Risk Factors of the Severity of COVID-19: a Meta-Analysis

Rahman, Abdur; Sathi, Nusrat Jahan

doi:10.1101/2020.04.30.20086744

Cited by 15 publications

(16 citation statements)

References 28 publications

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“…Moreover, they showed that patients with a history of smoking are roughly 14 times more likely to develop progressive disease. Two systematic reviews also demonstrated that smoking is a risk factor for more severe disease in COVID-19 patients (14,15). The main principle proposed for this increase in disease severity among smokers, is that smoking results in augmented expression of ACE-2 receptors in lung tissues, and as shown previously, SARS-CoV-2 exploits ACE-2 receptors to enter the pneumocytes and proliferate in them (20,21).…”

Section: Discussionmentioning

confidence: 63%

“…patients and severity of the disease (19,20), many original articles and meta-analyses also indicated that smoker patients have a poorer prognosis compared to non-smokers (11,(13)(14)(15)21). Liu et al, evaluated 78 patients with COVID-19 and reported that patients with progressive disease were more likely to be smokers compared to patients who had improvement/stabilization (13).…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have assessed the effect of smoking on the prognosis of COVID-19 however, the results of these studies are controversial (4,(6)(7)(8)(9)(10)(11)(12). Many studies suggest that smoking makes individuals more susceptible to COVID-19 and results in higher risk of severe disease and ICU admission (11,(13)(14)(15). Whereas, some studies implicate that not only smoking does not cause harm but also can have a protective role for patients with COVID-19, due to anti-in ammatory effects of nicotine (6,16,17).…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Clinical, Laboratory and Radiological Findings in Iranian Smokers and non- Smokers Patients with COVID-19: A Case Control Study

Nikpouraghdam

Bahramifar

Ahmadi

et al. 2020

Preprint

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Objective: The current study compared the clinical, laboratory and radiological findings between groups of smokers and non- smokers Iranian patients with COVID-19. Methods: This was a case-control study done on 120 patients with COVID-19 that were admitted to Baqiyatallah hospital, Tehran, Iran during March to May 2020. Our patients were categorized into two groups: smokers (40 patients) and non-smokers (80 age and sex matched controls). Demographic and clinical characteristics, laboratory findings, imaging manifestations, and outcomes were compared between two age groups. Results: Regarding the comorbidities, no significant difference was observed between the smoker and non-smoker patients. The distribution of COVID-19 symptoms was not significantly different between smokers and non-smokers, except for chest pain and weakness, which were significantly more common in smokers with COVID-19. A significantly lower white blood cell count and neutrophils in peripheral blood sample of smokers however, no difference was found concerning lymphocyte count. Moreover, the RDW of smokers was significantly lower. Regarding the findings on CT scan of COVID-19 patients, no significant difference was found between smoker and non-smoker COVID-19 patients. One (2.5%) of deaths occurred in the smoker and 3 (3.8%) occurred in the non- smoker groups.Conclusion: Current findings showed that the clinical picture of smoker and non-smoker COVID-19 patients does not differ significantly.

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Section: Discussionmentioning

confidence: 63%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Clinical, Laboratory and Radiological Findings in Iranian Smokers and non- Smokers Patients with COVID-19: A Case Control Study

Nikpouraghdam

Bahramifar

Ahmadi

et al. 2020

Preprint

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“…Another large-scale British primary care study of more than 17 million subjects revealed similar risk factors as above 17 . There is also a relatively large literature on the study of risk factors associated with severe or fatal disease [8][9][10][11][18][19][20][21] . Some commonly reported risk factors included age, sex, obesity, diabetes, hypertension, cardiometabolic and respiratory disorders.…”

