PDF(Pubmed)
Abstract：
Hypoplastic left heart syndrome (HLHS) is a congenital malformation commonly treated with palliative surgery and is associated with significant morbidity and mortality. Risk stratification models have often relied upon traditional survival analyses or outcomes data failing to extend beyond infancy. Individualized prediction of transplant-free survival (TFS) employing machine learning (ML) based analyses of outcomes beyond infancy may provide further valuable insight for families and healthcare providers along the course of a staged palliation. Data from both the Pediatric Heart Network (PHN) Single Ventricle Reconstruction (SVR) trial and Extension study (SVR II), which extended cohort follow up for five years was used to develop ML-driven models predicting TFS. Models incrementally incorporated features corresponding to successive phases of care, from pre-Stage 1 palliation (S1P) through the stage 2 palliation (S2P) hospitalization. Models trained with features from Pre-S1P, S1P operation, and S1P hospitalization all demonstrated time-dependent area under the curves (td-AUC) beyond 0.70 through 5 years following S1P, with a model incorporating features through S1P hospitalization demonstrating particularly robust performance (td-AUC 0.838 (95% CI 0.836-0.840)). Machine learning may offer a clinically useful alternative means of providing individualized survival probability predictions, years following the staged surgical palliation of hypoplastic left heart syndrome.
摘要：
左心发育不良综合征（HLHS）是一种先天性畸形，通常采用姑息性手术治疗，并与显着的发病率和死亡率相关。风险分层模型通常依赖于传统的生存分析或无法扩展到婴儿期的结果数据。采用基于机器学习（ML）的对婴儿期以外的结果进行分析的无移植生存（TFS）的个性化预测可能会在分阶段姑息治疗的过程中为家庭和医疗保健提供者提供进一步有价值的见解。来自小儿心脏网络（PHN）单心室重建（SVR）试验和扩展研究（SVRII）的数据，其中延长队列随访5年用于开发预测TFS的ML驱动模型。模型递增地结合了与连续护理阶段相对应的特征，从1期前缓解(S1P)到2期缓解(S2P)住院。使用来自Pre-S1P的特征训练的模型，S1P操作,和S1P住院均显示出在S1P后5年内超过0.70的曲线下时间依赖性面积（td-AUC），结合S1P住院特征的模型表现出特别稳健的表现(td-AUC0.838(95%CI0.836-0.840))。机器学习可能提供一种临床上有用的替代方法，提供个性化的生存概率预测。在左心发育不良综合征分阶段手术缓解后的几年。
