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Prolonged hospital length of stay in pediatric trauma: a model for targeted interventions
David Gibbs1, Louis Ehwerhemuepha2,3, Tatiana Moreno1
1CHOC Children's Hospital, Orange, CA, USA.
Insights
Identifying risk factors for prolonged hospital stays in pediatric trauma patients is crucial. Burns, corrosion, and child abuse significantly increase length of stay (LOS), enabling targeted interventions for better care.
Area of Science:
- Pediatric trauma care
- Health services research
- Clinical informatics
Background:
- Prolonged length of stay (LOS) in pediatric trauma increases healthcare costs and family burden.
- Identifying specific risk factors is essential for optimizing care and resource allocation.
Purpose of the Study:
- To examine trauma-specific risk factors associated with prolonged LOS in pediatric patients.
- To develop predictive models for identifying at-risk individuals to improve care quality and reduce costs.
Main Methods:
- Analysis of 81,929 pediatric trauma hospitalizations across 27 hospitals with LOS >24 hours.
- Utilized a nested mixed effects model for statistical inference.
- Developed a stochastic gradient boosting model for predictive analysis, accounting for complex interactions.
Main Results:
- Over 18.7% of pediatric trauma cases exceeded a one-week length of stay.
- Burns, corrosion, and suspected/confirmed child abuse were identified as the strongest predictors of prolonged LOS.
- The machine learning model achieved a high predictive performance (AUC 0.912).
Conclusions:
- Advanced statistical and machine learning models effectively identify pediatric trauma patients at risk for prolonged LOS.
- These models facilitate targeted interventions to enhance patient care quality and reduce hospitalization duration.
- Optimizing LOS management can significantly alleviate the burden on families and the healthcare system.
Background:
In this study, trauma-specific risk factors of prolonged length of stay (LOS) in pediatric trauma were examined. Statistical and machine learning models were used to proffer ways to improve the quality of care of patients at risk of prolonged length of stay and reduce cost.
Methods:
Data from 27 hospitals were retrieved on 81,929 hospitalizations of pediatric patients with a primary diagnosis of trauma, and for which the LOS was >24 h. Nested mixed effects model was used for simplified statistical inference, while a stochastic gradient boosting model, considering high-order statistical interactions, was built for prediction.
Results:
Over 18.7% of the encounters had LOS >1 week. Burns and corrosion and suspected and confirmed child abuse are the strongest drivers of prolonged LOS. Several other trauma-specific and general pediatric clinical variables were also predictors of prolonged LOS. The stochastic gradient model obtained an area under the receiver operator characteristic curve of 0.912 (0.907, 0.917).
Conclusions:
The high performance of the machine learning model coupled with statistical inference from the mixed effects model provide an opportunity for targeted interventions to improve quality of care of trauma patients likely to require long length of stay.
Impact:
Targeted interventions on high-risk patients would improve the quality of care of pediatric trauma patients and reduce the length of stay. This comprehensive study includes data from multiple hospitals analyzed with advanced statistical and machine learning models. The statistical and machine learning models provide opportunities for targeted interventions and reduction in prolonged length of stay reducing the burden of hospitalization on families.
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