基于机器学习的严重主动脉反流患者死亡率风险分层。

IF 3.9 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS European heart journal. Digital health Pub Date : 2023-05-01 DOI:10.1093/ehjdh/ztad006
Vidhu Anand, Hanwen Hu, Alexander D Weston, Christopher G Scott, Hector I Michelena, Sorin V Pislaru, Rickey E Carter, Patricia A Pellikka
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引用次数: 1

摘要

目的:目前的指南推荐对有症状、左心室增大或收缩功能障碍的严重主动脉瓣反流(AR)患者进行主动脉瓣干预。最近的研究表明,我们可能会因为遵循指南而错过大量患者早期干预的窗口期。方法和结果:总体目标是确定是否可以训练基于机器学习(ML)的算法来识别独立于主动脉瓣置换术(AVR)的AR死亡风险患者。模型在1035名患者的数据集上进行了五倍交叉验证,并在207名患者的独立数据集上报告了性能。条件随机生存森林模型预测效果最佳。选择了19/41个变量的子集纳入最终模型。变量选择采用随机生存森林模型进行10倍交叉验证。最重要的变量包括年龄、体表面积、体重指数、舒张压、纽约心脏协会分级、AVR、合并症、射血分数、舒张末期容积和收缩末期尺寸,并对每次重复交叉验证的五次平均相对变量重要性进行评估。预测最佳模型1年生存率的一致性指数为0.84,2年生存率为0.86,总体生存率为0.87。结论:利用常见的超声心动图参数和患者特征,我们成功地训练了多个ML模型来预测严重AR患者的生存。该技术可用于识别高危患者,并从早期干预中获益,从而改善患者预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Machine learning-based risk stratification for mortality in patients with severe aortic regurgitation.

Aims: The current guidelines recommend aortic valve intervention in patients with severe aortic regurgitation (AR) with the onset of symptoms, left ventricular enlargement, or systolic dysfunction. Recent studies have suggested that we might be missing the window of early intervention in a significant number of patients by following the guidelines.

Methods and results: The overarching goal was to determine if machine learning (ML)-based algorithms could be trained to identify patients at risk for death from AR independent of aortic valve replacement (AVR). Models were trained with five-fold cross-validation on a dataset of 1035 patients, and performance was reported on an independent dataset of 207 patients. Optimal predictive performance was observed with a conditional random survival forest model. A subset of 19/41 variables was selected for inclusion in the final model. Variable selection was performed with 10-fold cross-validation using random survival forest model. The top variables included were age, body surface area, body mass index, diastolic blood pressure, New York Heart Association class, AVR, comorbidities, ejection fraction, end-diastolic volume, and end-systolic dimension, and the relative variable importance averaged across five splits of cross-validation in each repeat were evaluated. The concordance index for predicting survival of the best-performing model was 0.84 at 1 year, 0.86 at 2 years, and 0.87 overall, respectively.

Conclusion: Using common echocardiographic parameters and patient characteristics, we successfully trained multiple ML models to predict survival in patients with severe AR. This technique could be applied to identify high-risk patients who would benefit from early intervention, thereby improving patient outcomes.

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