Details

Autor(en) / Beteiligte

• Gadekar, Veerendra P.
• Damaraju, Nikhita
• Xavier, Ashley
• Thakur, Shambo Basu
• Vijayram, Ramya
• Desiraju, Bapu Koundinya
• Misra, Sumit
• Bhatnagar, Shinjini
• Wadhwa, Nitya
• Chandra Mouli Natchu, Uma
• Das, Bhabatosh
• Kshetrapal, Pallavi S.
• Sopory, Shailaja
• Thiruvengadam, Ramachandran
• Sharma, Dharmendra
• Sachdeva, Kanika
• Singh, Amanpreet
• Nair, Balakrish G.
• Rath, Satyajit
• Bal, Vineeta
• Sharma, Alka
• Sharma, Sunita
• Mehta, Umesh
• Sindhu, Brahmdeep
• Mittal, Pratima
• Bharti, Rekha
• Chellani, Harish
• Gera, Rani
• Suri, Jyotsna
• Debata, Pradeep
• Arya, Sugandha
• Maitra, Arindam
• Maiti, Tushar K.
• Salunke, Dinakar M.
• Tandon, Nikhil
• Gupta, Yashdeep
• Goyal, Alpesh
• Hari, Smriti
• Sharma K, Aparna
• Rana, Anubhuti
• Ramji, Siddarth
• Garg, Anju
• Khurana, Ashok
• Tripathi, Reva
• Gupta, Rakesh
• Sinha, Himanshu
• Rengaswamy, Raghunathan
• Majumder, Partha P.
• Rathore, Swati
• Abraham, Anuja
• Benjamin, Santosh
• Cherian, Anne George

Titel

Development and external validation of Indian population-specific Garbhini-GA2 model for estimating gestational age in second and third trimesters

Ist Teil von

• The Lancet regional health. Southeast Asia, 2024-06, Vol.25, p.100362, Article 100362

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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. 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. 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. 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. 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.