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The Deterioration Risk Index: Developing and Piloting a Machine Learning Algorithm to Reduce Pediatric Inpatient
Laura O H Rust1,2,3,4,5,6,7, Tyler J Gorham5, Sven Bambach5
1Division of Clinical Informatics, Department of Pediatrics, The Ohio State University College of Medicine, Nationwide Children's Hospital, Columbus, OH.
Summary
A new machine learning algorithm, the Deterioration Risk Index (DRI), significantly improves early detection of clinical deterioration in hospitalized children. This advanced system is more sensitive and generates fewer alarms than previous methods, enhancing patient safety.
Area of Science:
- Pediatric critical care medicine
- Machine learning in healthcare
- Clinical informatics
Background:
- Timely identification of clinical deterioration in pediatric inpatients is crucial for improving outcomes.
- Existing situational awareness programs may lack the sensitivity and specificity needed for optimal patient monitoring.
- Machine learning offers potential for developing more accurate predictive algorithms.
Purpose of the Study:
- To develop and deploy a disease cohort-based machine learning algorithm for early identification of hospitalized pediatric patients at risk for clinical deterioration.
- To create an algorithm that outperforms the existing situational awareness program in sensitivity and alarm frequency.
- To validate the algorithm's performance and assess its impact on clinical deterioration events.
Main Methods:
- A retrospective cohort study was conducted at a quaternary-care children's hospital.
- Lasso-regularized logistic regression was used to develop separate algorithms for cardiac, malignancy, and general patient cohorts.
- Predictors included vital signs, oxygen use, nursing assessments, early warning scores, diagnoses, and lab results.
- Model performance was compared to the existing situational awareness program using a novel retrospective validation approach.
- Simulations with frontline staff informed user experience and workflow refinement prior to pilot implementation.
Main Results:
- The Deterioration Risk Index (DRI) demonstrated 2.4 times greater sensitivity than the existing program (53% vs. 22%, p < 0.001).
- The DRI required 2.3 times fewer alarms per detected event (121 vs. 276).
- Significant sensitivity gains were observed in cardiac (73% vs. 18%) and malignancy (81% vs. 27%) cohorts.
- A pilot implementation showed a 77% reduction in deterioration events (3 observed vs. 13.1 expected, p = 0.001).
Conclusions:
- Machine learning algorithms, tailored to specific patient cohorts, can create more sensitive early warning systems for pediatric clinical deterioration.
- Acknowledging unique pathophysiology in cardiology and oncology patients is vital for accurate risk prediction.
- Successful bedside translation requires leveraging existing platforms and considering operational impacts of model implementation.
