Construction and evaluation of two nomograms for screening major depressive disorder and subthreshold depression individuals based on anxiety, depression, and sleep items.

BACKGROUND

Current evidence is insufficient to support specific tools for screening Major Depressive Disorder (MDD). Early detection of subthreshold depression (SD) is crucial in preventing its progression to MDD. This study aims to develop nomograms that visualize the weights of predictors to improve the performance of screening tools.

METHODS

Participants were recruited from Peking University Sixth Hospital and Beijing Physical Examination Center between October 2022 and April 2024. The Mini-International Neuropsychiatric Interview (MINI) 5.0.0 was employed as the diagnostic gold standard, and Generalized Anxiety Disorder questionnaire-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and Pittsburgh Sleep Quality Index (PSQI) were employed to assess anxiety, depression, and sleep state. The nomograms were constructed by incorporating optimal predictors, selected through the Least Absolute Shrinkage and Selection Operator (LASSO), into a multivariate logistic regression model to estimate the probability of MDD and SD.

RESULTS

After matching age and education, 164 participants were included in each group for analysis. Both nomograms demonstrated superior discrimination, calibration, and clinical applicability compared to PHQ-9. Anxiety emerged as a most significant predictor for SD, while sleep problems exhibited high rankings for both SD and MDD. The two predictors subsequently affect concentration and daytime functioning.

LIMITATIONS

With a lack of external validation data, the performance of nomograms may be overestimated.

CONCLUSIONS

This study is the first attempt to develop a nomogram for predicting SD, while also providing a nomogram for MDD. The crucial predictors offer valuable insights into potential variables for clinical intervention.