key: cord-0738864-ml2jtvbt authors: Patel, Urvish; Malik, Preeti; Usman, Muhammad Shariq; Mehta, Deep; Sharma, Ashish; Malik, Faizan Ahmad; Khan, Nashmia; Siddiqi, Tariq Jamal; Ahmed, Jawad; Patel, Achint; Sacks, Henry title: Age-Adjusted Risk Factors Associated with Mortality and Mechanical Ventilation Utilization Amongst COVID-19 Hospitalizations—a Systematic Review and Meta-Analysis date: 2020-08-29 journal: SN Compr Clin Med DOI: 10.1007/s42399-020-00476-w sha: 52ccde32e8956de0c6057f907e69ff6650fad5ca doc_id: 738864 cord_uid: ml2jtvbt The increasing COVID-19 cases in the USA have led to overburdening of healthcare in regard to invasive mechanical ventilation (IMV) utilization as well as mortality. We aim to identify risk factors associated with poor outcomes (IMV and mortality) of COVID-19 hospitalized patients. A meta-analysis of observational studies with epidemiological characteristics of COVID-19 in PubMed, Web of Science, Scopus, and medRxiv from December 1, 2019 to May 31, 2020 following MOOSE guidelines was conducted. Twenty-nine full-text studies detailing epidemiological characteristics, symptoms, comorbidities, complications, and outcomes were included. Meta-regression was performed to evaluate effects of comorbidities, and complications on outcomes using a random-effects model. The pooled correlation coefficient (r), 95% CI, and OR were calculated. Of 29 studies (12,258 confirmed cases), 17 reported IMV and 21 reported deaths. The pooled prevalence of IMV was 23.3% (95% CI: 17.1–30.9%), and mortality was 13% (9.3–18%). The age-adjusted meta-regression models showed significant association of mortality with male (r: 0.14; OR: 1.15; 95% CI: 1.07–1.23; I(2): 95.2%), comorbidities including pre-existing cerebrovascular disease (r: 0.35; 1.42 (1.14–1.77); I(2): 96.1%), and chronic liver disease (r: 0.08; 1.08 (1.01–1.17); I(2): 96.23%), complications like septic shock (r: 0.099; 1.10 (1.02–1.2); I(2): 78.12%) and ARDS (r: 0.04; 1.04 (1.02–1.06); I(2): 90.3%), ICU admissions (r: 0.03; 1.03 (1.03–1.05); I(2): 95.21%), and IMV utilization (r: 0.05; 1.05 (1.03–1.07); I(2): 89.80%). Similarly, male (r: 0.08; 1.08 (1.02–1.15); I(2): 95%), comorbidities like pre-existing cerebrovascular disease (r: 0.29; 1.34 (1.09–1.63); I(2):93.4%), and cardiovascular disease (r: 0.28; 1.32 (1.1–1.58); I(2): 89.7%) had higher odds of IMV utilization. COVID-19 patients with comorbidities including cardiovascular disease, cerebrovascular disease, and chronic liver disease had poor outcomes. Diabetes and hypertension had higher prevalence but no association with mortality and IMV. Our study results will be helpful in right allocation of resources towards patients who need them the most. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s42399-020-00476-w) contains supplementary material, which is available to authorized users. The first confirmed case of coronavirus disease 2019 in the USA was reported on 20 January 2020 [1] . The USA now has more confirmed cases than any other country in the world. The number of cases exceeds 1.2 million with a death toll crossing 70,000 [2] . COVID-19 disease affects mainly the respiratory system [3] but there are studies showing the involvement of other systems as well [4, 5] . Studies have shown that a large number of admitted patients required mechanical ventilation [3, 6, 7] . The common point that these studies show is that the majority of these patients had some associated comorbid condition. The prevalence of diabetes is 10.5% [8] and hypertension is 29% [9] in the USA indicating how widespread some of these conditions are. Some other studies revealed that certain risk factors like preexisting cardiovascular, cerebrovascular diseases, age ≥ 65, CD3+CD8+ T cells ≤ 75 cell/μL, and cardiac troponin I ≥ 0.05 ng/mL and d-dimer > 1 μg/mL are associated with increased in-hospital mortality [5, [10] [11] [12] . Predicting the risk factors associated with the need for IMV and poor prognosis are thus of utmost importance given the overwhelming number of admissions of critical patients to the hospitals. Studying the correlation of various factors like demographics, comorbidities, and complications in COVID-19 patients with IMV utilization can help to redirect the limited resources towards patients who require them the most. The other aim of the paper is to identify predictors of mortality adjusted by age based on the same parameters. The predictors of mortality will also help clinicians in early identification of such patients in the course of admission which can save lives and decrease mortality due to COVID-19. The objective of this study was to evaluate the risk factors including comorbidities, and complications associated with the poor outcomes amongst COVID-19 patients. Endpoints Primary aim of this study was to evaluate the risk factors (ageadjusted) associated with poor outcomes (IMV and mortality) amongst patients with confirmed COVID-19 infection. Secondary outcome of the study was to evaluate demographic and clinical characteristics, comorbidities, and complications of COVID-19 patients. We have not considered recovery and ICU admission as outcomes due to variability in the definitions of recovery and utilization of IMV outside ICU. A systematic review was performed using MOOSE guidelines [13] . We searched PubMed, Web of Science, Scopus, and medRxiv for observational studies that described characteristics of COVID- 19 