Predicting hospital outcomes in concussion and TBI: A mixed-effects analysis utilizing the nationwide readmissions
Anubhav Chandla1, Shane Shahrestani2, Gabrielle E A Hovis1
1Departments of Neurosurgery, Los Angeles, CA, United States.
Background And Objectives:
Traumatic brain injury (TBI) is characterized by a wide range in severity. This variation presents a challenge for predicting outcomes and making management decisions, particularly for patients sustaining less severe injury. We present a novel statistical model for the prediction of hospital outcomes in two propensity-matched cohorts to optimize TBI patient management and counseling.
Methods:
Hospitalized patients diagnosed with TBI were selected from the Nationwide Readmissions Database (NRD) from 2010 to 2019 using ICD-9 and ICD-10 codes. Using propensity score matching for baseline characteristics, patients were sorted by GCS score into two cohorts: 1188 patients with mild to moderate TBI (mTBI, GCS > 8) and 1219 patients with severe TBI (sTBI, GCS ≤ 8). Mixed-effects modeling was implemented, and model performance was evaluated using the Area Under the Curve (AUC). Any variance in ROC model prediction between cohorts was compared using DeLong's test.
Results:
After bivariate analysis, the mean length of stay (LOS), hospital cost, and mortality were significantly lower in the mTBI cohort relative to sTBI. GCS scores within the range of 9-15 were predictive of LOS (p < 0.01), with a trend towards significance in the prediction of non-routine discharge (p = 0.06).
Conclusion:
Using an advanced mixed-effects model, our study found that GCS is an accurate predictor of hospital outcomes after a TBI diagnosis. These results provide insight that may aid in the development of preventative strategies, management decisions, and patient counseling to ensure a safe return to daily life for patients diagnosed with concussion.
