key: cord-274324-obhrbxu4 authors: Tian, Wenjie; Jiang, Wanlin; Yao, Jie; Nicholson, Christopher J.; Li, Rebecca H.; Sigurslid, Haakon H.; Wooster, Luke; Rotter, Jerome I.; Guo, Xiuqing; Malhotra, Rajeev title: Predictors of mortality in hospitalized COVID‐19 patients: A systematic review and meta‐analysis date: 2020-05-22 journal: J Med Virol DOI: 10.1002/jmv.26050 sha: doc_id: 274324 cord_uid: obhrbxu4 Mortality rates of coronavirus disease 2019 (COVID‐19) continue to rise across the world. Information regarding the predictors of mortality in COVID‐19 patients remains scarce. Herein, we performed a systematic review of published articles, from January 1 to April 24, 2020, to evaluate the risk factors associated with mortality in COVID‐19. Two investigators independently searched the articles and collected the data, in accordance with PRISMA guidelines. We looked for associations between mortality and patient characteristics, comorbidities, and laboratory abnormalities. A total of 14 studies documenting the outcomes of 4659 patients were included. The presence of comorbidities such as hypertension (OR 2.5; 95% CI 2.1‐3.1; P<0.00001), coronary heart disease (OR 3.8; 95% CI 2.1‐6.9; P<0.00001) and diabetes (OR 2.0; 95% CI 1.7‐2.3; P<0.00001) were associated with significantly higher risk of death amongst COVID‐19 patients. Those who died, compared to those who survived, differed on multiple biomarker levels on admission including elevated levels of cardiac troponin (+44.2 ng/L, 95% CI 19.0‐69.4; P=0.0006); C‐reactive protein (+66.3 µg/mL, 95% CI 46.7‐85.9; P<0.00001); interleukin‐6 (+4.6 ng/mL, 95% CI 3.6‐5.6; P<0.00001); D‐dimer (+4.6 µg/mL, 95% CI 2.8‐6.4; P<0.00001); creatinine (+15.3 µmol/L, 95% CI 6.2‐24.3; P=0.001) and alanine transaminase (+5.7 U/L, 95% CI 2.6‐8.8; P=0.0003); as well as decreased levels of albumin (‐3.7 g/L, 95% CI ‐5.3 to ‐2.1; P<0.00001). Individuals with underlying cardiometabolic disease and that present with evidence for acute inflammation and end‐organ damage are at higher risk of mortality due to COVID‐19 infection and should be managed with greater intensity. This article is protected by copyright. All rights reserved. Since its emergence in Wuhan, China in late 2019, COVID-19 has rapidly become a global threat and was officially declared a pandemic by the World Health Organization (WHO) on March 11, 2020. As of April 30, 2020, there have been more than 3.0 million global confirmed cases and greater than 230,000 fatalities due to COVID-19. The U.S. is now the epicenter of the outbreak, having recorded over 60,000 fatalities. However, the factors that predispose an individual to a higher risk of death from COVID-19 are poorly This article is protected by copyright. All rights reserved. understood. To optimize patient care and appropriately deploy health care resources during this pandemic, effective patient risk stratification is essential. Although prior COVID-19 meta-analyses have been published, they have focused on severity of disease rather than the clinical outcome of mortality. [1] [2] [3] [4] [5] [6] [7] These studies have begun to answer key clinical questions on COVID-19 evolution and outcomes, as well as potential risk factors leading to hospital and ICU admission. Indeed, it is now understood that old age, male sex, elevated inflammatory markers, and comorbidities such as hypertension and cardiovascular disease are strong risk factors for COVID-19-related hospitalization. 1, [3] [4] [5] [6] To date, several important meta-analyses have reported on the relationships between COVID-19 disease severity and mortality with specific comorbidities, 8-13 lab and imaging results, 7,14-17 and medication use, 18,19 although the assessment of mortality was limited in sample size. We aimed to add to our understanding of COVID-19 by conducting a systematic meta-analysis of published articles to comprehensively elucidate predictors of mortality in hospitalized COVID-19 patients. This article has been reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. 20 This article is protected by copyright. All rights reserved. We performed a retrospective, cross-sectional systematic review using PubMed, Google scholar, Web of Science, and China National Knowledge Infrastructure (CNKI) between January 1, 2020 and April 24, 2020 without language restriction. We used the following We also searched the references of meta-analyses or systematic review articles to avoid missing any eligible articles. The results from the initial search were screened for relevance by titles and abstracts by two independent investigators. The full texts were reviewed for the eligibility criteria ( Figure 1 ). Duplicate publications, reviews, editorials, case reports, family-based studies and those that reported pediatric-only cases were excluded. Clinical studies that did not clearly report death as on outcome were excluded. In addition, if two or more studies were published based on the same sample of patients by the same author, only the article with the highest quality was included. This article is protected by copyright. All rights reserved. Data extraction forms, including information on the authors, year of publication, country, region, hospital, sample size, age, gender, comorbidities (e.g. hypertension, diabetes), clinical symptoms (e.g. fever), and laboratory parameters (e.g. creatinine, D-dimer) were obtained independently by two investigators (post-doctoral fellows with either MD or MBBS-PhD and clinical research experience). For one study published in Chinese, data were extracted by Drs Tian and Jiang, who are fluent in Chinese. A third investigator checked the article list and corresponding data to ensure that no duplications were made and adjudicated any discrepancies. For quality assessment, we used the Agency for Healthcare Research and Quality (AHRQ) score checklist to assess the methodological quality of cross-sectional studies in this meta-analysis. 