Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL USA.
Department of Psychiatry, Faculty of Medicine, University of Southampton, Southampton, UK; and Southern Health NHS Foundation Trust, Southampton, UK.
Psychiatry Res. 2021 Dec;306:114269. doi: 10.1016/j.psychres.2021.114269. Epub 2021 Nov 2.
Prior work suggested that trichotillomania may have four subtypes based on the extent to which pulling is automatic or focused in nature. 238 adults with trichotillomania undertook clinical and cognitive assessments and were assigned into four subtypes based on k-means clustering of Milwaukee Inventory for Subtypes of Trichotillomania-Adult Version (MIST-A) scores. We examined whether a cluster solution was apparent using conventional metrics. Based on prior literature, we then force-fitted a four subtype model (low-low, low-high, high-low, high-high). Subtypes were compared and validity of the MIST-A subtyping approach was evaluated. A cluster solution did not converge based on conventional metrics. Following force-fitting, subtypes did not differ on demographic variables, age at symptom onset, nor duration of illness. The high-focused high-automatic subtype had worse symptom severity than other subtypes. Co-morbid depression was more common in the low-focused low-automatic and high-focused low-automatic subtypes. This study suggests that MIST-A subtypes may not be valid or clinically useful based on several issues. First, k-means models indicated that the MIST-A data did not generate any cluster solutions. Second, when a forced cluster solution was fitted, the subtypes did not differ on the vast majority of measures. Third, force-fitting four subtypes yielded findings that were logically inconsistent (e.g. worse quality of life in one group, but higher rates of comorbid anxiety/depression in others). Overall, we suggest that both focused and automatic pulling may characterize the same pulling episode, or certainly the same person across episodes. Thus they may be clinically relevant variables, but not forming coherent subtypes.
先前的研究表明,拔毛癖可能有四个亚型,其依据是拔毛行为的自动性或专注性程度。238 名患有拔毛癖的成年人接受了临床和认知评估,并根据《成人拔毛症量表-修订版(MIST-A)》的 k-均值聚类分析被分为四个亚型。我们检查了常规指标是否明显出现聚类解决方案。基于先前的文献,我们强制拟合了一个四亚型模型(低低、低高、高低、高高)。比较了不同亚型,并评估了 MIST-A 分型方法的有效性。基于常规指标,聚类解决方案并未收敛。强制拟合后,亚型在人口统计学变量、发病年龄和疾病持续时间上没有差异。高专注高自动亚型的症状严重程度比其他亚型更严重。低专注低自动和高专注低自动亚型中更常见共患抑郁。本研究表明,基于几个问题,MIST-A 亚型可能无效或临床上无意义。首先,k-均值模型表明,MIST-A 数据没有产生任何聚类解决方案。其次,当拟合强制聚类解决方案时,绝大多数测量指标的亚型之间没有差异。第三,强制拟合四个亚型得出的结论在逻辑上不一致(例如,一个组的生活质量更差,但其他组的共患焦虑/抑郁率更高)。总的来说,我们认为专注和自动拔毛可能描述了相同的拔毛发作,或者肯定是同一患者在不同发作期间的特征。因此,它们可能是临床相关的变量,但不能形成连贯的亚型。