A Vital Sign-Based Model to Predict Clinical Deterioration in Hospitalized Children

Abstract

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.

Figure 1:. Model discrimination for predicting ICU transfers.

AUC comparisons between our derived vital sign-based model and the modified BedsidePEWS for predicting ICU transfers at 6–36 hours prior to the transfer. Bars indicate AUC 95% confidence intervals at each time point.

Figure 2:. Model discrimination for predicting critical deterioration events.

AUC comparisons between our derived of vital sign-based model and the modified BedsidePEWS for predicting critical deterioration events (ICU transfer followed by mechanical ventilation, vasopressor administration, or mortality within 12 hours) at 6–36 prior to event. Bars indicate AUC 95% confidence intervals at each time point

Figure 3:. Predictive performance of each variable in the vital sign-based model.

AUC (with 95% confidence interval) comparison of the overall vital sign-based model and individual features for predicting ICU transfers. Respiratory rate and heart rate were highly predictive of deterioration, as compared to other vital signs (temperature, systolic and diastolic blood pressure, and oxygen saturation). Time in the hospital was also predictive of deterioration. Apart from patient age (continuous variable) other patient characteristics such as sex, race (black/white/other), and ethnicity (Hispanic/Not-Hispanic) showed poor predictive accuracy.