Rahmati, Maryam, Nazarpour, Sima, Minooee, Sonia, Behboudi-Gandevani, Samira, Azizi, Fereidoun, and Ramezani Tehrani, Fahimeh (2023) A Bayesian model to estimate the cutoff value of TSH for management of preterm birth. PLoS ONE, 18 (3). e0283503.

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Abstract

Background Determining a thyroid hormone cutoff value in pregnancy is challenging issue and several approaches have been introduced to optimize a utility function. We aimed to estimate the cutoff value of TSH using Bayesian method for prediction of preterm-birth.

Methods This study was a secondary-analysis of the population-based data collected prospectively within the framework of the Tehran Thyroid and Pregnancy Study. A total of 1,538 pregnant women attending prenatal clinics.

Results Using Bayesian method resulted a TSH-cutoff of (3.97mIU/L,95%CI:3.95–4.00) for distinguishing pregnant women at risk of preterm-birth. The cutoff was associated with acceptable positive predictive and negative predictive values (0.84,95% CI:0.80–0.88) and 0.92 (95%CI: 0.91–0.94), respectively). In women who were negative for thyroid peroxides antibody (TPOAb) with sufficient urinary iodine concentration (UIC), the TSH cutoff of 3.92 mIU/L(95%CI:3.70–4) had the highest predictive value; whereas in TPOAb positive women with insufficient UIC, the cutoff of 4.0 mIU/L(95%:CI 3.94–4) could better predict preterm birth. Cutoffs estimated in this study are close to the revised TSH value of 4.0mIU/L which is currently recommended by the American Thyroid Association.

Conclusion Regardless of TPOAb status or iodine insufficiency, risk of preterm labor is increased in pregnant women with TSH value of > 3.92 mIU/L; these women may benefit from Levothyroxine (LT4) therapy for preventing preterm birth.

Item ID:	81199
Item Type:	Article (Research - C1)
ISSN:	1932-6203
Copyright Information:	© 2023 Rahmati et al. 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.
Date Deposited:	28 Nov 2023 00:52
FoR Codes:	32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320208 Endocrinology @ 70% 32 BIOMEDICAL AND CLINICAL SCIENCES > 3215 Reproductive medicine > 321502 Obstetrics and gynaecology @ 30%
SEO Codes:	20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 100%
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