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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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Ward VC, Hawken S, Chakraborty P, Darmstadt GL, Wilson K. Estimating Gestational Age and Prediction of Preterm Birth Using Metabolomics Biomarkers. Clin Perinatol 2024; 51:411-424. [PMID: 38705649 DOI: 10.1016/j.clp.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Preterm birth (PTB) is a leading cause of morbidity and mortality in children aged under 5 years globally, especially in low-resource settings. It remains a challenge in many low-income and middle-income countries to accurately measure the true burden of PTB due to limited availability of accurate measures of gestational age (GA), first trimester ultrasound dating being the gold standard. Metabolomics biomarkers are a promising area of research that could provide tools for both early identification of high-risk pregnancies and for the estimation of GA and preterm status of newborns postnatally.
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Affiliation(s)
- Victoria C Ward
- Department of Pediatrics, Stanford University School of Medicine, 291 Campus Drive Li Ka Shing Building, Stanford, CA 94305, USA
| | - Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Centre for Practice Changing Research, 501 Smyth Road, Box 201-B, Ottawa, Ontario, Canada K1H 8L6; School of Epidemiology and Public Health, University of Ottawa, 600 Peter Morand Crescent, Ottawa, Ontario, Canada K1G 5Z3.
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, 415 Smyth Road, Ottawa, Ontario K1H 8M8, Canada; Department of Pediatrics, University of Ottawa, Roger Guindon Hall, 451 Smyth Rd, Ottawa Ontario, Canada K1H 8M5
| | - Gary L Darmstadt
- Prematurity Research Center, Department of Pediatrics, Stanford University School of Medicine, 453 Quarry Road, Palo Alto, CA 94304, USA
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Centre for Practice Changing Research, 501 Smyth Road, Box 201-B, Ottawa, Ontario, Canada K1H 8L6; Department of Medicine, University of Ottawa, Roger Guindon Hall, 451 Smyth Road, Ottawa, Ontario, Canada K1H 8M5; Bruyère Research Institute, 85 Primrose Avenue, Ottawa, Ontario, Canada K2A2E5
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Bradburn E, Conde-Agudelo A, Roberts NW, Villar J, Papageorghiou AT. Accuracy of prenatal and postnatal biomarkers for estimating gestational age: a systematic review and meta-analysis. EClinicalMedicine 2024; 70:102498. [PMID: 38495518 PMCID: PMC10940947 DOI: 10.1016/j.eclinm.2024.102498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 01/21/2024] [Accepted: 02/02/2024] [Indexed: 03/19/2024] Open
Abstract
Background Knowledge of gestational age (GA) is key in clinical management of individual obstetric patients, and critical to be able to calculate rates of preterm birth and small for GA at a population level. Currently, the gold standard for pregnancy dating is measurement of the fetal crown rump length at 11-14 weeks of gestation. However, this is not possible for women first presenting in later pregnancy, or in settings where routine ultrasound is not available. A reliable, cheap and easy to measure GA-dependent biomarker would provide an important breakthrough in estimating the age of pregnancy. Therefore, the aim of this study was to determine the accuracy of prenatal and postnatal biomarkers for estimating gestational age (GA). Methods Systematic review prospectively registered with PROSPERO (CRD42020167727) and reported in accordance with the PRISMA-DTA. Medline, Embase, CINAHL, LILACS, and other databases were searched from inception until September 2023 for cohort or cross-sectional studies that reported on the accuracy of prenatal and postnatal biomarkers for estimating GA. In addition, we searched Google Scholar and screened proceedings of relevant conferences and reference lists of identified studies and relevant reviews. There were no language or date restrictions. Pooled coefficients of correlation and root mean square error (RMSE, average deviation in weeks between the GA estimated by the biomarker and that estimated by the gold standard method) were calculated. The risk of bias in each included study was also assessed. Findings Thirty-nine studies fulfilled the inclusion criteria: 20 studies (2,050 women) assessed prenatal biomarkers (placental hormones, metabolomic profiles, proteomics, cell-free RNA transcripts, and exon-level gene expression), and 19 (1,738,652 newborns) assessed postnatal biomarkers (metabolomic profiles, DNA methylation profiles, and fetal haematological components). Among the prenatal biomarkers assessed, human chorionic gonadotrophin measured in maternal serum between 4 and 9 weeks of gestation showed the highest correlation with the reference standard GA, with a pooled coefficient of correlation of 0.88. Among the postnatal biomarkers assessed, metabolomic profiling from newborn blood spots provided the most accurate estimate of GA, with a pooled RMSE of 1.03 weeks across all GAs. It performed best for term infants with a slightly reduced accuracy for preterm or small for GA infants. The pooled RMSEs for metabolomic profiling and DNA methylation profile from cord blood samples were 1.57 and 1.60 weeks, respectively. Interpretation We identified no antenatal biomarkers that accurately predict GA over a wide window of pregnancy. Postnatally, metabolomic profiling from newborn blood spot provides an accurate estimate of GA, however, as this is known only after birth it is not useful to guide antenatal care. Further prenatal studies are needed to identify biomarkers that can be used in isolation, as part of a biomarker panel, or in combination with other clinical methods to narrow prediction intervals of GA estimation. Funding The research was funded by the Bill and Melinda Gates Foundation (INV-000368). ATP is supported by the Oxford Partnership Comprehensive Biomedical Research Centre with funding from the NIHR Biomedical Research Centre funding scheme. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, the Department of Health, or the Department of Biotechnology. The funders of this study had no role in study design, data collection, analysis or interpretation of the data, in writing the paper or the decision to submit for publication.
