Birla Shweta, Patel Vinit, Gupta Dinesh, Gupta Rishi, Balhara Yatan Pal Singh, Sarkar Siddharth
Translational Bioinformatics Group, International Centre for Genetic Engineering Biotechnology (ICGEB), New Delhi, India.
National Drug Dependence Treatment Centre, AIIMS, New Delhi, India.
Ind Psychiatry J. 2025 Jan-Apr;34(1):32-38. doi: 10.4103/ipj.ipj_430_24. Epub 2025 Apr 18.
Opioid use disorder (OUD) is a global concern with a reported shift in changing demographic and biopsychosocial profiles. Characterization of clusters based on diagnostic symptom criteria can help to understand the underlying associations between these criteria.
The present study identifies clusters based on OUD diagnostic criteria, which may reveal clinically relevant subgroups of individuals with OUDs.
The DSM5 diagnostic system OUD diagnosis was made for 204 male participants. An unsupervised clustering analysis focused on the individual 11 DSM5 diagnostic criteria.
Using the DSM5 diagnostic criteria, we obtained two clusters based on severity. Further, analyzing clinical information along with DSM5 criteria, two groups varying in OUD severity, presence of injecting drug use, and employment were identified.
Based on cluster analysis, two main clusters of DSM5 criteria emerged. Rather than DSM5 symptoms clustering with each other based on the similarity of symptomatology, they aggregate numerically reflecting severity.
阿片类物质使用障碍（OUD）是一个全球性问题，据报道其在人口统计学和生物心理社会学特征方面发生了变化。基于诊断症状标准对聚类进行特征描述有助于理解这些标准之间的潜在关联。
本研究基于OUD诊断标准识别聚类，这可能揭示患有OUD个体的临床相关亚组。
对204名男性参与者进行了DSM5诊断系统的OUD诊断。一项无监督聚类分析聚焦于11项个体DSM5诊断标准。
使用DSM5诊断标准，我们根据严重程度获得了两个聚类。此外，结合DSM5标准分析临床信息，识别出了两组在OUD严重程度、注射吸毒情况和就业方面存在差异的人群。
基于聚类分析，出现了DSM5标准的两个主要聚类。DSM5症状并非基于症状学相似性相互聚类，而是在数值上聚合以反映严重程度。
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