A hierarchical procedure to select intrauterine and extrauterine factors for methodological validation of preterm birth risk estimation

被引:40
作者
Della Rosa, Pasquale Anthony [2 ,3 ]
Miglioli, Cesare [4 ]
Caglioni, Martina [1 ,2 ]
Tiberio, Francesca [1 ,2 ]
Mosser, Kelsey H. H. [2 ,3 ]
Vignotto, Edoardo [4 ]
Canini, Matteo [2 ,3 ]
Baldoli, Cristina [2 ,3 ]
Falini, Andrea [2 ,3 ]
Candiani, Massimo [1 ,2 ]
Cavoretto, Paolo [1 ,2 ]
机构
[1] IRCCS San Raffaele Hosp, Obstet & Gynaecol Dept, Via Olgettina 62, I-20132 Milan, Italy
[2] Univ Milan, Via Olgettina 62, I-20132 Milan, Italy
[3] IRCCS San Raffaele Hosp, Neuroradiol Dept, Via Olgettina 62, I-20132 Milan, Italy
[4] Univ Geneva, Res Ctr Stat, Blvd Pont dArve 40, CH-1205 Geneva, Switzerland
关键词
Preterm delivery; Pregnancy; Risk factors; Intrauterine; Extrauterine; Akaike information criterion; Random forest; Fuzzy clustering; Precision medicine; CERVICAL LENGTH; MATERNAL CHARACTERISTICS; SONOGRAPHIC MEASUREMENT; PERINATAL OUTCOMES; GROWTH RESTRICTION; NEONATAL OUTCOMES; PREGNANCY; DELIVERY; WOMEN; METAANALYSIS;
D O I
10.1186/s12884-021-03654-3
中图分类号
R71 [妇产科学];
学科分类号
100211 ;
摘要
Background 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. Methods 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. Results 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. Conclusions 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.
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页数:17
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