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

epidemiology
psychiatry-and-psychology
data-mining-and-machine-learning

Abstract

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 ASD (Acute Stress Disorder, ASD). However, the ability to identify ASD early is still limited. This study aimed to establish a visual prediction model for post-traumatic ASD and provided a theoretical basis for clinical interventions and treatments.

Methods: General demographic characteristics and clinical information were collected. The participants were divided into the ASD group and the control group according to the diagnostic criteria of ASD. To establish a prediction model, the 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: 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 the AUC (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 a good calibration and better 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 superior effects in calibration and clinical usefulness. It may have potential value for the early diagnosis of ASD.

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