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Accurate prediction of gestational age using newborn screening analyte data
Kumanan Wilson1, Steven Hawken2, Beth K Potter3
1Clinical 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.
Insights
Newborn screening metabolites can accurately estimate infant gestational age, offering a valuable tool for preterm birth identification, especially in low-resource settings.
Area of Science:
- Biochemistry
- Neonatal Medicine
- Public Health
Background:
- Accurate gestational age estimation is crucial for neonatal care.
- Estimating gestational age is challenging in developing countries.
- Newborn screening metabolites vary by gestational age and can potentially estimate it.
Purpose of the Study:
- To develop an algorithm for estimating gestational age at birth.
- The algorithm is based on analytes from newborn infant screening.
Main Methods:
- Population-based cross-sectional study of 249,700 infants.
- Used multivariable regression analyses to predict gestational age.
- Incorporated newborn screening metabolite measurements and physical characteristics (birthweight, sex).
Main Results:
- The metabolic gestational dating algorithm showed excellent predictive ability (R-square 0.65, RMSE 1.06 weeks).
- Average deviation between observed and expected gestational age was approximately 1 week.
- The model accurately predicted gestational age within ±1 week for two-thirds of infants and discriminated well between term and premature infants (c-statistic >0.99).
Conclusions:
- Metabolic gestational dating provides accurate gestational age prediction.
- This method holds significant potential value in low-resource settings for guiding neonatal care.
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.
