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Predicting the Risk of Macrosomia at Mid-Pregnancy Among Non-Diabetics: A Retrospective Cohort Study

Published:August 02, 2017DOI:https://doi.org/10.1016/j.jogc.2017.05.032

      Abstract

      Objective

      To identify factors known in mid-pregnancy to be associated with risk of macrosomia (≥4000 g) among non-diabetic women and to develop a risk score to allow early identification of women at high risk.

      Methods

      Data were obtained from a population-based perinatal database and a hospital laboratory database in Nova Scotia, Canada. The study included singleton live births born to non-diabetic women between 1998 and 2005. Logistic regression was used to identify risk factors significantly associated with macrosomia. Risk scoring systems were developed for nulliparous and parous women separately and validated using the C-statistic.

      Results

      Of the 23 857 mother-infant pairs included in the study, 16.7% of the infants were macrosomic. In nulliparous women, seven risk factors were identified, of which pre-pregnancy weight ≥90 kg with an OR of 4.8 (95% CI: 3.9 to 6.0) contributed a greater number of points to the risk score. The resulting risk score corresponded to a range of estimated risk of 0.2% to 47.0% and had a C-statistic of 0.70. In parous women, the most points were assigned to women with a previous large birth (OR: 3.7; 95% CI: 3.2–4.0) and a pre-pregnancy weight ≥90 kg (OR: 3.8; 95% CI: 3.1–4.7). The resulting risk score corresponded to a range of estimated risk of 0.4% to 88.0% and had a C-statistic of 0.75.

      Conclusions

      Macrosomia risk can be estimated by a simple calculation based on a woman’s risk factor profile at mid-pregnancy.

      Résumé

      Objectif

      Mettre en évidence des facteurs qui, à la moitié de la grossesse, sont associés à un risque de macrosomie (≥4 000 g) chez les femmes non diabétiques, et mettre au point un score de risque permettant la détection précoce des femmes à risque élevé.

      Méthodologie

      Les données ont été tirées d’une base de données périnatales représentative de la population et d’une base de données de laboratoire en milieu hospitalier de la Nouvelle-Écosse. L’étude s’est penchée sur les naissances vivantes uniques survenues entre 1998 et 2005 pour lesquelles la mère ne souffrait pas de diabète. Les facteurs de risque présentant une association significative avec la macrosomie ont été mis en évidence par régression logistique, et des systèmes de scores de risque distincts ont été mis au point pour les femmes nullipares et pares, puis validés au moyen de la statistique C.

      Résultats

      Parmi les 23 857 paires mères-enfants retenues dans l’étude, 16,7 % des nouveau-nés présentaient une macrosomie. Chez les mères nullipares, sept facteurs de risque ont été mis en évidence; celui qui valait le plus de points dans le score de risque était un poids avant grossesse de 90 kg ou plus, associé à un rapport de cotes (RC) de 4,8 (IC à 95 % : 3,9–6,0). Le score de risque résultant correspondait à une étendue de risque estimé de 0,2 % à 47,0 %, et la statistique C était de 0,70. Chez les femmes pares, les facteurs qui comptaient le plus dans le score étaient la naissance précédente d’un bébé présentant une macrosomie (RC : 3,7; IC à 95 % : 3,2 à 4,0), ainsi qu’un poids avant grossesse de 90 kg ou plus (RC : 3,8; IC à 95 % : 3,1–4,7). Le score de risque résultant correspondait à une étendue de risque estimé de 0,4 % à 88,0 %, et la statistique C était de 0,75.

      Conclusions

      Le risque de macrosomie peut être estimé par un simple calcul fondé sur le profil de risque de la femme à la moitié de la grossesse.

      Key Words

      Abbreviations:

      GCT (glucose challenge test), GDM (gestational diabetes), LBW (low birth weight), MSAFP (maternal serum alpha-fetoprotein levels), NSAPD (Nova Scotia Atlee Perinatal Data), ROC (receiver operator characteristic)
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      References

