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Published on: August 2, 2019
Death One Hour After Terminal Extubation in Children: Validation of a Machine Learning Model to Predict Cardiac Death
Meredith C Winter1,2, Alice X Zhou1,3, Eugene Laksana1,3
1Department of Anesthesiology and Critical Care Medicine, Children's Hospital Los Angeles, Los Angeles, CA.
Insights
A machine learning model accurately predicts death within 1 hour of pediatric terminal extubation (TE). This tool aids end-of-life care and organ donation decisions in pediatric intensive care units (PICUs).
Area of Science:
- Pediatric critical care medicine
- Machine learning applications in healthcare
- Biomedical informatics
Background:
- Predicting mortality after terminal extubation (TE) in pediatric intensive care units (PICUs) is crucial for family support and organ donation.
- Identifying patients at high risk of imminent death aids in end-of-life care and resource allocation.
Purpose of the Study:
- To develop and validate a machine learning model for predicting death within one hour following TE in pediatric patients.
- To support clinical decision-making in PICUs regarding family counseling and organ donation after circulatory determination of death (DCDD).
Main Methods:
- Utilized the multicenter retrospective Death One Hour After Terminal Extubation (DONATE) database (2009-2021).
- Collected data included demographics, clinical features, vital signs, labs, ventilator settings, medications, and procedures.
- Trained and validated extra-trees machine learning models on data from 10 U.S. PICUs, including 957 pediatric patients (0-21 years) who underwent TE.
Main Results:
- A parsimonious extra-trees model with 21 features achieved an area under the receiver operating characteristic curve of 0.84 (95% CI, 0.81-0.87).
- At 90% sensitivity, the model demonstrated a positive predictive value (PPV) of 88% and a negative predictive value (NPV) of 70% for predicting death within 1 hour.
- For potential organ donors, the PPV was 86% and NPV was 74% at 90% sensitivity.
Conclusions:
- The developed machine learning model accurately predicts pediatric death within 1 hour of TE with high discrimination and low false alarm rates.
- This validated model offers significant potential for improving end-of-life counseling and optimizing institutional resource use for organ donation.
Objectives:
In the PICU, predicting death within 1 hour after terminal extubation (TE) is valuable in augmenting family counseling and in identifying suitable candidates for organ donation after circulatory determination of death (DCDD). The objective of this study was to train and validate a machine learning model to predict death within 1 hour after TE.
Design:
The Death One Hour After Terminal Extubation (DONATE) database was generated using multicenter retrospective data from 2009 to 2021. Data covering demographics, clinical features, vital signs, laboratory values, ventilator settings, medications, and procedures were collected. Machine learning models were trained to predict whether a pediatric patient would die within 1 hour after TE and evaluated on a holdout set.
Setting:
Ten U.S. PICUs.
Patients:
Children and adolescents, 0-21 years old, who died after TE ( n = 957).
Interventions:
None.
Measurements And Main Results:
The final model was a parsimonious extra-trees model with 21 input features. It was trained on the 2009-2018 data from eight sites ( n = 634) and evaluated on a holdout set comprised of the 2019-2021 data of all ten sites ( n = 323), representing temporal and external validation. The area under the receiver operating characteristic curve and 95% CI was 0.84 (95% CI, 0.81-0.87). At a sensitivity of 90%, the positive predictive value (PPV) was 88%, the negative predictive value (NPV) was 70%, and the number needed to alert (NNA) was 1.14. Among potential organ donors, at the same sensitivity level, the PPV was 86%, the NPV was 74%, and the NNA was 1.17.
Conclusions:
Our model, trained and validated on multisite data, predicted whether a child will die within 1 hour of TE with high discrimination and a low false alarm rate. This finding has important applications to end-of-life counseling and institutional resource utilization when families wish to attempt DCDD.
