AUTHOR=Wang Jiayi , Zhu Enzhao , Ai Pu , Liu Jun , Chen Zhihao , Wang Feng , Chen Fazhan , Ai Zisheng 

TITLE=The potency of psychiatric questionnaires to distinguish major mental disorders in Chinese outpatients

JOURNAL=Frontiers in Psychiatry

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2022.1091798

DOI=10.3389/fpsyt.2022.1091798

ISSN=1664-0640

ABSTRACT=Background: Mental disorders are highly prevalent globally, and considering the huge population in China, the available mental health resources are inadequate. Thus, our study aimed to evaluate whether mental questionnaires, serving as auxiliary diagnostic tools, have efficient diagnostic ability in a realistic hospital environment.Methods: We conducted a retrospective study of Chinese psychiatric outpatients. Altogether 1182, 5069, and 4958 records of Symptom Checklist-90 (SCL-90), Hamilton Anxiety Rating Scale (HAM-A), and Hamilton Depression Rating Scale (HAM-D), respectively, were collected from March 2021 to July 2022. The Mann–Whitney U test was applied to subscale scores and total scores of SCL-90, HAM-A, and HAM-D between the two sexes (male and female groups), different age groups, and four diagnostic groups (anxiety disorder, depressive disorder, bipolar disorder, and schizophrenia). Kendall's tau coefficient analysis and machine learning were also conducted in the diagnostic groups. The calculated multiple accuracy indicators of machine learning included the accuracy score, F-measure (F1), precision score (prec.), and the area under the receiver operating characteristic curve (AUC).Results: We found significant differences in most subscale scores for both age and gender groups. Among them, the female group and the early adulthood group showed higher scores in many subscales. Using the Mann–Whitney U test and Kendall's tau coefficient analysis, we found that there were no statistically significant differences in diseases in total scale scores and nearly all subscale scores. The results of machine learning (ML) showed that for HAM-A, anxiety had a small degree of differentiation with an AUC of 0.56, while other diseases had an AUC close to 0.50. As for HAM-D, bipolar disorder was slightly distinguishable with an AUC of 0.60, while the AUC of other diseases was lower than 0.50. In SCL-90, all diseases had a similar AUC; among them, bipolar disorder had the lowest score , schizophrenia had the highest score, while anxiety and depression both had an AUC of approximately 0.56. Conclusions: Our results indicated that the univariate subscale scores did not have statistical significance among our four diagnostic groups, which highlights the limit of their practical use by doctors in identifying different mental diseases in Chinese outpatient psychiatric services.