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Affiliation(s)
- Elizabeth Bradburn
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK
| | - Agustin Conde-Agudelo
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Nia W. Roberts
- Bodleian Health Care Libraries, University of Oxford, Oxford, UK
| | - Jose Villar
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Aris T. Papageorghiou
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
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Aktar S, Nu UT, Rahman M, Pervin J, Rahman SM, El Arifeen S, Persson LÅ, Rahman A. Trends and risk of recurrent preterm birth in pregnancy cohorts in rural Bangladesh, 1990-2019. BMJ Glob Health 2023; 8:e012521. [PMID: 37984897 PMCID: PMC10660812 DOI: 10.1136/bmjgh-2023-012521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 10/08/2023] [Indexed: 11/22/2023] Open
Abstract
INTRODUCTION A history of preterm birth reportedly increases the risk of subsequent preterm birth. This association has primarily been studied in high-income countries and not in low-income settings in transition with rapidly descending preterm birth figures. We evaluated the population-based trends of preterm births and recurrent preterm births and the risk of preterm birth recurrence in the second pregnancy based on prospectively studied pregnancy cohorts over three decades in Matlab, Bangladesh. METHODS A population-based cohort included 72 160 live births from 1990 to 2019. We calculated preterm birth and recurrent preterm birth trends. We assessed the odds of preterm birth recurrence based on a subsample of 14 567 women with live-born singletons in their first and second pregnancies. We used logistic regression and presented the associations by OR with a 95% CI. RESULTS The proportion of preterm births decreased from 25% in 1990 to 13% in 2019. The recurrent preterm births had a similar, falling pattern from 7.4% to 3.1% across the same period, contributing 27% of the total number of preterm births in the population. The odds of second pregnancy preterm birth were doubled (OR 2.18; 95% CI 1.96 to 2.43) in women with preterm birth compared with the women with term birth in their first pregnancies, remaining similar over the study period. The lower the gestational age at the first birth, the higher the odds of preterm birth in the subsequent pregnancy (test for trend p<0.001). CONCLUSION In this rural Bangladeshi setting, recurrent preterm births contributed a sizeable proportion of the total number of preterm births at the population level. The increased risk of recurrence remained similar across three decades when the total proportion of preterm births was reduced from 25% to 13%.
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Affiliation(s)
- Shaki Aktar
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - U Tin Nu
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Monjur Rahman
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Jesmin Pervin
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | | | - Shams El Arifeen
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Lars Åke Persson
- Department of Disease Control, Faculty of infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Anisur Rahman
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh
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5
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Hawken S, Olibris B, Ducharme R, Bota AB, Murray JC, Potter BK, Walker M, Chakraborty P, Wilson K. Validation of gestational age determination from ultrasound or a metabolic gestational age algorithm using exact date of conception in a cohort of newborns conceived using assisted reproduction technologies. AJOG GLOBAL REPORTS 2022; 2:100091. [PMID: 36536852 PMCID: PMC9758343 DOI: 10.1016/j.xagr.2022.100091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Accurate estimates of gestational age in pregnancy are important for the provision of optimal care. Although current guidelines generally recommend estimating gestational age via first-trimester ultrasound measurement of crown-rump length, error associated with this method can range from 3 to 8 days of gestation. In pregnancies resulting from assisted reproductive technology, estimated due date can be calculated on the basis of the age of the embryo and the date of embryo transfer, arguably providing the most accurate estimates possible. We have developed and extensively validated statistical models to estimate gestational age postnatally using metabolomic markers from blood samples in combination with clinical and demographic data. These models have shown high accuracy compared with first-trimester ultrasound, the recommended method for estimating gestational age in spontaneous pregnancies. We hypothesized that gestational age derived from date and stage of embryo at transfer in newborns conceived using assisted reproduction therapy would provide the most accurate reference standard possible to evaluate and compare the accuracy of both first-trimester ultrasound and metabolomic model-based gestational dating. OBJECTIVE This study aimed to validate both first-trimester ultrasound dating and postnatal metabolomic gestational age estimation models against gestational age derived from date and stage of embryo at transfer in a cohort of newborns conceived via assisted reproductive technology, both overall and in important subgroups of interest (preterm birth, small for gestational age, and multiple birth). STUDY DESIGN This was a retrospective cohort study of infants born in Ontario, Canada between 2015 and 2017 and captured in the provincial birth registry. Spontaneous conceptions were randomly partitioned into a model derivation sample (80%) and a test sample (20%) for model validation. A cohort of assisted conceptions resulting from fresh embryo transfers was derived to evaluate the accuracy of both ultrasound and model-based gestational dating. Postnatal gestational age estimation models were developed with multivariable linear regression using elastic-net regularization. Gestational age estimates from dating ultrasound and from postnatal metabolomic models were compared with date of embryo transfer reference gestational age in the independent test cohorts. Accuracy was quantified by calculating mean absolute error and the square root of mean squared error. RESULTS Our model derivation cohort included 202,300 spontaneous conceptions, and the testing cohorts included 50,735 spontaneous conceptions and 1924 assisted conceptions. In the assisted conception cohort, first-trimester dating ultrasound was accurate to within approximately ±1.5 days compared with date of embryo transfer reference overall (mean absolute error, 0.21 [95% confidence interval, 0.20-0.23]). When compared with gestational age derived from date of embryo transfer, the metabolomic estimation models were accurate to within approximately ±5 days overall (0.79 [0.76-0.81] weeks). When ultrasound was used as the reference in validating the metabolomic model, the mean absolute error was slightly higher overall (0.81 [0.78-0.84] weeks). In general, the accuracy of gestational age estimates derived from ultrasound or metabolomic models was highest in term infants and lower in preterm and small-for-gestational-age newborns. CONCLUSION Our findings support the accuracy of ultrasound as a gestational age dating tool. They also support the potential utility of metabolic gestational age dating algorithms in settings where ultrasound or other accurate methods of estimating gestational age are not available because of lack of infrastructure or specialized training (eg, low-income countries). However, the accuracy of metabolomic model-based dating was generally lower than that of ultrasound.
