Sodium-glucose cotransporter 2 Inhibitors and COVID-19 outcomes in type 2 diabetes patients: A population-based cohort study
Bibliographic record
Abstract
Introduction Sodium-glucose cotransporter 2 (SGLT2) inhibitors (SGLT-2i) have been suggested to be beneficial in the management of Coronavirus disease 2019 (COVID-19); however, animal and clinical data have been inconsistent. The objective of this study was to assess the risk of SARS-CoV-2 infection and poor COVID-19-related outcomes associated with SGLT-2i use in patients with type 2 diabetes. Methods This is a comparative population-based retrospective cohort study on new users of SGLT-2i or dipeptidyl peptidase-4 (DPP-4) inhibitors (DPP-4i) from January 1, 2012 to March 31, 2021 in Alberta, Canada. We assessed: 1) presence of a positive COVID-19 test (or seropositivity for SARS-CoV-2); 2) an all-cause event around positive COVID-19 test (hospital admission, emergency department visit, death); and 3) a COVID-19-specific-event(hospital admission, emergency department visit, death) around positive COVID-19 test. We estimated the hazard ratio (HR) and 95% Confidence interval (CI) using a conditional Cox proportional hazard regression after 1:1 high-dimensional propensity score (hdPS) matching. Results There were 37,079 SGLT-2i and 39,053 DPP-4i users (30,433 matched pairs). After adjustment, compared to DPP-4i, SGLT-2i use was minimally associated with a positive COVID-19 test [HR: 1.23; 95% CI: 1.02–1.49]. Results were statistically significant across secondary cohort comparators for the risk of a COVID-19-positive test. SGLT-2i was also associated with a higher risk in a COVID-19-specific event [HR: 1.66; 95% CI: 1.12–2.45] compared to DPP-4i. Conclusion SGLT-2i may be associated with a modest increase in positive COVID-19 tests across all compactors and COVID-19-specific events compared to DPP-4i among adults with type 2 diabetes. However, the clinical impact of this finding is uncertain. There is a need for further prospective studies to assess the relationship between SGLT-2i use and COVID-19-related outcomes in patients with type 2 diabetes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".