Development and Testing a New Online Dynamic Nomogram for Contrast-Induced Acute Kidney Injury in Elderly Patients with ST-Segment Elevation Myocardial Infarction.
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引用次数: 0
Abstract
Background: ST-segment elevation myocardial infarction (STEMI), the most severe form of acute coronary syndrome (ACS), requires timely percutaneous coronary intervention (PCI) to restore coronary blood flow. However, contrast-induced acute kidney injury (CI-AKI), the third most common cause of hospital-acquired renal failure, remains a critical complication of PCI.
Objective: To develop a machine learning model predicting CI-AKI risk in elderly patients with STEMI patients using clinical features.
Methods: Data from 2120 elderly patients with STEMI treated with PCI at Xuzhou Medical University Affiliated Hospital (2019-2023) were used for model development and testing. An external validation cohort, comprising 236 individuals, was derived from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (2008-2019). Lasso regression selected predictors, and nine Machine Learning (ML) algorithms were evaluated via Receiver Operating Characteristic (ROC) analysis. Overlapping top-ranked features from high-performing models (AUC >0.8) informed a nomogram. Performance was assessed using AUC and decision curve analysis (DCA).
Results: The final model included five independent predictors: lymphocyte-to-monocyte ratio, diuretic use, residual cholesterol, serum creatinine, and blood urea nitrogen. This model was developed as a simple-to-use online dynamic nomogram. It demonstrated robust discrimination, with C-statistics of 0.782 in the testing dataset and 0.791 in the validation dataset. DCA confirmed its clinical utility across a wide range of risk thresholds.
Conclusion: A new online dynamic nomogram was developed to provide a practical tool for CI-AKI risk stratification in elderly STEMI patients, aiding personalized prevention strategies.
背景：st段抬高型心肌梗死（STEMI）是急性冠脉综合征（ACS）的最严重形式，需要及时行经皮冠状动脉介入治疗（PCI）以恢复冠状动脉血流。然而，造影剂诱导的急性肾损伤（CI-AKI）是医院获得性肾衰竭的第三大常见原因，仍然是PCI的一个重要并发症。目的：建立利用临床特征预测老年STEMI患者CI-AKI风险的机器学习模型。方法：采用徐州医科大学附属医院2019-2023年2120例行PCI治疗的老年STEMI患者的数据进行模型开发和检验。外部验证队列包括236人，来自重症监护医疗信息市场- iv （MIMIC-IV）数据库（2008-2019）。Lasso回归选择预测因子，并通过受试者工作特征（ROC）分析对9种机器学习（ML）算法进行评估。来自高性能模型（AUC >0.8）的重叠排名最高的特征通知了nomogram。使用AUC和决策曲线分析（DCA）评估绩效。结果：最终的模型包括五个独立的预测因子：淋巴细胞与单核细胞的比例、利尿剂的使用、残留胆固醇、血清肌酐和血尿素氮。该模型是作为一个简单易用的在线动态图开发的。测试数据集的c统计量为0.782，验证数据集的c统计量为0.791。DCA证实了它在广泛的风险阈值范围内的临床应用。结论：开发了一种新的在线动态图，为老年STEMI患者的CI-AKI风险分层提供了实用工具，有助于个性化预防策略。
期刊介绍：
Clinical Interventions in Aging, is an online, peer reviewed, open access journal focusing on concise rapid reporting of original research and reviews in aging. Special attention will be given to papers reporting on actual or potential clinical applications leading to improved prevention or treatment of disease or a greater understanding of pathological processes that result from maladaptive changes in the body associated with aging. This journal is directed at a wide array of scientists, engineers, pharmacists, pharmacologists and clinical specialists wishing to maintain an up to date knowledge of this exciting and emerging field.
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