A newly published retrospective study reports that a seven-variable predictive nomogram based on routine coagulation and biochemical indicators can effectively differentiate ectopic pregnancy from intrauterine pregnancy in symptomatic women. Reporting in BMC Pregnancy and Childbirth, a research team led by Huiyan Feng, Zhenhui Yang, and colleagues constructed the mathematical model to address the diagnostic difficulties clinicians face during early pregnancy evaluations. In a large derivation cohort of thousands of patients, the weighted nomogram demonstrated strong discriminative performance, securing an area under the receiver operating characteristic curve of 0.950. The tool was developed to identify ectopic pregnancies using common laboratory indicators that are routinely measured, offering potential diagnostic utility without requiring specialized testing platforms.

Accurate and early identification of ectopic pregnancy remains a significant clinical challenge worldwide, particularly within resource-limited medical settings where access to advanced diagnostic infrastructure may be constrained or delayed. In these clinical environments, distinguishing an ectopic pregnancy from a normal or abnormal intrauterine pregnancy is critical for patient management. To address this persistent diagnostic hurdle, the researchers sought to develop and validate a practical nomogram model constructed entirely from routine clinical and laboratory indicators. Their objective focused on determining whether standard blood tests could accurately predict ectopic pregnancy among women who present during early gestation with common clinical warning signs, including menstrual cessation accompanied by vaginal bleeding or lower abdominal pain.

The research team established their derivation dataset through a retrospective analysis of clinical records spanning a twelve-year period from January 2013 through December 2025. The study population was restricted to patients presenting with menstrual cessation who simultaneously experienced either vaginal bleeding or abdominal pain. In total, the derivation cohort included 4,207 patients diagnosed with ectopic pregnancy and 687 patients diagnosed with intrauterine pregnancy. To provide a robust comparative baseline, the intrauterine pregnancy cohort specifically incorporated 184 healthy intrauterine pregnancy control cases. Across these patient groups, the investigators methodically collected and compared demographic information, coagulation profiles, biochemical measurements, and routine laboratory parameters to detect systematic differences between the two pregnancy outcomes.

To identify which parameters possessed independent predictive power and to construct the nomogram, the researchers performed univariate and multivariate logistic regression analyses incorporating inverse probability weighting. The statistical modeling revealed seven distinct indicators that independently predicted the presence of an ectopic pregnancy. These seven variables comprised maternal age along with two routine coagulation metrics—fibrinogen concentration and the prothrombin time-international normalized ratio, evaluated per 0.1 unit increase. In addition, four standard biochemical markers were identified as independent predictors: serum albumin, creatinine, blood glucose, and serum calcium levels, forming a multi-marker panel derived entirely from routine clinical laboratory assessments.

When evaluated in the derivation cohort, the weighted seven-variable nomogram achieved an area under the receiver operating characteristic curve of 0.950, with a 95 percent confidence interval extending from 0.939 to 0.961. In addition to measuring discriminative capacity via receiver operating characteristic curves, the researchers assessed the agreement between predicted probabilities and observed outcomes using calibration curves. The model demonstrated excellent calibration across the derivation population, registering a mean absolute error of 0.008. Furthermore, the investigators conducted decision curve analysis to assess the clinical relevance of the nomogram, which confirmed a positive net benefit across a broad range of decision threshold probabilities.

A critical consideration in evaluating coagulation and metabolic markers during ectopic pregnancy is whether laboratory alterations reflect the underlying disease process itself or simply arise as secondary consequences of tissue damage and rupture. To address this biological distinction, the investigators carried out a dedicated subgroup analysis strictly restricted to 3,518 unruptured ectopic pregnancy cases. Within this unruptured patient subset, the nomogram maintained its high discriminative accuracy, achieving an area under the receiver operating characteristic curve of 0.952, with a 95 percent confidence interval spanning from 0.941 to 0.962. This sustained performance confirmed that the observed coagulation signature is an intrinsic characteristic of ectopic pregnancy pathophysiology rather than an artifact of tubal rupture.

To determine whether the predictive model could generalize beyond its primary development setting, the researchers performed external validation using an independent cohort treated at Chaozhou Central Hospital. This separate validation group comprised 200 patients, structured equally to include 100 confirmed ectopic pregnancy cases and 100 confirmed intrauterine pregnancy cases. Upon testing the seven-variable nomogram in this distinct clinical cohort, the model retained good discriminative capability, achieving an area under the receiver operating characteristic curve of 0.847, with a 95 percent confidence interval ranging between 0.779 and 0.914, thereby demonstrating solid generalizability across different institutional populations.

Despite the strong performance metrics observed in both the derivation and external validation cohorts, the authors emphasized several significant limitations that constrain immediate clinical application. The researchers highlighted that the study is subject to selection bias, assay heterogeneity between differing laboratory platforms, and the inherent constraints of retrospective validation. Consequently, the team noted that this seven-variable nomogram should presently be regarded as a preliminary, hypothesis-generating decision-support framework rather than a definitive clinical tool. The investigators concluded that prospective validation across consecutive patient cohorts, accompanied by rigorous assay harmonization across participating laboratories, remains essential before this diagnostic model can be integrated into routine clinical decision-making.

Subject of Research: Nomogram prediction of ectopic pregnancy using routine laboratory tests

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

Article References: Feng, H., Yang, Z., Xie, Q., Li, B., Xie, Q., Ye, H., & Zeng, Z. (2026). Differentiating ectopic pregnancy from intrauterine pregnancy in symptomatic women: a 7-variable nomogram based on routine coagulation and biochemical tests. BMC Pregnancy and Childbirth. https://doi.org/10.1186/s12884-026-10056-w

Image Credits: AI Generated

DOI: 10.1186/s12884-026-10056-w

Keywords: ectopic pregnancy, intrauterine pregnancy, nomogram, coagulation tests, biochemical markers, fibrinogen, albumin, prothrombin time, PT-INR, creatinine, glucose, calcium, unruptured ectopic pregnancy, decision curve analysis, receiver operating characteristic, inverse probability weighting, retrospective cohort study, external validation