The Prevalence of Underlying Diseases and Comorbidities in COVID-19 Patients; an Updated Systematic Review and Meta-analysis.
Bibliographic record
Abstract
INTRODUCTION: Gaining knowledge about underlying diseases and associated comorbidities in patients with COVID-19 can be beneficial in developing a proper understanding of the disease prognosis as well as comprehensive management, and treatment of the disease. The aim of this study was to determine the prevalence of underlying diseases and associated comorbidities in COVID-19 patients using a systematic review and meta-analysis. METHODS: Major biomedical electronic databases, including Scopus, PubMed, Web of Science, CINAHL and EMBASE were searched for all relevant literature published in English from January to July 2020. Cross-sectional and retrospective studies reporting the prevalence of comorbid conditions such as acute cardiac injury, acute myocardial infarction, acute kidney injury, acute liver injury, shock, acute respiratory disease, and acute respiratory distress syndrome in patients with COVID-19 were included in the study. After selecting eligible studies, two authors extracted data of each study, independently, and any inconsistency was resolved through discussion with the third reviewer until reaching a consensus. The risk of bias was assessed by two independent research experts using the Newcastle-Ottawa Scale (NOS). The variance in the meta-analyses on prevalence was stabilized by double arcsine transformations. RESULTS: The pooled prevalence of acute respiratory injury in patients with COVID-19 was estimated as 34% (95% Cl: 10 - 57%). Also, the prevalence of acute kidney injury, acute liver injury, acute respiratory distress syndrome, and shock were estimated as 10% (95% Cl: 6 - 14%), 19% (95% Cl: 10 - 27%), 23 % (95% Cl: 19 - 27%), and 12 % (95% Cl: 5 - 19 %). CONCLUSION: According to this meta-analysis, comorbidities such as hypertension, acute liver and kidney injury, acute respiratory distress syndrome, shock, diabetes, and coronary heart disease seem to be a predisposing factor for symptomatic and severe COVID-19 infection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.038 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".