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Affiliation(s)
- Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada (Drs Hawken and Olibris, Ms. Ducharme, and Drs Bota, Walker, and Wilson)
| | - Brieanne Olibris
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada (Drs Hawken and Olibris, Ms. Ducharme, and Drs Bota, Walker, and Wilson)
| | - Robin Ducharme
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada (Drs Hawken and Olibris, Ms. Ducharme, and Drs Bota, Walker, and Wilson)
| | - A. Brianne Bota
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada (Drs Hawken and Olibris, Ms. Ducharme, and Drs Bota, Walker, and Wilson)
| | - Jeffrey C. Murray
- Department of Pediatrics, University of Iowa, Iowa City, IA (Dr Murray)
| | - Beth K. Potter
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada (Dr Potter)
| | - Mark Walker
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada (Drs Hawken and Olibris, Ms. Ducharme, and Drs Bota, Walker, and Wilson)
| | - Pranesh Chakraborty
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, Canada (Dr Chakraborty)
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada (Drs Hawken and Olibris, Ms. Ducharme, and Drs Bota, Walker, and Wilson)
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6
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Bota B, Ward V, Lamoureux M, Santander E, Ducharme R, Hawken S, Potter BK, Atito R, Nyamanda B, Munga S, Otieno N, Chakraborty S, Saha S, Stringer JSA, Mwape H, Price JT, Mujuru HA, Chimhini G, Magwali T, Chakraborty P, Darmstadt GL, Wilson K. Unlocking the global health potential of dried blood spot cards. J Glob Health 2022; 12:03027. [PMID: 35841606 PMCID: PMC9288235 DOI: 10.7189/jogh.12.03027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Affiliation(s)
- Brianne Bota
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Victoria Ward
- Prematurity Research Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Monica Lamoureux
- Newborn Screening Ontario, Children’s Hospital of Eastern Ontario, Ottawa, Canada
| | - Emeril Santander
- Newborn Screening Ontario, Children’s Hospital of Eastern Ontario, Ottawa, Canada
| | - Robin Ducharme
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Beth K Potter
- Department of Pediatrics, University of Ottawa, Ottawa, Canada
| | - Raphael Atito
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Bryan Nyamanda
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Stephen Munga
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Nancy Otieno
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | | | - Samir Saha
- Child Health Research Foundation, Mirzapur, Bangladesh
| | - Jeffrey SA Stringer
- Department of Obstetrics and Gynecology, UNC School of Medicine, Chapel Hill, North Carolina, USA
- UNC Global Projects Zambia, Lusaka, Zambia
| | | | - Joan T Price
- Department of Obstetrics and Gynecology, UNC School of Medicine, Chapel Hill, North Carolina, USA
- UNC Global Projects Zambia, Lusaka, Zambia
| | - Hilda Angela Mujuru
- Department of Paediatrics and Child Health, University of Zimbabwe, Harare, Zimbabwe
| | - Gwendoline Chimhini
- Department of Paediatrics and Child Health, University of Zimbabwe, Harare, Zimbabwe
| | - Thulani Magwali
- Department of Obstetrics and Gynaecology, Faculty of Medicine and Health Sciences, University of Zimbabwe, Harare, Zimbabwe
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children’s Hospital of Eastern Ontario, Ottawa, Canada
- Department of Pediatrics, University of Ottawa, Ottawa, Canada
| | - Gary L Darmstadt
- Prematurity Research Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
- Department of Medicine, University of Ottawa, Ottawa, Canada
- Bruyere Research Institute, Ottawa, Ontario
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7
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Jasper EA, Oltman SP, Rogers EE, Dagle JM, Murray JC, Kamya M, Kakuru A, Kajubi R, Ochieng T, Adrama H, Okitwi M, Olwoch P, Jagannathan P, Clark TD, Dorsey G, Ruel T, Jelliffe-Pawlowski LL, Ryckman KK. Targeted newborn metabolomics: prediction of gestational age from cord blood. J Perinatol 2022; 42:181-186. [PMID: 35067676 PMCID: PMC8830770 DOI: 10.1038/s41372-021-01253-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 10/11/2021] [Accepted: 10/14/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Our study sought to determine whether metabolites from a retrospective collection of banked cord blood specimens could accurately estimate gestational age and to validate these findings in cord blood samples from Busia, Uganda. STUDY DESIGN Forty-seven metabolites were measured by tandem mass spectrometry or enzymatic assays from 942 banked cord blood samples. Multiple linear regression was performed, and the best model was used to predict gestational age, in weeks, for 150 newborns from Busia, Uganda. RESULTS The model including metabolites and birthweight, predicted the gestational ages within 2 weeks for 76.7% of the Ugandan cohort. Importantly, this model estimated the prevalence of preterm birth <34 weeks closer to the actual prevalence (4.67% and 4.00%, respectively) than a model with only birthweight which overestimates the prevalence by 283%. CONCLUSION Models that include cord blood metabolites and birth weight appear to offer improvement in gestational age estimation over birth weight alone.
