在中低收入国家(LMIC),很大一部分孕妇在妊娠14周后寻求首次产前护理。虽然末次月经期(LMP)仍然是确定胎龄(GA)的最普遍方法,基于超声的胎儿生物测量在孕中期和晚期被认为更准确。在LMIC设置中,Hadlock公式,最初是使用来自少数白种人的数据开发的,作为超声机器中的预编程公式,在全球范围内广泛用于估计GA和胎儿重量。这种方法在估计不同群体中的GA时会导致不准确。因此,这项研究旨在开发一种特定于人群的模型,用于估计妊娠晚期的GA,该模型与妊娠早期的GA估计一样准确。使用来自GARBH-Ini的数据,北印度地区医院的一个怀孕队列,随后在印度南部的一个独立队列中验证了该模型。
■通过纵向超声检查获得的所有妊娠中期的数据用于开发和验证妊娠中期和中期的GA模型。使用超声检查确定了妊娠早期GA评估的金标准。Garbhini-GA2,多项式回归模型,是使用基于遗传算法的方法开发的,展示所考虑的模型中的最佳性能。该模型包含了在第二和第三个三个月期间常规测量的五个超声参数中的三个。为了评估其性能,使用来自GARBH-Ini队列的测试集(N=1493)和来自基督教医学院(CMC)的独立验证数据集(N=948),将Garbhini-GA2模型与Hadlock和INTERGROWTH-21st模型进行了比较。Vellore队列。评估指标,包括均方根误差,偏见,和早产(PTB)率,用于综合评估模型的准确性和可靠性。
■以妊娠早期GA为基线,与Hadlock公式相比,Garbhini-GA2将GA估计中值误差降低了三倍以上。Further,与INTERGROWTH-21和Hadlock公式相比,使用Garbhini-GA2估计的PTB率更准确,高估了22.47%和58.91%,分别。
■Garbhini-GA2是第一个使用印度人口数据开发和验证的妊娠晚期GA估计模型。它在GA估计中具有更高的准确性,与妊娠早期和PTB分类的GA估计相当,强调了部署针对特定人群的GA配方以加强产前护理的重要性。
■GARBH-Ini队列研究由生物技术部资助,印度政府(BT/PR9983/MED/97/194/2013)。超声资料库得到了印度大挑战-所有儿童繁荣计划的部分支持,生物技术产业研究援助委员会,生物技术系,印度政府(BIRAC/GCI/0115/03/14-ACT)。本出版物中报告的研究是由生物技术产业研究援助委员会(BIRAC)的印度大挑战资助(BT/kiData0394/06/18),由DBT-BMGF-BIRAC联合支持的运营部门。CMCVellore的外部验证研究部分得到了印度生物技术产业研究援助委员会(BIRAC)的重大挑战资助(BT/kiData0394/06/18)的部分支持,由罗伯特·博世数据科学和人工智能中心(RBCDSAI)的DBT-BMGF-BIRAC和探索性研究资助(SB/20-21/0602/BT/RBCX/008481)共同支持的运营部门,马德拉斯研究所。PrakashArunachalam的明矾捐赠(BIO/18-19/304/ALUM/KARH)在综合生物学和系统医学中心部分资助了这项研究,马德拉斯研究所。
UNASSIGNED: A large proportion of pregnant women in lower and middle-income countries (LMIC) seek their first antenatal care after 14 weeks of gestation. While the last menstrual period (LMP) is still the most prevalent method of determining gestational age (GA), ultrasound-based foetal biometry is considered more accurate in the second and third trimesters. In LMIC settings, the Hadlock formula, originally developed using data from a small Caucasian population, is widely used for estimating GA and foetal weight worldwide as the pre-programmed formula in ultrasound machines. This approach can lead to inaccuracies when estimating GA in a diverse population. Therefore, this study aimed to develop a population-specific model for estimating GA in the late trimesters that was as accurate as the GA estimation in the first trimester, using data from GARBH-Ini, a pregnancy cohort in a North Indian district hospital, and subsequently validate the model in an independent cohort in South India.
UNASSIGNED: Data obtained by longitudinal ultrasonography across all trimesters of pregnancy was used to develop and validate GA models for the second and third trimesters. The gold standard for GA estimation in the first trimester was determined using ultrasonography. The Garbhini-GA2, a polynomial regression model, was developed using the genetic algorithm-based method, showcasing the best performance among the models considered. This model incorporated three of the five routinely measured ultrasonographic parameters during the second and third trimesters. To assess its performance, the Garbhini-GA2 model was compared against the Hadlock and INTERGROWTH-21st models using both the TEST set (N = 1493) from the GARBH-Ini cohort and an independent VALIDATION dataset (N = 948) from the Christian Medical College (CMC), Vellore cohort. Evaluation metrics, including root-mean-squared error, bias, and preterm birth (PTB) rates, were utilised to comprehensively assess the model\'s accuracy and reliability.
UNASSIGNED: With first trimester GA dating as the baseline, Garbhini-GA2 reduced the GA estimation median error by more than three times compared to the Hadlock formula. Further, the PTB rate estimated using Garbhini-GA2 was more accurate when compared to the INTERGROWTH-21st and Hadlock formulae, which overestimated the rate by 22.47% and 58.91%, respectively.
UNASSIGNED: The Garbhini-GA2 is the first late-trimester GA estimation model to be developed and validated using Indian population data. Its higher accuracy in GA estimation, comparable to GA estimation in the first trimester and PTB classification, underscores the significance of deploying population-specific GA formulae to enhance antenatal care.
UNASSIGNED: The GARBH-Ini cohort study was funded by the Department of Biotechnology, Government of India (BT/PR9983/MED/97/194/2013). The ultrasound repository was partly supported by the Grand Challenges India-All Children Thriving Program, Biotechnology Industry Research Assistance Council, Department of Biotechnology, Government of India (BIRAC/GCI/0115/03/14-ACT). The research reported in this publication was made possible by a grant (BT/kiData0394/06/18) from the Grand Challenges India at Biotechnology Industry Research Assistance Council (BIRAC), an operating division jointly supported by DBT-BMGF-BIRAC. The external validation study at CMC Vellore was partly supported by a grant (BT/kiData0394/06/18) from the Grand Challenges India at Biotechnology Industry Research Assistance Council (BIRAC), an operating division jointly supported by DBT-BMGF-BIRAC and by Exploratory Research Grant (SB/20-21/0602/BT/RBCX/008481) from Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras. An alum endowment from Prakash Arunachalam (BIO/18-19/304/ALUM/KARH) partly funded this study at the Centre for Integrative Biology and Systems Medicine, IIT Madras.