Citas bibligráficas
Espínola, M., (2021). Modelo predictivo de restricción del crecimiento intrauterino usando Doppler de arterias uterinas y edad materna [Tesis, Universidad Privada Antenor Orrego]. https://hdl.handle.net/20.500.12759/7226
Espínola, M., Modelo predictivo de restricción del crecimiento intrauterino usando Doppler de arterias uterinas y edad materna [Tesis]. PE: Universidad Privada Antenor Orrego; 2021. https://hdl.handle.net/20.500.12759/7226
@mastersthesis{renati/380639,
title = "Modelo predictivo de restricción del crecimiento intrauterino usando Doppler de arterias uterinas y edad materna",
author = "Espínola Sánchez, Marcos Augusto",
publisher = "Universidad Privada Antenor Orrego",
year = "2021"
}
Intrauterine growth restriction represents an important perinatal morbimortality and its detection varies according to clinical models and characteristics of each population. OBJECTIVES: To evaluate if uterine artery Doppler and maternal age conform a clinical model with predictive capacity of intrauterine growth restriction in a wide sample of Peruvian population. MATERIALS AND METHOD: Observational, analytical, diagnostic test study. A total of 1344 pregnant women attended a national maternal perinatal reference center Level III in Peru between 2010-2018. The sample was randomly selected and divided: training sample and validation sample. In the analysis, multivariate analysis and measurement of diagnostic and predictive capabilities were applied. RESULTS: The clinical model formed by the average pulsatility index greater than the 95th percentile of the uterine artery and maternal age greater than 35 years made up the model with the lowest Akaike's penalty indicator compared to the other clinical models developed in the present study, Youden's index was 0.53. The area under the ROC curve 0.75. Sensitivity 71.5%, specificity 72.1%, positive predictive value 65.8%, negative predictive value 91.2%. CONCLUSIONS: The use of the average pulsatility index of the uterine artery associated with maternal age contributes to the formation of a model to discriminate IUGR; however, it requires other factors to adjust the model for a higher detection rate.
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