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Affiliation(s)
| | - Scott P Oltman
- University of California, San Francisco, Department of Epidemiology & Biostatistics, Kampala, Uganda.,UCSF California Preterm Birth Initiative, Kampala, Uganda
| | - Elizabeth E Rogers
- University of California San Francisco, Department of Pediatrics, Kampala, Uganda
| | - John M Dagle
- University of Iowa, Department of Pediatrics, Kampala, Uganda
| | | | - Moses Kamya
- Infectious Diseases Research Collaboration, Kampala, Uganda.,Department of Medicine, Makerere University College of Health Sciences, Kampala, Uganda
| | - Abel Kakuru
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Richard Kajubi
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Teddy Ochieng
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Harriet Adrama
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Martin Okitwi
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Peter Olwoch
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | | | - Tamara D. Clark
- Department of Medicine, University of California, San Francisco School of Medicine, San Francisco, CA
| | - Grant Dorsey
- Department of Medicine, University of California, San Francisco School of Medicine, San Francisco, CA
| | - Theodore Ruel
- Department of Pediatrics, University of California, San Francisco School of Medicine, San Francisco, CA
| | - Laura L Jelliffe-Pawlowski
- University of California, San Francisco, Department of Epidemiology & Biostatistics, Kampala, Uganda.,UCSF California Preterm Birth Initiative, Kampala, Uganda
| | - Kelli K Ryckman
- Department of Epidemiology, University of Iowa, Iowa, IA, USA.
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8
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Hawken S, Ward V, Bota AB, Lamoureux M, Ducharme R, Wilson LA, Otieno N, Munga S, Nyawanda BO, Atito R, Stevenson DK, Chakraborty P, Darmstadt GL, Wilson K. Real world external validation of metabolic gestational age assessment in Kenya. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0000652. [PMID: 36962760 PMCID: PMC10021775 DOI: 10.1371/journal.pgph.0000652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 10/20/2022] [Indexed: 11/29/2022]
Abstract
Using data from Ontario Canada, we previously developed machine learning-based algorithms incorporating newborn screening metabolites to estimate gestational age (GA). The objective of this study was to evaluate the use of these algorithms in a population of infants born in Siaya county, Kenya. Cord and heel prick samples were collected from newborns in Kenya and metabolic analysis was carried out by Newborn Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were developed with data from Ontario with multivariable linear regression using ELASTIC NET regularization. Model performance was evaluated by applying the models to the data collected from Kenya and comparing model-derived estimates of GA to reference estimates from early pregnancy ultrasound. Heel prick samples were collected from 1,039 newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small for GA. Cord blood samples were also collected from 1,012 newborns. In data from heel prick samples, our best-performing model estimated GA within 9.5 days overall of reference GA [mean absolute error (MAE) 1.35 (95% CI 1.27, 1.43)]. In preterm infants and those small for GA, MAE was 2.62 (2.28, 2.99) and 1.81 (1.57, 2.07) weeks, respectively. In data from cord blood, model accuracy slightly decreased overall (MAE 1.44 (95% CI 1.36, 1.53)). Accuracy was not impacted by maternal HIV status and improved when the dating ultrasound occurred between 9 and 13 weeks of gestation, in both heel prick and cord blood data (overall MAE 1.04 (95% CI 0.87, 1.22) and 1.08 (95% CI 0.90, 1.27), respectively). The accuracy of metabolic model based GA estimates in the Kenya cohort was lower compared to our previously published validation studies, however inconsistency in the timing of reference dating ultrasounds appears to have been a contributing factor to diminished model performance.
