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Novel Claims-Based Outcome Phenotypes in Survivors of Pediatric Traumatic Brain Injury
Aline B Maddux1, Carter Sevick, Matthew Cox-Martin
1Section of Critical Care Medicine, University of Colorado School of Medicine and Children's Hospital Colorado, Aurora (Dr Maddux); Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus and Children's Hospital Colorado, Aurora (Mr Sevick and Dr Cox-Martin); and Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, and Children's Hospital Colorado, Aurora (Dr Bennett).
Insights
Researchers identified distinct groups of children with traumatic brain injury (TBI) based on their healthcare use after hospital discharge. Early hospitalization factors predict these functional outcome groups, aiding future clinical trial targeting.
Area of Science:
- Pediatric Traumatology
- Neuroscience
- Health Services Research
Background:
- Traumatic brain injury (TBI) in children leads to varied long-term functional outcomes.
- Understanding healthcare utilization patterns post-discharge is crucial for assessing functional outcomes.
- Predicting these outcomes requires identifying key patient and hospitalization characteristics.
Purpose of the Study:
- To identify healthcare utilization patterns representing functional outcome phenotypes in pediatric TBI survivors using postdischarge insurance claims.
- To determine patient and hospitalization characteristics that predict these identified outcome phenotypes.
Main Methods:
- Retrospective cohort study involving 289 pediatric TBI survivors.
- Unsupervised machine learning applied to postdischarge insurance claims to define functional outcome phenotypes.
- Regression analyses conducted to identify predictors of these phenotypes.
Main Results:
- Four distinct functional outcome phenotypes were identified.
- Phenotypes 3 and 4 exhibited the highest healthcare resource utilization.
- Morbidity burden was highest in the initial 4 months post-discharge, with respiratory issues persisting.
- Injury severity, mechanism, intracranial pressure monitoring, seizures, and length of hospital/ICU stay predicted phenotypes.
Conclusions:
- Machine learning effectively identified pediatric TBI postdischarge phenotypes associated with high morbidity risk.
- Predictors are often available early in hospitalization, enabling prognostic enrichment for clinical trials.
- Findings support targeted interventions for domain-specific morbidities in high-risk pediatric TBI populations.
Objective:
For children hospitalized with acute traumatic brain injury (TBI), to use postdischarge insurance claims to identify: (1) healthcare utilization patterns representative of functional outcome phenotypes and (2) patient and hospitalization characteristics that predict outcome phenotype.
Setting:
Two pediatric trauma centers and a state-level insurance claim aggregator.
Patients:
A total of 289 children, who survived a hospitalization after TBI between 2009 and 2014, were in the hospital trauma registry, and had postdischarge insurance eligibility.
Design:
Retrospective cohort study.
Main Measures:
Unsupervised machine learning to identify phenotypes based on postdischarge insurance claims. Regression analyses to identify predictors of phenotype.
Results:
Median age 5 years (interquartile range 2-12), 29% (84/289) female. TBI severity: 30% severe, 14% moderate, and 60% mild. We identified 4 functional outcome phenotypes. Phenotypes 3 and 4 were the highest utilizers of resources. Morbidity burden was highest during the first 4 postdischarge months and subsequently decreased in all domains except respiratory. Severity and mechanism of injury, intracranial pressure monitor placement, seizures, and hospital and intensive care unit lengths of stay were phenotype predictors.
Conclusions:
Unsupervised machine learning identified postdischarge phenotypes at high risk for morbidities. Most phenotype predictors are available early in the hospitalization and can be used for prognostic enrichment of clinical trials targeting mitigation or treatment of domain-specific morbidities.
