PDF(Pubmed)
Abstract：
UNASSIGNED: Transcatheter aortic valve replacement (TAVR) is an important treatment option for patients with severe symptomatic aortic stenosis. It is important to identify predictors of excellent outcomes (good clinical outcomes, more time spent at home) after TAVR that are potentially amenable to improvement.
UNASSIGNED: The purpose of the study was to use machine learning to identify potentially modifiable predictors of clinically relevant patient-centered outcomes after TAVR.
UNASSIGNED: We used data from 8,332 TAVR cases (January 2016-December 2021) from 21 hospitals to train random forest models with 57 patient characteristics (demographics, comorbidities, surgical risk score, lab values, health status scores) and care process parameters to predict the end point, a composite of parameters that designated an excellent outcome and included no major complications (in-hospital or at 30 days), post-TAVR length of stay of 1 day or less, discharge to home, no readmission, and alive at 30 days. We used recursive feature elimination with cross-validation and Shapley Additive Explanation feature importance to identify parameters with the highest predictive values.
UNASSIGNED: The final random forest model retained 29 predictors (15 patient characteristics and 14 care process components); the area under the curve, sensitivity, and specificity were 0.77, 0.67, and 0.73, respectively. Four potentially modifiable predictors with relatively high Shapley Additive Explanation values were identified: type of anesthesia, direct movement to stepdown unit post-TAVR, time between catheterization and TAVR, and preprocedural length of stay.
UNASSIGNED: This study identified four potentially modifiable predictors of excellent outcome after TAVR, suggesting that machine learning combined with hospital-level data can inform modifiable components of care, which could support better delivery of care for patients undergoing TAVR.
UNASSIGNED: The purpose of the study was to use machine learning to identify potentially modifiable predictors of clinically relevant patient-centered outcomes after TAVR.
UNASSIGNED: We used data from 8,332 TAVR cases (January 2016-December 2021) from 21 hospitals to train random forest models with 57 patient characteristics (demographics, comorbidities, surgical risk score, lab values, health status scores) and care process parameters to predict the end point, a composite of parameters that designated an excellent outcome and included no major complications (in-hospital or at 30 days), post-TAVR length of stay of 1 day or less, discharge to home, no readmission, and alive at 30 days. We used recursive feature elimination with cross-validation and Shapley Additive Explanation feature importance to identify parameters with the highest predictive values.
UNASSIGNED: The final random forest model retained 29 predictors (15 patient characteristics and 14 care process components); the area under the curve, sensitivity, and specificity were 0.77, 0.67, and 0.73, respectively. Four potentially modifiable predictors with relatively high Shapley Additive Explanation values were identified: type of anesthesia, direct movement to stepdown unit post-TAVR, time between catheterization and TAVR, and preprocedural length of stay.
UNASSIGNED: This study identified four potentially modifiable predictors of excellent outcome after TAVR, suggesting that machine learning combined with hospital-level data can inform modifiable components of care, which could support better delivery of care for patients undergoing TAVR.
摘要：
■经导管主动脉瓣置换术（TAVR）是严重症状性主动脉瓣狭窄患者的重要治疗选择。确定优秀结果的预测因素很重要(良好的临床结果，在TAVR之后花费更多的时间），这些时间可能会有所改善。
本研究的目的是使用机器学习来确定TAVR后临床相关的以患者为中心的结果的潜在可修改的预测因子。
■我们使用来自21家医院的8,332例TAVR病例（2016年1月至2021年12月）的数据来训练具有57例患者特征的随机森林模型（人口统计，合并症,手术风险评分,实验室值,健康状况评分)和护理过程参数来预测终点，一个复合参数，指定一个极好的结果，包括没有重大并发症（住院或30天），TAVR后的停留时间为1天或更短，出院回家，没有重新接纳，还活着30天.我们使用具有交叉验证的递归特征消除和Shapley加法解释特征重要性来识别具有最高预测值的参数。
■最终的随机森林模型保留了29个预测因子（15个患者特征和14个护理过程组件）；曲线下的面积，灵敏度,特异性分别为0.77、0.67和0.73。确定了具有相对较高的Shapley加法解释值的四个潜在可修改的预测因子：麻醉类型，直接移动到TAVR后的降压单元，导管插入和TAVR之间的时间，和程序前的停留时间。
■这项研究确定了TAVR后优异结局的四个潜在可修改的预测因子，这表明机器学习与医院层面的数据相结合可以为可修改的护理组件提供信息，这可以为接受TAVR的患者提供更好的护理。
本研究的目的是使用机器学习来确定TAVR后临床相关的以患者为中心的结果的潜在可修改的预测因子。
■我们使用来自21家医院的8,332例TAVR病例（2016年1月至2021年12月）的数据来训练具有57例患者特征的随机森林模型（人口统计，合并症,手术风险评分,实验室值,健康状况评分)和护理过程参数来预测终点，一个复合参数，指定一个极好的结果，包括没有重大并发症（住院或30天），TAVR后的停留时间为1天或更短，出院回家，没有重新接纳，还活着30天.我们使用具有交叉验证的递归特征消除和Shapley加法解释特征重要性来识别具有最高预测值的参数。
■最终的随机森林模型保留了29个预测因子（15个患者特征和14个护理过程组件）；曲线下的面积，灵敏度,特异性分别为0.77、0.67和0.73。确定了具有相对较高的Shapley加法解释值的四个潜在可修改的预测因子：麻醉类型，直接移动到TAVR后的降压单元，导管插入和TAVR之间的时间，和程序前的停留时间。
■这项研究确定了TAVR后优异结局的四个潜在可修改的预测因子，这表明机器学习与医院层面的数据相结合可以为可修改的护理组件提供信息，这可以为接受TAVR的患者提供更好的护理。
