Death One Hour After Terminal Extubation in Children: Validation of a Machine Learning Model to Predict Cardiac Death After Withdrawal of Life-Sustaining Treatment in a Multicenter Cohort, 2009-2021.

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.

Prehospitalization Trauma and Physiologic Factors Associated with the Presence of Post-traumatic Stress 3 Months After PICU Discharge.

Background: Children admitted to the pediatric intensive care unit (PICU) have post-traumatic stress (PTS) rates up to 64%, and up to 28% of them meet criteria for PTS disorder (PTSD). We aim to examine whether a prior trauma history and increased physiologic parameters due to a heightened sympathetic response are associated with later PTS. Our hypothesis was children with history of prehospitalization trauma, higher heart rates, blood pressures, cortisol, and extrinsic catecholamine administration during PICU admission are more likely to have PTS after discharge.

Ventilator Weaning and Terminal Extubation: Withdrawal of Life-Sustaining Therapy in Children. Secondary Analysis of the Death One Hour After Terminal Extubation Study.

Objective: Terminal extubation (TE) and terminal weaning (TW) during withdrawal of life-sustaining therapies (WLSTs) have been described and defined in adults. The recent Death One Hour After Terminal Extubation study aimed to validate a model developed to predict whether a child would die within 1 hour after discontinuation of mechanical ventilation for WLST. Although TW has not been described in children, pre-extubation weaning has been known to occur before WLST, though to what extent is unknown.

Narrowing the gap: expected versus deployment performance.

Objectives: Successful model development requires both an accurate a priori understanding of future performance and high performance on deployment. Optimistic estimations of model performance that are unrealized in real-world clinical settings can contribute to nonuse of predictive models. This study used 2 tasks, predicting ICU mortality and Bi-Level Positive Airway Pressure failure, to quantify: (1) how well internal test performances derived from different methods of partitioning data into development and test sets estimate future deployment performance of Recurrent Neural Network models and (2) the effects of including older data in the training set on models' performance.

Analgesia and Sedation at Terminal Extubation: A Secondary Analysis From Death One Hour After Terminal Extubation Study Data.

Objectives: To describe the doses of opioids and benzodiazepines administered around the time of terminal extubation (TE) to children who died within 1 hour of TE and to identify their association with the time to death (TTD).

Design: Secondary analysis of data collected for the Death One Hour After Terminal Extubation study.

Setting: Nine U.

Development of a deep learning model that predicts Bi-level positive airway pressure failure.

Delaying intubation for patients failing Bi-Level Positive Airway Pressure (BIPAP) may be associated with harm. The objective of this study was to develop a deep learning model capable of aiding clinical decision making by predicting Bi-Level Positive Airway Pressure (BIPAP) failure. This was a retrospective cohort study in a tertiary pediatric intensive care unit (PICU) between 2010 and 2020.

Continuous Prediction of Mortality in the PICU: A Recurrent Neural Network Model in a Single-Center Dataset.

Objectives: Develop, as a proof of concept, a recurrent neural network model using electronic medical records data capable of continuously assessing an individual child's risk of mortality throughout their ICU stay as a proxy measure of severity of illness.

Design: Retrospective cohort study.

Setting: PICU in a tertiary care academic children's hospital.

Machine Learning to Predict Cardiac Death Within 1 Hour After Terminal Extubation.

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.

Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit.

Objective: Quantify physiologically acceptable PICU-discharge vital signs and develop machine learning models to predict these values for individual patients throughout their PICU episode.

Methods: EMR data from 7256 survivor PICU episodes (5632 patients) collected between 2009 and 2017 at Children's Hospital Los Angeles was analyzed. Each episode contained 375 variables representing physiology, labs, interventions, and drugs.