Differentiating ectopic pregnancy from intrauterine pregnancy in symptomatic women: a 7-variable nomogram based on routine coagulation and biochemical tests

Abstract Background Early accurate diagnosis of ectopic pregnancy (EP) is challenging, especially in resource-limited settings. This study aims to develop and validate a nomogram model using routine clinical indicators to predict EP in symptomatic early pregnancy women. Methods This retrospective study included patients with menstrual cessation and vaginal bleeding or abdominal pain from Jan 2013 to Dec 2025. The derivation cohort comprised 4,207 EP patients and 687 intrauterine pregnancy (IUP) patients, including 184 healthy IUP controls. Demographic, coagulation, biochemical, and routine laboratory parameters were compared. Univariate and multivariate logistic regression with inverse probability weighting were used to identify independent predictors and construct the nomogram. Model performance was assessed via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). External validation was performed using an independent cohort of 200 patients (100 EP, 100 IUP) from Chaozhou Central Hospital. Results Multivariate analysis identified seven independent predictors: age, fibrinogen, albumin, PT-INR (per 0.1 increase), creatinine, glucose, and calcium. The weighted nomogram achieved an AUC of 0.950 (95% CI: 0.939–0.961) in the derivation cohort, with excellent calibration (mean absolute error 0.008) and positive net benefit across a wide range of risk thresholds in DCA. Subgroup analysis restricted to unruptured EP cases ( n = 3,518) demonstrated sustained performance (AUC = 0.952, 95% CI: 0.941–0.962), confirming that the coagulation signature is intrinsic to EP pathophysiology rather than a consequence of rupture. External validation yielded an AUC of 0.847 (95% CI: 0.779–0.914), indicating good generalizability. Conclusion The study developed a 7-variable nomogram incorporating routine coagulation and biochemical parameters for EP prediction. The model demonstrated excellent derivation performance and maintained good discriminative ability in external validation, with preserved utility in unruptured EP. However, due to selection bias, assay heterogeneity, and the retrospective nature of validation, this nomogram should be considered a preliminary, hypothesis-generating decision-support framework. Prospective, consecutive-cohort validation with assay harmonization is required before clinical implementation.