Introduction:Patients with chronic limb-threatening ischemia (CLTI) have high mortality rates after revascularization. Risk stratification for short-term outcomes is challenging. We aimed to develop machine-learning models to rank predictive variables for 30-day and 90-day all-cause mortality after peripheral vascular intervention (PVI).Methods:Patients undergoing PVI for CLTI in the Medicare-linked Vascular Quality Initiative were included. Sixty-six preprocedural variables were included. Random survival forest (RSF) models were constructed for 30-day and 90-day all-cause mortality in the training sample and evaluated in the testing sample. Predictive variables were ranked based on the frequency that they caused branch splitting nearest the root node by importance-weighted relative importance plots. Model performance was assessed by the Brier score, continuous ranked probability score, out-of-bag error rate, and Harrell’s C-index.Results:A total of 10,114 patients were included. The crude mortality rate was 4.4% at 30 days and 10.6% at 90 days. RSF models commonly identified stage 5 chronic kidney disease (CKD), dementia, congestive heart failure (CHF), age, urgent procedures, and need for assisted care as the most predictive variables. For both models, eight of the top 10 variables were either medical comorbidities or functional status variables. Models showed good discrimination (C-statistic 0.72 and 0.73) and calibration (Brier score 0.03 and 0.10).Conclusion:RSF models for 30-day and 90-day all-cause mortality commonly identified CKD, dementia, CHF, need for assisted care at home, urgent procedures, and age as the most predictive variables as critical factors in CLTI. Results may help guide individualized risk-benefit treatment conversations regarding PVI.

中文翻译：

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因慢性肢体威胁性缺血而接受血管内介入治疗的医疗保险患者的短期死亡率评估

简介：慢性肢体威胁性缺血（CLTI）患者在血运重建后死亡率很高。短期结果的风险分层具有挑战性。我们的目标是开发机器学习模型，对外周血管介入治疗 (PVI) 后 30 天和 90 天全因死亡率的预测变量进行排名。方法：纳入了在医疗保险相关血管质量计划中接受 PVI 治疗 CLTI 的患者。包括六十六个程序前变量。针对训练样本中的 30 天和 90 天全因死亡率构建了随机生存森林 (RSF) 模型，并在测试样本中进行了评估。通过重要性加权相对重要性图，根据预测变量引起最接近根节点的分支分裂的频率对预测变量进行排序。通过 Brier 评分、连续排序概率评分、袋外错误率和 Harrell C 指数来评估模型性能。 结果：总共纳入 10,114 名患者。30天时的粗死亡率为4.4%，90天时的粗死亡率为10.6%。RSF 模型通常将 5 期慢性肾病 (CKD)、痴呆、充血性心力衰竭 (CHF)、年龄、紧急手术和辅助护理需求视为最具预测性的变量。对于这两个模型，前 10 个变量中有 8 个是医疗合并症或功能状态变量。模型显示出良好的区分度（C 统计量 0.72 和 0.73）和校准度（Brier 评分 0.03 和 0.10）。结论：RSF 模型的 30 天和 90 天全因死亡率通常识别出 CKD、痴呆、CHF、辅助护理需求在家中、紧急手术和年龄是 CLTI 中最具预测性的变量和关键因素。结果可能有助于指导有关 PVI 的个性化风险收益治疗对话。