Objective Finding a predictive model for persistent low lying placenta (LLP) based on early clinical and laboratory parameters. Methods This retrospective cohort study included patients with LLP detected during early anatomy scan. Additional transvaginal ultrasound exams assessed for resolution at 22–24 weeks and 36–39 weeks. Patients were categorized as: Group 1–LLP resolved by the second trimester scan, Group 2- LLP resolved by the third, and Group 3–LLP persisted to delivery. Clinical and laboratory parameters were compared between groups. A linear support vector machine classification was used to define a prediction model for persistence. Results Among 236 pregnancies with LLP, 80% resolved by 22–24 weeks, 10.5% resolved by 36–39 weeks and 9.5% persisted until delivery. Second trimester hCG levels were higher the longer the LLP persisted (0.8MoM + 0.7 vs. 1.13MoM + 0.4 vs. 1.7MoM + 1.5, P = 0.03, respectively) and Cervical length was shorter (P = 0.008, P = 0.02, respectively). A linear SVM classification model was calculated based on these parameters. The predictive accuracy of this model was 90.3%. Conclusion LLP persistence can be predicted with an accuracy of 90.3%, as early as the beginning of the second trimester. Persistence vs. resolution of LLP may represent different entities and not a spectrum of the same condition over time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.