ArticlePharmacology research & perspectives2026
Development and Validation of an XGBoost-SHAP Model for Predicting Adverse Outcomes in Elderly Cardiovascular Patients With Polypharmacy: A Retrospective Cohort Study.
Article in Pharmacology research & perspectives, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to develop and validate an interpretable machine learning model using Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP) analysis to predict adverse outcomes in elderly cardiovascular patients with polypharmacy. This retrospective cohort study included 1200 patients aged ≥ 65 years with cardiovascular disease and polypharmacy (≥ 5 medications) from The First Affiliated Hospital of Soochow University between January 2021 and December 2024. Data were split into training (60%), validation (20%), and test (20%) sets. The primary outcome was adverse drug events (ADEs); secondary outcomes included 30-day readmission, 90-day readmission, and 1-year mortality. XGBoost models were developed and compared with logistic regression, random forest, and support vector machine algorithms. Model interpretability was enhanced using SHAP values to identify key predictive features and their contributions. The cohort had a mean age of 76.34 ± 8.12 years, with 18.92% experiencing ADEs during follow-up. The XGBoost model demonstrated strong discriminative ability for ADE prediction (AUC 0.857, 95% CI: 0.790-0.910) compared to logistic regression (AUC 0.795, p < 0.001). The model also performed well for secondary outcomes: 30-day readmission (AUC 0.843, 95% CI: 0.779-0.899), 90-day readmission (AUC 0.848, 95% CI: 0.784-0.895), and 1-year mortality (AUC 0.873, 95% CI: 0.807-0.929), all significantly superior to logistic regression (all p < 0.001). At optimal thresholds, the model achieved sensitivity ranging from 62.71% to 90.24% and specificity from 67.84% to 87.85% across outcomes. SHAP analysis identified age (importance: 0.196), estimated glomerular filtration rate (0.181), medication count (0.159), serum albumin (0.147), and Charlson Comorbidity Index (0.138) as the most influential predictors. The model maintained good calibration (Brier scores: 0.156-0.164) and consistent performance across patient subgroups. SHAP threshold analysis identified eGFR < 45 mL/min/1.73 m
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.