A nomogram to predict the risk of cognitive impairment in patients with depressive disorder

Abstract

This study was to describe the cognitive function status in patients with depressive disorder and to construct a nomogram model to predict the risk factors of cognitive impairment in these patients. From October 2019 to February 2021, a total of 141 patients with depressive disorder completed the survey in two hospitals. The Montreal cognitive assessment (MoCA) was used with a cutoff score of 26 to differentiate cognitive impairment. Univariable and multivariable logistic regression analyses were conducted to identify independent risk factors. A nomogram was then constructed based on the results of the multivariable logistic regression analysis. The patients had an average MoCA score of 23.99 ± 3.02. The multivariable logistic regression analysis revealed that age (OR: 1.096, 95% CI: 1.042–1.153, p < 0.001), education (OR: 0.065, 95% CI: 0.016–0.263, p < 0.001), depression severity (OR: 1.878, 95% CI: 1.021–3.456, p = 0.043), and sleep quality (OR: 2.454, 95% CI: 1.400–4.301, p = 0.002) were independent risk factors for cognitive impairment in patients with depressive disorder. The area under receiver operating characteristic (ROC) curves was 0.868 (95% CI: 0.807–0.929), indicating good discriminability of the model. The calibration curve of the model and the Hosmer–Lemeshow test (p = 0.571) demonstrated a well‐fitted model with high calibration. Age, education, depression severity, and sleep quality were found to be significant predictors of cognitive function. A nomogram model was developed to predict cognitive impairment in patients with depressive disorder, providing a solid foundation for clinical interventions.