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Affiliation(s)
- Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| | - Victoria Ward
- Prematurity Research Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
| | - A Brianne Bota
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Monica Lamoureux
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, Canada
| | - Robin Ducharme
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Lindsay A Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Nancy Otieno
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Stephen Munga
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Bryan O Nyawanda
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Raphael Atito
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - David K Stevenson
- Prematurity Research Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, Canada
- Departments of Pediatrics, and of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Canada
| | - Gary L Darmstadt
- Prematurity Research Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States of America
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Department of Medicine, University of Ottawa, Ottawa, Canada
- Bruyère Research Institute, Ottawa, Canada
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9
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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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10
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Sazawal S, Ryckman KK, Mittal H, Khanam R, Nisar I, Jasper E, Rahman S, Mehmood U, Das S, Bedell B, Chowdhury NH, Barkat A, Dutta A, Deb S, Ahmed S, Khalid F, Raqib R, Ilyas M, Nizar A, Ali SM, Manu A, Yoshida S, Baqui AH, Jehan F, Dhingra U, Bahl R. Using AMANHI-ACT cohorts for external validation of Iowa new-born metabolic profiles based models for postnatal gestational age estimation. J Glob Health 2021; 11:04044. [PMID: 34326994 PMCID: PMC8285766 DOI: 10.7189/jogh.11.04044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Globally, 15 million infants are born preterm and another 23.2 million infants are born small for gestational age (SGA). Determining burden of preterm and SGA births, is essential for effective planning, modification of health policies and targeting interventions for reducing these outcomes for which accurate estimation of gestational age (GA) is crucial. Early pregnancy ultrasound measurements, last menstrual period and post-natal neonatal examinations have proven to be not feasible or inaccurate. Proposed algorithms for GA estimation in western populations, based on routine new-born screening, though promising, lack validation in developing country settings. We evaluated the hypothesis that models developed in USA, also predicted GA in cohorts of South Asia (575) and Sub-Saharan Africa (736) with same precision. METHODS Dried heel prick blood spots collected 24-72 hours after birth from 1311 new-borns, were analysed for standard metabolic screen. Regression algorithm based, GA estimates were computed from metabolic data and compared to first trimester ultrasound validated, GA estimates (gold standard). RESULTS Overall Algorithm (metabolites + birthweight) estimated GA to within an average deviation of 1.5 weeks. The estimated GA was within the gold standard estimate by 1 and 2 weeks for 70.5% and 90.1% new-borns respectively. Inclusion of birthweight in the metabolites model improved discriminatory ability of this method, and showed promise in identifying preterm births. Receiver operating characteristic (ROC) curve analysis estimated an area under curve of 0.86 (conservative bootstrap 95% confidence interval (CI) = 0.83 to 0.89); P < 0.001) and Youden Index of 0.58 (95% CI = 0.51 to 0.64) with a corresponding sensitivity of 80.7% and specificity of 77.6%. CONCLUSION Metabolic gestational age dating offers a novel means for accurate population-level gestational age estimates in LMIC settings and help preterm birth surveillance initiatives. Further research should focus on use of machine learning and newer analytic methods broader than conventional metabolic screen analytes, enabling incorporation of region-specific analytes and cord blood metabolic profiles models predicting gestational age accurately.
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Affiliation(s)
- Sunil Sazawal
- Center for Public Health Kinetics, Global Division, New Delhi, India
- Public Health Laboratory-IDC, Chake Chake, Pemba,Tanzania
| | - Kelli K Ryckman
- University of Iowa, College of Public Health, Department of Epidemiology, Iowa City, Iowa, USA
| | - Harshita Mittal
- Center for Public Health Kinetics, Global Division, New Delhi, India
| | - Rasheda Khanam
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Imran Nisar
- Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, Pakistan
| | - Elizabeth Jasper
- University of Iowa, College of Public Health, Department of Epidemiology, Iowa City, Iowa, USA
| | | | - Usma Mehmood
- Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, Pakistan
| | - Sayan Das
- Center for Public Health Kinetics, Global Division, New Delhi, India
| | - Bruce Bedell
- University of Iowa, College of Public Health, Department of Epidemiology, Iowa City, Iowa, USA
| | | | - Amina Barkat
- Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, Pakistan
| | - Arup Dutta
- Center for Public Health Kinetics, Global Division, New Delhi, India
| | - Saikat Deb
- Center for Public Health Kinetics, Global Division, New Delhi, India
- Public Health Laboratory-IDC, Chake Chake, Pemba,Tanzania
| | | | - Farah Khalid
- Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, Pakistan
| | - Rubhana Raqib
- International Center for Diarrheal Disease Research, Bangladesh, Mohakhali, Dhaka, Bangladesh
| | - Muhammad Ilyas
- Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, Pakistan
| | - Ambreen Nizar
- Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, Pakistan
| | | | - Alexander Manu
- World Health Organization (MCA/MRD), Geneva, Switzerland
| | | | - Abdullah H Baqui
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Fyezah Jehan
- Aga Khan University, Department of Paediatrics and Child Health, Karachi, Sindh, Pakistan
| | - Usha Dhingra
- Center for Public Health Kinetics, Global Division, New Delhi, India
| | - Rajiv Bahl
- World Health Organization (MCA/MRD), Geneva, Switzerland
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11
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Hawken S, Murphy MSQ, Ducharme R, Bota AB, Wilson LA, Cheng W, Tumulak MAJ, Alcausin MML, Reyes ME, Qiu W, Potter BK, Little J, Walker M, Zhang L, Padilla C, Chakraborty P, Wilson K. External validation of machine learning models including newborn metabolomic markers for postnatal gestational age estimation in East and South-East Asian infants. Gates Open Res 2021; 4:164. [PMID: 34104876 PMCID: PMC8160452 DOI: 10.12688/gatesopenres.13131.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/07/2021] [Indexed: 11/30/2022] Open
Abstract
Background: Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. To date, model development and validation have been carried out in North American settings. Validation outside of these settings is warranted. Methods: This was a retrospective database study using data from newborn screening programs in Canada, the Philippines and China. ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent sample of Canadian infants, and externally validated in infant cohorts from the Philippines and China. Results: Cohorts included 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China. For the full model including sex, birth weight and metabolomic markers, GA estimates were within ±5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within ±6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model. Conclusions: Accuracy of metabolic GA algorithms was attenuated when applied in external settings. Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility
