Development and validation of a concussion risk prediction model using 2023 National Health Interview Survey (NHIS) data

Concussions are complex, as patients often present with nonspecific symptoms, requiring timely evaluation and accurate diagnosis. This study, using the National Health Interview Survey database, aimed to explore and validate a concussion risk model to support diagnostic decision-making and patient treatment supervision. This study included demographic and clinical data of 14,275 subjects in 2023. Predictive indicators were selected using least baseline characteristics and least absolute shrinkage and selection operator regression analysis, and a risk nomogram model was constructed. The model was evaluated using calibration curves, the area under curve of receiver operating characteristic, and decision curve analysis. The eligible concussion group (n = 363) and the nonconcussion group (n = 13,912) from the National Health Interview Survey database exhibited significant differences in 9 baseline characteristics (P