Predicting inpatient rehabilitation length of stay for adults with traumatic spinal cord injury

• Published in: The journal of spinal cord medicine pp. 1 - 11
• Main Authors: Whitten, Tara A, Loyola Sanchez, Adalberto, Gyawali, Bina, Papathanassoglou, Elisavet D E, Bakal, Jeffrey A, Krysa, Jacqueline A
• Format: Journal Article
• Language: English
• Published: England 11.03.2024
• Subjects: Spinal cord injury; Inpatient rehabilitation; Length of stay
• ISSN: 1079-0268; 2045-7723
• DOI: 10.1080/10790268.2024.2325165

Most post-injury traumatic spinal cord injury (TSCI) care occurs in the inpatient rehabilitation setting. The inpatient rehabilitation length of stay (R-LOS) has been shown to be a significant predictor of motor function restoration in persons with TSCI. Due to the complexity, and heterogeneity of individuals with TSCI, the R-LOS is challenging to predict at admission. To identify the main predictors of R-LOS and derive an equation to estimate R-LOS in persons with TSCI. This is a retrospective analysis of data from adults with TSCI from The Rick Hansen Spinal Cord Injury Registry in Alberta, Canada, who received rehabilitation care between May 10, 2005, and January 28, 2020. Multiple linear regression analysis was used to determine significant relationships between R-LOS and measures of participant demographics, length of stay, impairment and injury classification, and comorbidities. The analysis included 736 adults with TSCI from an eligible cohort of 1365. The median R-LOS was 65 days (IQR 39-99 days), ranging from 1 to 469 days. Multivariate linear regression analysis identified two significant predictors of R-LOS, total FIM score and the injury classification. This model was used to derive a R-LOS prediction equation, which explained 34% of the variance in R-LOS. We developed a simple equation to predict R-LOS based on the level of impairment and total FIM scores in persons with TSCI. These data have implications for health system planning, improvement, and innovation, and provide insights to support further research into the predictors of R-LOS, identification of higher-risk individuals.