Roostami Tahereh, Farhadian Maryam, Yazdi Amirhossein, Mahjub Hossein
Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Research Center for Health Sciences, Institute of Health Sciences and Technologies, Avicenna Health Research Institute, Hamadan University of Medical Sciences, Hamadan, Iran.
BMC Med Inform Decis Mak. 2025 Aug 18;25(1):311. doi: 10.1186/s12911-025-03154-w.
Identifying predictors of in-hospital mortality in patients with ST-segment elevation myocardial infarction (STEMI) is a major concern in cardiology. The aim of this study was to identify risk factors and develop a nomogram to predict in-hospital mortality in STEMI patients.
This single-center study was a retrospective cohort analysis of all STEMI patients consecutively referred to Farshchian Hospital (Hamadan Province-Iran) from April 2021 to August 2024. Four different feature selection methods were used to select common important variables in the prediction model: Boruta, Recursive Feature Elimination (RFE), Random Forest (RF) and LASSO. The uneven distribution of the different classes 2,356 (91.7%) alive and 212 (8.3%) dead) was dealt with using the SMOTE method. After splitting the data into a training (70%) and a test (30%) dataset, a multiple logistic regression model was formulated using the significant variables identified. A nomogram predicting in-hospital mortality was then constructed and validated.
The findings indicate that age (OR = 1.05: 95% CI: 1.03–1.06), gender (female OR = 1.71: 95% CI: 1.22–2.41), length of stay (OR = 0.89: 95% CI: 0.83–0.96), blood urea nitrogen level (OR = 1.02: 95% CI: 1.00-0.03), white blood cell count (OR = 1.07: 95% CI: 1.03–1.1), Creatinine (OR = 1.74: 95% CI: 1.36–2.22), fasting blood glucose (OR = 1.007: 95% CI: 1.005–1.009), uric acid (OR = 1.07: 95% CI: 1.02–1.12), potassium (OR = 1.2: 95% CI: 0.95–1.52) and systolic blood pressure (OR = 0.98: 95% CI: 0.97–0.99) are pivotal factor in predicting in-hospital mortality in STEMI patients. The predictive model demonstrated high accuracy (84%) and excellent discriminatory ability with an AUC of 0.91. The calibration plot demonstrated the model’s strong discriminatory performance in distinguishing between the two classes.
The development of a nomogram for reliably predicting in-hospital mortality in STEMI patients provides clinicians with a practical visual aid for identifying high-risk patients. By enabling tailored care strategies, this tool improves therapeutic precision and ultimately leads to better clinical outcomes.
Not applicable.
识别ST段抬高型心肌梗死（STEMI）患者院内死亡的预测因素是心脏病学中的一个主要关注点。本研究的目的是识别危险因素并开发一种列线图来预测STEMI患者的院内死亡率。
本单中心研究是对2021年4月至2024年8月连续转诊至法尔什奇安医院（伊朗哈马丹省）的所有STEMI患者进行的回顾性队列分析。使用四种不同的特征选择方法在预测模型中选择共同的重要变量：Boruta、递归特征消除（RFE）、随机森林（RF）和套索（LASSO）。使用SMOTE方法处理不同类别（2356例（91.7%）存活和212例（8.3%）死亡）的不均衡分布。将数据拆分为训练集（70%）和测试集（30%）后，使用确定的显著变量建立多元逻辑回归模型。然后构建并验证了预测院内死亡率的列线图。
研究结果表明，年龄（OR = 1.05：95%CI：1.03 - 1.06）、性别（女性OR = 1.71：95%CI：1.22 - 2.41）、住院时间（OR = 0.89：95%CI：0.83 - 0.96）、血尿素氮水平（OR = 1.02：95%CI：1.00 - 0.03）、白细胞计数（OR = 1.07：95%CI：1.03 - 1.1）、肌酐（OR = 1.74：95%CI：1.36 - 2.22）、空腹血糖（OR = 1.00）、尿酸（OR = 1.07：95%CI：1.02 - 1.12）、钾（OR = 1.2：95%CI：0.95 - 1.52）和收缩压（OR = 0.98：95%CI：0.97 - 0.99）是预测STEMI患者院内死亡率的关键因素。预测模型显示出高准确性（84%）和出色的区分能力，AUC为0.91。校准图表明该模型在区分两类患者方面具有很强的区分性能。
开发用于可靠预测STEMI患者院内死亡率的列线图为临床医生识别高危患者提供了实用的视觉辅助工具。通过制定个性化的护理策略，该工具提高了治疗的精准度，并最终带来更好的临床结果。
不适用。
