Predictive model for macrosomia using maternal parameters without sonography information.
Objective: We aimed to develop new predictive models for excluding macrosomia using only maternal physical parameters, without sonographic examination. Methods: The present study retrospectively analyzed the medical records of pregnant women who delivered singleton infants at term at one obstetric h...
| Publicado en: | Journal of Maternal-Fetal & Neonatal Medicine Vol. 32; no. 22; pp. 3859 - 3864 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Nov2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137844256&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137844256 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14767058 O6A jtl: Journal of Maternal-Fetal & Neonatal Medicine issn: 14767058 maglogo: Y pubinfo: dt: Nov2019 vid: 32 iid: 22 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 137844256 137844256 NLM29852791 137844256 10.1080/14767058.2018.1484090 NLM29852791 137844256 ppf: 3859 ppct: 5 formats: tig: atl: Predictive model for macrosomia using maternal parameters without sonography information. aug: au: Shigemi, Daisuke Yamaguchi, Satoru Aso, Shotaro Yasunaga, Hideo affil: Yamaguchi Women's Hospital, Chiba, Japan sug: subj: Fetal Macrosomia Etiology Fetal Macrosomia Diagnosis Mothers Statistics and Numerical Data Models, Statistical Prognosis Female Retrospective Design Pregnancy Complications Diagnosis Pregnancy Complications Epidemiology Sensitivity and Specificity Infant, Newborn Pregnancy Japan Adult Predictive Value of Tests Risk Factors Ultrasonography, Prenatal Human Infant, Newborn: birth-1 month Adult: 19-44 years Female ab: Objective: We aimed to develop new predictive models for excluding macrosomia using only maternal physical parameters, without sonographic examination. Methods: The present study retrospectively analyzed the medical records of pregnant women who delivered singleton infants at term at one obstetric hospital in an urban area in Japan from May 2005 to April 2017. We performed logistic regression analysis to predict macrosomia and created an integer risk scoring system based on the significant predictors. We also developed an alternative predictive regression model using machine learning with the random forest algorithm. Results: There were 203 cases of macrosomia among 15,263 eligible women. Although our scoring system had low specificity and positive predictive value, the negative predictive value for screening macrosomia was very high (0.996-1.000). The other model, using machine learning with the random forest algorithm to predict macrosomia, showed a negative predictive value of 0.99, which was similar to the results of our scoring system. Conclusions: Our integer scoring system is an easy and useful method for excluding macrosomia among pregnant women without sonographic examination. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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