Factors associated with suicidal attempts in female patients with mood disorder

Bibliographic Data

ID22069560
AuthorsJinhe Zhang (0000-0001-5770-2976, Capital Medical University), Sixiang Liang (Capital Medical University), Xinyu Liu (0000-0001-5087-3385, Capital Medical University), Dan Li (0009-0009-7330-3225, Capital Medical University), Fuchun Zhou (0000-0002-9431-7708, Capital Medical University), Le Xiao (0000-0003-4323-036X, Capital Medical University), Jun Liu (0000-0001-5393-3187, Capital Medical University), Sha Sha (0000-0002-7179-611X, Capital Medical University, corresponding author)
Year2023
Volume11
Pages1157606-1157606
Publication date2023-09-25
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2023.1157606
PMID37818303
OpenAlexW4387021137
LanguageEN
References cited44

Aim: This study aims to establish a nomogram model to predict the relevance of SA in Chinese female patients with mood disorder (MD). Method: The study included 396 female participants who were diagnosed with MD Diagnostic Group (F30-F39) according to the 10th Edition of Disease and Related Health Problems (ICD-10). Assessing the differences of demographic information and clinical characteristics between the two groups. LASSO Logistic Regression Analyses was used to identify the risk factors of SA. A nomogram was further used to construct a prediction model. Bootstrap re-sampling was used to internally validate the final model. The Receiver Operating Characteristic (ROC) curve and C-index was also used to evaluate the accuracy of the prediction model. Result: = -0.11, SE = 0.06). We assessed the ability of the nomogram model to recognize suicidality, with good results (AUC = 0.76, 95% CI: 0.71-0.80). Indicating that the nomogram had a good consistency (C-index: 0.756, 95% CI: 0.750-0.758). The C-index of bootstrap resampling with 100 replicates for internal validation was 0.740, which further demonstrated the excellent calibration of predicted and observed risks. Conclusion: Five factors, namely BMI, social dysfunction, time interval between first onset and first dose, polarity at onset, and times of hospitalization, were found to be significantly associated with the development of suicidality in patients with MD. By incorporating these factors into a nomogram model, we can accurately predict the risk of suicide in MD patients. It is crucial to closely monitor clinical factors from the beginning and throughout the course of MD in order to prevent suicide attempts

Confidence interval · Logistic regression · Mood · Nomogram · Psychiatry · Receiver operating characteristic · Statistics · Mathematics · Medicine · Mental Health Treatment and Access · Mental Health via Writing · Suicide and Self-Harm Studies · Internal Medicine

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