European journal of obstetrics, gynecology, and reproductive biology · 2026
Classification of patients with adenomyosis based on clusters of coexisting diseases: An illustration of clinical diversity.
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Wang Xin, Fu YuDan, Zhao RuoXi, Liu Yong, Zhao RuiHua
Аннотация
OBJECTIVE: Adenomyosis (AM) is a complex disease with poorly understood pathogenesis and considerable clinical phenotypic heterogeneity. Cluster analysis of coexisting diseases may help identify distinct clinical subtypes of AM and explore the potential association between AM and its coexisting diseases.
METHODS: In this study, data were extracted from a cross-sectional questionnaire. AM patients were clustered by Ward's method according to the coexisting diseases. Cluster of patients with similar coexisting diseases pattern was captured and characterized. Subsequently, the clinical characteristics of these clusters were compared.
RESULTS: Data from 2080 AM patients were collected. Sixteen types of coexisting diseases were included in the cluster analysis, resulting in six distinct clusters: cluster 1 (leiomyoma of the uterus); cluster 2 (endometrial lesions); cluster 3 (benign breast and thyroid masses); cluster 4 (pelvic inflammatory disease); cluster 5 (benign ovarian cyst); cluster 6 (low burden of comorbidity).
CONCLUSION: Identifying patient clusters with similar coexisting diseases pattern can improve our understanding of the multidimensional nature of AM, reveal the complex inter-disease relationship, aid in early screening for specific diseases in particular subgroups, support the identification of risk factors for AM prevention, and guide the development of tailored management strategies for different subgroups.
METHODS: In this study, data were extracted from a cross-sectional questionnaire. AM patients were clustered by Ward's method according to the coexisting diseases. Cluster of patients with similar coexisting diseases pattern was captured and characterized. Subsequently, the clinical characteristics of these clusters were compared.
RESULTS: Data from 2080 AM patients were collected. Sixteen types of coexisting diseases were included in the cluster analysis, resulting in six distinct clusters: cluster 1 (leiomyoma of the uterus); cluster 2 (endometrial lesions); cluster 3 (benign breast and thyroid masses); cluster 4 (pelvic inflammatory disease); cluster 5 (benign ovarian cyst); cluster 6 (low burden of comorbidity).
CONCLUSION: Identifying patient clusters with similar coexisting diseases pattern can improve our understanding of the multidimensional nature of AM, reveal the complex inter-disease relationship, aid in early screening for specific diseases in particular subgroups, support the identification of risk factors for AM prevention, and guide the development of tailored management strategies for different subgroups.