Abstracts were reviewed, and articles were retrieved and reviewed for availability of data on epidemiology of COVID-19. Studies mentioned details on IMV and mortality had been selected for quantitative analysis. UP and PM independently screened all identified studies and assessed full texts to decide eligibility. Any disagreement was resolved through discussion with other reviewers (SU and DM). From the included studies, data relating to patient characteristics like age and sex, symptoms like headache, fever, cough, diarrhea, dyspnea hemoptysis, myalgia/fatigue, nausea/ vomiting, sore throat, nasal congestion/rhinorrhea, and sputum production, comorbidities and risk factors like smoker, diabetes, hypertension, malignancy, pulmonary disease, chronic liver disease, cerebrovascular disease, and cardiovascular disease, complications like pneumonia, acute respiratory distress syndrome, septic shock, secondary infections, and cardiac complications, details on discharged/recovery and ICU admission, and outcomes like mortality and needs for IMV were collected using prespecified data collection forms by two authors (UP and PM) with a common consensus of authors (SU and TJ) upon disagreement. We have presented the study characteristics like publication year, country of origin, and sample size. Data on the following outcomes which were IMV utilization and mortality were extracted. The Newcastle-Ottawa Quality Assessment Scale [14] was used to evaluate the quality of the included studies and the risk of bias. We used all studies containing details on epidemiological characteristics in order to calculate pooled prevalence, 95% confidence interval (CI), and weights of demographic features, symptoms, comorbidities, risk factors, and complications rate amongst COVID-19 patients precisely. Metaregression was performed to evaluate the effects of comorbidities, risk factors, and complications on outcomes of COVID-19 patients. We used comprehensive meta-analysis software to estimate correlation coefficient (r) and 95% confidence interval (95% CI) and odds ratios (OR) (e^coefficient) with corresponding 95% CI were pooled using a random-effects model. The proportion of total between-study variance explained by the model identified using analogous index (R 2 ) and statistical heterogeneity across studies was reported using the I 2 statistics. The I 2 statistic of > 75% was considered significant heterogeneity. p < 0.05 was considered significant. Age-adjusted and unadjusted meta-regression were performed. Sensitivity analysis was also performed using the "leave-one-out method" to probe sources of heterogeneity. As of May 31, 2020, we included 29 observational studies (eSupplemental file (2)) with 12 Meta-regression random-effects models quantified the study level impact of comorbidities, risk factors, and complications in COVID-19 patients on IMV utilization, and mortality. Amongst COVID-19 patients, the age-adjusted meta-regression models showed strong association of mortality with male (r: 0.14; OR: 1.15; 95% CI: 1.07-1.23; p = 0.0001; I 2 : 95.2%), comorbidities including pre-existing cerebrovascular disease (r: 0.35; OR: 1.42; 95% CI: 1.14-1.77; p = 0.0018; I 2 : 96.1%), and chronic liver disease (r: 0.08; OR: 1.08; 95% CI: (Table 3) . eSupplemental file (4) shows age-adjusted meta-regression suggests incremental association between mortality (logevent) and pooled prevalence of male, ICU admission, IMV utilization, cerebrovascular disease, chronic liver disease, acute respiratory distress syndrome, septic shock, and cardiac complications. eSupplemental file (5) shows age-adjusted meta-regression suggests incremental association between IMV utilization (log-event) and pooled prevalence of male, cerebrovascular disease, chronic liver disease, cardiovascular disease, and acute respiratory distress syndrome. The heterogeneity analysis of the age-adjusted mortality and IMV showed 67-96% and 77-96% dispersion observed between studies, respectively. Additionally, overall studies had moderate risk of bias (eSupplemental file (6)). In our meta-regression analysis of 29 observational studies with 12,258 confirmed cases of COVID-19 patients, the pooled prevalence of IMV was 23.3%, and mortality was 13%. Male (57.3%) and those with pre-existing hypertension Meta-regression models are based on random effects * Not enough data to run the analysis # Statistically significant at p < 0.001 (28.2%), diabetes (15.4%), cardiovascular disease (12.2%), and cerebrovascular diseases (4.4%) had the highest prevalence in our study cohort. Our results are consistent with other studies from China and outside China [3, 6, 11, [15] [16] [17] [18] . Regardless of the variations in the sample size and the geographical locations, cardiovascular disease and hypertension remain the most common comorbidity PM [15, [19] [20] [21] [22] . The mortality rate for SARS-CoV was more than 10% and for MERS-CoV was more than 35%, and both are highly pathogenic organisms [23, 24] . The decreased vulnerability of females to viral infections may be assigned to X chromosome and sex hormone protectiveness, both of which play an important role in innate and adaptive immunity [25] . Furthermore, studies have reported that the majority of the COVID-19 patients had coexisting comorbidities, mainly cardiovascular and cerebrovascular diseases [17] and diabetes, similar to MERS-CoV [26] or any type of severe infectious disease