A Practical Electronic Health Record-Based Dry Weight Supervision Model for Hemodialysis Patients
A Practical Electronic Health Record-Based Dry Weight Supervision Model for Hemodialysis Patients
复制标题
一种实用的基于电子健康记录的血液透析患者干体重监控模型
DOI：
10.1109/jtehm.2019.2948604
复制
发表时间：
2019-01-01
影响因子：
3.4
通讯作者：
Chen, Jing
中科院分区：
文献类型：
--
作者：
Bi, Zhaori;Wang, Mengjing;Chen, Jing
目的：干体重（DW）是终末期肾病（ESRD）患者的典型血液透析（HD）处方。然而，由于身体成分的复杂性和个体差异，准确的DW评估是困难的。我们的目标是建立一个临床上可行的DW估计模型。方法：我们提出一个基于时间序列的回归方法来评估血液透析患者的体重波动，根据电子健康记录（EHR）。共选择了34例患者（5100次HD治疗数据），并将其分为三组：HD稳定组、HD不耐受组和濒死组。每组最近的150个HD会话数据被用来评估所提出的模型。结果如下：在0.5 kg的绝对误差范围内，我们的模型对HD稳定组、HD不耐受组和濒死组的透析后体重预测准确率分别为95.44%、91.95%和83.12%。在1%的相对误差范围内，所提出的方法分别获得了97.99%、95.36%和66.38%的准确率。对于HD稳定患者，拟定方法的平均绝对误差（MAE）为0.17 kg ± 0.04 kg。在模型比较实验中，性能测试表明，所提出的模型的质量优于那些国家的最先进的模型上级。结论：本研究的结果表明，所提出的模型可能会自动化的HD患者的临床体重管理。临床影响：这项工作可以帮助医生监测和估计DW。它也可以是HD患者的健康风险指标。
Objective: Dry Weight (DW) is a typical hemodialysis (HD) prescription for End-Stage Renal Disease (ESRD) patients. However, an accurate DW assessment is difficult due to the complication of body components and individual variations. Our objective is to model a clinically practicable DW estimator. Method: We proposed a time series-based regression method to evaluate the weight fluctuation of HD patients according to Electronic Health Record (EHR). A total of 34 patients with 5100 HD sessions data were selected and partitioned into three groups; in HD-stabilized, HD-intolerant, and near-death. Each group’s most recent 150 HD sessions data were adopted to evaluate the proposed model. Results: Within a 0.5 kg absolute error margin, our model achieved 95.44%, 91.95%, and 83.12% post-dialysis weight prediction accuracies for the HD-stabilized, HD-intolerant, and near-death groups, respectively. Within a 1%relative error margin, the proposed method achieved 97.99%, 95.36%, and 66.38% accuracies. For HD-stabilized patients, the Mean Absolute Error (MAE) of the proposed method was 0.17 kg ± 0.04 kg. In the model comparison experiment, the performance test showed that the quality of the proposed model was superior to those of the state-of-the-art models. Conclusion: The outcome of this research indicates that the proposed model could potentially automate the clinical weight management for HD patients. Clinical Impact: This work can aid physicians to monitor and estimate DW. It can also be a health risk indicator for HD patients.