Exploring negative symptoms heterogeneity in patients diagnosed with schizophrenia and schizoaffective disorder using cluster analysis

BMC Psychiatry

Research

Romy Hajje¹  Chadia Haddad²  Souheil Hallit³  Jocelyne Azar⁴  Feten Fekih-Romdhane⁵ 

[1] Faculty of Science, Lebanese University, Fanar, Lebanon;Research Department, Psychiatric Hospital of the Cross, Jal Eddib, Lebanon;INSPECT-LB (Institut National de Santé Publique, d’Épidémiologie Clinique et de Toxicologie-Liban), Beirut, Lebanon;School of Health Sciences, Modern University for Business and Science, Beirut, Lebanon;Research Department, Psychiatric Hospital of the Cross, Jal Eddib, Lebanon;School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, P.O. Box 446, Jounieh, Lebanon;Applied Science Research Center, Applied Science Private University, Amman, Jordan;School of Medicine, Lebanese American University, Byblos, Lebanon;The Tunisian Center of Early Intervention in Psychosis, Department of Psychiatry “Ibn Omrane”, Razi hospital, 2010, Manouba, Tunisia;Faculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia;

关键词: Psychosis;    Schizophrenia;    Heterogeneity;    Negative symptoms;    Cluster analysis;   

DOI  :  10.1186/s12888-023-05101-3

 received in 2023-06-16, accepted in 2023-08-10,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundDissecting the heterogeneity of schizophrenia may help foster progress in understanding its etiology and lay the groundwork for the development of new treatment options for primary or enduring negative symptoms (NS). In this regard, the present study aimed to: (1) to use cluster analysis to identify subgroups of Lebanese patients diagnosed with either schizophrenia or schizoaffective disorder based on NS clusters, and (2) to relate the statistically-derived subgroups to clinically relevant external validators (including measures if state and trait depression, stigma, insight, loneliness, social support).MethodA total of 202 adult long-stay, chronic, and clinically remitted patients (166 diagnosed with schizophrenia and 36 with schizoaffective disorder) were enrolled. A cluster analysis approach was adopted to classify patients based on the five NS domains social withdrawal, emotional withdrawal, alogia, avolition and anhedonia.ResultsA three-cluster solution was obtained based on unique NS profiles, and divided patients into (1) low NS (LNS; 42.6%) which characterized by the lowest mean scores in all NS domains, (2) moderate NS (MNS; 25.7%), and (3) high NS (HNS; 31.7%). Post-hoc comparisons showed that depression (state and trait), loneliness and social support could accurately distinguish the schizophrenia subgroups. Additionally, individuals in the HNS cluster had longer duration of illness, longer duration of hospitalization, and were given higher dosages of antipsychotic medication compared to those in the other clusters, but these differences did not achieve the statistical significance.ConclusionFindings provide additional support to the categorical model of schizophrenia by confirming the existence of three alternate subtypes based on NS. The determination of distinct NS subgroups within the broad heterogeneous population of people diagnosed with schizophrenia may imply that each subgroup possibly has unique underlying mechanisms and necessitates different treatment approaches.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023