that require hospital or ICU admission [27] . In our study, comorbidities like pre-existing cerebrovascular disease, cardiovascular disease, and chronic liver disease were significantly associated with increased odds of mortality and IMV utilization in COVID-19 patients. The outcomes in many studies are similar to ours [16, 28] . It is well known that some comorbidities frequently coexist, and such patients are more likely to have poor well-being. A study by Guan et al. has found significantly increased risk of poor outcomes in COVID-19 patients with at least one comorbidity, or even more compared with patients with no comorbidity [29] . They also reported that severe cases were more likely to have hypertension, cardiovascular diseases, cerebrovascular diseases, and diabetes compared with non-severe cases, suggesting that both the category and number of comorbidities should be taken into account when predicting COVID-19 patients' prognosis. There is an assumption that immune dysregulation and prolonged inflammation might be the key drivers of the poor clinical outcomes in COVID-19 but await verification in more mechanistic studies [29] . However, we found no association of hypertension and diabetes with mortality and IMV. To support our findings, a study predicting factors associated with mortality in COVID-19 pneumonia reported that mortality was not associated with malignancy or diabetes [10] . Until now, it is not evident whether the severity or level of control of pre-existing health conditions has affected the risk for severe disease in COVID-19 patients. Additionally, many of these comorbidities have high prevalence in the USA. According to the AHA 2020 report [30] , the prevalence of cardiovascular disease (excluding hypertension) was 10.6%. Considering the findings of our study, both highly prevalent comorbidities in COVID-19 patients in the USA and potential risk for more severe COVID-19 disease in patients with these comorbidities highlight the importance of COVID-19 prevention in people with underlying health conditions. Therefore, CDC continues to develop and update resources for persons with underlying health conditions to reduce the risk of acquiring COVID-19 [31] . Interestingly, there has not been published literature on the association of COVID-19 complications with poor outcomes. To our knowledge, this is the first study to report that COVID-19 patients with complications of ARDS have higher odds of mortality and IMV compared with those without ARDS. Hence, our study findings have added to the existing literature of common coexisting comorbidities and complications in patients with COVID-19 and its associated outcomes based on the large sample size and representing global population. To our knowledge, this is the first large population study that shows association between risk factors and outcomes, using meta-regression of 12,258 RT-PCR confirmed COVID-19 patients. Our findings may provide early insights into designing models for early identification of high-risk patients and prioritizing their treatment based on disease severity, which will help in prudent use of limited healthcare resources during this pandemic. A limitation of this study is missing details on severity of these risk factors. In addition, we have analyzed the group data of COVID-19 hospitalized patients, and individual patient meta-analysis would probably be able to better tease out relationships between multiple factors and reduce the risk of ecological fallacy while attempting to make inferences about individuals using study-level information. Also, since the primary studies are from very different healthcare systems, there may be uncaptured differences in ancillary care, criteria for IMV, ICU care, and etc. Due to non-identical effects being estimated in studies analyzed in our meta-regression, our study has high heterogeneity which we tried to justify using random-effects model and sensitivity analysis. Our study suggests that COVID-19 patients with coexisting comorbidities such as cardiovascular disease, cerebrovascular disease, and chronic liver disease had poor outcomes of death and IMV compared with those without it. Hence, our study results might be helpful for clinicians in proper triage of patients by watchfully talking about the medical history, as this will help in early identification of high-risk patients who would be more likely to develop serious adverse outcomes of COVID-19 which in turn will be helpful in appropriate allocation of healthcare resources. However, diabetes and hypertension had higher prevalence in the study cohort but no association with mortality and IMV. Future studies should focus specifically on these comorbidities and their associated outcomes. Data Availability The data is collected from the studies published online, publicly available, and specific details related to data and/or analysis will be made available upon request. Conflict of Interest The authors declare that they have no conflict of interest. Ethical Approval Though this article does not contain any studies with direct involvement of human participants or animals performed by any of the authors, all procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The data used in this study is deidentified and collected from the studies published online; thus, informed consent or IRB approval was not needed for this study. 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