An interpretable machine learning model for predicting 1-year major adverse cardiovascular events in patients with type 2 diabetes and hypertension.

Background: Patients with coexisting type 2 diabetes mellitus (T2DM) and hypertension (HTN) face a synergistically elevated risk of major adverse cardiovascular events (MACE). Evidence for prediction models developed specifically in established T2DM-HTN comorbidity population remains limited. Objective: To methodologically explore and preliminarily evaluate an interpretable machine learning framework for 1-year MACE prediction in hospitalized patients with coexisting T2DM and HTN using routine clinical data. Methods: This retrospective study included 1,054 hospitalized patients with T2DM and HTN, of whom 249 (23.6%) experienced MACE during 1-year follow-up. The dataset was randomly divided into training (60%), validation (20%), and independent test (20%) cohorts using stratified sampling. LASSO regression was applied for feature selection from 69 clinical variables. Four algorithms, including logistic regression, random forest, support vector machine, and XGBoost, were developed and compared. Model performance was assessed using discrimination, calibration, and clinical utility metrics. SHapley Additive exPlanations (SHAP) were used to interpret the final model. Results: LASSO identified six stable predictors: HbA1c, age, hypertension duration, cystatin C (CysC), T2DM duration, and carotid intima-media thickness (CIMT). Sex was additionally incorporated based on clinical relevance. Multivariable logistic regression showed that HbA1c, age, hypertension duration, T2DM duration, CysC, and CIMT were associated with 1-year MACE risk, whereas sex was not statistically significant. Logistic regression showed the best relative balance between discrimination, calibration, and simplicity on the validation set, although learning curves indicated limited incremental improvement with increasing training sample size. After isotonic regression recalibration, the final logistic regression model achieved an ROC-AUC of 0.828, a PR-AUC of 0.656, and a Brier score of 0.116 on the independent test set. Decision curve analysis indicated potential clinical net benefit. SHAP linked model predictions to glycemic burden, aging, cumulative disease exposure, renal-related risk, and subclinical atherosclerosis. Conclusion: An interpretable logistic regression model based on seven routine clinical variables showed relatively good internal performance for predicting 1-year composite MACE risk in hospitalized patients with coexisting T2DM and HTN. CysC provided additional prognostic information beyond its conventional role as a renal filtration marker, although this association should be interpreted as prognostic rather than causal. External validation is required before the model can be considered for clinical decision support.