Dapagliflozin and Kidney Outcomes in Hospitalized Patients with COVID-19 Infection
Bibliographic record
Abstract
Background and objectives Patients who were hospitalized with coronavirus disease 2019 (COVID-19) infection are at high risk of AKI and KRT, especially in the presence of CKD. The Dapagliflozin in Respiratory Failure in Patients with COVID-19 (DARE-19) trial showed that in patients hospitalized with COVID-19, treatment with dapagliflozin versus placebo resulted in numerically fewer participants who experienced organ failure or death, although these differences were not statistically significant. We performed a secondary analysis of the DARE-19 trial to determine the efficacy and safety of dapagliflozin on kidney outcomes in the overall population and in prespecified subgroups of participants defined by baseline eGFR. Design, setting, participants, & measurements The DARE-19 trial randomized 1250 patients who were hospitalized (231 [18%] had eGFR <60 ml/min per 1.73 m 2 ) with COVID-19 and cardiometabolic risk factors to dapagliflozin or placebo. Dual primary outcomes (time to new or worsened organ dysfunction or death, and a hierarchical composite end point of recovery [change in clinical status by day 30]), and the key secondary kidney outcome (composite of AKI, KRT, or death), and safety were assessed in participants with baseline eGFR <60 and ≥60 ml/min per 1.73 m 2 . Results The effect of dapagliflozin versus placebo on the primary prevention outcome (hazard ratio, 0.80; 95% confidence interval, 0.58 to 1.10), primary recovery outcome (win ratio, 1.09; 95% confidence interval, 0.97 to 1.22), and the composite kidney outcome (hazard ratio, 0.74; 95% confidence interval, 0.50 to 1.07) were consistent across eGFR subgroups ( P for interaction: 0.98, 0.67, and 0.44, respectively). The effects of dapagliflozin on AKI were also similar in participants with eGFR <60 ml/min per 1.73 m 2 (hazard ratio, 0.71; 95% confidence interval, 0.29 to 1.77) and ≥60 ml/min per 1.73 m 2 (hazard ratio, 0.69; 95% confidence interval, 0.37 to 1.29). Dapagliflozin was well tolerated in participants with eGFR <60 and ≥60 ml/min per 1.73 m 2 . Conclusions The effects of dapagliflozin on primary and secondary outcomes in hospitalized participants with COVID-19 were consistent in those with eGFR below/above 60 ml/min per 1.73 m 2 . Dapagliflozin was well tolerated and did not increase the risk of AKI in participants with eGFR below or above 60 ml/min per 1.73 m 2 .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".