Abstract：
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).
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.
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).
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.
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.
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).
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.
摘要：
■慢性威胁肢体缺血（CLTI）患者血运重建后死亡率高。短期结果的风险分层具有挑战性。我们旨在开发机器学习模型，对外周血管介入（PVI）后30天和90天全因死亡率的预测变量进行排名。
纳入Medicare相关血管质量计划中接受CLTI的PVI患者。包括66个术前变量。在训练样本中构建30天和90天全因死亡率的随机生存森林(RSF)模型,并在测试样本中进行评价。预测变量通过重要性加权的相对重要性图根据它们引起最接近根节点的分支分裂的频率进行排序。模型性能通过Brier评分进行评估，连续排名概率得分，包外错误率，和哈雷尔的C指数。
■共纳入10,114例患者。粗死亡率在30天为4.4%,在90天为10.6%。RSF模型通常识别为5期慢性肾脏病（CKD），痴呆症,充血性心力衰竭（CHF），年龄,紧急程序,并且需要辅助护理作为最具预测性的变量。对于这两种型号，前10个变量中有8个是医学合并症或功能状态变量.模型显示出良好的辨别(C统计量0.72和0.73)和校准(Brier评分0.03和0.10)。
■30天和90天全因死亡率的RSF模型通常被确定为CKD，痴呆症,CHF,在家需要辅助护理，紧急程序,年龄是CLTI中最具预测性的变量，也是CLTI的关键因素。结果可能有助于指导有关PVI的个性化风险收益治疗对话。
纳入Medicare相关血管质量计划中接受CLTI的PVI患者。包括66个术前变量。在训练样本中构建30天和90天全因死亡率的随机生存森林(RSF)模型,并在测试样本中进行评价。预测变量通过重要性加权的相对重要性图根据它们引起最接近根节点的分支分裂的频率进行排序。模型性能通过Brier评分进行评估，连续排名概率得分，包外错误率，和哈雷尔的C指数。
■共纳入10,114例患者。粗死亡率在30天为4.4%,在90天为10.6%。RSF模型通常识别为5期慢性肾脏病（CKD），痴呆症,充血性心力衰竭（CHF），年龄,紧急程序,并且需要辅助护理作为最具预测性的变量。对于这两种型号，前10个变量中有8个是医学合并症或功能状态变量.模型显示出良好的辨别(C统计量0.72和0.73)和校准(Brier评分0.03和0.10)。
■30天和90天全因死亡率的RSF模型通常被确定为CKD，痴呆症,CHF,在家需要辅助护理，紧急程序,年龄是CLTI中最具预测性的变量，也是CLTI的关键因素。结果可能有助于指导有关PVI的个性化风险收益治疗对话。
