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Li W, Zhang Q, Tang Y, Park SC, Park Y, Yang SY, Chen LY, Lin SK, Najoan E, Kallivayalil RA, Viboonma K, Jamaluddin R, Javed A, Thi Quynh Hoa D, Iida H, Sim K, Swe T, He YL, Ahmed HU, De Alwis A, Chiu HFK, Sartorius N, Tan CH, Chong MY, Shinfuku N, Avasthi A, Grover S, Ungvari GS, Ng CH, Xiang YT. Network analysis of psychiatric symptoms in schizophrenia: Findings from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics (REAP-AP). Asian J Psychiatr 2022; 75:103200. [PMID: 35850062 DOI: 10.1016/j.ajp.2022.103200] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 06/22/2022] [Accepted: 07/01/2022] [Indexed: 11/02/2022]
Abstract
AIMS Schizophrenia is a major mental disorder with a wide range of psychiatric symptoms. This study explored the structure of psychiatric symptoms of schizophrenia using network analysis in a large representative Asian sample based on a survey of clinical features and treatment used in schizophrenia patients across 15 countries/territories in Asia. METHODS Data on the demographic characteristics and psychiatric symptoms in schizophrenia patients were extracted from the dataset of the fourth Research on Asia Psychotropic Prescription for Antipsychotics (REAP-AP) project. The presence of the following psychiatric symptoms including delusions, hallucinations, disorganized speech, grossly disorganized or catatonic behavior, negative symptoms, social/occupational dysfunction, verbal aggression, physical aggression, and affective symptoms were analyzed. RESULTS A total of 3681 patients were included. The network analysis revealed that verbal aggression, hallucinations, and social/occupational dysfunction were the most central symptoms, while the connections between social/occupational dysfunction and verbal aggression, and between hallucinations and disorganized speech were the two strongest edges. There were significant gender differences in the network structure based on the network structure invariance test (M=0.74, P = 0.03) and invariant edge strength test. The positive correlation between verbal aggression and hallucinations was significantly stronger in the female network than that in the male network (P = 0.03), while a negative correlation between affective symptoms and negative symptoms was found in the female, but not the male network (P < 0.01). CONCLUSION Central symptoms including verbal aggression, hallucinations, and socio-occupational dysfunction should be addressed in developing targeted treatment strategy for schizophrenia patients.
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Affiliation(s)
- Wen Li
- Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, China, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai, China
| | - Qinge Zhang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital & the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yilang Tang
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA; Atlanta Veterans Affairs Medical Center, Decatur, GA, USA
| | - Seon-Cheol Park
- Department of Psychiatry, Hanyang University College of Medicine, Seoul, the Republic of Korea
| | - Yongchon Park
- Department of Psychiatry, Hanyang University, Seoul, the Republic of Korea
| | - Shu-Yu Yang
- Department of Pharmacy, Taipei City Hospital, Taipei, Taiwan
| | - Lian-Yu Chen
- Kunming Prevention and Control Center, Taipei City Hospital; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taiwan
| | - Shih-Ku Lin
- Kunming Prevention and Control Center, Taipei City Hospital; Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital, Linkou, Taoyuan, Taiwan
| | | | | | | | - Ruzita Jamaluddin
- Department of Psychiatry & Mental Health, Hospital Tuanku Fauziah, Kangar, Perlis, Malaysia
| | - Afzal Javed
- Pakistan Psychiatric Research Centre, Fountain House, Lahore, Pakistan
| | | | - Hitoshi Iida
- Department of Psychiatry, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Kang Sim
- Institute of Mental Health, Buangkok Green Medical Park, Singapore
| | - Thiha Swe
- Department of Mental Health, University of Medicine, Magway, Myanmar
| | - Yan-Ling He
- Department of Psychiatric Epidemiology, Shanghai Mental Health Center, Shanghai, China
| | | | | | - Helen F K Chiu
- Department of Psychiatry, Chinese University of Hong Kong, Hong Kong, China
| | - Norman Sartorius
- Association for the Improvement of Mental Health Programs, Geneva, Switzerland
| | - Chay-Hoon Tan
- Department of Pharmacology, National University of Singapore, Singapore
| | - Mian-Yoon Chong
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung & Chang Gung University School of Medicine, Taoyuan, Taiwan
| | - Naotaka Shinfuku
- International Center for Medical Research, Kobe University School of Medicine, Kobe, Japan
| | - Ajit Avasthi
- Department of Psychiatry, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Sandeep Grover
- Department of Psychiatry, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Gabor S Ungvari
- University of Notre Dame Australia, Fremantle, Australia; Division of Psychiatry, Medical School, University of Western Australia, Perth, Australia
| | - Chee H Ng
- Department of Psychiatry, The Melbourne Clinic and St Vincent's Hospital, University of Melbourne, Richmond, Victoria, Australia.
| | - Yu-Tao Xiang
- Unit of Psychiatry, Department of Public Health and Medicinal Administration, & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao; Centre for Cognitive and Brain Sciences, University of Macau, Macao; Institute of Advanced Studies in Humanities and Social Sciences, University of Macau, Macao.
