Development and validation of a prediction nomogram for non-suicidal self-injury in female patients with mood disorder

Sixiang Liang; Dan Li; Xinyu Liu; Isadora Jiang; Jinhe Zhang; Jun Liu; Sha Sha

doi:10.3389/fpsyt.2023.1130335

Development and validation of a prediction nomogram for non-suicidal self-injury in female patients with mood disorder

Front Psychiatry. 2023 Apr 17:14:1130335. doi: 10.3389/fpsyt.2023.1130335. eCollection 2023.

Authors

Sixiang Liang^#¹, Dan Li^#¹, Xinyu Liu¹, Isadora Jiang¹, Jinhe Zhang¹, Jun Liu¹, Sha Sha¹

Affiliation

¹ National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.

^# Contributed equally.

Abstract

Background: Non-suicidal self-injury (NSSI) is a highly prevalent behavioral problem among people with mental disorders that can result in numerous adverse outcomes. The present study aimed to systematically analyze the risk factors associated with NSSI to investigate a predictive model for female patients with mood disorders.

Methods: A cross-sectional survey among 396 female patients was analyzed. All participants met the mood disorder diagnostic groups (F30-F39) based on the Diseases and Related Health Problems 10th Revision (ICD-10). The Chi-Squared Test, t-test, and the Wilcoxon Rank-Sum Test were used to assess the differences of demographic information and clinical characteristics between the two groups. Logistic LASSO Regression Analyses was then used to identify the risk factors of NSSI. A nomogram was further used to construct a prediction model.

Results: After LASSO regression selection, 6 variables remained significant predictors of NSSI. Psychotic symptom at first-episode (β = 0.59) and social dysfunction (β = 1.06) increased the risk of NSSI. Meanwhile, stable marital status (β = -0.48), later age of onset (β = -0.01), no depression at onset (β = -1.13), and timely hospitalizations (β = -0.10) can decrease the risk of NSSI. The C-index of the nomogram was 0.73 in the internal bootstrap validation sets, indicated that the nomogram had a good consistency.

Conclusion: Our findings suggest that the demographic information and clinical characteristics of NSSI can be used in a nomogram to predict the risk of NSSI in Chinese female patients with mood disorders.

Keywords: LASSO regression; mood disorder; nomogram; non-suicidal self-injury; prediction model.

Grants and funding

This study was funded by the High-level Talents-the Second Batch of Scientific Backbone (KY480-11) and Beijing Anding Hospital, Capital Medical University (YJ2021-05).