Efficient Serum Metabolic Fingerprints for Ectopic Pregnancy Diagnosis and Rupture Risk Prediction

Ectopic pregnancy (EP) is a leading cause of maternal morbidity and mortality in early pregnancy. Current diagnostic approaches, which rely on ultrasound scanning and blood measurements, are limited by low detection sensitivity. Herein, we propose a one-step system that uses EP-associated serum metabolic fingerprints (ESF) for efficient EP diagnosis and risk prediction. The system employs nanoparticle-assisted laser desorption/ionization mass spectrometry to rapidly record ESF. A machine learning-based diagnostic model was then used to analyze ESF from 722 participants, achieving an area under the curve (AUC) of 0.913. Simultaneously, potential metabolic biomarkers from ESF were annotated, enabling accurate diagnosis of EP across various clinical profiles (AUC 0.922). Moreover, a rupture risk prediction model was constructed, yielding an AUC of 0.885, significantly surpassing conventional clinical indicators (AUC 0.702, p < 0.05). Our work offers a rapid, effective tool for early EP diagnosis and risk stratification, marking a pivotal advancement toward precision diagnostics.