Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study.
Background: Hemodialysis (HD) therapy is an indispensable tool used in critical care management. Patients undergoing HD are at risk for intradialytic adverse events, ranging from muscle cramps to cardiac arrest. So far, there is no effective HD device-integrated algorithm to assist medical staff in...
| Published in: | Journal of Medical Internet Research Vol. 23; no. 9 |
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| Main Authors: | , , , , , , , |
| Format: | research Journal Article |
| Published: |
JMIR Publications Inc.
Sep2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152822494&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152822494 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14394456 DNC jtl: Journal of Medical Internet Research issn: 14394456 maglogo: N pubinfo: dt: Sep2021 vid: 23 iid: 9 pid: 21567 pub: JMIR Publications Inc. place: Toronto, Ontario artinfo: ui: 152822494 152822494 NLM34491204 152822494 10.2196/27098 NLM34491204 152822494 ppct: 1 formats: tig: atl: Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study. aug: au: Liu, Yi-Shiuan Yang, Chih-Yu Chiu, Ping-Fang Lin, Hui-Chu Lo, Chung-Chuan Lai, Alan Szu-Han Chang, Chia-Chu Lee, Oscar Kuang-Sheng affil: 1 Institute of Clinical Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei, Taiwan sug: subj: Hypotension Hemodialysis Human Algorithms Comparative Studies Multicenter Studies Evaluation Research Validation Studies ab: Background: Hemodialysis (HD) therapy is an indispensable tool used in critical care management. Patients undergoing HD are at risk for intradialytic adverse events, ranging from muscle cramps to cardiac arrest. So far, there is no effective HD device-integrated algorithm to assist medical staff in response to these adverse events a step earlier during HD.Objective: We aimed to develop machine learning algorithms to predict intradialytic adverse events in an unbiased manner.Methods: Three-month dialysis and physiological time-series data were collected from all patients who underwent maintenance HD therapy at a tertiary care referral center. Dialysis data were collected automatically by HD devices, and physiological data were recorded by medical staff. Intradialytic adverse events were documented by medical staff according to patient complaints. Features extracted from the time series data sets by linear and differential analyses were used for machine learning to predict adverse events during HD.Results: Time series dialysis data were collected during the 4-hour HD session in 108 patients who underwent maintenance HD therapy. There were a total of 4221 HD sessions, 406 of which involved at least one intradialytic adverse event. Models were built by classification algorithms and evaluated by four-fold cross-validation. The developed algorithm predicted overall intradialytic adverse events, with an area under the curve (AUC) of 0.83, sensitivity of 0.53, and specificity of 0.96. The algorithm also predicted muscle cramps, with an AUC of 0.85, and blood pressure elevation, with an AUC of 0.93. In addition, the model built based on ultrafiltration-unrelated features predicted all types of adverse events, with an AUC of 0.81, indicating that ultrafiltration-unrelated factors also contribute to the onset of adverse events.Conclusions: Our results demonstrated that algorithms combining linear and differential analyses with two-class classification machine learning can predict intradialytic adverse events in quasi-real time with high AUCs. Such a methodology implemented with local cloud computation and real-time optimization by personalized HD data could warn clinicians to take timely actions in advance. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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