Using AMANHI-ACT cohorts for external validation of Iowa new-born metabolic profiles based models for postnatal gestational age estimation

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.

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

“…The metabolic screening data from 330 samples used for this analysis was a subset of 1318 individual samples set generated as part of the AMANHI collaboration with Department of Epidemiology, College of Public Health, University of Iowa, for evaluating external validity of the GA estimation methods, developed based on Iowa samples [ 17 , 18 ]. Heel prick blood spots were obtained on a protein saver card (Whatman R 903, GE Healthcare, USA), within 24-72 hours of birth from newborns as per standard procedures.…”

“…A normal probability distribution ( dnorm ) was used so that the means and standard deviation for the vector points remained consistent. To evaluate if the 330 selected samples and their simulated data were in fact an unbiased representation of the parent data set used in the validation exercise [ 18 , 19 ], we used a published machine learning model to predict the GA in the simulated data set, and compared the results with those, obtained from 1283 participants in published validation study [ 19 ]. Stacked percentage and distribution plots were made to compare the predictability.…”

“…Algorithms developed in North American settings, deriving gestational age (GA) estimates through the biochemical analysis of newborn dried blood spots (DBS), have been shown to provide accurate estimates to within 2 weeks of best obstetric estimate [ 3 ]. One of these models, based on conventional multivariable linear and logistic regression methods, have been internally validated in US population [ 17 ], and externally validated in Alliance for Maternal and Newborn Health Improvement (AMANHI) LMICs cohort, demonstrating satisfactory performance [ 18 ]. We have further demonstrated improvement in validity with an error of around 1 week by using machine learning models [ 19 ].…”

“…Therefore, in a pilot study we investigated the difference in the recovery of the analytes in paired samples shipped at ambient temperature and on dry ice, from LMICs sites to IOWA (USA) for tandem mass spectrometric (TMS) analysis. Realizing the loss of recovery, our published external validations consequently used only DBS shipped on dry ice [ 18 ].…”

“…We also investigated models using machine learning with a restricted set of analytes, resistant to temperature effects. In this non-interventional, international validation study; we report changes in recovery of analytes and the accuracy of gestational age estimation comparing two shipment methods, and results from machine learning models using analytes resistant to temperature change, compared to those obtained by conventional methods using all analytes [ 18 ].…”