A data-driven model for early prediction of need for invasive mechanical ventilation in pediatric intensive care unit patients

Acute respiratory failure is a life-threatening clinical outcome in critically ill pediatric patients. In severe cases, patients can require mechanical ventilation (MV) for survival. Early recognition of these patients can potentially help clinicians alter the clinical course and lead to improved ou...

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Bibliographic Details
Published in:	PloS one 2023-08, Vol.18 (8), p.e0289763-e0289763
Main Authors:	Bose, Sanjukta N, Defante, Andrew, Greenstein, Joseph L, Haddad, Gabriel G, Ryu, Julie, Winslow, Raimond L
Format:	Article
Language:	eng
Subjects:	Acute respiratory distress syndrome Airway management Artificial neural networks Artificial respiration Asthma Biology and Life Sciences Cardiovascular disease Care and treatment Child Children Clustering Complications and side effects Datasets Decision trees Diagnosis Electronic health records Failure Generalized linear models Health aspects Health risks Humans Intensive care Intensive Care Units Intensive Care Units, Pediatric Internet of Things Intubation Mechanical ventilation Medicine and Health Sciences Modelling Mortality Neural networks Nosocomial infections Observational studies Ostomy Oxygen saturation Patient outcomes Patients Pediatrics Physical Sciences Prediction models Research and Analysis Methods Respiration, Artificial Respiratory distress syndrome Respiratory failure Respiratory Insufficiency - therapy Retrospective Studies Risk ROC Curve Trajectories Variables Ventilation Ventilators Vital signs
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Summary:	Acute respiratory failure is a life-threatening clinical outcome in critically ill pediatric patients. In severe cases, patients can require mechanical ventilation (MV) for survival. Early recognition of these patients can potentially help clinicians alter the clinical course and lead to improved outcomes. To build a data-driven model for early prediction of the need for mechanical ventilation in pediatric intensive care unit (PICU) patients. The study consists of a single-center retrospective observational study on a cohort of 13,651 PICU patients admitted between 1/01/2010 and 5/15/2018 with a prevalence of 8.06% for MV due to respiratory failure. XGBoost (extreme gradient boosting) and a convolutional neural network (CNN) using medication history were used to develop a prediction model that could yield a time-varying "risk-score"-a continuous probability of whether a patient will receive MV-and an ideal global threshold was calculated from the receiver operating characteristics (ROC) curve. The early prediction point (EPP) was the first time the risk-score surpassed the optimal threshold, and the interval between the EPP and the start of the MV was the early warning period (EWT). Spectral clustering identified patient groups based on risk-score trajectories after EPP. A clinical and medication history-based model achieved a 0.89 area under the ROC curve (AUROC), 0.6 sensitivity, 0.95 specificity, 0.55 positive predictive value (PPV), and 0.95 negative predictive value (NPV). Early warning time (EWT) median [inter-quartile range] of this model was 9.9[4.2-69.2] hours. Clustering risk-score trajectories within a six-hour window after the early prediction point (EPP) established three patient groups, with the highest risk group's PPV being 0.92. This study uses a unique method to extract and apply medication history information, such as time-varying variables, to identify patients who may need mechanical ventilation for respiratory failure and provide an early warning period to avert it.
ISSN:	1932-6203 1932-6203