Kumanan Wilson1, Steven Hawken2, Beth K Potter3, Pranesh Chakraborty4, Mark Walker5, Robin Ducharme6, Julian Little7. 1. Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Institute for Clinical Evaluative Sciences, University of Ottawa, Ottawa, Ontario, Canada; School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada; Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada; Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada. Electronic address: kwilson@ohri.ca. 2. Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Institute for Clinical Evaluative Sciences, University of Ottawa, Ottawa, Ontario, Canada; School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada; Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada. 3. Institute for Clinical Evaluative Sciences, University of Ottawa, Ottawa, Ontario, Canada; School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada; Newborn Screening Ontario, Ottawa, Ontario, Canada. 4. Department of Pediatrics, University of Ottawa, Ottawa, Ontario, Canada; Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada; Newborn Screening Ontario, Ottawa, Ontario, Canada. 5. Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Department of Obstetrics & Gynecology, University of Ottawa, Ottawa, Ontario, Canada; Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada. 6. Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Institute for Clinical Evaluative Sciences, University of Ottawa, Ottawa, Ontario, Canada. 7. School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Ottawa, Ontario, Canada.
Abstract
BACKGROUND: Identification of preterm births and accurate estimates of gestational age for newborn infants is vital to guide care. Unfortunately, in developing countries, it can be challenging to obtain estimates of gestational age. Routinely collected newborn infant screening metabolic analytes vary by gestational age and may be useful to estimate gestational age. OBJECTIVE: We sought to develop an algorithm that could estimate gestational age at birth that is based on the analytes that are obtained from newborn infant screening. STUDY DESIGN: We conducted a population-based cross-sectional study of all live births in the province of Ontario that included 249,700 infants who were born between April 2007 and March 2009 and who underwent newborn infant screening. We used multivariable linear and logistic regression analyses to build a model to predict gestational age using newborn infant screening metabolite measurements and readily available physical characteristics data (birthweight and sex). RESULTS: The final model of our metabolic gestational dating algorithm had an average deviation between observed and expected gestational age of approximately 1 week, which suggests excellent predictive ability (adjusted R-square of 0.65; root mean square error, 1.06 weeks). Two-thirds of the gestational ages that were predicted by our model were accurate within ±1 week of the actual gestational age. Our logistic regression model was able to discriminate extremely well between term and increasingly premature categories of infants (c-statistic, >0.99). CONCLUSION: Metabolic gestational dating is accurate for the prediction of gestational age and could have value in low resource settings.
BACKGROUND: Identification of preterm births and accurate estimates of gestational age for newborn infants is vital to guide care. Unfortunately, in developing countries, it can be challenging to obtain estimates of gestational age. Routinely collected newborn infant screening metabolic analytes vary by gestational age and may be useful to estimate gestational age. OBJECTIVE: We sought to develop an algorithm that could estimate gestational age at birth that is based on the analytes that are obtained from newborn infant screening. STUDY DESIGN: We conducted a population-based cross-sectional study of all live births in the province of Ontario that included 249,700 infants who were born between April 2007 and March 2009 and who underwent newborn infant screening. We used multivariable linear and logistic regression analyses to build a model to predict gestational age using newborn infant screening metabolite measurements and readily available physical characteristics data (birthweight and sex). RESULTS: The final model of our metabolic gestational dating algorithm had an average deviation between observed and expected gestational age of approximately 1 week, which suggests excellent predictive ability (adjusted R-square of 0.65; root mean square error, 1.06 weeks). Two-thirds of the gestational ages that were predicted by our model were accurate within ±1 week of the actual gestational age. Our logistic regression model was able to discriminate extremely well between term and increasingly premature categories of infants (c-statistic, >0.99). CONCLUSION: Metabolic gestational dating is accurate for the prediction of gestational age and could have value in low resource settings.
Authors: Karl G Sylvester; Zachary J Kastenberg; R Larry Moss; Gregory M Enns; Tina M Cowan; Gary M Shaw; David K Stevenson; Tiffany J Sinclair; Curt Scharfe; Kelli K Ryckman; Laura L Jelliffe-Pawlowski Journal: J Pediatr Date: 2016-11-08 Impact factor: 4.406
Authors: Steven Hawken; Malia S Q Murphy; Robin Ducharme; A Brianne Bota; Lindsay A Wilson; Wei Cheng; Ma-Am Joy Tumulak; Maria Melanie Liberty Alcausin; Ma Elouisa Reyes; Wenjuan Qiu; Beth K Potter; Julian Little; Mark Walker; Lin Zhang; Carmencita Padilla; Pranesh Chakraborty; Kumanan Wilson Journal: Gates Open Res Date: 2021-06-21
Authors: Kelli K Ryckman; Abhismitha Ramesh; Hyunkeun Cho; Scott P Oltman; Elizabeth E Rogers; John M Dagle; Laura L Jelliffe-Pawlowski Journal: Clin Biochem Date: 2021-10-22 Impact factor: 3.281
Authors: Brianne Bota; Victoria Ward; Monica Lamoureux; Emeril Santander; Robin Ducharme; Steven Hawken; Beth K Potter; Raphael Atito; Bryan Nyamanda; Stephen Munga; Nancy Otieno; Sowmitra Chakraborty; Samir Saha; Jeffrey Sa Stringer; Humphrey Mwape; Joan T Price; Hilda Angela Mujuru; Gwendoline Chimhini; Thulani Magwali; Pranesh Chakraborty; Gary L Darmstadt; Kumanan Wilson Journal: J Glob Health Date: 2022-07-16 Impact factor: 7.664
Authors: Steven Hawken; Robin Ducharme; Malia S Q Murphy; Katherine M Atkinson; Beth K Potter; Pranesh Chakraborty; Kumanan Wilson Journal: BMJ Open Date: 2017-09-03 Impact factor: 2.692
Authors: Deshayne B Fell; Steven Hawken; Coralie A Wong; Lindsay A Wilson; Malia S Q Murphy; Pranesh Chakraborty; Thierry Lacaze-Masmonteil; Beth K Potter; Kumanan Wilson Journal: Sci Rep Date: 2017-12-21 Impact factor: 4.379
Authors: Malia S Q Murphy; Steven Hawken; Wei Cheng; Lindsay A Wilson; Monica Lamoureux; Matthew Henderson; Beth Potter; Julian Little; Pranesh Chakraborty; Kumanan Wilson Journal: Gates Open Res Date: 2018-05-30
Authors: Kumanan Wilson; Steven Hawken; Malia S Q Murphy; Katherine M Atkinson; Beth K Potter; Ann Sprague; Mark Walker; Pranesh Chakraborty; Julian Little Journal: EBioMedicine Date: 2016-12-01 Impact factor: 8.143
Authors: Malia S Q Murphy; Steven Hawken; Katherine M Atkinson; Jennifer Milburn; Jesmin Pervin; Courtney Gravett; Jeffrey S A Stringer; Anisur Rahman; Eve Lackritz; Pranesh Chakraborty; Kumanan Wilson Journal: BMJ Glob Health Date: 2017-07-27