether the eligible studies applied a multivariable analysis in which they adjusted the associations of specific prognostic factors for other prognostic factors. For studies examining multiple prognostic factors and/or outcomes, we assessed the pertinent question with the highest risk of bias grade for the ascertainment of the prognostic factor and outcome. The eligible studies were assessed on the basis of the prognostic factors and the outcomes that participated in a meta-analysis. Note: For studies examining multiple prognostic factors and/or outcomes, we assessed the pertinent question with the highest risk of bias grade for the ascertainment of the prognostic factor and outcome. ** Two studies did not report the number of cases and/or total number of participants. *** Three studies did not report the number of cases and/or total number of participants. † The annotated meta-analyses fulfilled the criteria for the presence of small-study effects (a statistically significant Egger's test at P-value <0.10 and a more conservative effect in the largest study than in the random-effects meta-analysis. 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