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©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Subgrouping time-dependent prescribing patterns of first-onset major depressive episodes by psychotropics dissection
Hsi-Chung Chen, Hui-Hsuan Hsu, Mong-Liang Lu, Ming-Chyi Huang, Chun-Hsin Chen, Tzu-Hua Wu, Wei-Chung Mao, Chuhsing K Hsiao, Po-Hsiu Kuo
Hsi-Chung Chen, Department of Psychiatry & Center of Sleep Disorders, National Taiwan University Hospital, Taipei 100, Taiwan
Hui-Hsuan Hsu, Center of Statistical Consultation and Research, National Taiwan University Hospital, Taipei 100, Taiwan
Mong-Liang Lu, Chun-Hsin Chen, Department of Psychiatry, Wan-Fang Hospital & School of Medicine, College of Medicine, Taipei Medical University, Taipei 100, Taiwan
Ming-Chyi Huang, Department of Psychiatry, Taipei City Hospital, Songde Branch, Taipei 100, Taiwan
Tzu-Hua Wu, Department of Clinical Pharmacy, School of Pharmacy, College of Pharmacy, Taipei Medical University,Taipei 110, Taiwan
Wei-Chung Mao, Department of Psychiatry, Cheng-Hsin General Hospital, Taipei 100, Taiwan
Chuhsing K Hsiao, Po-Hsiu Kuo, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei 100, Taiwan
Author contributions: Chen HC and Hsu HH drafted the manuscript; Kuo PH, Hsiao CK and Chen HC conceived and designed the study, and critically revised the manuscript; Kuo PH owns primary responsibility for the final content; Lu ML, Huang MC, and Chen CH assisted to refer the patients as participants; Lu ML, Huang MC, Chen CH, Wu TH, and Mao WC gave critical opinions on the study design and the manuscript; all authors have read and approve the final manuscript.
Supported by the Ministry of Science and Technology, Taiwan, No. MOST 107-2314-B-002-219 and No. MOST 108-2314-B-002-110-MY2; the National Taiwan University Hospital, No. UN110-021.
Institutional review board statement: This study was approved by the research ethics committees of National Taiwan University Hospital, Taipei Municipal Wanfang Hospital, and Taipei City Hospital, Songde Branch.
Informed consent statement: All study participants provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The authors declare no competing interests.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
http://creativecommons.org/Licenses/by-nc/4.0/ Corresponding author: Po-Hsiu Kuo, PhD, Professor, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Rm 521, No. 17, Xuzhou Road, Taipei 100, Taiwan. phkuo@ntu.edu.tw
Received: July 8, 2021
Peer-review started: July 8, 2021
First decision: July 28, 2021
Revised: August 5, 2021
Accepted: September 22, 2021
Article in press: September 22, 2021
Published online: November 19, 2021
Processing time: 132 Days and 0.3 Hours
BACKGROUND
Subgrouping patients with major depressive disorder is a promising solution for the issue of heterogeneity. However, the link between available subtypes and distinct pathological mechanisms is weak and yields disappointing results in clinical application.
AIM
To develop a novel approach for classification of patients with time-dependent prescription patterns at first onset in real-world settings.
METHODS
Drug-naive patients experiencing their first major depressive episode (n = 105) participated in this study. Psychotropic agents prescribed in the first 24 mo following disease onset were recorded monthly and categorized as antidepressants, augmentation agents, and hypnosedatives. Monthly cumulative doses of agents in each category were converted into relevant equivalents. Four parameters were used to summarize the time-dependent prescription patterns for each psychotropic load: Stability, amount, frequency, and the time trend of monthly prescriptions. A K-means cluster analysis was used to derive subgroups of participants based on these input parameters of psychotropic agents across 24 mo. Clinical validity of the resulting data-driven clusters was compared using relevant severity indicators.
RESULTS
Four distinct clusters were derived from K-means analysis, which matches experts’ consent: "Short-term antidepressants use", "long-term antidepressants use", "long-term antidepressants and sedatives use", and "long-term antidepressants, sedatives, and augmentation use". At the first 2 years of disease course, the four clusters differed on the number of antidepressants used at adequate dosage and duration, frequency of outpatient service use, and number of psychiatric admissions. After the first 2 years following disease onset, depression severity was differed in the four subgroups.
CONCLUSION
Our findings suggested a new approach to optimize the subgrouping of patients with major depressive disorder, which may assist future etiological and treatment response studies.
Core Tip: This study evaluated the time-dependent prescription patterns in drug-naive patients experiencing their first major depressive episode with data collected over the first 2 years after disease onset. The K-means clustering analysis was performed, along with the evaluation of four input parameters to generate data-based subgroups. Four feature-based clusters were identified, differentiated by the time-dependent prescription profiles and burden of the disease. Our novel parameters successfully captured the reciprocal interaction between physicians' prescriptions and disease status in a real-world setting. This study presents a novel clustering strategy that can be used to generate prescription-based subtypes.
