Machine Learning-Based Pediatric Early Warning Score: Patient Outcomes in a Pre- Versus Post-Implementation Study,...

Area of Science:

• Pediatric critical care medicine
• Machine learning in healthcare
• Clinical decision support systems

Background:

• Direct ward to ICU transfers in children pose significant risks.
• Accurate and timely risk stratification is crucial for pediatric patient management.
• Existing methods may not adequately predict the need for intensive care.

Purpose of the Study:

• To describe the implementation of the pediatric Calculated Assessment of Risk and Triage (pCART) machine learning model.
• To evaluate the impact of pCART on predicting direct ward to ICU transfers within 12 hours.
• To assess the associated improvements in outcomes for hospitalized children.

Main Methods:

• A pre- vs. post-implementation study design was employed.
• The study included pediatric admissions (<18 years) at an urban, tertiary-care academic hospital.
• Data were collected from May 2019 to April 2023, divided into baseline and pCART implementation cohorts.

Main Results:

• pCART implementation was associated with a significant decrease in critical events (from 1.4% to 0.4%, p < 0.001).
• High-risk patients identified by pCART had over two-thirds lower adjusted odds of critical events (OR, 0.22; p < 0.001).
• No significant association was found with overall hospital/ICU length-of-stay, but a difference was noted in LOS for ICU-transferred patients.

Conclusions:

• Deployment of the pCART machine learning tool improved clinical decision support for pediatric ward patients.
• pCART implementation was linked to reduced odds of critical events in high-risk pediatric patients.
• The study highlights the potential of AI-driven tools in enhancing pediatric critical care outcomes.