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Applying a Risk Prediction Algorithm to Women with Gestational Diabetes to Predict Adverse Pregnancy Outcomes | OMICS International| Abstract

Journal of Clinical Diabetes
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  • Short Communication   
  • J Clin Diabetes 2023, Vol 7(5): 191
  • DOI: 10.4172/JCDS.1000191

Applying a Risk Prediction Algorithm to Women with Gestational Diabetes to Predict Adverse Pregnancy Outcomes

Stephanie Guerrero*
Department of Medicine, School of Clinical Sciences, Monash University, Melbourne, Australia
*Corresponding Author : Stephanie Guerrero, Department of Medicine, School of Clinical Sciences, Monash University, Melbourne, Australia, Email: stephanie.guerr89@gmail.com

Received Date: Feb 27, 2023 / Published Date: May 09, 2023

Abstract

The capability to calculate absolutely the threat of adverse physiological condition issues for a private lady with physiological condition diabetes (GDM) would enable precautionary and remedial interventions to be delivered to girls at bad, frugal girls at low threat from spare care. We tend to aimed to develop, validate and guesstimate the clinical mileage of a vaticination model for adverse physiological condition issues in girls with GDM. A vaticination model development and confirmation study was conducted on information from an experimental cohort. Actors enclosed all girls with GDM from 3 metropolitan tertiary tutoring hospitals in Melbourne, Australia. The event cohort comprised those that delivered between one Gregorian time table months 2017 to thirty Gregorian time table month 2018 and thus the confirmation cohort those that delivered between one Gregorian time table months 2018 to thirty one Gregorian time table month 2018.

Keywords: Gestational diabetes, Adverse pregnancy outcomes postpartum, Prediabetes diabetes, Gregorian, Precautionary

Citation: Guerrero S (2023) Applying a Risk Prediction Algorithm to Women with Gestational Diabetes to Predict Adverse Pregnancy Outcomes. J Clin Diabetes 7: 191. Doi: 10.4172/JCDS.1000191

Copyright: © 2023 Guerrero S. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

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