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Ramasamy T, Varughese B, Singh M, Tailor P, Rao A, Misra S, Sharma N, Desiraju K, Thiruvengadam R, Wadhwa N, Kapoor S, Bhatnagar S, Kshetrapal P. Post-natal gestational age assessment using targeted metabolites of neonatal heel prick and umbilical cord blood: A GARBH-Ini cohort study from North India. J Glob Health 2024; 14:04115. [PMID: 38968007 PMCID: PMC11225965 DOI: 10.7189/jogh.14.04115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/07/2024] Open
Abstract
Background Accurate assessment of gestational age (GA) and identification of preterm birth (PTB) at delivery is essential to guide appropriate post-natal clinical care. Undoubtedly, dating ultrasound sonography (USG) is the gold standard to ascertain GA, but is not accessible to the majority of pregnant women in low- and middle-income countries (LMICs), particularly in rural areas and small secondary care hospitals. Conventional methods of post-natal GA assessment are not reliable at delivery and are further compounded by a lack of trained personnel to conduct them. We aimed to develop a population-specific GA model using integrated clinical and biochemical variables measured at delivery. Methods We acquired metabolic profiles on paired neonatal heel prick (nHP) and umbilical cord blood (uCB) dried blood spot (DBS) samples (n = 1278). The master data set consists of 31 predictors from nHP and 24 from uCB after feature selection. These selected predictors including biochemical analytes, birth weight, and placental weight were considered for the development of population-specific GA estimation and birth outcome classification models using eXtreme Gradient Boosting (XGBoost) algorithm. Results The nHP and uCB full model revealed root mean square error (RMSE) of 1.14 (95% confidence interval (CI) = 0.82-1.18) and of 1.26 (95% CI = 0.88-1.32) to estimate the GA as compared to actual GA, respectively. In addition, these models correctly estimated 87.9 to 92.5% of the infants within ±2 weeks of the actual GA. The classification models also performed as the best fit to discriminate the PTB from term birth (TB) infants with an area under curve (AUC) of 0.89 (95% CI = 0.84-0.94) for nHP and an AUC of 0.89 (95% CI = 0.85-0.95) for uCB. Conclusion The biochemical analytes along with clinical variables in the nHP and uCB data sets provide higher accuracy in predicting GA. These models also performed as the best fit to identify PTB infants at delivery.
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Affiliation(s)
- Thirunavukkarasu Ramasamy
- Lab of Perinatal Research, Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Bijo Varughese
- Genetics Laboratory, Department of Paediatrics, Maulana Azad Medical College, New Delhi, India
| | - Mukesh Singh
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
| | - Pragya Tailor
- Lab of Perinatal Research, Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Archana Rao
- Lab of Perinatal Research, Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Sumit Misra
- Gurugram Civil Hospital, GCH, Haryana, India
| | - Nikhil Sharma
- Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Koundiya Desiraju
- Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Ramachandran Thiruvengadam
- Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Nitya Wadhwa
- Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - GARBH-Ini Study Group6
- Lab of Perinatal Research, Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
- Genetics Laboratory, Department of Paediatrics, Maulana Azad Medical College, New Delhi, India
- Department of Gastroenterology, All India Institute of Medical Sciences, New Delhi, India
- Gurugram Civil Hospital, GCH, Haryana, India
- Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
- Interdisciplinary Group for Advanced Research on Birth Outcomes - DBT India Initiative, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Seema Kapoor
- Genetics Laboratory, Department of Paediatrics, Maulana Azad Medical College, New Delhi, India
| | - Shinjini Bhatnagar
- Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
| | - Pallavi Kshetrapal
- Lab of Perinatal Research, Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
- Maternal and Child Health, Translational Health Science and Technology Institute, Faridabad, Haryana, India
