Interpretable Machine Learning Model for Predicting 1-Year Unplanned Readmissions in Ischemic Stroke Patients with Diabetes: A Synergistic View of Inflammation and Metabolism.

BACKGROUND

To develop and validate an interpretable machine learning (ML) model integrating inflammatory and metabolic biomarkers for predicting the risk of 1-year unplanned readmission in patients with ischemic stroke (IS) and type 2 diabetes mellitus (T2DM).

METHODS

This retrospective study included IS patients with comorbid T2DM who were hospitalized between June 2022 and December 2023. A total of 49 clinical variables were extracted. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection. The dataset was randomly divided into a training set (70%) and a validation set (30%). Seven widely used ML algorithms were applied to construct predictive models, and model performance was evaluated using a validation set. No external validation was performed in this study. The best-performing model was further interpreted using Shapley Additive Explanations (SHAP), and a dynamic nomogram was developed for individualized risk assessment.

RESULTS

A total of 833 patients were included, with a 1-year unplanned readmission rate of 34.3%. LASSO regression identified nine key variables: age, neutrophil-to-lymphocyte ratio (NLR), homocysteine (HCY), glycated hemoglobin A1c (HbA1c), triglyceride-glucose (TyG) index, metformin use, and the presence of hyperlipidemia, pulmonary infection, and renal insufficiency. The random forest model demonstrated the best overall performance (area under the curve [AUC] = 0.78, F1 score = 0.70). SHAP analysis indicated that NLR, HCY, HbA1c, and TyG index were the most influential predictors, suggesting that chronic inflammation and metabolic dysregulation play pivotal roles in readmission risk.

CONCLUSION

The ML model based on inflammatory and metabolic biomarkers effectively predicts 1-year unplanned readmission risk in IS patients with T2DM, with good interpretability and clinical potential. The dynamic nomogram enables real-time, individualized risk prediction to support early identification of high-risk patients, tailored follow-up, and targeted allocation of healthcare resources.