Effects of dapagliflozin on hospitalisations in people with type 2 diabetes: post-hoc analyses of the DECLARE-TIMI 58 trial
Bibliographic record
Abstract
BACKGROUND: In people with type 2 diabetes at high risk of cardiovascular or kidney disease, sodium-glucose co-transporter 2 (SGLT2) inhibitors consistently reduce the risk of hospitalisations for heart failure. Less is known about their effects on hospitalisation from any cause, especially in people with type 2 diabetes without atherosclerotic cardiovascular disease, which includes most of the global population of people with type 2 diabetes. We aimed to assess the effect of the SGLT2 inhibitor, dapagliflozin, on the risks of hospitalisations for any cause and for specific causes in people with type 2 diabetes with and without atherosclerotic cardiovascular disease. METHODS: The DECLARE-TIMI 58 trial was a double-blind, multicentre, randomised, placebo-controlled study. People with type 2 diabetes and either risk factors for or established atherosclerotic cardiovascular disease were randomly assigned (1:1) to receive oral dapagliflozin 10 mg or placebo once daily. In these post-hoc analyses, the effects of dapagliflozin on risks of first non-elective any-cause and cause-specific hospitalisation were assessed with Cox proportional hazards regression models overall and in the subset of participants without prevalent atherosclerotic cardiovascular disease. The risk of total (first plus subsequent) non-elective hospitalisations was assessed with Lin-Wei-Ying-Yang model. Investigator-reported System Organ Class terms were used to classify cause-specific hospitalisations. The trial is registered with ClinicalTrials.gov, NCT01730534. FINDINGS: Between April 25, 2013, and Sept 18, 2018, 17 160 people (6422 [37·4%] women, 10 738 [62·6%] men; mean age 63·9 years [SD 6·8]) were enrolled in the original trial, of whom 10186 (59·4%) had multiple risk factors for but did not have established atherosclerotic cardiovascular disease, and 6835 (39·8%) had both no evidence of atherosclerotic cardiovascular disease and low KDIGO risk. Over a median follow-up of 4·2 years (IQR 3·9-4·4), dapagliflozin was associated with a lower risk of first non-elective hospitalisation for any cause (2779 [32·4%] of 8582 people in the dapagliflozin group vs 3036 [35·4%] of 8578 people in the placebo group; hazard ratio [HR] 0·89 [95% CI 0·85-0·94]) and total (first plus subsequent) non-elective hospitalisations for any cause (risk ratio 0·92 [95% CI 0·86-0·97]). The association between dapagliflozin use and the risk of first non-elective hospitalisation for any cause was consistent in subgroups of participants with (HR 0·92 [95% CI 0·85-0·99] and without (0·87 [0·81-0·94]) atherosclerotic cardiovascular disease at baseline (p interaction=0·31). Compared with the placebo group, the dapagliflozin group had lower risk of first hospitalisations due to cardiac disorders (HR 0·91 [95% CI 0·84-1·00]), metabolism and nutrition disorders (0·73 [0·60-0·89]), renal and urinary disorders (0·61 [0·49-0·77]), and due to any other cause excluding these three causes (0·90 [0·85-0·96]). Treatment with dapagliflozin was also associated with a lower risk of hospitalisations due to musculoskeletal and connective tissue disorders (HR 0·81 [0·67-0·99]) and infections and infastations (HR 0·86 [0·78-0·96]). INTERPRETATION: Dapagliflozin reduced the risk of first and total non-elective hospitalisations for any cause in people with type 2 diabetes, regardless of the presence of atherosclerotic cardiovascular disease, including hospitalisations not directly attributed to cardiac, kidney, or metabolic causes. These findings might have implications on health-related quality of life for people with type 2 diabetes and on health-care costs attributable this condition. FUNDING: AstraZeneca.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".