Machine Learning to Predict Cardiac Death Within 1 Hour After Terminal Extubation*. Issue 2 (February 2021)

• Record Type: Journal Article
• Authors: Winter, Meredith C.; Day, Travis E.; Ledbetter, David R.; Aczon, Melissa D.; Newth, Christopher J. L.; Wetzel, Randall C.; Ross, Patrick A.
• Is Part Of: Pediatric critical care medicine. Volume 22:Issue 2(2021)
• Publication Date: 2021-02
• Subjects: data science -- intensive care units -- pediatric -- machine learning -- palliative care -- respiration -- artificial -- terminal care; Pediatric intensive care -- Periodicals; Pediatric emergencies -- Periodicals; 618.05
• DOI: 10.1097/PCC.0000000000002612
• Languages: English
• ISSNs: 1529-7535

Abstract

Abstract : Objectives: Accurate prediction of time to death after withdrawal of life-sustaining therapies may improve counseling for families and help identify candidates for organ donation after cardiac death. The study objectives were to: 1) train a long short-term memory model to predict cardiac death within 1 hour after terminal extubation, 2) calculate the positive predictive value of the model and the number needed to alert among potential organ donors, and 3) examine associations between time to cardiac death and the patient's characteristics and physiologic variables using Cox regression. Design: Retrospective cohort study. Setting: PICU and cardiothoracic ICU in a tertiary-care academic children's hospital. Patients: Patients 0–21 years old who died after terminal extubation from 2011 to 2018 ( n = 237). Interventions: None. Measurements and Main Results: The median time to death for the cohort was 0.3 hours after terminal extubation (interquartile range, 0.16–1.6 hr); 70% of patients died within 1 hour. The long short-term memory model had an area under the receiver operating characteristic curve of 0.85 and a positive predictive value of 0.81 at a sensitivity of 94% when predicting death within 1 hour of terminal extubation. About 39% of patients who died within 1 hour met organ procurement and transplantation network criteria for liver and kidney donors. The long short-term memory identified 93% of potential organ donors with a number needed to alert of 1.08, meaning that 13 of 14 prepared operating rooms would have yielded a viable organ. A Cox proportional hazard model identified independent predictors of shorter time to death including low Glasgow Coma Score, high PaO2 -to-FIO2 ratio, low-pulse oximetry, and low serum bicarbonate. Conclusions: Our long short-term memory model accurately predicted whether a child will die within 1 hour of terminal extubation and may improve counseling for families. Our model can identify potential candidates for donation after cardiac death while minimizing unnecessarily prepared operating rooms. Abstract : Supplemental Digital Content is available in the text.