Key result
SGLT2 inhibitor use linked to ~15-point lower 12-month mortality.
Why the study?
Older adults with HF and T2DM face high 12-month mortality, motivating the development of a routine-variable mortality prediction model and exploration of SGLT2i-related absolute risk differences across model-defined risk strata.
Does a routine-variable machine-learning model predict 12-month mortality and identify SGLT2 inhibitor-related absolute risk differences in hospitalized patients aged ≥ 75 years with heart failure and type 2 diabetes?
Cohort (n=500)
Does a routine-variable machine-learning model predict 12-month mortality and identify SGLT2 inhibitor-related absolute risk differences in hospitalized patients aged ≥ 75 years with heart failure and type 2 diabetes?
Routine clinical variables can moderately stratify 12-month mortality risk in very elderly patients (≥75 years) hospitalized with heart failure and type 2 diabetes, among whom SGLT2 inhibitor use is associated with a 15.0 percentage point lower adjusted 12-month mortality.
Answers are grounded in this paper and cite their sources.
SGLT2 inhibitor use should not yet change practice in very elderly HF-T2D patients; leaves open whether routine-variable ML improves mortality stratification.
Aim: Older adults with heart failure (HF) and type 2 diabetes mellitus (T2DM) face high 12-month mortality. We developed a routine-variable model for mortality prediction and explored SGLT2 inhibitor (SGLT2i)-related absolute risk differences across model-defined risk strata. Methods: This retrospective study included 500 hospitalized patients aged ≥ 75 years with HF and T2DM. Three feature sets (guideline-informed core, primary analysis, and complete imputed candidate) and ten algorithms were compared using repeated five-fold outer/four-fold inner cross-validation with training-fold preprocessing. Cox and multiple-imputation sensitivity analyses were performed. SGLT2i associations were assessed using propensity-score overlap weighting. Results: The binary modelling cohort included 434 patients with 167 deaths (38.5%). The nested selection workflow achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.729, area under the precision-recall curve of 0.612, and Brier score of 0.206. Cox sensitivity analysis yielded a censoring-adjusted 365-day AUROC of 0.755; multiple imputation gave an AUROC of 0.758 for the fixed gradient-boosting reference. Among 381 patients, weighted 12-month mortality was 16.1% in SGLT2i users vs. 31.1% in non-users, a difference of 15.0 percentage points (95%CI: 5.9-23.9). Stratum-specific differences were 6.5, 18.1, and 19.7 percentage points, with wide confidence intervals. A six-variable interactive interface supported risk communication. Conclusion: Routine clinical variables supported moderately discriminative mortality risk stratification under nested validation. SGLT2i use was associated with lower adjusted 12-month mortality overall; larger studies are needed to establish whether associations differ across risk strata. External and prospective validation remains necessary.
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Qin et al. (2026) conducted a cohort in Heart failure and type 2 diabetes mellitus (n=500). SGLT2 inhibitor use (and machine-learning mortality prediction model) vs. SGLT2 inhibitor non-users was evaluated on 12-month mortality (ARR 15.0 percentage points, 95% CI 5.9-23.9). SGLT2 inhibitor use was associated with lower 12-month mortality (16.1% vs 31.1%; ARR 15.0 percentage points, 95% CI 5.9-23.9), while ML risk stratification achieved an AUROC of 0.729.
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