21 Two independent assessors evaluated the quality of studies as low (0-3), moderate (4-7) or high (8-11). Although there were varying levels of confidence based on the AHRQ score (Supplemental Table 1 ), we included all available studies to maximize the sample size and to enhance the generalizability of our findings. A meta-analysis was performed using the program Review Manager (RevMan) (https://training.cochrane.org/online-learning/core-software-cochrane-reviews/revman) to compare clinical features (hypertension, coronary heart disease/cardiovascular disease, This article is protected by copyright. All rights reserved. Accepted Article cerebrovascular disease, diabetes, chronic renal disease, smoking history, COPD) between COVID-19 patients who survived and those who did not survive. For laboratory data (i.e. continuous measures) we calculated weighted mean differences (WMD) and 95% confidence intervals (95% CI) in COVID-19 patients who survived versus those who did not survive whenever two or more studies reported a given parameter. We used the generic inverse variance method in RevMan to weight studies involved in the metaanalysis. A random-effects meta-analysis model was assumed given the fact that the effects being estimated in different studies may not be identical but follow some distribution. The width of this distribution describes the degree of heterogeneity. RevMan was also used to calculate measures of heterogeneity such as the Chi 2 and I 2 statistics and the Tau 2 statistic for random effects analysis. 22 The systematic search of articles published on or before April 24, 2020, identified 170 topic-related articles, of which 14 articles were included in the final study (Figure 1) . [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] The main characteristics of the included studies are reported in Table 1 . The results of the meta-analyses are summarized in Tables 2 and 3. In total, 4659 patients were included in the studies from China (2025, 13 studies) and New York (2634, 1 study combining data from 12 hospitals). Overall, 2681 participants (57.5%) were male, and the mean age of the patients enrolled, excluding instances where only partial age ranges were analyzed, was 59.8 years. Across all patients, significant comorbidities included This article is protected by copyright. All rights reserved. hypertension (43.6%), diabetes (23.8%), and coronary heart disease (CHD)/cardiovascular disease (12.4%). Fever (88.0%), fatigue (44.5%), and myalgia (21.1%) were common clinical manifestations. Overall, 1189 patients died (25.5%). The mortality rate amongst Chinese patients was 31.4% and for New York was 21.0%. However, unlike the New York study, some of the Chinese studies only included critically ill patients in their studies and therefore a comparison cannot be performed. Older age was associated with a higher risk of death (Supplemental Figure 1A , mean difference 15.6; 95% CI 12.5-18.6, P<0.00001). Further, male sex was associated with a higher risk of mortality ( Table 2 and Supplemental Figure 1B , OR 1.8; 95% CI 1.3-2.4, P=0.0003). There was no clear association between death and the presence of fever, myalgia, diarrhea, or hemoptysis in COVID-19 patients (Supplemental Figure 2) . Fatigue was more prevalent in patients that succumbed to COVID-19 versus those that survived (Supplemental Figure 2B , OR 1.6; 95% CI 1.1-2.5, P=0.03). We also observed a nonsignificant trend to suggest that increased length of time between the onset of symptoms and hospital admission correlated with a greater odds for death (Supplemental This article is protected by copyright. All rights reserved. In our meta-analysis, we found several comorbidities were associated with risk of mortality due to COVID-19 (summarized in Table 2 Common laboratory tests were evaluated for their association with mortality (Table 3, This article is protected by copyright. All rights reserved. Our systematic review and meta-analysis of 14 published articles involving 4659 patients is the first to provide a comprehensive analysis of the demographic features, comorbidities, and laboratory abnormalities that are associated with mortality in COVID-19. Across all studies included in the meta-analysis, a quarter of hospitalized patients died, which is higher than previously reported. 5 The majority of patients across both groups were male, which supports previous studies. 3, 5, 6 However, we report for the first time that a higher proportion of admitted men died than did admitted women. Consistent with other meta-analyses, hypertension was a common underlying condition amongst all patients across the collected studies (39.9%), and the prevalence was substantially higher (56.8%) in the non-survival group. 3, 5, 6 We observed that hypertension confers a greater than 2.5-fold increase in the odds of death from COVID-19, supporting previous studies. 8, 12 The second most prevalent comorbidity we assessed was diabetes, which was observed in approximately a quarter of all patients. Diabetes was associated with a 2-fold higher chance of death from COVID-19, which is consistent with previous meta-analyses. 