BMC medical informatics and decision making · 2026
Phenotypic subclassification of preeclampsia through cluster analysis of preterm birth-related factors.
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Liang Zewei, Xu Yanhong, Liu Xinying, Zhang Ying, Jin Chengcheng, Qi Xingyi, Xu Xia, Yan Jianying
Аннотация
BACKGROUND: Preeclampsia is a multifactorial hypertensive disorder of pregnancy with heterogeneous clinical presentation and outcomes. Current classification systems inadequately capture the substantial clinical heterogeneity of preeclampsia. This study aimed to identify and characterize distinct phenotypic clusters of the disease using preterm birth-related risk factors.
METHODS: This retrospective cohort study included 4,132 singleton pregnancies with preeclampsia between January 2014 and December 2024. We performed unsupervised k-means clustering based on 22 preterm birth-related clinical and biochemical variables. Maternal characteristics and pregnancy outcomes were compared across the identified clusters. Random forest algorithm was employed to evaluate feature importance.
RESULTS: Five distinct clinical clusters were identified. Cluster 1 (N = 740) exhibited metabolic-inflammatory features. Cluster 2 (N = 1090) was characterized by multiple comorbidities, including pre-gestational diabetes (39.2%) and fetal growth restriction (47.8%). Cluster 3 (N = 732) featured a heightened systemic inflammatory response. Cluster 4 (N = 707) presented with the most benign clinical features. Cluster 5 (N = 863) was distinguished by a high prevalence of uterine mechanical factors. These clusters demonstrated significant differences in preterm birth rates and other adverse pregnancy outcomes (P < 0.05). Random forest analysis confirmed the discriminative power of the selected variables.
CONCLUSION: Cluster analysis reveals five clinically distinct preeclampsia phenotypes with direct implications for preterm birth risk. This refined classification provides a foundation for investigating distinct pathophysiological mechanisms and supports the development of individualized management strategies.
METHODS: This retrospective cohort study included 4,132 singleton pregnancies with preeclampsia between January 2014 and December 2024. We performed unsupervised k-means clustering based on 22 preterm birth-related clinical and biochemical variables. Maternal characteristics and pregnancy outcomes were compared across the identified clusters. Random forest algorithm was employed to evaluate feature importance.
RESULTS: Five distinct clinical clusters were identified. Cluster 1 (N = 740) exhibited metabolic-inflammatory features. Cluster 2 (N = 1090) was characterized by multiple comorbidities, including pre-gestational diabetes (39.2%) and fetal growth restriction (47.8%). Cluster 3 (N = 732) featured a heightened systemic inflammatory response. Cluster 4 (N = 707) presented with the most benign clinical features. Cluster 5 (N = 863) was distinguished by a high prevalence of uterine mechanical factors. These clusters demonstrated significant differences in preterm birth rates and other adverse pregnancy outcomes (P < 0.05). Random forest analysis confirmed the discriminative power of the selected variables.
CONCLUSION: Cluster analysis reveals five clinically distinct preeclampsia phenotypes with direct implications for preterm birth risk. This refined classification provides a foundation for investigating distinct pathophysiological mechanisms and supports the development of individualized management strategies.