Neuroradiology Department, IRCCS San Raffaele Hospital and University, via Olgettina 62, Milan, 20132, Italy.
Research Center for Statistics, University of Geneva, Boulevard du Pont-d'Arve 40, Geneva, 1205, Switzerland.
BMC Pregnancy Childbirth. 2021 Apr 16;21(1):306. doi: 10.1186/s12884-021-03654-3.
Etiopathogenesis of preterm birth (PTB) is multifactorial, with a universe of risk factors interplaying between the mother and the environment. It is of utmost importance to identify the most informative factors in order to estimate the degree of PTB risk and trace an individualized profile. The aims of the present study were: 1) to identify all acknowledged risk factors for PTB and to select the most informative ones for defining an accurate model of risk prediction; 2) to verify predictive accuracy of the model and 3) to identify group profiles according to the degree of PTB risk based on the most informative factors.
The Maternal Frailty Inventory (MaFra) was created based on a systematic review of the literature including 174 identified intrauterine (IU) and extrauterine (EU) factors. A sample of 111 pregnant women previously categorized in low or high risk for PTB below 37 weeks, according to ACOG guidelines, underwent the MaFra Inventory. First, univariate logistic regression enabled p-value ordering and the Akaike Information Criterion (AIC) selected the model including the most informative MaFra factors. Second, random forest classifier verified the overall predictive accuracy of the model. Third, fuzzy c-means clustering assigned group membership based on the most informative MaFra factors.
The most informative and parsimonious model selected through AIC included Placenta Previa, Pregnancy Induced Hypertension, Antibiotics, Cervix Length, Physical Exercise, Fetal Growth, Maternal Anxiety, Preeclampsia, Antihypertensives. The random forest classifier including only the most informative IU and EU factors achieved an overall accuracy of 81.08% and an AUC of 0.8122. The cluster analysis identified three groups of typical pregnant women, profiled on the basis of the most informative IU and EU risk factors from a lower to a higher degree of PTB risk, which paralleled time of birth delivery.
This study establishes a generalized methodology for building-up an evidence-based holistic risk assessment for PTB to be used in clinical practice. Relevant and essential factors were selected and were able to provide an accurate estimation of degree of PTB risk based on the most informative constellation of IU and EU factors.
早产(PTB)的病因学是多因素的,母体和环境中的各种危险因素相互作用。确定最重要的因素非常重要,以便估计 PTB 的风险程度并追踪个体化的风险特征。本研究的目的是:1)确定所有公认的 PTB 危险因素,并选择最有信息的因素来确定准确的风险预测模型;2)验证模型的预测准确性;3)根据最有信息的因素,根据 PTB 风险程度确定群体特征。
基于对包括 174 个宫内(IU)和宫外(EU)因素的文献系统综述,创建了母体脆弱性指数(MaFra)。对 111 名孕妇进行了 MaFra 指数测试,这些孕妇先前根据 ACOG 指南分为早产(PTB)风险低或高的 37 周以下。首先,单变量逻辑回归使 p 值排序,Akaike 信息准则(AIC)选择包括最有信息的 MaFra 因素的模型。其次,随机森林分类器验证了模型的整体预测准确性。第三,模糊 c-均值聚类根据最有信息的 MaFra 因素分配组隶属关系。
通过 AIC 选择的最有信息和最简约的模型包括前置胎盘、妊娠高血压、抗生素、宫颈长度、体育锻炼、胎儿生长、产妇焦虑、子痫前期、抗高血压药物。仅包括最有信息的 IU 和 EU 因素的随机森林分类器实现了 81.08%的整体准确性和 0.8122 的 AUC。聚类分析根据最有信息的 IU 和 EU 危险因素确定了三组典型孕妇,根据 PTB 风险程度从低到高的程度对其进行了分类,这与分娩时间相对应。
本研究建立了一种基于证据的综合 PTB 风险评估方法,用于临床实践。选择了相关和必要的因素,能够根据 IU 和 EU 因素中最有信息的组合准确估计 PTB 风险程度。