目的:透析中低血压(IDH)与HD患者的不良临床结局密切相关。IDH预测模型对于IDH风险筛查和临床决策很重要。在这项研究中,我们使用机器学习(ML)开发了用于HD患者风险预测的IDH模型.
方法:将62,227个透析会话随机分为训练数据(70%),测试数据(20%),和验证数据(10%)。基于27个变量的IDH-A模型被构建用于下一次HD治疗的风险预测。基于来自64,870个透析疗程的10个变量开发了IDH-B模型,用于在每次HD治疗之前进行风险评估。光梯度升压机(LightGBM),线性判别分析,支持向量机,XGBoost,TabNet,和多层感知器用于开发预测模型。
结果:在IDH-A模型中,我们将LightGBM方法确定为性能最佳和可解释的模型,在Fall30Nadir90定义中C-统计量为0.82,高于其他模型(P<0.01)。在Nadir90,Nadir100,Fall20,Fall30和Fall20Nadir90的其他IDH标准中,LightGBM方法的性能为C统计量为0.77至0.89。作为一个补充应用,IDH-B模型中的LightGBM模型在Fall30Nadir90定义中的C-统计量为0.68,在其他五个IDH标准中的C-统计量为0.69至0.78,也高于其他方法,分别。
结论:使用ML,我们将LightGBM方法确定为性能良好且可解释的模型。我们确定了顶级变量是HD患者中IDH事件的高危因素。IDH-A和IDH-B模型可以有效地相互补充以进行风险预测,并通过应用于不同的临床环境进一步促进及时干预。
OBJECTIVE: Intradialytic hypotension (IDH) is closely associated with adverse clinical outcomes in HD-patients. An IDH predictor model is important for IDH risk screening and clinical decision-making. In this
study, we used Machine learning (ML) to develop IDH model for risk prediction in HD patients.
METHODS: 62,227 dialysis sessions were randomly partitioned into training data (70%), test data (20%), and validation data (10%). IDH-A model based on twenty-seven variables was constructed for risk prediction for the next HD treatment. IDH-B model based on ten variables from 64,870 dialysis sessions was developed for risk assessment before each HD treatment. Light Gradient Boosting Machine (LightGBM), Linear Discriminant Analysis, support vector machines, XGBoost, TabNet, and multilayer perceptron were used to develop the predictor model.
RESULTS: In IDH-A model, we identified the LightGBM method as the best-performing and interpretable model with C- statistics of 0.82 in Fall30Nadir90 definitions, which was higher than those obtained using the other models (P<0.01). In other IDH standards of Nadir90, Nadir100, Fall20, Fall30, and Fall20Nadir90, the LightGBM method had a performance with C- statistics ranged 0.77 to 0.89. As a complementary application, the LightGBM model in IDH-B model achieved C- statistics of 0.68 in Fall30Nadir90 definitions and 0.69 to 0.78 in the other five IDH standards, which were also higher than the other methods, respectively.
CONCLUSIONS: Use ML, we identified the LightGBM method as the good-performing and interpretable model. We identified the top variables as the high-risk factors for IDH incident in HD-patient. IDH-A and IDH-B model can usefully complement each other for risk prediction and further facilitate timely intervention through applied into different clinical setting.