Inflammatory indices, machine learning and artificial intelligence in tubal ectopic pregnancy management

To assess the predictive value of hematologic and biochemical inflammatory indices for methotrexate (MTX) treatment outcomes in tubal ectopic pregnancy (TEP) and to develop machine learning (ML) models for individualized risk stratification.

This retrospective cohort included 293 hemodynamically stable TEP patients who were treated with a single dose of MTX between January 2019 and December 2023. Demographic, clinical, ultrasonographic, and laboratory data were analyzed. Inflammatory indices-including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio, systemic immune-inflammation index, systemic inflammation response index (SIRI), aggregate index of systemic inflammation (AISI), and fibrinogen-to-albumin ratio (FAR)-were calculated. Outcomes were categorized as single-dose MTX success, requirement for additional MTX, or surgery. Predictive accuracy of five supervised ML algorithms was evaluated using receiver operating characteristic analysis.

Single-dose MTX was successful in 65.5% of patients; 18.4% required an additional dose, and 16.0% underwent surgery. AISI had the highest predictive accuracy for surgery [area under the curve (AUC)=0.929], followed by SIRI (AUC=0.899) and FAR (AUC=0.847). NLR best predicted the need for additional MTX (AUC=0.675). Naïve Bayes achieved the highest performance for surgical prediction (accuracy=98.3%, AUC=0.998), while random forest and gradient boosting were most effective in predicting the need for additional MTX (accuracy=83.1%, AUC=0.884-0.896). Feature importance analyses consistently ranked AISI, SIRI, and FAR as top predictors.

AISI, SIRI, and FAR are strong predictors of MTX failure and surgical intervention in TEP. Combining these biomarkers with ML models markedly improves predictive performance and supports a personalized approach to TEP management. Multicenter prospective validation is needed before clinical application.