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A preliminary clinical risk model for probable post-traumatic stress disorder symptoms among intensive care unit
Bingjie Xiang1, Yuan Gao2, Yuanyuan Ren2
1Department of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan, China; Department of Psychiatry and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China.
Background:
Post-traumatic stress disorder (PTSD) is a disabling sequela among survivors of critical illness, yet early identification of high-risk individuals during hospitalization remains challenging. We aimed to develop a clinically interpretable prediction model for probable PTSD symptoms among ICU survivors using routinely collected clinical data.
Methods:
In this single-center retrospective cohort study, adult ICU survivors were assessed for probable PTSD symptoms after discharge using the PC-PTSD-5 questionnaire. A multivariable logistic regression model was developed with predictor selection via bootstrap-resampled LASSO regression. Internal validation was performed using bootstrap optimism correction and random split-sample testing. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis. A nomogram was constructed to facilitate individual risk estimation.
Results:
Among 207 ICU survivors, 18 (8.7%) screened positive for PTSD symptoms. Six predictors were retained: minimum fibrinogen, maximum white blood cell count, minimum serum sodium, minimum blood glucose, history of heart disease, and hospital length of stay. Lower minimum blood glucose, longer hospital length of stay, and lower minimum fibrinogen were independently associated with increased odds of PTSD symptom screening positivity. The model showed good discrimination (AUC 0.867), and bootstrap optimism-corrected validation confirmed stable performance (corrected AUC 0.866). Calibration was satisfactory with minimal overfitting. A nomogram was developed for clinical use.
Conclusions:
We developed an interpretable prediction model for probable PTSD symptoms among ICU survivors using routine clinical data. This model may support early identification of vulnerable patients and inform targeted psychological monitoring and follow-up after ICU discharge.
