Development and validation of an early predictive model for acute stress disorder after trauma: a clinical cohort study

Background Injury is sudden and unpredictable and has become a major public health problem in the world, and many trauma patients may experience cognitive or psychological problems including acute stress disorder (ASD). However, the ability to identify ASD early is still limited. The aim of this study was to investigate the risk factors for post-traumatic ASD and to establish a visual prediction model. Methods This prospective cohort study was conducted from the trauma center of one general hospital in Zunyi. Cases of 216 inpatients with trauma were selected from September 2020 to August 2021. The participants were divided into the ASD ( n = 49) and non-ASD ( n = 167) groups according to the diagnostic criteria for ASD. General demographic characteristics and clinical information were collected. To establish a prediction model, the Least Absolute Shrinkage and Selection Operator (LASSO) regression method was applied to filter variables, and multivariable logistic regression analysis was used to construct a nomogram. The nomogram performance was determined by its discrimination, calibration, and clinical usefulness. Results Patients in the ASD group differed significantly from those in the non-ASD group in terms of general demographic and clinical characteristics ( e.g. , work or life pressure, trauma history, cause of trauma, trauma site, coma, fear, psychological burden, critical condition, sleep quality, limb activity). The ASD group exhibited higher levels compared to the control group in inflammatory markers, which indicated that ASD might be associated with inflammation in trauma patients. The predictive model yielded an Area Under the Curve (AUC) of 0.846 (95% Confidence Interval (CI) 0.781–0.911), sensitivity was 67.35%, specificity was 91.62%, and in the internal validation, the AUC was 0.845 (95% CI 0.783–0.911). This model showed good calibration and positive net benefits in decision curve analysis when the risk threshold of ASD was between 10% and 83%. Conclusions Our prediction model had a good discriminatory capacity and showed better effects in calibration. It may have potential value for the early identification of ASD.