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Prediction of 7-Day Readmission Risk for Pediatric Trauma Patients
Patrick T Delaplain1, Yigit S Guner2, William Feaster3
1Department of Surgery, University of California, Irvine, Orange, California.
Insights
Pediatric trauma patients have unique readmission risks. Condition-specific models can predict unplanned 7-day readmissions, identifying high-risk factors like poisoning and complications.
Area of Science:
- Pediatric trauma care
- Healthcare analytics
- Predictive modeling in medicine
Background:
- Pediatric trauma admissions present unique unplanned readmission risks.
- Condition-specific predictive models are needed for accuracy in this population.
- This study analyzes risk factors for unplanned 7-day readmissions in pediatric trauma patients.
Purpose of the Study:
- To identify risk factors associated with unplanned 7-day readmissions in pediatric trauma patients.
- To develop and evaluate a predictive model for 7-day readmissions.
- To compare 7-day readmission predictors with those for 30-day readmissions.
Main Methods:
- Utilized a multicenter dataset of 82,532 pediatric trauma encounters.
- Developed a random intercept, mixed-effects regression model using 75% of the data.
- Included demographics, payer, healthcare utilization, diagnoses, medications, and procedures as variables.
Main Results:
- Poisoning and medical/surgical complications increase readmission odds.
- Specific trauma sites (thorax, knee, etc.) are associated with reduced readmission odds.
- Healthcare utilization and medication count are significant predictors; the 7-day model achieved an AUC of 0.737.
Conclusions:
- The type of trauma influences readmission risk in pediatric patients.
- Targeted quality improvement measures are necessary for high-risk trauma conditions.
- Condition-specific models enhance prediction accuracy for pediatric trauma readmissions.
Background:
Pediatric patients admitted for trauma may have unique risk factors of unplanned readmission and require condition-specific models to maximize accuracy of prediction. We used a multicenter data set on trauma admissions to study risk factors and predict unplanned 7-day readmissions with comparison to the 30-day metric.
Methods:
Data from 28 hospitals in the United States consisting of 82,532 patients (95,158 encounters) were retrieved, and 75% of the data were used for building a random intercept, mixed-effects regression model, whereas the remaining were used for evaluating model performance. The variables included were demographics, payer, current and past health care utilization, trauma-related and other diagnoses, medications, and surgical procedures.
Results:
Certain conditions such as poisoning and medical/surgical complications during treatment of traumatic injuries are associated with increased odds of unplanned readmission. Conversely, trauma-related conditions, such as trauma to the thorax, knee, lower leg, hip/thigh, elbow/forearm, and shoulder/upper arm, are associated with reduced odds of readmission. Additional predictors include the current and past health care utilization and the number of medications. The corresponding 7-day model achieved an area under the receiver operator characteristic curve of 0.737 (0.716, 0.757) on an independent test set and shared similar risk factors with the 30-day version.
Conclusions:
Patients with trauma-related conditions have risk of readmission modified by the type of trauma. As a result, additional quality of care measures may be required for patients with trauma-related conditions that elevate their risk of readmission.
