Incidence Trends and Risk Prediction Nomogram for Suicidal Attempts in Patients With Major Depressive Disorder

Qian Zhao,Sha Sha,Yuan Liu,Xiaoning Shi,Juan Huang,Ling Zhang,Amanda Wilson,Yuanyuan Wang,Sixiang Liang,Jinhe Zhang

doi:10.3389/fpsyt.2021.644038

Abstract

Background: Major depressive disorder (MDD) is often associated with suicidal attempt (SA). Therefore, predicting the risk factors of SA would improve clinical interventions, research, and treatment for MDD patients. This study aimed to create a nomogram model which predicted correlates of SA in patients with MDD within the Chinese population.Method: A cross-sectional survey among 474 patients was analyzed. All subjects met the diagnostic criteria of MDD according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10). Multi-factor logistic regression analysis was used to explore demographic information and clinical characteristics associated with SA. A nomogram was further used to predict the risk of SA. Bootstrap re-sampling was used to internally validate the final model. Integrated Discrimination Improvement (IDI) and Akaike Information Criteria (AIC) were used to evaluate the capability of discrimination and calibration, respectively. Decision Curve Analysis (DCA) and the Receiver Operating Characteristic (ROC) curve was also used to evaluate the accuracy of the prediction model.Result: Multivariable logistic regression analysis showed that being married (OR = 0.473, 95% CI: 0.240 and 0.930) and a higher level of education (OR = 0.603, 95% CI: 0.464 and 0.784) decreased the risk of the SA. The higher number of episodes of depression (OR = 1.854, 95% CI: 1.040 and 3.303) increased the risk of SA in the model. The C-index of the nomogram was 0.715, with the internal (bootstrap) validation sets was 0.703. The Hosmer–Lemeshow test yielded a P-value of 0.33, suggesting a good fit of the prediction nomogram in the validation set.Conclusion: Our findings indicate that the demographic information and clinical characteristics of SA can be used in a nomogram to predict the risk of SA in Chinese MDD patients.

Highlights

Suicide is widely prevalent and constitutes a clinical and public health concern
This study aimed to provide a prediction model to identify the correlates with suicide attempts (SA) and diagnosed Major depressive disorder (MDD)
Four hundred and seventy-four hospitalized MDD patients were involved in the present analysis

Summary

Introduction

Suicide is widely prevalent and constitutes a clinical and public health concern. Close to eight hundred thousand people die annually by suicide every year. Suicide is the second leading cause of death among people aged 15–29 globally. Suicide and SA are widely prevalent on a global scale among psychiatric patients [3]. A previous SA is the strongest risk factor for subsequent SA and suicide death [5, 6]. MDD is the most common psychiatric disorder among people who die by suicide [7] and a wellestablished risk factor for SA [8]. The lifetime risk of SA in people with MDD is estimated between 16 and 40% [9], showing a wide range that requires further exploration to better understand the actual risk percentage. Predicting the risk factors of SA would improve clinical interventions, research, and treatment for MDD patients. This study aimed to create a nomogram model which predicted correlates of SA in patients with MDD within the Chinese population

Objectives

Methods

Results

Conclusion