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Machine Learning to Predict Cardiac Death Within 1 Hour After Terminal Extubation.
Winter, Meredith C; Day, Travis E; Ledbetter, David R; Aczon, Melissa D; Newth, Christopher J L; Wetzel, Randall C; Ross, Patrick A.
Afiliación
  • Winter MC; Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA.
  • Day TE; Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA.
  • Ledbetter DR; Laura P. and Leland K. Whittier Virtual Pediatric Intensive Care Unit, Los Angeles, CA.
  • Aczon MD; Department of Computer Science, University of Southern California Viterbi School of Engineering, Los Angeles, CA.
  • Newth CJL; Department of Pediatrics, University of Southern California Keck School of Medicine, Los Angeles, CA.
  • Wetzel RC; Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA.
  • Ross PA; Laura P. and Leland K. Whittier Virtual Pediatric Intensive Care Unit, Los Angeles, CA.
Pediatr Crit Care Med ; 22(2): 161-171, 2021 02 01.
Article en En | MEDLINE | ID: mdl-33156210
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Obtención de Tejidos y Órganos / Extubación Traqueal Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Child / Child, preschool / Humans / Infant / Newborn Idioma: En Revista: Pediatr Crit Care Med Asunto de la revista: PEDIATRIA / TERAPIA INTENSIVA Año: 2021 Tipo del documento: Article País de afiliación: Canadá

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Obtención de Tejidos y Órganos / Extubación Traqueal Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Child / Child, preschool / Humans / Infant / Newborn Idioma: En Revista: Pediatr Crit Care Med Asunto de la revista: PEDIATRIA / TERAPIA INTENSIVA Año: 2021 Tipo del documento: Article País de afiliación: Canadá