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Affiliation(s)
- Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.,School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Malia S Q Murphy
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Robin Ducharme
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - A Brianne Bota
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Lindsay A Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Wei Cheng
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
| | - Ma-Am Joy Tumulak
- Newborn Screening Reference Centre, University of the Philippines Manila, Manila, Philippines
| | | | - Ma Elouisa Reyes
- Newborn Screening Reference Centre, University of the Philippines Manila, Manila, Philippines
| | - Wenjuan Qiu
- Pediatric Endocrinology and Genetic Metabolism, XinHua Hospital, Shanghai, Shanghai, China
| | - Beth K Potter
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Julian Little
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Mark Walker
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.,Better Outcomes Registry & Network, Ottawa, Canada
| | - Lin Zhang
- Department of Gynecology and Obsetrics, XinHua Hospital, Shanghai, Shanghai, China.,MOE-Shanghai Key Lab of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Carmencita Padilla
- Department of Pediatrics, University of the Philippines Manila, Manilla, Philippines.,Institute of Human Genetics, National Institutes of Health, University of Philippines Manila, Manila, Philippines
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada.,Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.,Department of Medicine, University of Ottowa, Ottowa, ON, Canada.,Bruyère Research Institute, Ottowa, ON, Canada
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12
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Bota AB, Ward V, Hawken S, Wilson LA, Lamoureux M, Ducharme R, Murphy MSQ, Denize KM, Henderson M, Saha SK, Akther S, Otieno NA, Munga S, Atito RO, Stringer JSA, Mwape H, Price JT, Mujuru HA, Chimhini G, Magwali T, Mudawarima L, Chakraborty P, Darmstadt GL, Wilson K. Metabolic gestational age assessment in low resource settings: a validation protocol. Gates Open Res 2021; 4:150. [PMID: 33501414 PMCID: PMC7801859 DOI: 10.12688/gatesopenres.13155.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/22/2021] [Indexed: 11/20/2022] Open
Abstract
Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children's Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario's newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable.
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Affiliation(s)
- A. Brianne Bota
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Victoria Ward
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen Hawken
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Lindsay A. Wilson
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Monica Lamoureux
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada
| | - Robin Ducharme
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Malia S. Q. Murphy
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
| | - Kathryn M. Denize
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada
| | - Matthew Henderson
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada
| | - Samir K. Saha
- Child Health Research Foundation, Mizapur, Bangladesh
| | - Salma Akther
- Child Health Research Foundation, Mizapur, Bangladesh
| | - Nancy A. Otieno
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Stephen Munga
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | - Raphael O. Atito
- Kenya Medical Research Institute (KEMRI), Center for Global Health Research, Kisumu, Kenya
| | | | | | - Joan T. Price
- Department of Obstetrics and Gynecology, UNC School of Medicine, Chapel Hill, NC, USA
| | - Hilda Angela Mujuru
- Department of Paediatrics and Child Health, University of Zimbabwe, Avondale, Zimbabwe
| | - Gwendoline Chimhini
- Department of Paediatrics and Child Health, University of Zimbabwe, Avondale, Zimbabwe
| | - Thulani Magwali
- Department of Obstetrics and Gynaecology, University of Zimbabwe, Avondale, Zimbabwe
| | - Louisa Mudawarima
- Department of Paediatrics and Child Health, University of Zimbabwe, Avondale, Zimbabwe
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, ON, Canada
| | - Gary L. Darmstadt
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Health Research Institute, Ottawa, ON, Canada
- Department of Medicine, University of Ottawa, Ottawa, Canada
- Bruyère Research Institute, Otttawa, Canada
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13
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Rahman M, Rahman SM, Pervin J, Aktar S, El Arifeen S, Rahman A. Body mass index in early-pregnancy and selected maternal health outcomes: Findings from two cohorts in Bangladesh. J Glob Health 2020; 10:020419. [PMID: 33110579 PMCID: PMC7568936 DOI: 10.7189/jogh.10.020419] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Maternal nutrition is one of the most influential factors that affect the health of the mother and her offspring and remains a significant public health challenge globally. There is a lack of studies evaluating the trends of maternal nutrition and its impact on the burden of pregnancy complications from low-income countries, including Bangladesh. We aimed to determine the burden of early-pregnancy nutrition status based on body mass index (BMI), and the associations of nutritional status with pregnancy-induced hypertension (PIH), cesarean section (CS) delivery, perineal tear and postpartum hemorrhage (PPH) in a rural area in Bangladesh. METHODS This prospective study analyzed data from two cohorts: the Maternal, Neonatal, and Child Health (MNCH) project carried out from January 2008 to June 2010, and the Preterm and Stillbirth Study, Matlab (PreSSMat) conducted from October 2015 to March 2018. In total, information of 9287 women who gave birth from the two cohorts was available for analysis. Early-pregnancy BMI was categorized into underweight, normal-weight, and overweight groups. The change in the burden of malnutrition between two cohort periods and the associations between women's BMI and maternal health outcomes were presented in odds ratios (ORs) with their 95% confidence interval (CI). RESULTS Between the two cohort periods, the prevalence of underweight decreased from 17.5% to 15.4%, and overweight increased from 10.8% to 20.9%. The risk of being overweight in pregnant women was about two times (OR = 2.19; 95% CI = 1.94-2.46) higher in the PreSSMat cohort than in the MNCH cohort. After multivariate-adjustment for socio-demographic factors, the pooled ORs of PIH, CS delivery, perineal tear, and PPH were 2.41 (95% CI = 1.95-2.99), 2.12 (95% CI = 1.86-2.41), 2.46 (95% CI = 1.54-3.92), and 1.68 (95% CI = 1.12-2.53), respectively, in women with overweight compared to the normal-weight group. CONCLUSIONS The results confirmed the existence of a double burden of malnutrition in rural women in Bangladesh. Women with overweight had an increased risk of selected pregnancy complications. The findings call for the adoption of appropriate prenatal counseling and preparedness tailored to women's nutritional status to prevent possible adverse health outcomes.
