Suppr超能文献

基于标准化超声标志物的胎盘植入严重程度风险预测模型。

A Risk-Prediction Model for Placenta Accreta Spectrum Severity From Standardized Ultrasound Markers.

机构信息

Medical Sciences Division, University of Oxford, Oxford, UK.

Centre for Statistics in Medicine, University of Oxford, Oxford, UK.

出版信息

Ultrasound Med Biol. 2023 Feb;49(2):512-519. doi: 10.1016/j.ultrasmedbio.2022.09.021. Epub 2022 Nov 6.

Abstract

We aimed to generate a model to predict the risk of a woman having normal, abnormally adherent (AAP) or abnormally invasive placentation (AIP) based on the presence of recently codified ultrasound (US) markers and disease definitions of placenta accreta spectrum (PAS). We recruited women with anterior low-lying placenta or placenta previa and a history of previous caesarean delivery to a prospective cohort study. US markers of abnormal placentation were recorded on a standardized pro forma. The presence and International Federation of Gynecology and Obstetrics grade of PAS was evaluated clinically and histologically at delivery. Markers demonstrating a predictive relationship to PAS were incorporated into a logistic regression model. A total of 106 women were included, of whom 42 (40%) were normal, 24 (23%) had AAP and 40 (38%) had AIP. A model including just four key variables (loss of clear zone, abnormal placental lacunae, placental bulge and bladder wall interruption) was shown to reliably predict presence and severity of PAS, with an optimism-corrected C-index of 0.901. A simple model incorporating four US markers can predict likelihood and severity of PAS with high accuracy. This is the first time this has been demonstrated using the recently codified definitions of the US signs and disease definitions. Further work will see our model applied prospectively to a large patient cohort, ideally through a smartphone-based application, for external validation.

摘要

我们旨在基于最近编码的超声(US)标志物和胎盘植入谱系(PAS)的疾病定义,生成一个预测女性正常、异常附着(AAP)或异常浸润性胎盘(AIP)风险的模型。我们招募了有前位低置胎盘或前置胎盘且有既往剖宫产史的女性,进行前瞻性队列研究。异常胎盘附着的 US 标志物在标准化表格上记录。在分娩时通过临床和组织学评估 PAS 的存在和国际妇产科联合会(FIGO)分级。将与 PAS 具有预测关系的标志物纳入逻辑回归模型。共纳入 106 名女性,其中 42 名(40%)正常,24 名(23%)有 AAP,40 名(38%)有 AIP。结果显示,仅包含四个关键变量(清晰带缺失、异常胎盘腔隙、胎盘膨出和膀胱壁中断)的模型能够可靠地预测 PAS 的存在和严重程度,经乐观校正的 C 指数为 0.901。一个简单的包含四个 US 标志物的模型可以准确预测 PAS 的可能性和严重程度。这是首次使用最近编码的 US 征象和疾病定义来证明这一点。进一步的工作将通过智能手机应用程序,前瞻性地将我们的模型应用于更大的患者队列中,进行外部验证。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验