Cardiology Department, Faculty of Medicine, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia.
Cardiac Vascular and Lung Research Institute, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia.
PLoS One. 2024 Feb 15;19(2):e0298036. doi: 10.1371/journal.pone.0298036. eCollection 2024.
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.
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.
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.
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.
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.
传统的风险评估工具在预测非 ST 段抬高型心肌梗死（NSTEMI）或不稳定型心绞痛（UA）患者的短期和长期死亡率时，往往准确性不足。
运用机器学习（ML）和堆叠集成学习（EL）方法预测亚洲 NSTEMI/UA 患者的短期和长期死亡率，并识别相关特征，随后将这些发现与已建立的风险评分进行评估。
我们分析了来自马来西亚国家心血管疾病数据库（2006-2019 年）的数据，该数据库代表了一个多样化的亚洲 NSTEMI/UA 患者群体。算法开发利用了 9518 名住院患者的住院记录、7133 名患者的 30 天数据和 7031 名患者的 1 年数据。本研究使用了 39 个特征，包括人口统计学、心血管风险、药物和临床特征。在堆叠 EL 模型的开发中，使用了四种基础学习算法：极端梯度提升（XGB）、支持向量机（SVM）、朴素贝叶斯（NB）和随机森林（RF），广义线性模型（GLM）作为元学习器。使用 ML 特征重要性进行特征选择和向后消除来选择显著特征并对其进行排名。使用曲线下面积（AUC）作为指标评估算法的预测性能。使用来自独立验证数据集的 TIMI 对 NSTEMI/UA 进行算法验证，并随后确定净重新分类指数（NRI）。
使用完整和简化特征，算法性能的 AUC 范围为 0.73 至 0.89。性能最佳的 ML 算法始终优于 TIMI 风险评分，用于住院、30 天和 1 年的预测（AUC 值分别为 0.88、0.88 和 0.81，均 p<0.001），而 TIMI 评分分别为 0.55、0.54 和 0.61，这表明 TIMI 评分往往低估了患者的死亡风险。对于这些时期的 NSTEMI/UA 患者，最佳 ML 算法的净重新分类指数（NRI）在 40-60%（p<0.001）之间，相对于 TIMI NSTEMI/UA 风险评分。确定短期和长期死亡率的关键特征包括年龄、Killip 分级、心率和低分子肝素（LMWH）的应用。
在广泛的多民族人群中，ML 方法在对 NSTEMI 和 UA 患者进行分类方面优于传统的 TIMI 评分。ML 允许在个体亚洲人群中精确识别独特特征，从而提高死亡率预测的准确性。这些 ML 算法的不断开发、测试和验证有望提高风险分层的准确性，从而彻底改变未来的管理策略和患者结局。
