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Using AMANHI-ACT cohorts for external validation of Iowa new-born metabolic profiles based models for postnatal
Sunil Sazawal1,2, Kelli K Ryckman3, Harshita Mittal1
1Center for Public Health Kinetics, Global Division, New Delhi, India.
Insights
Accurate gestational age estimation for newborns is crucial for monitoring preterm and small for gestational age births. A novel metabolic screening method using dried blood spots shows promise for reliable gestational age assessment in low-resource settings.
Area of Science:
- Neonatal screening
- Biomarker discovery
- Public health surveillance
Background:
- High global burden of preterm and small for gestational age (SGA) births necessitates accurate gestational age (GA) estimation.
- Current methods like ultrasound and last menstrual period are often inaccurate or infeasible, especially in developing countries.
- Existing GA estimation algorithms developed in Western populations require validation in diverse settings.
Purpose of the Study:
- To evaluate the precision of GA estimation models developed in the USA for South Asian and Sub-Saharan African newborn cohorts.
- To assess the feasibility of using routine newborn screening metabolic data for accurate GA estimation.
- To determine if metabolic algorithms can effectively identify preterm births.
Main Methods:
- Dried heel prick blood spots from 1311 newborns were analyzed for metabolic profiles.
- Regression algorithms computed GA estimates from metabolic data.
- Estimates were compared against first-trimester ultrasound-validated GA (gold standard).
Main Results:
- The combined metabolite and birthweight algorithm estimated GA with an average deviation of 1.5 weeks.
- 70.5% and 90.1% of newborns had GA estimates within 1 and 2 weeks of the gold standard, respectively.
- The model demonstrated good discriminatory ability for identifying preterm births, with an Area Under the ROC Curve of 0.86.
Conclusions:
- Metabolic gestational age dating provides a novel and accurate method for population-level GA estimation in low- and middle-income countries (LMICs).
- This approach can significantly aid preterm birth surveillance initiatives.
- Future research should explore machine learning and broader analytic methods, including region-specific analytes and cord blood profiles, for enhanced accuracy.
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.
