背景:体力活动是血友病患儿积极生活方式的重要组成部分。然而,对出血的恐惧已被认为是血友病患儿参与体力活动的障碍,即使有预防.缺乏证据和数据驱动的指标是关键问题。
目的:我们的目标是开发基于临床数据的机器学习模型,并考虑多种潜在因素来预测预防血友病患儿的体力活动出血风险。
方法:从这项队列研究来看,我们收集了98例接受适当预防的血友病患儿的信息(FVIII谷:C水平>1%).涉及的潜在预测变量包括人口统计信息,治疗信息,身体活动,联合评估,和药代动力学参数,等。我们应用了CoxPH,随机生存森林(RSF)和DeepSurv构建体育活动期间出血风险的预测模型。所有三个生存分析模型均经过内部和外部验证。
结果:本研究共纳入98例患者。他们的中位年龄为7.9(5.5,10.2)岁。CoxPH,RSF和DeepSurv模型的判别和校准能力都很高,RSF模型具有最佳性能(内部验证:C指数,0.7648±0.0139;Brier评分,0.1098±0.0015;外部验证:C指数,0.7260±0.0154;Brier评分,0.0930±0.0018)。预测曲线表明,开发的RSF模型可以很好地区分出血和非出血患者的风险。以及不同体力活动水平的患者。同时,特征重要性分析证实,体力活动出血是由各种因素的综合作用推演的,不同因素对出血结局的重要性存在差异。
结论:这项研究从机制上揭示了有必要纳入多种因素来准确预测体力活动相关的出血风险。在临床实践中,设计的机器学习模型可以为A型血友病患儿积极参与体育活动提供指导。
Physical activity is a crucial part of an active lifestyle for haemophiliac children. However, the fear of bleeds has been identified as barriers to participating physical activity for haemophiliac children even with prophylaxis. Lack of evidence and metrics driven by data is key problem.
We aim to develop machine learning models based on clinical data with multiple potential factors considered to predict risk of physical activity bleeding for
haemophilia children with prophylaxis.
From this cohort study, we collected information on 98 haemophiliac children with adequate prophylaxis (trough FVIII:C level > 1 %). The involved potential predictor variables include demographic information, treatment information, physical activity, joint evaluation, and pharmacokinetic parameters, etc. We applied CoxPH, Random Survival Forests (RSF) and DeepSurv to construct prediction models for the risk of bleeding during physical activities. All three survival analysis models were internally and externally validated.
A total of 98 patients were enrolled in this study. Their median age was 7.9 (5.5, 10.2) years. The CoxPH, RSF and DeepSurv models\' discriminative and calibration abilities were all high, and the RSF model had the best performance (Internal validation: C-index, 0.7648 ± 0.0139; Brier Score, 0.1098 ± 0.0015; External validation: C-index, 0.7260 ± 0.0154; Brier Score, 0.0930 ± 0.0018). The prediction curves demonstrated that the developed RSF model can distinguish the risks well between bleeding and non-bleeding patients, as well as patients with different levels of physical activity. Meanwhile, the feature importance analysis confirmed that physical activity bleeding was deduced by comprehensive effects of various factors, and the importance of different factors on bleeding outcome is discrepant.
This study revealed from the mechanism that it is necessary to incorporate multiple factors to accurately predict physical activity related bleeding risk. In clinical practice, the designed machine learning models can provide guidance for children with
haemophilia A to positively participate in physical activity.