9,10 Here, we found ~12% of hospitalized patients have underlying CHD/cardiovascular disease, which was also associated with a 3.8-fold increase in the odds of death. The prevailing data seems to suggest patients with underlying cardiovascular disease are more prone to severe outcomes of COVID-19, including death, as we found in our metaanalyses. 1, 5, 6, 8, 16, [39] [40] [41] [42] [43] [44] [45] The mechanisms underlying the association between cardiovascular This article is protected by copyright. All rights reserved. disease and COVID-19 remain to be determined but might be due to infection-related demand ischemia that devolves into myocardial injury or myocardial dysfunction and/or a viral-induced inflammatory storm causing shock and ensuing ischemic-related injury. In addition, a previous case report found evidence for direct viral infection of the myocardium. 46 Our meta-analysis found evidence that both myocardial injury and increased inflammation were more prevalent in the non-survival group. We and others have shown that higher levels of troponin I are found in non-survivors compared to survivors of COVID-19. 15, 16, 43, 45 Furthermore, we found that increased levels of inflammatory markers, such as CRP, IL-6, and ESR, were also observed in the nonsurvival group. In the current study, levels of BUN, creatinine, albumin, total bilirubin, ALT, and AST were indicative of abnormal kidney and liver function at the time of admission in nonsurvivors compared to survivors. Mortality was also associated with lower platelet count and elevated D-dimer levels, suggesting a possible coagulopathy in these patients. Moreover, we observed that patients in the non-survival group were more likely to have a higher WBC count and lower lymphocyte and CD4 + /CD8 + T cell counts. Taken together, these findings suggest that initial laboratory assessment is important for risk stratification of COVID-19 patients and that those demonstrating markers of end-organ dysfunction, inflammation, or coagulopathy are at increased risk of a poor outcome. Whether the virus alters biomarker levels directly, or that abnormal baseline levels predispose a higher individual risk for mortality to COVID-19, is not currently This article is protected by copyright. All rights reserved. understood. Various infections, including those caused by the SARS family of viruses, cause endothelial dysfunction, which is characterized by a diminished ability to produce nitric oxide and the release of inflammatory markers, such as CRP, ICAM-1 and VCAM-1. 47, 48 The unique marked affinity of coronaviruses to the host angiotensin-converting enzyme 2 (ACE2) receptor, which is expressed in endothelial cells of blood vessels, means a direct effect of SARS-COV-2 on the vascular endothelium is distinctly possible. 39, 43, 45, [49] [50] [51] Endothelial dysfunction manifested by reduced nitric oxide bioavailability is thought to be an early event in hypertension, diabetes, CHD, and even kidney dysfunction, which were shown here to be significantly associated with mortality in COVID-19 patients. [52] [53] [54] [55] [56] Interestingly, nitric oxide donors inhibit SARS-CoV infection of cells and improve cell survival. 57 It is possible, therefore, that underlying endothelial dysfunction, which is further exacerbated by COVID-19 infection, promotes a sequalae leading to adverse clinical events and death. Further studies will need to be conducted to investigate the specific role of endothelial dysfunction and nitric oxide in COVID-19 infection. The studies included in this meta-analysis were not randomized controlled trials, but retrospective studies, which are the only studies available during this pandemic. Furthermore, as with all meta-analyses, the limitations are mainly the availability of data, possible underlying heterogeneity of data, and the potential for publication bias. This was particularly apparent in analyzing some of the comorbidities (e.g. COPD and This article is protected by copyright. All rights reserved. cerebrovascular disease) and laboratory abnormalities, which were not uniformly assessed across all 14 studies. In general, few studies that report associations with death in COVID-19 are available at this time. Since all but one of our included studies were from China, the overall generalizability of the meta-analysis results must be interpreted with caution. Additional data from other geographical areas will be required to have a more complete picture of the predictors of mortality in COVID-19. The current understanding of COVID-19 epidemiology will be enhanced when clinical data from nations across the globe are available. Furthermore, because of the lack of access to individual patient data, we were unable to perform multivariable regression analyses to adjust for potential confounders in the non-survival vs. survival groups (e.g. age). In this meta-analysis, we found that baseline cardiometabolic disease and evidence of increased acute inflammation and end-organ damage (cardiac, renal, liver and hematologic) on admission were associated with increased risk of mortality due to COVID-19 infection. This information adds important pieces of clinical knowledge to the armamentarium that physicians need to manage COVID-19 patients and may help to inform discussions between patients and caregivers about risk stratification, management strategies, and allocation of healthcare resources and personnel during the COVID-19 pandemic. Sizes of data markers indicate weight of studies. IV = inverse variance, CI = confidence intervals, df = degrees of freedom. 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The authors confirm that there are no financial conflicts of interest. This article is protected by copyright. All rights reserved.