无监督机器学习识别子痫前期三种表型的前瞻性队列研究
该研究在西班牙巴塞罗那三级母胎医学中心纳入482例子痫前期孕妇，采用UMAP降维联合k-means聚类，基于母体年龄、体重指数、血压、血管生成因子、尿白蛋白/肌酐比值及分娩孕周和出生体重百分位等数据识别表型。结果识别出3种表型，其在分娩孕周、血管生成失衡程度、胎儿生长受限及母儿并发症发生率上差异明显，提示可用于未来风险分层和个体化管理，尚需前瞻性外部验证。
要点
- 基于常规临床与实验室数据可无监督聚类出3种子痫前期表型
- A型分娩最早、血管生成失衡重、胎儿生长受限64%、母儿并发症最高
- C型多为足月、血管生成失衡最轻、肥胖与糖尿病最多但并发症最少
核心结论
- 无监督学习可划分出3种子痫前期表型，可能支持未来风险分层与更个体化的管理，尚需前瞻性外部验证。
研究设计与样本
- 前瞻性队列研究，纳入2013年8月至2024年4月在西班牙巴塞罗那BCNatal确诊子痫前期的482例孕妇。
- 前瞻性收集母体人口学、临床、超声和实验室数据及分娩与母儿并发症资料。
- 以母体年龄、身高、体重、体重指数、血压、血管生成因子、尿白蛋白/肌酐比值、分娩孕周及出生体重百分位构建患者表征。
- 采用UMAP降维后行k-means聚类识别表型。
主要发现
- 识别出3种表型：A型223例（46.2%）、B型147例（30.5%）、C型112例（23.2%）。
- A型分娩较早（平均33.1±3.3周），血管生成失衡明显，胎儿生长受限占64%，母体并发症23%、新生儿并发症41%，均最高。
- B型分娩较晚（37.0±2.1周），血管生成失衡中等，出生体重百分位为15.5±9.7。
- C型多为足月（38.2±1.5周），血管生成失衡最轻，出生体重百分位最高（67.9±27.3），肥胖31%、糖尿病15%最常见，母体并发症4%、新生儿并发症9%最少。
临床怎么用
- 可依据分娩孕周、血管生成因子与尿白蛋白/肌酐比值、出生体重百分位等常规数据对子痫前期进行表型归类，辅助风险分层。
- 对早期分娩伴明显血管生成失衡者应加强母儿监测，警惕胎儿生长受限与母儿并发症。
局限性
- 为单中心三级中心队列，结论需前瞻性外部验证。
- 原文摘要未报告各表型定义的具体阈值及模型性能指标。
以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。
原文
OBJECTIVE: To explore clinically meaningful phenotypes of preeclampsia using unsupervised machine learning. DESIGN: Prospective cohort study. SETTING: BCNatal, a tertiary maternal-foetal medicine centre (Barcelona, Spain). POPULATION: A total of 482 pregnant women diagnosed with preeclampsia between August 2013 and April 2024. METHODS: Maternal demographic, clinical, ultrasound and laboratory data were prospectively collected, together with delivery details and maternal and neonatal complications. We generated a patient representation using maternal age, height, weight, body-mass index, blood pressure, angiogenic factors, urinary albumin/creatinine ratio, gestational age at birth and birthweight centile. Dimensionality reduction was performed using Uniform Manifold Approximation and Projection, followed by k-means clustering to identify phenotypes. MAIN OUTCOME MEASURES: Maternal and neonatal characteristics and complication rates were compared across clusters to evaluate the clinical significance of the data-driven phenotypes. RESULTS: Three phenotypes were identified. Cluster A (n = 223; 46.2%) showed earlier delivery (mean 33.1 ± SD 3.3 weeks), marked angiogenic imbalance, prevalent foetal growth restriction (64%) and the highest rates of maternal (23%) and neonatal (41%) complications. Cluster B (n = 147; 30.5%) had later delivery (37.0 ± 2.1 weeks), moderate angiogenic imbalance and intermediate birthweight centiles (15.5 ± 9.7). Cluster C (n = 112; 23.2%) comprised mostly term cases (38.2 ± 1.5 weeks) with the lowest angiogenic imbalance and the highest birthweight centile (67.9 ± 27.3); obesity (31%) and diabetes (15%) were most prevalent and maternal (4%) and neonatal (9%) complications were least frequent. CONCLUSIONS: Unsupervised learning delineated three preeclampsia phenotypes. These phenotypes could support the need for future risk stratification and more personalised management; prospective external validation is warranted.
引用
- Houri O, Youssef L, Crovetto F, et al. Phenotyping Preeclampsia Using Unsupervised Machine Learning: A Prospective Cohort Study. BJOG. 2026. PMID: 42136148. doi:10.1111/1471-0528.70262
供执业医师工作参考，不替代临床判断；数值与分级以指南原文为准。