Risk prediction models for non-suicidal self-injurious behavior in patient with depressive disorder: a protocol for systematic review and mata-analyisis

TL;DR

This study aims to evaluate risk prediction models for non-suicidal self-injury in patients with depressive disorders to improve clinical decision-making.

Contribution

The paper introduces a systematic review and meta-analysis protocol to assess the quality and applicability of NSSI prediction models in depressive disorder patients.

Findings

Various risk prediction models for NSSI show significant differences in performance.

The study will summarize model characteristics and validate them using meta-analysis.

The review aims to guide clinicians in selecting effective models for clinical use.

Abstract

Non-suicidal self-injury (NSSI) frequently occurs in patients with depressive disorder and often presents as burning or severe scratching. NSSI plays a crucial role in increasing the risk of self-injury in individuals with depressive disorder. Despite the progressive development of various risk prediction models to identify NSSI, there are significant differences in their overall predictive performance. This systematic review aims to evaluate the quality and applicability of these models in predicting NSSI among patients with depressive disorders.

A systematic review with meta-analysis was conducted targeting patients with depressive disorder. We included studies on risk prediction models for NSSI behavior in this population that were developed and published. The primary outcome was NSSI behavior as reported by the prediction models. Predictive variables were measured at different disease stages in patients with depressive disorder, with no specific limitations on the prediction horizon. The intended use of the risk prediction model is to individualize the prediction of NSSI behavior of in patients with depressive disorder, thus facilitating the implementation of preventive measures to avoid adverse events. Databases, including China National Knowledge Infrastructure (CNKI), Wanfang Database, VIP Database, PubMed, Web of Science, Medline, and Embase, were searched from inception to March 2024 by two independently reviewers. Data extraction followed the guidelines outlined in the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). The risk of bias and applicability of the included studies were assessed using PROBAST. Descriptive statistical methods were employed to summarize the characteristics of the NSSI models and meta-analysis for model validation was conducted using Stata software.

The study will systematically review the prediction models for NSSI in patients with depressive disorders to enhance clinical practice. This research will also assist clinicians in selecting effective prediction models for NSSI in this patient population.

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