ArticleBMC medical informatics and decision making2025
Applied machine learning to predict 1-year major adverse cardiovascular events in elderly patients after percutaneous coronary intervention.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- J-shaped relationship between creatinine levels and the risk of three major adverse events in patients after percutaneous coronary intervention.Frontiers in endocrinology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiovascular diseases remain the leading cause of mortality worldwide, with elderly patients experiencing the worst prognosis following ST-elevation myocardial infarction (STEMI). Traditional risk stratification models demonstrate suboptimal performance in geriatric patients due to complex risk profiles involving frailty and multiple comorbidities.
objectivesTo develop machine learning-based predictive models for one-year major adverse cardiovascular events (MACE) in elderly patients (≥65 years) undergoing percutaneous coronary intervention (PCI) for STEMI.
methodsThis retrospective cohort study analyzed 1,358 elderly patients who underwent PCI between 2015 and 2021. MACE included cardiovascular death, myocardial infarction, stroke, and revascularization within one year. Eight machine learning algorithms were evaluated: XGradient Boosting (XGB), Random Forest (RF), Logistic Regression, Neural Networks, Support Vector Machines, K-Nearest Neighbors, Decision Trees, and Naive Bayes. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance. Model performance was evaluated using various metrics, and SHAP (Shapley Additive Explanations) was used to enhance model interpretability and support clinical decision-making by identifying key risk factors driving predictions.
resultsAmong the patients (mean age 74.1 ± 6.7 years, 31.8% female), 152 (11.2%) experienced MACE within one year. XGB and RF emerged as the most robust models, achieving area under the receiver operating characteristic curve (AUC) values of 94% and 95%, respectively, with RF demonstrating higher sensitivity (79%) and specificity (96%). SHAP analysis revealed pre-PCI ejection fraction, age, creatinine levels, fasting blood sugar, BMI, and LDL/HDL ratio as the most influential predictors.
conclusionMachine learning models demonstrated strong predictive performance in predicting one-year MACE in elderly post-PCI patients. By combining high-performance models with SHAP-based explanations, this approach supports transparent, clinically actionable risk stratification and personalized care.
Indexed as
Identifiers
What Socratic 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 Socratic graph.