ArticleInternational journal of clinical practice2022
USMRI Features and Clinical Data-Based Model for Predicting the Degree of Placenta Accreta Spectrum Disorders and Developing Prediction Models.
Article in International journal of clinical practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk of Placenta Accreta Spectrum Disorder After Prior Non-Cesarean Delivery Uterine Surgery: A Systematic Review and Meta-analysis.Obstetrics and gynecology · 2025Pooled it
- Application value of an artificial intelligence-based diagnosis and recognition system in gastroscopy training for graduate students in gastroenterology: a preliminary study.Wiener medizinische Wochenschrift (1946) · 2024Trial
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6 authors.
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Abstract
Aim: This study aimed to investigate the ability of ultrasound/magnetic resonance imaging (MRI) signature and clinical data-based model for preoperatively predicting the degree of placenta accreta spectrum disorders and develop combined prediction models. Methods: The clinicopathological characteristics, prenatal ultrasound images, and MRI features of 132 pregnant women with placenta accreta spectrum disorders at Xiangyang No. 1 People's Hospital were retrospectively reviewed from January 2016 to December 2020. In the training set of 99 patients, the ultrasound/MRI features model, clinical characteristics model, and combined model were developed by multivariate logistic regression analysis to predict the degree of placenta accreta spectrum disorders. The prediction performance of different models was compared using the Delong test. The developed models were validated by assessing their prediction performance in a test set of 33 patients. Results: The multivariate logistic regression analysis identified history of abortion, history of endometrial injury, and blurred boundary between the placenta and the myometrium/between the uterine serosa and the bladder to construct a combined model for predicting the degree of placenta accreta spectrum disorders (area under the curve (AUC) = 0.931; 95% confidence interval (CI): 0.882-0.980). The AUC of the clinical characteristics model and ultrasound/MRI features model was 0.858 (95% CI 0.794-0.921) and 0.709 (95% CI 0.624-0.798), respectively. The AUC of the combined model was significantly higher than that of the ultrasound/MRI features model ( Conclusions: Ultrasound/MRI-based signature is a powerful predictor for the degree of placenta accreta spectrum disorders in an early stage. A combined model (constructed with history of abortion, history of endometrial injury, and blurred boundary between the placenta and the myometrium/between the uterine serosa and the bladder) can improve the accuracy for predicting the degree of placenta accreta spectrum disorders in an early stage.
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