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Published on: August 25, 2019
[Optimizing follow-up of pregnancies of unknown location: a simplified clinical algorithm for ectopic pregnancy risk]
Marie Le Naelou1, Thibault Thubert2, Manon Degez3
1Service de médecine et biologie de la reproduction, gynécologie médicale, CHU Nantes, Nantes, France.
Objectives:
Management of pregnancies of unknown location (PUL) relies on close monitoring with serial human chorionic gonadotropin (hCG) measurements and repeated ultrasound examinations to exclude ectopic pregnancy (EP). Although safe, this strategy leads to multiple consultations and significant organizational and economic burden. This paper aims to develop and preliminarily validate a simplified clinical algorithm to stratify the risk of EP in women presenting with PUL, in order to adapt follow-up frequency.
Methods:
The prospective monocentric OPTIGLI study was conducted at Nantes University Hospital between May and September 2023. All women presenting with PUL were included after informed non-opposition. Fifteen clinical, biological, and ultrasound variables were analyzed using univariate and multivariate logistic regression. A composite predictive score was constructed from significant variables, and its positive predictive value (PPV) and negative predictive value (NPV) were calculated. Economic evaluation and patient satisfaction assessment were also performed.
Results:
A total of 162 patients were included; the EP rate was 19.8%. Pregnancy occurring with a copper intrauterine device (IUD) was significantly associated with EP (p = 0.0089). The presence of a lateral uterine mass was associated with earlier EP diagnosis (p = 0.0169). The algorithm showed an NPV of 99.0% and a PPV of 22.0%, allowing reduced follow-up intensity in two-thirds of patients, with an estimated cost reduction of nearly euro10,000 over four months.
Conclusion:
The OPTIGLI algorithm is a simple and safe tool for rationalizing PUL management. Multicenter validation is required to confirm its robustness and potential integration into national clinical protocols.