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Liang Q, Wang D, Zhou H, Chen D, Xiu M, Cui L, Zhang X. Tardive dyskinesia in Chinese patients with schizophrenia: Prevalence, clinical correlates and relationship with cognitive impairment. J Psychiatr Res 2022; 151:181-187. [PMID: 35489178 DOI: 10.1016/j.jpsychires.2022.04.029] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 04/04/2022] [Accepted: 04/20/2022] [Indexed: 01/26/2023]
Abstract
OBJECTIVE Tardive dyskinesia (TD) has a high prevalence and is one of the distressing side effects of antipsychotic medications. Few studies have explored the relationship between TD, clinical correlates, and cognition. The aim of this study was to assess the prevalence, clinical correlates and cognitive impairment of co-occurring TD in Chinese patients with schizophrenia. METHODS We recruited 655 patients with chronic schizophrenia who met the DSM-IV diagnostic criteria for schizophrenia and collected clinical and demographic data. All patients were assessed using the Abnormal Involuntary Movement Scale (AIMS) for the severity of TD, Positive and Negative Syndrome Scale (PANSS) for psychopathological symptoms, and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) for cognition. RESULTS The overall TD prevalence was 41.1%, 42.9% (246/574) in men and 28.4% (23/81) in women (χ2 = 6.1 df = 1, p < 0.05). There were significant differences in age, sex, duration of illness, number of hospitalizations, drug type, smoking and PANSS negative symptom subscore between TD and non-TD groups (all p < 0.05). Moreover, patients with TD scored lower for immediate memory, attention, delayed memory, and RBANS total scores (all p < 0.05). Logistic regression showed a significant correlation between TD and age, sex, drug type and attention subscore. CONCLUSION Our results suggest that multiple demographic and clinical variables may be associated with the development of TD. Moreover, TD patients may exhibit more cognitive impairment than non-TD patients.
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Affiliation(s)
- Qilin Liang
- School of Psychology, Beijing Key Laboratory of Learning and Cognition and School of Psychology, Capital Normal University, Beijing, China
| | - Dongmei Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Huixia Zhou
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Dachun Chen
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Meihong Xiu
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Lixia Cui
- School of Psychology, Beijing Key Laboratory of Learning and Cognition and School of Psychology, Capital Normal University, Beijing, China.
| | - Xiangyang Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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Oh HS, Lee BJ, Lee YS, Jang OJ, Nakagami Y, Inada T, Kato TA, Kanba S, Chong MY, Lin SK, Si T, Xiang YT, Avasthi A, Grover S, Kallivayalil RA, Pariwatcharakul P, Chee KY, Tanra AJ, Rabbani G, Javed A, Kathiarachchi S, Myint WA, Cuong TV, Wang Y, Sim K, Sartorius N, Tan CH, Shinfuku N, Park YC, Park SC. Machine Learning Algorithm-Based Prediction Model for the Augmented Use of Clozapine with Electroconvulsive Therapy in Patients with Schizophrenia. J Pers Med 2022; 12:969. [PMID: 35743753 PMCID: PMC9224640 DOI: 10.3390/jpm12060969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/10/2022] [Accepted: 06/12/2022] [Indexed: 12/17/2022] Open
Abstract
The augmentation of clozapine with electroconvulsive therapy (ECT) has been an optimal treatment option for patients with treatment- or clozapine-resistant schizophrenia. Using data from the Research on Asian Psychotropic Prescription Patterns for Antipsychotics survey, which was the largest international psychiatry research collaboration in Asia, our study aimed to develop a machine learning algorithm-based substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in terms of precision medicine. A random forest model and least absolute shrinkage and selection operator (LASSO) model were used to develop a substantial prediction model for the augmented use of clozapine with ECT. Among the 3744 Asian patients with schizophrenia, those treated with a combination of clozapine and ECT were characterized by significantly greater proportions of females and inpatients, a longer duration of illness, and a greater prevalence of negative symptoms and social or occupational dysfunction than those not treated. In the random forest model, the area under the curve (AUC), which was the most preferred indicator of the prediction model, was 0.774. The overall accuracy was 0.817 (95% confidence interval, 0.793−0.839). Inpatient status was the most important variable in the substantial prediction model, followed by BMI, age, social or occupational dysfunction, persistent symptoms, illness duration > 20 years, and others. Furthermore, the AUC and overall accuracy of the LASSO model were 0.831 and 0.644 (95% CI, 0.615−0.672), respectively. Despite the subtle differences in both AUC and overall accuracy of the random forest model and LASSO model, the important variables were commonly shared by the two models. Using the machine learning algorithm, our findings allow the development of a substantial prediction model for the augmented use of clozapine with ECT in Asian patients with schizophrenia. This substantial prediction model can support further studies to develop a substantial prediction model for the augmented use of clozapine with ECT in patients with schizophrenia in a strict epidemiological context.