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Sazawal S, Das S, Ryckman KK, Khanam R, Nisar I, Deb S, Jasper EA, Rahman S, Mehmood U, Dutta A, Chowdhury NH, Barkat A, Mittal H, Ahmed S, Khalid F, Ali SM, Raqib R, Ilyas M, Nizar A, Manu A, Russell D, Yoshida S, Baqui AH, Jehan F, Dhingra U, Bahl R. Machine learning prediction of gestational age from metabolic screening markers resistant to ambient temperature transportation: Facilitating use of this technology in low resource settings of South Asia and East Africa. J Glob Health 2022; 12:04021. [PMID: 35493781 PMCID: PMC9022771 DOI: 10.7189/jogh.12.04021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background Knowledge of gestational age is critical for guiding preterm neonatal care. In the last decade, metabolic gestational dating approaches emerged in response to a global health need; because in most of the developing world, accurate antenatal gestational age estimates are not feasible. These methods initially developed in North America have now been externally validated in two studies in developing countries, however, require shipment of samples at sub-zero temperature. Methods A subset of 330 pairs of heel prick dried blood spot samples were shipped on dry ice and in ambient temperature from field sites in Tanzania, Bangladesh and Pakistan to laboratory in Iowa (USA). We evaluated impact on recovery of analytes of shipment temperature, developed and evaluated models for predicting gestational age using a limited set of metabolic screening analytes after excluding 17 analytes that were impacted by shipment conditions of a total of 44 analytes. Results With the machine learning model using all the analytes, samples shipped in dry ice yielded a Root Mean Square Error (RMSE) of 1.19 weeks compared to 1.58 weeks for samples shipped in ambient temperature. Out of the 44 screening analytes, recovery of 17 analytes was significantly different between the two shipment methods and these were excluded from further machine learning model development. The final model, restricted to stable analytes provided a RMSE of 1.24 (95% confidence interval (CI) = 1.10-1.37) weeks for samples shipped on dry ice and RMSE of 1.28 (95% CI = 1.15-1.39) for samples shipped at ambient temperature. Analysis for discriminating preterm births (gestational age <37 weeks), yielded an area under curve (AUC) of 0.76 (95% CI = 0.71-0.81) for samples shipped on dry ice and AUC of 0.73 (95% CI = 0.67-0.78) for samples shipped in ambient temperature. Conclusions In this study, we demonstrate that machine learning algorithms developed using a sub-set of newborn screening analytes which are not sensitive to shipment at ambient temperature, can accurately provide estimates of gestational age comparable to those from published regression models from North America using all analytes. If validated in larger samples especially with more newborns <34 weeks, this technology could substantially facilitate implementation in LMICs.
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Affiliation(s)
- Sunil Sazawal
- Center for Public Health Kinetics, New Delhi, India,Public Health Laboratory-IDC, Chake Chake, Tanzania
| | - Sayan Das
- Center for Public Health Kinetics, New Delhi, India
| | | | - Rasheda Khanam
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | | | - Saikat Deb
- Center for Public Health Kinetics, New Delhi, India,Public Health Laboratory-IDC, Chake Chake, Tanzania
| | | | | | | | - Arup Dutta
- Center for Public Health Kinetics, New Delhi, India
| | | | | | | | | | | | | | - Rubhana Raqib
- International Center for Diarrheal Disease Research, Dhaka, Bangladesh
| | | | | | - Alexander Manu
- Department of Maternal, Newborn, Child and Adolescent Health, and Ageing, Geneva, Switzerland
| | | | - Sachiyo Yoshida
- Department of Maternal, Newborn, Child and Adolescent Health, and Ageing, Geneva, Switzerland
| | - Abdullah H Baqui
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | | | - Usha Dhingra
- Center for Public Health Kinetics, New Delhi, India
| | - Rajiv Bahl
- Department of Maternal, Newborn, Child and Adolescent Health, and Ageing, Geneva, Switzerland
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Sazawal S, Ryckman KK, Das S, Khanam R, Nisar I, Jasper E, Dutta A, Rahman S, Mehmood U, Bedell B, Deb S, Chowdhury NH, Barkat A, Mittal H, Ahmed S, Khalid F, Raqib R, Manu A, Yoshida S, Ilyas M, Nizar A, Ali SM, Baqui AH, Jehan F, Dhingra U, Bahl R. Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa. BMC Pregnancy Childbirth 2021; 21:609. [PMID: 34493237 PMCID: PMC8424940 DOI: 10.1186/s12884-021-04067-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 08/14/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, advocacy, resources allocation and program evaluation and at an individual level for targeted care. Early prenatal ultrasound examination is not available in these settings, gestational age (GA) is estimated using new-born assessment, last menstrual period (LMP) recalls and birth weight, which are unreliable. Algorithms in developed settings, using metabolic screen data, provided GA estimates within 1-2 weeks of ultrasonography-based GA. We sought to leverage machine learning algorithms to improve accuracy and applicability of this approach to LMICs settings. METHODS This study uses data from AMANHI-ACT, a prospective pregnancy cohorts in Asia and Africa where early pregnancy ultrasonography estimated GA and birth weight are available and metabolite screening data in a subset of 1318 new-borns were also available. We utilized this opportunity to develop machine learning (ML) algorithms. Random Forest Regressor was used where data was randomly split into model-building and model-testing dataset. Mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate performance. Bootstrap procedures were used to estimate confidence intervals (CI) for RMSE and MAE. For pre-term birth identification ROC analysis with bootstrap and exact estimation of CI for area under curve (AUC) were performed. RESULTS Overall model estimated GA had MAE of 5.2 days (95% CI 4.6-6.8), which was similar to performance in SGA, MAE 5.3 days (95% CI 4.6-6.2). GA was correctly estimated to within 1 week for 85.21% (95% CI 72.31-94.65). For preterm birth classification, AUC in ROC analysis was 98.1% (95% CI 96.0-99.0; p < 0.001). This model performed better than Iowa regression, AUC Difference 14.4% (95% CI 5-23.7; p = 0.002). CONCLUSIONS Machine learning algorithms and models applied to metabolomic gestational age dating offer a ladder of opportunity for providing accurate population-level gestational age estimates in LMICs settings. These findings also point to an opportunity for investigation of region-specific models, more focused feasible analyte models, and broad untargeted metabolome investigation.
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Affiliation(s)
- Sunil Sazawal
- Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India.
| | - Kelli K Ryckman
- College of Public Health, Department of Epidemiology, University of Iowa, 145 N. Riverside Dr. , S435, Iowa City, IA, 52242, USA
| | - Sayan Das
- Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India
| | - Rasheda Khanam
- Department of International Health, Johns Hopkins Bloomberg School for Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA
| | - Imran Nisar
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan
| | - Elizabeth Jasper
- College of Public Health, Department of Epidemiology, University of Iowa, 145 N. Riverside Dr. , S435, Iowa City, IA, 52242, USA
| | - Arup Dutta
- Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India
| | - Sayedur Rahman
- Projahnmo Research Foundation, Abanti, Flat # 5B, House # 37, Road # 27, Banani, Dhaka, 1213, Bangladesh
| | - Usma Mehmood
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan
| | - Bruce Bedell
- College of Public Health, Department of Epidemiology, University of Iowa, 145 N. Riverside Dr. , S435, Iowa City, IA, 52242, USA
| | - Saikat Deb
- Public Health Laboratory-IDC, Chake Chake, Pemba, Tanzania
| | - Nabidul Haque Chowdhury
- Projahnmo Research Foundation, Abanti, Flat # 5B, House # 37, Road # 27, Banani, Dhaka, 1213, Bangladesh
| | - Amina Barkat
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan
| | - Harshita Mittal
- Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India
| | - Salahuddin Ahmed
- Projahnmo Research Foundation, Abanti, Flat # 5B, House # 37, Road # 27, Banani, Dhaka, 1213, Bangladesh
| | - Farah Khalid
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan
| | - Rubhana Raqib
- International Centre for Diarrhoeal Disease Research, Mohakhali, Dhaka, 1212, Bangladesh
| | - Alexander Manu
- Department of Maternal, Newborn, Child and Adolescent Health and Ageing, Avenue Appia 20, 1211, Geneva, Switzerland
| | - Sachiyo Yoshida
- Department of Maternal, Newborn, Child and Adolescent Health and Ageing, Avenue Appia 20, 1211, Geneva, Switzerland
| | - Muhammad Ilyas
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan
| | - Ambreen Nizar
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan
| | | | - Abdullah H Baqui
- Department of International Health, Johns Hopkins Bloomberg School for Public Health, 615 N. Wolfe Street, Baltimore, MD, 21205, USA
| | - Fyezah Jehan
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Sindh, Pakistan
| | - Usha Dhingra
- Center for Public Health Kinetics, Global Division, 214 A, LGL Vinoba Puri, Lajpat Nagar II, New Delhi, India
| | - Rajiv Bahl
- Department of Maternal, Newborn, Child and Adolescent Health and Ageing, Avenue Appia 20, 1211, Geneva, Switzerland.
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