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Revista de la Facultad de Medicina Humana

versión impresa ISSN 1814-5469versión On-line ISSN 2308-0531

Resumen

HUACACHI TREJO, Kelly  y  CORREA-LOPEZ, Lucy E.. Maternal characteristics associated with the fetal macrosomy diagnosis in a Hospital III-1 of the capital of Peru. Rev. Fac. Med. Hum. [online]. 2020, vol.20, n.1, pp.76-81. ISSN 1814-5469.  http://dx.doi.org/10.25176/rfmh.v20i1.2549.

Objective:

To identify the maternal characteristics associated with the diagnosis of fetal macrosomia at Sergio E. Bernales Hospital from January to December 2018.

Methods:

An observational, analytical, retrospective, case-control study was carried out. The population studied was pregnant women with a diagnosis of fetal macrosomia treated in the gynecoobstetrics service of Sergio E. Bernales Hospital from January to December 2018. Through a data collection sheet, the information from the medical records was extracted; the data was then processed according to the IBM SPSS Statistics v25 program.

Results:

Of 532 patients studied, 133 cases and 399 controls were obtained. Maternal age varies between 14 and 45 years (average age of 27.01). A statistically significant association was found between fetal macrosomia and the following variables: post-term pregnancy (OR = 13,613 95% CI 2,901-63,891), gestational diabetes (OR 5.7 IC95% 2.5 -12.7), excessive weight gain (OR 1,833 95% CI 1,154-2,911), sex of the newborn (OR 1.83 95% CI 1.2-2.7) and age of the mother (OR 1.7 95% CI 1.0-2.9). When performing the multivariate analysis, no association was found with the variables age of the mother (P = 0.228, OR 1.510 95% CI 0.773-2.950) and BMI (P = 0.331, OR 0.740 95% CI 0.403-1.358), so they were considered confusing variables.

Conclusions:

The maternal characteristics associated with the diagnosis of fetal macrosomia are post-term delivery, gestational diabetes, excessive weight gain and sex of the newborn.

Palabras clave : macrosomia; Gestational diabetes; weight gain during pregnancy, maternal age, postnatal pregnancy (source: MeSH NLM)).

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