纵向临床数据使用机器学习改善了血液细胞移植后的生存预测
Yiwang Zhou1, Jesse Smith1, Dinesh Keerthi2
1Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN.
Blood advances
|November 22, 2023
概括
这项研究开发了一种新的算法,用于预测儿童在异构造血细胞移植 (allo-HCT) 后的生存率. 通过结合动态临床数据,该模型与传统方法相比,显著改善了死亡率预测.
科学领域:
- 血液学 血液学 血液学
- 在瘤学瘤学.
- 生物统计学 生物统计学
背景情况:
- 在全源造血细胞移植 (allo-HCT) 后进行序列预后评估对于识别高风险患者至关重要.
- 现有的预测算法往往缺乏准确性,因为它们无法纳入移植后患者临床状态的动态变化.
研究的目的:
- 开发和验证一个强大的风险预测算法,用于预测接受alo-HCT的儿科患者的短期和长期生存.
- 通过包括基线生物变量和移植后临床状态变化来提高预后模型的预测能力.
主要方法:
- 一个Naïve-Bayes机器学习模型是使用儿科患者的临床数据开发的.
- 该模型结合了从alo-HCT30天前到30天后的纵向临床和实验室测量.
- 该算法在30%的队列中进行了内部验证,并在单独的第三级护理推中心进行了外部验证.
主要成果:
- 使用纵向数据的机器学习模型显示,在allo-HCT后的100天,1年和2年内,对患者存活率的预测明显更好.
- 这些动态模型的表现优于仅基于基线变量的传统模型.
- 该研究证实了通过将临床和实验室数据的动态变异性纳入来改善死亡率的预测.
结论:
- 将动态的临床和实验室数据纳入风险评估模型可以显著提高儿科all-HCT患者死亡率的预测.
- 这种方法为依赖固定变量的传统预测工具提供了更准确和更动态的替代方案.
- 开发的算法代表了一种概念验证,用于改善alo-HCT接受者的预后评估.
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