Volume 16, Issue 1 (Vol.16, No.1 2020)
irje 2020, 16(1): 71-80 |
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1- PhD Candidate of Biostatistics, Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
2- Professor of Biostatistics, Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran ,kazem_an@modares.ac.ir
3- Professor of Biostatistics, Proteomics Research Center and Department of Biostatistics, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4- Instructor of Midwifery, Department of Midwifery, School of Nursing and Midwifery, Jiroft University of Medical Sciences, Jiroft, Iran
5- Associate Professor, Department of Midwifery and Reproductive Health, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2- Professor of Biostatistics, Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran ,
3- Professor of Biostatistics, Proteomics Research Center and Department of Biostatistics, Faculty of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4- Instructor of Midwifery, Department of Midwifery, School of Nursing and Midwifery, Jiroft University of Medical Sciences, Jiroft, Iran
5- Associate Professor, Department of Midwifery and Reproductive Health, School of Nursing and Midwifery, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Abstract: (3006 Views)
Background and Objectives: Gestational diabetes mellitus (GDM) is a medical problem in pregnancy, and its late diagnosis can cause adverse effects in the mother and fetus. The purpose of this research was to estimate the accuracy parameters of a biomarker for early prediction of gestational diabetes in the absence of a perfect reference standard test.
Methods: This study was conducted in 523 pregnant women who presented to Mahdieh Hospital and Taleghani Hospital affiliated with Shahid Beheshti University of Medical Sciences, Tehran, Iran 2017-2018. As a predictor for detecting GDM, beta- human chorionic gonadotropin (β-hCG) measurements were recorded during 14-17th weeks’ gestation in a checklist. The Bayesian latent variable model was used to estimate the sensitivity, specificity, and area under receiver operating characteristic curve (AUC). Bayesian parameter estimation was calculated using the R2OpenBUGS package in R version 3.5.3.
Results: The median gestational age was 33 years. In the absence of a perfect reference test, the applied model had a sensitivity, specificity, and AUC of 78% (95% credible interval (CrI): 0.66-0.83), 83% (95% CrI: 0.74-0.89), and 0.72 (95% CrI: 0.64-0.88) for β-hCG, respectively.
Conclusion: According to the results of this study, β-hCG may be an acceptable biomarker for early diagnosis of diabetes in pregnant women in the absence of a perfect reference test.
Methods: This study was conducted in 523 pregnant women who presented to Mahdieh Hospital and Taleghani Hospital affiliated with Shahid Beheshti University of Medical Sciences, Tehran, Iran 2017-2018. As a predictor for detecting GDM, beta- human chorionic gonadotropin (β-hCG) measurements were recorded during 14-17th weeks’ gestation in a checklist. The Bayesian latent variable model was used to estimate the sensitivity, specificity, and area under receiver operating characteristic curve (AUC). Bayesian parameter estimation was calculated using the R2OpenBUGS package in R version 3.5.3.
Results: The median gestational age was 33 years. In the absence of a perfect reference test, the applied model had a sensitivity, specificity, and AUC of 78% (95% credible interval (CrI): 0.66-0.83), 83% (95% CrI: 0.74-0.89), and 0.72 (95% CrI: 0.64-0.88) for β-hCG, respectively.
Conclusion: According to the results of this study, β-hCG may be an acceptable biomarker for early diagnosis of diabetes in pregnant women in the absence of a perfect reference test.
Keywords: Latent variable modeling, ROC curve, Bayesian approach, Gestational diabetes, Human chorionic gonadotropin
Type of Study: Research |
Subject:
Epidemiology
Received: 2020/06/28 | Accepted: 2020/06/28 | Published: 2020/06/28
Received: 2020/06/28 | Accepted: 2020/06/28 | Published: 2020/06/28
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