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Affiliation(s)
- Hong Seok Oh
- Department of Psychiatry, Konyang University Hospital, Daejeon 35356, Korea;
| | - Bong Ju Lee
- Department of Psychiatry, Inje University Haeundae Paik Hospital, Busan 48108, Korea;
| | - Yu Sang Lee
- Department of Psychiatry, Yong-In Mental Hospital, Yongin 17089, Korea;
| | - Ok-Jin Jang
- Department of Psychiatry, Bugok National Hospital, Changyeong 50365, Korea;
| | - Yukako Nakagami
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto 606-8501, Japan;
| | - Toshiya Inada
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan;
| | - Takahiro A. Kato
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (T.A.K.); (S.K.)
| | - Shigenobu Kanba
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan; (T.A.K.); (S.K.)
| | - Mian-Yoon Chong
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung & Chang Gung University School of Medicine, Taoyuan 83301, Taiwan;
| | - Sih-Ku Lin
- Department of Psychiatry, Linkou Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan;
| | - Tianmei Si
- Peking Institute of Mental Health (PIMH), Peking University, Beijing 100083, China;
| | - Yu-Tao Xiang
- Unit of Psychiatry, Department of Public Health and Medicinal Administration & Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China;
| | - Ajit Avasthi
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh 160012, India; (A.A.); (S.G.)
| | - Sandeep Grover
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh 160012, India; (A.A.); (S.G.)
| | | | - Pornjira Pariwatcharakul
- Department of Psychiatry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10400, Thailand;
| | - Kok Yoon Chee
- Tunku Abdul Rahman Institute of Neuroscience, Kuala Lumpur Hospital, Kuala Lumpur 502586, Malaysia;
| | - Andi J. Tanra
- Wahidin Sudirohusodo University, Makassar 90245, Sulawesi Selatan, Indonesia;
| | - Golam Rabbani
- National Institute of Mental Health, Dhaka 1207, Bangladesh;
| | - Afzal Javed
- Pakistan Psychiatric Research Centre, Fountain House, Lahore 39020, Pakistan;
| | - Samudra Kathiarachchi
- Department of Psychiatry, University of Sri Jayewardenepura, Nugegoda 10250, Sri Lanka;
| | - Win Aung Myint
- Department of Mental Health, University of Medicine (1), Yangon 15032, Myanmar;
| | | | - Yuxi Wang
- West Region, Institute of Mental Health, Singapore 119228, Singapore; (Y.W.); (K.S.)
| | - Kang Sim
- West Region, Institute of Mental Health, Singapore 119228, Singapore; (Y.W.); (K.S.)
- Research Division, Institute of Mental Health, Singapore 119228, Singapore
| | - Norman Sartorius
- Association of the Improvement of Mental Health Programs (AMH), 1209 Geneva, Switzerland;
| | - Chay-Hoon Tan
- Department of Pharmacology, National University Hospital, Singapore 119228, Singapore;
| | - Naotaka Shinfuku
- Department of Social Welfare, School of Human Sciences, Seinan Gakuin University, Fukuoka 814-8511, Japan;
| | - Yong Chon Park
- Department of Psychiatry, Hanyang University College of Medicine, Seoul 04763, Korea;
| | - Seon-Cheol Park
- Department of Psychiatry, Hanyang University College of Medicine, Seoul 04763, Korea;
- Department of Psychiatry, Hanyang University Guri Hospital, Guri 11923, Korea
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