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Affiliation(s)
- Monjur Rahman
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh
| | - Syed Moshfiqur Rahman
- International Maternal and Child Health, Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden
| | - Jesmin Pervin
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh
| | - Shaki Aktar
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh
| | - Shams El Arifeen
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh
| | - Anisur Rahman
- Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Bangladesh
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14
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Coyle K, Quan AML, Wilson LA, Hawken S, Bota AB, Coyle D, Murray JC, Wilson K. Cost-effectiveness of a gestational age metabolic algorithm for preterm and small-for-gestational-age classification. Am J Obstet Gynecol MFM 2020; 3:100279. [PMID: 33451597 PMCID: PMC7805344 DOI: 10.1016/j.ajogmf.2020.100279] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 10/22/2020] [Accepted: 11/16/2020] [Indexed: 11/04/2022]
Abstract
Background Preterm birth complications are the leading cause of death among children under 5 years of age, and this imposes a heavy burden on healthcare and social systems, particularly in low- and middle-income countries where reliable estimates of gestational age may be difficult to obtain. Metabolic analyte data can aid in accurately estimating gestational age. However, important costs are associated with this approach, which are related to the collection and analysis of newborn samples, and its cost-effectiveness has yet to be determined. Objective This study aimed to evaluate the cost-effectiveness of an internationally validated gestational age estimation algorithm based on neonatal blood spot metabolite data in combination with clinical and demographic variables (birthweight, sex, and multiple birth status) compared with a basic algorithm that uses only clinical and demographic variables in classifying infants as preterm or term (using a 37-week dichotomous preterm or term classification) and determining gestational age. Study Design The cost per correctly classified preterm infant and per correctly classified small-for-gestational-age infant for the metabolic algorithm vs the basic algorithm were estimated with data from an implementation study in Bangladesh. Results Over 1 year, the metabolic algorithm correctly classified an average of 8.7 (95% confidence interval, 1.3–14.7) additional preterm infants and 145.3 (95% confidence interval, 128.0–164.7) additional small-for-gestational-age infants per 1323 infants screened compared with the basic algorithm using only clinical and demographic variables. The incremental annual cost of adopting the metabolic algorithm was $100,031 (95% confidence interval, $86,354–$115,725). If setup costs were included, the cost was $120,496 (95% confidence interval, $106,322–$136,656). Compared with the basic algorithm, the incremental cost per preterm infant correctly classified by the metabolic algorithm is $11,542 ($13,903 with setup), and the incremental cost per small-for-gestational-age infant is $688 ($829 with setup). Conclusion This research quantifies the cost per detection of preterm or small-for-gestational-age infant in the implementation of a newborn screening program to aid in improved classification of preterm and, in particular, small-for-gestational-age infants in low- and middle-income countries.
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Affiliation(s)
- Kathryn Coyle
- Department of Health Sciences, Institute of Environment, Health and Societies, Brunel University London, Kingston Lane, Uxbridge, Middlesex, United Kingdom
| | - Amanda My Linh Quan
- Dalla School of Public Health, University of Toronto, Toronto, Ontario Canada; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Lindsay A Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - A Brianne Bota
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Doug Coyle
- Department of Health Sciences, Institute of Environment, Health and Societies, Brunel University London, Kingston Lane, Uxbridge, Middlesex, United Kingdom; Faculty of Medicine, School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | | | - Kumanan Wilson
- Department of Medicine, University of Ottawa, Ottowa, Ontario, Canada; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Bruyère Research Institute, Ottawa, Ontario, Canada.
