4.7 Article

Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study

期刊

出版社

JMIR PUBLICATIONS, INC
DOI: 10.2196/27098

关键词

hemodialysis; intradialytic adverse events; prediction algorithm; machine learning

资金

  1. Advantech Foundation [YM105C041]
  2. Ministry of Science and Technology (MOST), Taiwan [MOST 108-2923-B-010-002-MY3, MOST 109-2926-I-010-502, MOST 109-2823-8-010-003-CV, MOST 110-2923-B-A49A-501-MY3]
  3. The Ministry of Science and Technology (MOST), Taiwan [109-2314-B-010-053-MY3, 109-2321-B-009-007, MOST 109-2811-B-010-532, MOST 110-2811-B-010-510, MOST 110-2813-C-A49A-551-B, MOST 110-2321-B-A49-003, MOST 108-2633-B-009-001, MOST 109-2314-B-182-010]
  4. Taipei Veterans General Hospital, Taipei, Taiwan [V106D25-003-MY3, VGHUST107-G5-3-3, VGHUST109-V5-1-2, V110C-194]
  5. Yin Yen-Liang Foundation Development and Construction Plan of the School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan [107F-M01-0504]
  6. Chang Gung Medical Research Foundation [CMRPD1K0601]
  7. Featured Areas Research Center Program within by the Ministry of Education (MOE) in Taiwan

向作者/读者索取更多资源

The study aimed to develop machine learning algorithms to predict intradialytic adverse events during hemodialysis. By collecting dialysis and physiological time-series data, extracting features through linear and differential analyses, building models, and cross-validating them, the results showed that the algorithm achieved high AUC in quasi-real time prediction of adverse events during hemodialysis.
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.

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