Background

Predicting the clinical trajectory of individual patients with implantable cardioverter-defibrillators (ICDs) is essential to inform clinical care. Machine learning approaches can potentially overcome the limitations of conventional statistical methods and provide more accurate, personalized risk estimates.

Objectives

We sought to develop and externally validate a novel machine learning algorithm for predicting all-cause mortality and/or heart failure (HF) hospitalization in ICD patients with and without cardiac resynchronization therapy (CRT) using variables that are readily available to treating clinicians. We also sought to identify key factors that separate patients along a continuum of risk.

Methods

Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict 3-month and 1-year risks for all-cause mortality and a composite outcome of death/HF hospitalization during the first 5 years of device implant. Models were trained using a nationwide cohort from the Veterans Health Administration. Three models were sequentially tested, and external validation was performed in a separate nonveteran clinical registry.

Results

The training and validation cohorts included 12,043 patients (age 67.5 ± 9.4 years) and 1,394 patients (age 66.3 ± 11.9 years), respectively. Median follow-up was 3.3 years for the training cohort and 3.6 years for validation cohort. The most accurate models for both outcomes included baseline demographics entered at the time of ICD implant (age, sex, CRT therapy) and time-varying ICD data with area under the receiver-operating characteristic curve for predicting death at 3 months (0.91; 95% CI: 0.87-0.94) and 1 year (0.80; 95% CI: 0.78-0.82); death/HF hospitalization at 3 months (0.81; 95% CI: 0.79-0.83) and 1 year (0.71; 95% CI: 0.70-0.72). Models demonstrated high discrimination and good calibration in the validation cohort. Additionally, time-varying physiologic data from ICDs, especially daily physical activity, had substantial importance in predicting outcomes.

Conclusions

The RF-SLAM algorithm accurately predicted all-cause mortality and death/HF hospitalization at 3 months and 1 year during the first 5 years of device implant, demonstrating good internal and external validity. Prospective studies and randomized trials are needed to evaluate model performance in other populations and settings and to determine its impact on patient outcomes.

中文翻译：

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基于机器学习的植入式心律转复除颤器患者死亡和住院预测

 背景

预测植入式心律转复除颤器 （ICD） 个体患者的临床轨迹对于为临床护理提供信息至关重要。机器学习方法有可能克服传统统计方法的局限性，并提供更准确、个性化的风险估计。

 目标

我们试图开发和外部验证一种新的机器学习算法，用于使用治疗临床医生容易获得的变量预测有和没有心脏再同步治疗 （CRT） 的 ICD 患者的全因死亡率和/或心力衰竭 （HF） 住院。我们还试图确定沿连续风险将患者区分开来的关键因素。

 方法

应用随机森林生存、纵向和多变量 （RF-SLAM） 数据分析来预测设备植入前 5 年全因死亡的 3 个月和 1 年风险以及死亡/HF 住院的复合结果。模型是使用退伍军人健康管理局的全国队列进行训练的。依次测试了三个模型，并在单独的非退伍军人临床登记处进行了外部验证。

 结果

训练和验证队列分别包括 12,043 名患者 （年龄 67.5 ± 9.4 岁） 和 1,394 名患者 （年龄 66.3 ± 11.9 岁）。训练队列的中位随访时间为 3.3 年，验证队列的中位随访时间为 3.6 年。两种结局最准确的模型包括植入 ICD 时输入的基线人口统计学（年龄、性别、CRT 治疗）和时变 ICD 数据，其中受试者工作特征曲线下面积预测 3 个月 （0.91;95% CI： 0.87-0.94） 和 1 年 （0.80;95% CI： 0.78-0.82） 死亡;3 个月 （0.81;95% CI： 0.79-0.83） 和 1 年 （0.71;95% CI： 0.70-0.72） 的死亡/心力衰竭住院。模型在验证队列中表现出高鉴别力和良好的校准性。此外，来自 ICD 的时变生理数据，尤其是日常身体活动，在预测结果方面具有重要意义。

 结论

RF-SLAM 算法准确预测了设备植入前 5 年 3 个月和 1 年的全因死亡率和死亡/HF 住院率，表现出良好的内部和外部效度。需要前瞻性研究和随机试验来评估模型在其他人群和环境中的性能，并确定其对患者结局的影响。