Kasim, Sazzli and Rudin, Putri Nur Fatin Amir and Malek, Sorayya and Aziz, Firdaus and Ahmad, Wan Azman Wan and Ibrahim, Khairul Shafiq and Hamidi, Muhammad Hanis Muhmad and Shariff, Raja Ezman Raja and Fong, Alan Yean Yip and Song, Cheen (2024) Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians. PLoS ONE, 19 (2). e0298036. ISSN 1932-6203, DOI https://doi.org/10.1371/journal.pone.0298036.
Full text not available from this repository.Abstract
Background Traditional risk assessment tools often lack accuracy when predicting the short- and long-term mortality following a non-ST-segment elevation myocardial infarction (NSTEMI) or Unstable Angina (UA) in specific population. Objective To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores. Methods We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006-2019), representing a diverse NSTEMI/UA Asian cohort. Algorithm development utilized in-hospital records of 9,518 patients, 30-day data from 7,133 patients, and 1-year data from 7,031 patients. This study utilized 39 features, including demographic, cardiovascular risk, medication, and clinical features. In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. Significant features were chosen and ranked using ML feature importance with backward elimination. The predictive performance of the algorithms was assessed using the area under the curve (AUC) as a metric. Validation of the algorithms was conducted against the TIMI for NSTEMI/UA using a separate validation dataset, and the net reclassification index (NRI) was subsequently determined. Results Using both complete and reduced features, the algorithm performance achieved an AUC ranging from 0.73 to 0.89. The top-performing ML algorithm consistently surpassed the TIMI risk score for in-hospital, 30-day, and 1-year predictions (with AUC values of 0.88, 0.88, and 0.81, respectively, all p < 0.001), while the TIMI scores registered significantly lower at 0.55, 0.54, and 0.61. This suggests the TIMI score tends to underestimate patient mortality risk. The net reclassification index (NRI) of the best ML algorithm for NSTEMI/UA patients across these periods yielded an NRI between 40-60% (p < 0.001) relative to the TIMI NSTEMI/UA risk score. Key features identified for both short- and long-term mortality included age, Killip class, heart rate, and Low-Molecular-Weight Heparin (LMWH) administration. Conclusions In a broad multi-ethnic population, ML approaches outperformed conventional TIMI scoring in classifying patients with NSTEMI and UA. ML allows for the precise identification of unique characteristics within individual Asian populations, improving the accuracy of mortality predictions. Continuous development, testing, and validation of these ML algorithms holds the promise of enhanced risk stratification, thereby revolutionizing future management strategies and patient outcomes.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Acute Coronary Syndrome; In-Hospital Mortality; Timi Risk Score; Admission Electrocardiogram; 30-Day Mortality; Prognostic Value; Cardiac Events; Model; Outcomes; Disease |
| Subjects: | Q Science > QH Natural history R Medicine > R Medicine (General) |
| Divisions: | Faculty of Science > Institute of Biological Sciences Universiti Malaya Medical Centre (UMMC) |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 06 Nov 2024 04:04 |
| Last Modified: | 06 Nov 2024 04:04 |
| URI: | http://eprints.um.edu.my/id/eprint/45617 |
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