Citation

Huang ZX, Wang Q, Lei S, Zhang W, Huang Y, Zhang C, Zhang X. Soc. Psychiatry Psychiatr. Epidemiol. 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s00127-023-02572-3

PMID

37831079

Abstract

Early identification of high-risk patients with Major depressive disorder (MDD) having suicide attempts (SAs) is essential for timely targeted and tailored psychological interventions and medications. This study aimed to develop and validate a web-based dynamic nomogram as a personalized predictor of SA in MDD patients. A dynamic nomogram was developed using data collected from 1718 patients in China. The dynamic model was established based on a machine learning-based regression technique in the training cohort. We validated the nomogram internally using 1000 bootstrap replications. The nomogram performance was assessed using estimates of discrimination (via the concordance index) and calibration (calibration plots). The nomogram incorporated five predictors, including Hamilton anxiety rating scale (odds ratio [OR]: 1.255), marital status (OR: 0.618), clinical global impressions (OR: 2.242), anti-thyroid peroxidase antibodies (OR: 1.002), and systolic pressure levels (OR: 1.037). The model demonstrated good overall discrimination (Harrell's C-index = 0.823). Using decision curve analysis, this model also demonstrated good clinical applicability. An online web server was constructed ( https://odywong.shinyapps.io/PRSM/ ) to facilitate the use of the nomogram. Based on these results, our study developed a nomogram to predict SA in MDD patients. The application of this nomogram may help for patients and clinicians to make decisions.

Language: en

Keywords

Suicide; Depression; Mood disorders; Anxiety disorders; Psychotic disorder