A network meta‐analysis of association between cardiometabolic risk factors and <scp>COVID</scp>‐19 outcome severity
Bibliographic record
Abstract
BACKGROUND: Cardiometabolic comorbidities have been associated with a higher risk of COVID-19 severity and mortality, but more investigations are needed to determine which comorbidity is more detrimental. METHODS: Embase, Emcare, and MEDLINE were searched systematically for prospective and retrospective studies assessing the associations of cardiometabolic risk factors and COVID-19 outcomes of hospitalization, severity, and mortality among COVID-19-diagnosed patients. Literature search was performed from first publication to May 19, 2021. Study quality was assessed by the Newcastle-Ottawa Scale. RESULTS: From the literature search, 301 studies suggested that all included cardiometabolic risk factors were associated with a higher risk of COVID-19 hospitalization, severity, and mortality, except that overweight was associated with a decreased risk of mortality (relative risk [RR] 0.88; 95% CI, 0.80-0.98). Patients with diabetes (RR 1.46; 95% CI, 1.45-1.47) were most likely to be hospitalized; patients with heart failure had the highest risk for severe COVID-19 outcomes (RR 1.89; 95% CI, 1.71-2.09); while patients with stroke were most susceptible to overall mortality (RR 1.99; 95% CI, 1.90-2.08). In the network meta-analysis, cerebrovascular disease had the highest impact (RR 1.69; 95% CI, 1.65-1.73) on COVID-19 outcomes compared to other cardiometabolic risk factors. For different combinations of risk factors, cardiovascular disease and diabetes combined (RR 6.98; 95% CI, 5.28-9.22) was more detrimental than others. CONCLUSIONS: Considering the high prevalence of cardiometabolic comorbidities and risk of all severe outcomes, patients with cardiometabolic comorbidities should be prioritized in vaccination and treatment development of COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".