Dipeptidyl peptidase-4 inhibitor use and mortality in COVID-19 patients with diabetes mellitus: an updated systematic review and meta-analysis
Bibliographic record
Abstract
Background: Few observational studies have shown a beneficial effect of dipeptidyl peptidase-4 inhibitors (DPP4i) in patients with coronavirus disease 2019 (COVID-19), although results are not consistent. The present systematic review and meta-analysis was undertaken to provide a precise summary of the effect of DPP4i use (preadmission or in-hospital) and mortality in COVID-19 patients with diabetes mellitus (DM). Methods: PubMed and Google Scholar databases were systematically searched using appropriate keywords to 4 January 2021, to identify observational studies reporting mortality in COVID-19 patients with DM using DPP4i versus those not using DPP4i. Preadmission and in-hospital use of DPP4i were considered. Study quality was assessed using the Newcastle–Ottawa Scale. Unadjusted and adjusted pooled odds ratio (OR) with 95% confidence intervals (CIs) were calculated. Subgroup analysis was performed for studies reporting preadmission and in-hospital use of DPP4i. Results: We identified nine observational studies of high quality pooling data retrieved from 7008 COVID-19 patients with DM. The pooled analysis of unadjusted and adjusted data did not show any significant association between DPP4i use and mortality in COVID-19 patients with DM. However, on subgroup analysis, we found that in-hospital (and not preadmission) DPP4i use was associated with reduced mortality (unadjusted OR 0.37, 95% CI 0.23, 0.58, p < 0.0001, I 2 = 0% and adjusted OR 0.27, 95% CI 0.13, 0.55, p = 0.0003, I 2 = 12%). Conclusions: In-hospital use of DPP4i is associated with a significant reduction in COVID-19 mortality. Hence, it would be prudent to initiate or continue DPP4i in COVID-19 patients with DM if not contraindicated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".