Machine Learning Approach to Predict In-Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States.
Machine Learning Approach to Predict In-Hospital Mortality in Patients Admitted for Peripheral Artery Disease in the United States.
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DOI：
10.1161/jaha.122.026987
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发表时间：
2022-10-18
影响因子：
5.4
通讯作者：
Annex, Brian H.
中科院分区：
文献类型：
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作者：
Zhang, Donglan;Li, Yike;Kalbaugh, Corey Andrew;Shi, Lu;Divers, Jasmin;Islam, Shahidul;Annex, Brian H.
外周动脉疾病（PAD）影响美国超过1000万人。PAD与不良结局相关，包括过早死亡。机器学习（ML）越来越多地用于大数据来预测临床结果。本研究旨在基于国家数据库开发ML模型来预测因PAD住院的患者的院内死亡率。患者住院数据来自2016年至2019年全国住院患者样本。使用国际疾病分类第10版临床修订版（ICD-10-CM）和国际疾病分类第10版手术编码系统（ICD-10-PCS）的代码，共确定了150 921例主要诊断为PAD和PAD相关手术的住院患者。训练了四个ML模型，包括逻辑回归、随机森林、光梯度增强和极端梯度增强模型，以根据选择的变量（包括患者特征、合并症、手术和医院相关因素）预测院内死亡风险。1.8%的患者发生院内死亡。4个模型的性能相当，受试者工作特征曲线下面积范围为0.83至0.85，灵敏度为77%至82%，特异性为72%至75%。这些结果表明临床决策具有足够的可预测性。在所有4个模型中，诊断和手术总数、年龄、血管内血运重建术、充血性心力衰竭、糖尿病和糖尿病并发症是院内死亡率的关键预测因素。本研究证明了ML在预测原发性PAD诊断患者的住院死亡率方面的可行性。研究结果强调了ML模型在识别结果不佳的高风险患者和指导个性化干预方面的潜力。
Peripheral artery disease (PAD) affects >10 million people in the United States. PAD is associated with poor outcomes, including premature death. Machine learning (ML) has been increasingly used on big data to predict clinical outcomes. This study aims to develop ML models to predict in‐hospital mortality in patients hospitalized for PAD based on a national database. Inpatient hospitalization data were obtained from the 2016 to 2019 National Inpatient Sample. A total of 150 921 inpatients were identified with a primary diagnosis of PAD and PAD‐related procedures using codes of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM) and International Classification of Diseases, Tenth Revision, Procedure Coding System (ICD‐10‐PCS). Four ML models, including logistic regression, random forest, light gradient boosting, and extreme gradient boosting models, were trained to predict the risk of in‐hospital death based on a selection of variables, including patient characteristics, comorbidities, procedures, and hospital‐related factors. In‐hospital mortality occurred in 1.8% of patients. The performance of the 4 models was comparable, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.85, sensitivity of 77% to 82%, and specificity of 72% to 75%. These results suggest adequate predictability for clinical decision‐making. In all 4 models, the total number of diagnoses and procedures, age, endovascular revascularization procedure, congestive heart failure, diabetes, and diabetes with complications were critical predictors of in‐hospital mortality. This study demonstrates the feasibility of ML in predicting in‐hospital mortality in patients with a primary PAD diagnosis. Findings highlight the potential of ML models in identifying high‐risk patients for poor outcomes and guiding personalized intervention.