Abstract
BACKGROUND/AIMS: Shift workers, such as medical personnel, and pilots, are facing an increased risk of depressive symptoms. Depressive symptoms significantly impact an individual's quality of life and affect work performance, decision-making abilities, and overall public safety. This study aims to establish a multidimensional depressive symptom prediction model based on a large sample of commercial airline pilots to facilitate early identification, prevention, and personalized intervention strategies. METHODS: This population-based study included 11,111 participants, with 7918 pilots in the training set and 3193 pilots in the external validation set. Depressive symptom severity was assessed using the Patient Health Questionnaire-9 (PHQ-9). Physiological, psychological, and lifestyle factors potentially associated with depressive symptom risk were collected. The optimal predictors for model development were selected using the Boruta algorithm combined with the LASSO method, and a nomogram was developed using multivariate logistic regression to predict depressive symptoms in pilots. The model performance was evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, and accuracy measures, such as the Brier score and Spiegelhalter z-test. Additionally, decision curve analysis (DCA) was performed to assess the model's clinical utility. RESULTS: A total of 7918 pilots were included in the training set and 3193 were included in the external validation set. Five characteristic indicators were selected based on their significance in the prediction of depressive symptom risk: living status, alcohol drinking, family history of mental health disorder, subjective health, and subjective sleep quality. The model showed acceptable overall discrimination (AUC(train) = 0.836, 95%CI 0.818 to 0.854; AUC(validation) = 0.840, 95%CI 0.811 to 0.868) and calibration (Brier score(train) = 0.048; Brier score(validation) = 0.051). The decision curve analysis showed that the net benefit was superior to intervening on all participants or not intervening on all participants. CONCLUSIONS: This study provides a reliable tool for early prediction and customized management of depressive symptoms among commercial airline pilots. This approach promotes the development of the field by transitioning from passive mental health care to active mental health prevention, emphasizing personalized prevention strategies.