A Predictive Model for Post-Percutaneous Coronary Intervention Readmission: Insights from Machine Learning on Clinical Risk Factors.
Introduction: Unplanned one-year readmission after percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI) poses serious clinical and economic challenges. This study developed and validated a random forest (RF) model to predict one-year all-cause unplanned rea...
| Publicado en: | Journal of Research in Health Sciences Vol. 26; no. 3; pp. 1 - 10 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Hamadan University of Medical Sciences, School of Public Health
Summer2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=197020402&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 197020402 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22287795 903Q jtl: Journal of Research in Health Sciences issn: 22287795 maglogo: N pubinfo: dt: Summer2026 vid: 26 iid: 3 pid: 54266 pub: Hamadan University of Medical Sciences, School of Public Health artinfo: ui: 197020402 10.34172/jrhs.13875 197020402 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: A Predictive Model for Post-Percutaneous Coronary Intervention Readmission: Insights from Machine Learning on Clinical Risk Factors. aug: au: Farhadian, Maryam Hosseini, Seyed Kianoosh Roostami, Tahereh affil: Research Center for Health Sciences, Institute of Health Sciences and Technologies, Avicenna Health Research Institute, Hamadan University of Medical Sciences, Hamadan, Iran. sug: ab: Introduction: Unplanned one-year readmission after percutaneous coronary intervention (PCI) for ST-segment elevation myocardial infarction (STEMI) poses serious clinical and economic challenges. This study developed and validated a random forest (RF) model to predict one-year all-cause unplanned readmission in STEMI-PCI patients and identify key risk factors. Study Design: A single-center retrospective cohort study. Methods: This single-center retrospective cohort study was conducted at Farshchian Heart Hospital from September 2021 to September 2024. Overall, 1,836 STEMI patients undergoing primary PCI were analyzed to predict 365-day unplanned readmission. The primary outcome was 365-day unplanned readmission (binary: readmitted or not). A RF classifier was developed on an 80/20 training-test split, optimized through repeated cross-validation with SMOTE for class imbalance. A SMOTE-RF model was also employed to mitigate the significant class imbalance inherent in the 17.6% readmission rate. Finally, key predictors were identified using mean decrease in Gini impurity. Results: Operating at an optimized threshold, the RF model demonstrated moderate discriminative ability (AUC: 0.765) with a sensitivity of 62.5% and specificity of 78.5%. Moreover, the RF model outperformed logistic regression in terms of AUC (0.695 vs. 0.765) and specificity (78.5% vs. 52%), although logistic regression achieved higher sensitivity (78.5% vs. 62.5%). Multivessel disease, smoking, length of stay, hypertension, ejection fraction, and age were key predictors of readmission identified by the RF. Conclusion: The RF model exhibited acceptable predictive performance for one-year hospital readmission, enabling clinically interpretable risk stratification and identifying key clinical risk factors to inform tailored post-discharge care. However, external validation is required to confirm its generalizability and clinical applicability. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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