Section: Prediction Of Severity/mortalitymentioning

confidence: 99%

Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data

Wong

Xiang

2020

Preprint

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Background: COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with severe disease. Accurate prediction of those at risk of developing severe infections is also important clinically. Methods: Based on the UK Biobank (UKBB data), we built machine learning(ML) models to predict the risk of developing severe or fatal infections, and to evaluate the major risk factors involved. We first restricted the analysis to infected subjects, then performed analysis at a population level, considering those with no known infections as controls. Hospitalization was used as a proxy for severity. Totally 93 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (e.g. hematological/liver and renal function/metabolic parameters etc.), anthropometric measures and other risk factors (e.g. smoking/drinking habits) were included as predictors. XGboost (gradient boosted trees) was used for prediction and predictive performance was assessed by cross-validation. Variable importance was quantified by Shapley values and accuracy gain. Shapley dependency and interaction plots were used to evaluate the pattern of relationship between risk factors and outcomes. Results: A total of 1191 severe and 358 fatal cases were identified. For the analysis among infected individuals (N=1747), our prediction model achieved AUCs of 0.668 and 0.712 for severe and fatal infections respectively. Since only pre-diagnostic clinical data were available, the main objective of this analysis was to identify baseline risk factors. The top five contributing factors for severity were age, waist-hip ratio(WHR), HbA1c, number of drugs taken(cnt_tx) and gamma-glutamyl transferase levels. For prediction of mortality, the top features were age, systolic blood pressure, waist circumference (WC), urea and WHR. In subsequent analyses involving the whole UKBB population (N for controls=489987), the corresponding AUCs for severity and fatality were 0.669 and 0.749. The same top five risk factors were identified for both outcomes, namely age, cnt_tx, WC, WHR and cystatin C. We also uncovered other features of potential relevance, including testosterone, IGF-1 levels, red cell distribution width (RDW) and lymphocyte percentage. Conclusions: We identified a number of baseline clinical risk factors for severe/fatal infection by an ML approach. For example, age, central obesity, impaired renal function, multi-comorbidities and cardiometabolic abnormalities may predispose to poorer outcomes. The presented prediction models may be useful at a population level to help identify those susceptible to developing severe/fatal infections, hence facilitating targeted prevention strategies. Further replications in independent cohorts are required to verify our findings.

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“…Based on phylogeny, taxonomy, and established practice, on 11 February 2020, the World Health Organization (WHO) named the disease as COVID-19 [2]. WHO declared COVID-19 as a global emergency on 30 January 2020 [3] and as pandemic on 11 March 2020 [4]. Globally 213 countries are confronting the grave consequences of the ongoing COVID-19 pandemic.…”

mentioning

confidence: 99%

Risk factors associated with morbidity and mortality outcomes of COVID-19 patients on the 14^th and 28^th day of the disease course: a retrospective cohort study in Bangladesh

Islam¹,

Riaz²,

Islam³

et al. 2020

Preprint

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Diverse risk factors intercede the outcomes of COVID-19. We conducted this retrospective cohort study to identify the risk factors associated with morbidity and mortality outcomes with a cohort of 1016 COVID-19 patients diagnosed in May 2020. Data were collected by telephone-interview and reviewing records using a questionnaire and checklist. Morbidity (64.4% Vs. 6.0%) and mortality (2.3% Vs. 2.5%) outcomes varied between the 14th and 28th day. Morbidity risk factors included chronic obstructive pulmonary disease (COPD) (RR=1.19, RR=2.68) both on the 14th and 18th day while elderly (AOR=2.56) and smokeless tobacco (SLT) (AOR=2.17) on the 28th day. Mortality risk factors included elderly (AOR=10.14), COPD (RR=5.93), and SLT (AOR=2.25) on the 14th day, and elderly (AOR=24.37) and COPD (RR=2.72) on the 28th day. The morbidity risk was higher with chronic kidney disease (CKD) (RR=3.33) and chronic liver disease (CLD) (RR=3.99) on the 28th day. The mortality risk was higher with coronary heart disease (RR=4.54) and CLD (RR=9.66) on the 14th while with diabetes mellitus (RR=3.08, RR=2.08), hypertension (RR=3.14, RR=2.30), CKD (RR=8.97, RR=2.71), and malignant diseases (RR=10.29) on both 14th and 28th day. We must espouse program interventions considering the morbidity and mortality risk factors to condense the aggressive outcomes of COVID-19.

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Risk Factors of the Severity of COVID-19: a Meta-Analysis

Cited by 15 publications

References 28 publications

Comparison of Clinical, Laboratory and Radiological Findings in Iranian Smokers and non- Smokers Patients with COVID-19: A Case Control Study

Comparison of Clinical, Laboratory and Radiological Findings in Iranian Smokers and non- Smokers Patients with COVID-19: A Case Control Study

Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data

Risk factors associated with morbidity and mortality outcomes of COVID-19 patients on the 14^th and 28^th day of the disease course: a retrospective cohort study in Bangladesh

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Risk Factors of the Severity of COVID-19: a Meta-Analysis

Cited by 15 publications

References 28 publications

Comparison of Clinical, Laboratory and Radiological Findings in Iranian Smokers and non- Smokers Patients with COVID-19: A Case Control Study

Comparison of Clinical, Laboratory and Radiological Findings in Iranian Smokers and non- Smokers Patients with COVID-19: A Case Control Study

Uncovering clinical risk factors and prediction of severe COVID-19: A machine learning approach based on UK Biobank data

Risk factors associated with morbidity and mortality outcomes of COVID-19 patients on the 14th and 28th day of the disease course: a retrospective cohort study in Bangladesh

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Product

Resources

About

Risk factors associated with morbidity and mortality outcomes of COVID-19 patients on the 14^th and 28^th day of the disease course: a retrospective cohort study in Bangladesh