Building and Validation of an Acute Event Prediction Model for Severe Mental Disorders

Abstract

Background

The global incidence of acute events in psychiatric patients is intensifying, and models to successfully predict acute events have attracted much attention.

Objective

To explore the influence factors of acute incident severe mental disorders (SMDs) and the application of Rstudio statistical software, and build and verify a nomogram prediction model.

Methods

SMDs were taken as research objects. The questionnaire survey method was adopted to collect data. Patients with acute event independent factors were screened. R software multivariable Logistic regression model was constructed and a nomogram was drawn.

Results

A total of 342 patients with SMDs were hospitalized, and the number of patients who encountered acute events was 64, which accounted for 18.70% of all patients. Statistical significances were found in many aspects (all P ˂ 0.05). Such aspects included Medication adherence, disease diagnosis, marital status, caregivers, social support and the hospitalization environment (odds ratio (OR) = 4.08, 11.62, 12.06, 10.52, 0.04 and 0.61, respectively) were independent risk factors for the acute events of patients with SMDs. The prediction model was modeled, and the AUC was 0.77 and 0.80. The calibration curve shows that the model has good calibration. The clinical decision curve shows that the model has a good clinical effect.

Conclusion

The constructed risk prediction model shows good prediction effectiveness in the acute events of patients with SMDs, which is helpful for the early detection of clinical mental health staff at high risk of acute events.

Keywords:

• SMDs
• acute event
• influencing factors
• predictive model and nomogram

Data Sharing Statement

All data of this study can be obtained by contacting the Email address of the corresponding author. The Email address of the corresponding author is [email protected].

Ethics Statement

This study was approved by the Ethics Committee of the Yangzhou Wutai Mountain Hospital (WTSLL2023001). All participants signed the informed consent form before entering the study, and all procedures performed in this study involving human participants were in accordance with the Declaration of HelsinkiEthics approval and consent to.

Acknowledgments

This study were supported by the 2022 annual hospital project funding fund of Yangzhou Wutai Mountain Hospital in Jiangsu Province (Ting Wang WTS2022021) and the 2023 Annual Medical Scientific Research Project of Yangzhou Municipal Health Commission (2023-2-35). The MMAS-8 Scale (US Copyright Registration No. TX0008632533), content, name, and trademarks are protected by US copyright and trademark laws. Permission for use of the scale and its coding is required. A license agreement is available from MMAR, LLC., www.moriskyscale.com.

Disclosure

The authors report no conflicts of interest in this work.