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15
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Hawken S, Murphy MSQ, Ducharme R, Bota AB, Wilson LA, Cheng W, Tumulak MAJ, Alcausin MML, Reyes ME, Qiu W, Potter BK, Little J, Walker M, Zhang L, Padilla C, Chakraborty P, Wilson K. External validation of ELASTIC NET regression models including newborn metabolomic markers for postnatal gestational age estimation in East and South-East Asian infants. Gates Open Res 2020; 4:164. [DOI: 10.12688/gatesopenres.13131.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/14/2020] [Indexed: 11/20/2022] Open
Abstract
Background: Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. To date, model development and validation have been carried out in North American settings. Validation outside of these settings is warranted. Methods: This was a retrospective database study using data from newborn screening programs in Canada, the Philippines and China. ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent group of infants, and externally validated in cohorts of infants from the Philippines and China. Results: Cohorts included 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China. For the full model including sex, birth weight and metabolomic markers, GA estimates were within 5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within 6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model. Conclusions: Accuracy of metabolic GA algorithms was attenuated when applied in external settings. Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility.
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Bota AB, Ward V, Hawken S, Wilson LA, Lamoureux M, Ducharme R, Murphy MSQ, Denize KM, Henderson M, Saha SK, Akther S, Otieno NA, Munga S, Atito RO, Stringer JSA, Mwape H, Price JT, Mujuru HA, Chimhini G, Magwali T, Mudawarima L, Chakraborty P, Darmstadt GL, Wilson K. Metabolic gestational age assessment in low resource settings: a validation protocol. Gates Open Res 2020. [DOI: 10.12688/gatesopenres.13155.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Preterm birth is the leading global cause of neonatal morbidity and mortality. Reliable gestational age estimates are useful for quantifying population burdens of preterm birth and informing allocation of resources to address the problem. However, evaluating gestational age in low-resource settings can be challenging, particularly in places where access to ultrasound is limited. Our group has developed an algorithm using newborn screening analyte values derived from dried blood spots from newborns born in Ontario, Canada for estimating gestational age within one to two weeks. The primary objective of this study is to validate a program that derives gestational age estimates from dried blood spot samples (heel-prick or cord blood) collected from health and demographic surveillance sites and population representative health facilities in low-resource settings in Zambia, Kenya, Bangladesh and Zimbabwe. We will also pilot the use of an algorithm to identify birth percentiles based on gestational age estimates and weight to identify small for gestational age infants. Once collected from local sites, samples will be tested by the Newborn Screening Ontario laboratory at the Children’s Hospital of Eastern Ontario (CHEO) in Ottawa, Canada. Analyte values will be obtained through laboratory analysis for estimation of gestational age as well as screening for other diseases routinely conducted at Ontario’s newborn screening program. For select conditions, abnormal screening results will be reported back to the sites in real time to facilitate counseling and future clinical management. We will determine the accuracy of our existing algorithm for estimation of gestational age in these newborn samples. Results from this research hold the potential to create a feasible method to assess gestational age at birth in low- and middle-income countries where reliable estimation may be otherwise unavailable.
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Wilson LA, Murphy MS, Ducharme R, Denize K, Jadavji NM, Potter B, Little J, Chakraborty P, Hawken S, Wilson K. Postnatal gestational age estimation via newborn screening analysis: application and potential. Expert Rev Proteomics 2019; 16:727-731. [PMID: 31422714 PMCID: PMC6816481 DOI: 10.1080/14789450.2019.1654863] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Introduction: Preterm birth is a major global health concern, contributing to 35% of all neonatal deaths in 2016. Given the importance of accurately ascertaining estimates of preterm birth and in light of current limitations in postnatal gestational age (GA) estimation, novel methods of estimating GA postnatally in the absence of prenatal ultrasound are needed. Previous work has demonstrated the potential for metabolomics to estimate GA by analyzing data captured through routine newborn screening. Areas covered: Circulating analytes found in newborn blood samples vary by GA. Leveraging newborn screening and demographic data, our group developed an algorithm capable of estimating GA postnatally to within approximately 1 week of ultrasound-validated GA. Since then, we have built on the model by including additional analytes and validating the model's performance through internal and external validation studies, and through implementation of the model internationally. Expert opinion: Currently, using metabolomics to estimate GA postnatally holds considerable promise but is limited by issues of cost-effectiveness and resource access in low-income settings. Future work will focus on enhancing the precision of this approach while prioritizing point-of-care testing that is both accessible and acceptable to individuals in low-resource settings.
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Affiliation(s)
- Lindsay A Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute , Ottawa , Canada
| | - Malia Sq Murphy
- Clinical Epidemiology Program, Ottawa Hospital Research Institute , Ottawa , Canada
| | - Robin Ducharme
- Clinical Epidemiology Program, Ottawa Hospital Research Institute , Ottawa , Canada
| | - Kathryn Denize
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario , Ottawa , Canada
| | - Nafisa M Jadavji
- Clinical Epidemiology Program, Ottawa Hospital Research Institute , Ottawa , Canada
| | - Beth Potter
- Department of Epidemiology and Community Health, University of Ottawa , Ottawa , Canada
| | - Julian Little
- Department of Epidemiology and Community Health, University of Ottawa , Ottawa , Canada
| | - Pranesh Chakraborty
- Newborn Screening Ontario, Children's Hospital of Eastern Ontario , Ottawa , Canada
| | - Steven Hawken
- Clinical Epidemiology Program, Ottawa Hospital Research Institute , Ottawa , Canada
| | - Kumanan Wilson
- Clinical Epidemiology Program, Ottawa Hospital Research Institute , Ottawa , Canada
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