Prediction tool for discharge disposition and 30-day readmission using electronic health records among patients hospitalized for traumatic brain injury

Tianjian Zhou^1*James Graham²Davis Davalos-DeLosh³Amartya Maulik¹Jessica Edelstein⁴Amanda Hoffman⁵Haonan Wang¹Deana Davalos⁶

• ¹Department of Statistics, Colorado State University, Fort Collins, United States
• ²Department of Occupational Therapy, Colorado State University, Fort Collins, United States
• ³Department of Computer Science, University of Colorado Boulder, Boulder, Colorado, United States
• ⁴Shirley Ryan AbilityLab and Department of Physical Medicine & Rehabilitation, Northwestern Medicine, Chicago, Illinois, United States
• ⁵UCHealth, University of Colorado Hospital, Aurora, Colorado, United States
• ⁶Department of Psychology, Colorado State University, Fort Collins, United States

Background: Traumatic brain injury (TBI) is one of the most common and complex neurological conditions. Many TBI patients require ongoing rehabilitation beyond acute care, making treatment and discharge decisions critical. While individual risk factors for TBI outcomes are known, integrating comprehensive electronic health record (EHR) data into practical, validated prediction tools for personalized discharge planning and readmission risk assessment remains a key challenge. EHRs offer a valuable resource by integrating sociodemographic information, clinical care details, and prior healthcare encounters, providing an opportunity to develop models that predict key outcomes for TBI patients, such as discharge disposition and 30-day readmission.Methods: This retrospective cohort study utilized EHRs from a large multi-hospital health system (2017-2023) to develop and validate statistical models predicting discharge disposition and 30-day readmission among hospitalized TBI patients, and to translate these models into an accessible clinical prediction tool. Descriptive statistics were calculated to summarize patient characteristics. Multinomial logistic regression was used to model discharge disposition, and logistic regression was used for 30-day readmission. Forward stepwise regression based on the Akaike information criterion was used for variable selection. Cross-validation using the area under the receiver operating characteristic evaluated predictive performance.Results: Several factors were significantly associated with both outcomes. Older age was positively associated with discharge to Inpatient Rehabilitation Facility/Skilled Nursing Facility or Hospice/Died versus Home (p < 0.001), and with 30-day readmission (p = 0.002). Ethnicity, significant other status, insurance, prior inpatient stays, length of stay, as well as Glasgow Coma Scale, activities of daily living, and mobility were all significantly associated with discharge disposition (p < 0.001). Prior mental health diagnosis (p = 0.062), prior inpatient stays (p < 0.001), and intensive care unit admission (p = 0.002) were associated with higher odds of 30-day readmission, while Commercial insurance was associated with lower odds compared to Medicare (p = 0.024). A prediction tool is available at https://tjzhou.shinyapps.io/INREACHapp/.We developed and validated predictive models using EHR data, culminating in a practical tool that may enhance the management of patients hospitalized with TBI by supporting personalized discharge planning and risk stratification.