        • Jolly M.C.
        • Sebire N.J.
        • Harris J.P.
        • et al.
        Risk factors for macrosomia and its clinical consequences: a study of 350,311 pregnancies.
        Eur J Obstet Gynecol Reprod Biol. 2003; 111: 9-14
        • Mocanu E.V.
        • Greene R.A.
        • Byrne B.M.
        • et al.
        Obstetric and neonatal outcome of babies weighing more than 4.5 kg: an analysis by parity.
        Eur J Obstet Gynecol Reprod Biol. 2000; 92: 229-233
        • Gaudet L.
        • Ferraro Z.M.
        • Wen S.W.
        • et al.
        Maternal obesity and occurrence of fetal macrosomia: a systematic review and meta-analysis.
        BioMed Res Int. 2014; 2014: 640291
        • Legardeur H.
        • Girard G.
        • Journy N.
        • et al.
        Factors predictive of macrosomia in pregnancies with a positive oral glucose challenge test: importance of fasting plasma glucose.
        Diabetes Metab. 2014; 40: 43-48
      1. Perinatal Epidemiology Research Unit, Dalhousie University. Nova Scotia Atlee Perinatal Database Report of Indicators: 2002-2011 [Internet]. 2012 Dec [cited].
        (Available from) (Accessed July 21, 2017)
        • Goetzinger K.R.
        • Odibo A.O.
        • Shanks A.L.
        • et al.
        Clinical accuracy of estimated fetal weight in term pregnancies in a teaching hospital.
        J Matern Fetal Neonatal Med. 2014; 27: 89-93
        • Scioscia M.
        • Vimercati A.
        • Ceci O.
        • et al.
        Estimation of birth weight by two-dimensional ultrasonography: a critical appraisal of its accuracy.
        Obstet Gynecol. 2008; 111: 57-65
        • Hartling L.
        • Dryden D.M.
        • Guthrie A.
        • et al.
        Benefits and harms of treating gestational diabetes mellitus: a systematic review and meta- analysis for the U.S. Preventive Services Task Force and the National Institutes of Health Office of Medical Applications of Research.
        Ann Intern Med. 2013; 159: 123-129
        • Boulvain M.
        • Senat M.
        • Perrotin F.
        • et al.
        Induction of labour versus expectant management for large-for-date fetuses: a randomised controlled trial.
        Lancet. 2015; 385: 2600-2605
        • DeSilva M.
        • Munoz F.M.
        • Mcmillan M.
        • et al.
        Congenital anomalies: Case definition and guidelines for data collection, analysis, and presentation of immunization safety data.
        Vaccine. 2016; 34: 6015-6026
        • Joseph K.S.
        • Fahey J.
        • Canadian Perinatal Surveillance System
        Validation of perinatal data in the Discharge Abstract Database of the Canadian Institute for Health Information.
        Chronic Dis Can. 2009; 29: 96-100
        • Ashley-Martin J.
        • Woolcott C.
        Gestational weight gain and postpartum weight retention in a cohort of Nova Scotian women.
        Matern Child Health J. 2014; 18: 1927-1935
        • Sullivan L.M.
        • Massaro J.M.
        • D'Agostino R.B.
        Presentation of multivariate data for clinical use: The Framingham Study risk score functions.
        Stat Med. 2004; 23: 1631-1660
        • D'Agostino R.B.
        • Grundy S.
        • Sullivan L.M.
        • et al.
        Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation.
        JAMA. 2001; 286: 180-187
        • Thompson D.
        • Berger H.
        • Feig D.
        • et al.
        Diabetes and pregnancy.
        Can J Diabetes. 2013; 37: S168-S183
        • Baschat A.A.
        • Harman C.R.
        • Farid G.
        • et al.
        Very low second-trimester maternal serum alpha-fetoprotein: Association with high birth weight.
        Obstet Gynecol. 2002; 99: 531-536
        • Statistics Canada
        2001 Census: Proportion of visible minorities, census metropolitan areas, 2001, 1996 and 1991 [Internet]. [cited].
        (Available from:) (Accessed July 21, 2017)
        • Practice Bulletin No. 173: Fetal Macrosomia
        The American College of Obstetricians and Gynecologists.
        Obstet Gynecol. 2016; 128: e195-209
        • Grote N.K.
        • Bridge J.A.
        • Gavin A.R.
        • et al.
        A meta-analysis of depression during pregnancy and the risk of preterm birth, low birth weight, and intrauterine growth restriction.
        Arch Gen Psychiatry. 2010; 67: 1012-1024
        • Banderali G.
        • Martelli A.
        • Landi M.
        • et al.
        Short and long term health effects of parental tobacco smoking during pregnancy and lactation: a descriptive review.
        J Transl Med. 2015; 13: 327
        • Clark J.M.
        • Hulme E.
        • Devendrakumar V.
        • et al.
        Effect of maternal asthma on birthweight and neonatal outcome in a British inner-city population.
        Paediatr Perinat Epidemiol. 2007; 21: 154-162
        • Motta M.
        • Rodriguez-Perez C.
        • Tincani A.
        • et al.
        Neonates born from mothers with autoimmune disorders.
        Early Hum Dev. 2009; 85: S67-70
        • Hosmer D.W.
        • Lemeshow S.
        Applied logistic regression.
        2nd ed. Wiley, New York2000