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- Title
Data-driven prediction of continuous renal replacement therapy survival.
- Authors
Zamanzadeh, Davina; Feng, Jeffrey; Petousis, Panayiotis; Vepa, Arvind; Sarrafzadeh, Majid; Karumanchi, S. Ananth; Bui, Alex A. T.; Kurtz, Ira
- Abstract
Continuous renal replacement therapy (CRRT) is a form of dialysis prescribed to severely ill patients who cannot tolerate regular hemodialysis. However, as the patients are typically very ill to begin with, there is always uncertainty whether they will survive during or after CRRT treatment. Because of outcome uncertainty, a large percentage of patients treated with CRRT do not survive, utilizing scarce resources and raising false hope in patients and their families. To address these issues, we present a machine learning-based algorithm to predict short-term survival in patients being initiated on CRRT. We use information extracted from electronic health records from patients who were placed on CRRT at multiple institutions to train a model that predicts CRRT survival outcome; on a held-out test set, the model achieves an area under the receiver operating curve of 0.848 (CI = 0.822–0.870). Feature importance, error, and subgroup analyses provide insight into bias and relevant features for model prediction. Overall, we demonstrate the potential for predictive machine learning models to assist clinicians in alleviating the uncertainty of CRRT patient survival outcomes, with opportunities for future improvement through further data collection and advanced modeling. Despite decades of use in clinical care, only half of individuals who receive continuous renal replacement therapy (CRRT) benefit, and no consensus exists around who should be placed on CRRT. Here, the authors use electronic health record data from multiple institutions to improve prediction of CRRT response before initiating treatment
- Subjects
RENAL replacement therapy; MACHINE learning; ELECTRONIC health records; SURVIVAL analysis (Biometry); OVERALL survival; SURVIVAL rate
- Publication
Nature Communications, 2024, Vol 15, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-024-49763-3