Impact of diabetes on <scp>COVID</scp>‐19 mortality and hospital outcomes from a global perspective: An umbrella systematic review and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: To date, COVID-19 has claimed 4.9 million lives. Diabetes has been identified as an independent risk factor of serious outcomes in people with COVID-19 infection. Whether that holds true across world regions uniformly has not been previously assessed. METHODS: This study offers the first umbrella systematic review and meta-analysis to analyse the collective and geographically stratified mortality, ICU admission, ventilation requirement, illness severity and discharge rate amongst patients with diabetes. Five databases (EMBASE, MEDLINE, CAB Abstracts, PsychInfo and Web of Science) and 3 additional sources (SSRN's eLibrary, Research Square and MedRxiv) were searched from inception to 30 August 2021. Prospective and retrospective cohort studies, reporting the association between diabetes and one or more COVID-19 hospitalization outcomes, were included. This meta-analysis was registered on PROSPERO, CRD42021278579. Abbreviated MeSH terms used for search were as follows: (Diabetes) AND (2019 Novel Coronavirus Disease), adapted per database requirements. Exclusion criteria exclusion criteria were as follows: (1) none of the primary or secondary outcomes of meta-analysis reported, (2) no confirmed COVID-19 infection (laboratory or clinical) and (3) no unexposed population (solely patients with diabetes included). Quality of the included studies were assessed using the Newcastle-Ottawa Scale (NOS) whilst quality of evidence by the GRADE framework. Studies that were clinically homogeneous were pooled. Summative data and heterogeneity were generated by the Cochrane platform RevMan (V. 5.4). RESULTS: Overall, 158 observational studies were included, with a total of 270,212 of participants, median age 59 [53-65 IQR] of who 56.5% were male. A total of 22 studies originated from EU, 90 from Far East, 16 from Middle East and 30 from America. Data were synthesized with mixed heterogeneity across outcomes. Pooled results highlighted those patients with diabetes were at a higher risk of COVID-19-related mortality, OR 1.87 [95%CI 1.61, 2.17]. ICU admissions increased across all studies for patients with diabetes, OR 1.59 [95%CI 1.15, 2.18], a result that was mainly skewed by Far East-originating studies, OR 1.94 [95%CI 1.51, 2.49]. Ventilation requirements were also increased amongst patients with diabetes worldwide, OR 1.44 [95%CI 1.20, 1.73] as well as their presentation with severe or critical condition, OR 2.88 [95%CI 2.29, 3.63]. HbA1C levels under <70 mmol and metformin use constituted protective factors in view of COVID-19 mortality, whilst the inverse was true for concurrent insulin use. CONCLUSIONS: Whilst diabetes constitutes a poor prognosticator for various COVID-19 infection outcomes, variability across world regions is significant and may skew overall trends.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".