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A Vital Sign-Based Model to Predict Clinical Deterioration in Hospitalized Children
Anoop Mayampurath1,2, Priti Jani1, Yangyang Dai2
1Department of Pediatrics, University of Chicago, Chicago, IL.
Insights
A new model using vital signs can predict clinical deterioration in hospitalized children, outperforming existing systems. This early warning aims to improve patient outcomes and reduce mortality risks.
Area of Science:
- Pediatric critical care medicine
- Health informatics
- Clinical prediction modeling
Background:
- Clinical deterioration in hospitalized children poses significant mortality and morbidity risks.
- Early identification of at-risk pediatric patients is crucial for timely intervention and improved outcomes.
- Existing early warning systems may have limitations in predicting deterioration accurately.
Purpose of the Study:
- To develop and validate a predictive model for clinical deterioration in pediatric ward patients.
- To utilize electronic health record (EHR) data, specifically vital signs, for predicting deterioration.
- To compare the performance of the developed model against existing scoring systems.
Main Methods:
- An observational cohort study was conducted at an urban, tertiary-care medical center.
- A discrete-time logistic regression model was developed using six vital signs and patient characteristics.
- Data from 31,899 pediatric admissions (2009-2018) were split into derivation and validation cohorts.
Main Results:
- The primary outcome, clinical deterioration, was defined as direct ward-to-ICU transfer.
- The developed vital sign model accurately predicted ICU transfers 12 hours in advance.
- The model demonstrated superior predictive performance (C-statistic 0.78) compared to a modified Bedside Pediatric Early Warning System score (0.72) in the validation cohort.
Conclusions:
- A novel model using six vital signs effectively predicts clinical deterioration in hospitalized children.
- This vital sign-based model shows improved accuracy over the modified Bedside Pediatric Early Warning System.
- The model offers potential for earlier interventions, risk mitigation, and improved pediatric patient survival and long-term health.
Objectives:
Clinical deterioration in hospitalized children is associated with increased risk of mortality and morbidity. A prediction model capable of accurate and early identification of pediatric patients at risk of deterioration can facilitate timely assessment and intervention, potentially improving survival and long-term outcomes. The objective of this study was to develop a model utilizing vital signs from electronic health record data for predicting clinical deterioration in pediatric ward patients.
Design:
Observational cohort study.
Setting:
An urban, tertiary-care medical center.
Patients:
Patients less than 18 years admitted to the general ward during years 2009-2018.
Interventions:
None.
Measurements And Main Results:
The primary outcome of clinical deterioration was defined as a direct ward-to-ICU transfer. A discrete-time logistic regression model utilizing six vital signs along with patient characteristics was developed to predict ICU transfers several hours in advance. Among 31,899 pediatric admissions, 1,375 (3.7%) experienced the outcome. Data were split into independent derivation (yr 2009-2014) and prospective validation (yr 2015-2018) cohorts. In the prospective validation cohort, the vital sign model significantly outperformed a modified version of the Bedside Pediatric Early Warning System score in predicting ICU transfers 12 hours prior to the event (C-statistic 0.78 vs 0.72; p < 0.01).
Conclusions:
We developed a model utilizing six commonly used vital signs to predict risk of deterioration in hospitalized children. Our model demonstrated greater accuracy in predicting ICU transfers than the modified Bedside Pediatric Early Warning System. Our model may promote opportunities for timelier intervention and risk mitigation, thereby decreasing preventable death and improving long-term health.
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Objectives and Importance:
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