A Practical Electronic Health Record-Based Dry Weight Supervision Model for Hemodialysis Patients

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. M...

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Bibliographic Details
Published in:IEEE journal of translational engineering in health and medicine 2019-01, Vol.7, p.1-9
Main Authors: Bi, Zhaori, Wang, Mengjing, Ni, Li, Ye, Guoxin, Zhou, Dian, Yan, Changhao, Zeng, Xuan, Chen, Jing
Format: Article
Language:English
Subjects:
Aging
Autoregressive processes
Biomedical monitoring
Data models
Diseases
electronic health record
Electronic health records
Errors
Hemodialysis
Hospitals
Monitoring
Performance tests
Personalized prognosis
personalized risk prediction
Physicians
Variations
Weight
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Summary: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.
ISSN:2168-2372
2168-2372
DOI:10.1109/JTEHM.2019.2948604