Abstract
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.
Highlights
Chronic Kidney Disease (CKD) characterized by the gradual loss of kidney function is commonly recognized as one of the most severe global public health problems
ERROR DISTRIBUTIONS We evaluated the accuracy of the proposed method with the absolute and relative error matrices, where the absolute error is a non-negative kilogram weight difference between patients’ actual post-dialysis weights
As a rule of thumb, the absolute weight error would be kept within 0.5 kg, and the relative error would be kept within 1% by the clinical staff to gain the best treatment for HD patients
Summary
Chronic Kidney Disease (CKD) characterized by the gradual loss of kidney function is commonly recognized as one of the most severe global public health problems. CKD affected more than 753 million people and caused 1.2 million deaths in the year of 2016 [1]. The disease is the 18-th leading cause of death in globally [2]. The end-stage of CKD is called EndStage Renal Disease (ESRD), and patients with ESRD must take Renal Replacement Therapy (RRT) including dialysis and transplantation. By the year of 2030, the number is expected to double roughly [3]. Developing countries provide the most market for HD consumptions, the medical conditions and quality of HD there are limited. The availability of experienced clinicians and well-conditioned HD centers [4] can hardly meet patients’ demands.
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