1. Epidemiology and Global Health
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External validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh

  1. Malia SQ Murphy
  2. Steven Hawken
  3. Wei Cheng
  4. Lindsay A Wilson
  5. Monica Lamoureux
  6. Matthew Henderson
  7. Jesmin Pervin
  8. Azad Chowdhury
  9. Courtney Gravett
  10. Eve Lackritz
  11. Beth K Potter
  12. Mark Walker
  13. Julian Little
  14. Anisur Rahman
  15. Pranesh Chakraborty
  16. Kumanan Wilson  Is a corresponding author
  1. Ottawa Hospital Research Institute, Canada
  2. University of Ottawa, Canada
  3. Children’s Hospital of Eastern Ontario, Canada
  4. International Centre for Diarrhoeal Disease Research, Bangladesh
  5. Dhaka Shishu (Children) Hospital, Bangladesh
  6. Global Alliance to Prevent Prematurity and Stillbirth, United Stares
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Cite this article as: eLife 2019;8:e42627 doi: 10.7554/eLife.42627

Abstract

This study sought to evaluate the performance of metabolic gestational age estimation models developed in Ontario, Canada in infants born in Bangladesh. Cord and heel prick blood spots were collected in Bangladesh and analyzed at a newborn screening facility in Ottawa, Canada. Algorithm-derived estimates of gestational age and preterm birth were compared to ultrasound-validated estimates. 1036 cord blood and 487 heel prick samples were collected from 1069 unique newborns. The majority of samples (93.2% of heel prick and 89.9% of cord blood) were collected from term infants. When applied to heel prick data, algorithms correctly estimated gestational age to within an average deviation of 1 week overall (root mean square error = 1.07 weeks). Metabolic gestational age estimation provides accurate population-level estimates of gestational age in this data set. Models were effective on data obtained from both heel prick and cord blood, the latter being a more feasible option in low-resource settings.

https://doi.org/10.7554/eLife.42627.001

eLife digest

Complications from preterm birth are the leading cause of death among children under five. Ultrasounds are routinely used in wealthy countries to track babies' development. In countries with limited resources, however, ultrasounds are rare, making it harder to estimate how many children are born prematurely. Blood tests may offer a way to determine whether a newborn was born too early when ultrasounds are not available. Many countries already require clinicians to collect a drop of blood from newborns via a heel-prick or from their umbilical cord. Testing these blood spots identifies babies at risk of rare conditions so they can receive prompt treatment.

Chemicals in the blood vary depending on how long the newborn spent growing in its mother’s womb. Scientists have developed a mathematical formula that can estimate a baby’s gestational age based on these chemicals. Using blood spots to estimate gestational age worked well when this strategy was tested in Canada, a high-income country. More tests are needed to determine if it works in low-income countries.

Now, Murphy et al. show their blood spot-testing strategy also reliably predicts the gestational age of babies in Matlab, Bangladesh. In the experiments, blood spots were collected from 1,069 newborns. This included 1,036 cord blood samples and 487 heel prick samples. Nearly all the samples came from full-term infants. A mathematical model estimated the infants' gestational age to within an average of one week of their true age when applied to heel-prick blood samples and to within two weeks of the baby’s true gestational age 94% of the time.

The model also provided reliable estimates of babies’ gestational ages when cord blood samples were tested, which is useful as the Bangladeshi parents were more comfortable with this method of blood collection. Using this strategy to estimate how many babies are born too early in low-income countries may help the countries develop strategies to reduce preterm births. The estimates might also help identify preterm babies who need special care.

https://doi.org/10.7554/eLife.42627.002

Introduction

Complications related to preterm birth are the leading cause of death among children under 5 years of age (March of Dimes, 2012). Estimating the burden of preterm birth in low-resource settings is challenging due to the absence of ultrasound technology and the unreliability of recall of last menstrual period. Commonly used estimates obtained late in gestation or postnatally (e.g. fundal height, and Ballard or Dubowitz scores) are subject to high inter-user variability and poor reliability in small for gestational age and preterm infants (Taylor et al., 2010; Spinnato et al., 1984; Robillard et al., 1992). In addition, data on preterm birth are not routinely documented in some countries and may not be classified according to international standards (Quinn et al., 2016), thus impeding the development of strategies for resource allocation to support global and local health initiatives. Strengthened data surveillance systems to more accurately assess and track changes in preterm birth across jurisdictions are urgently required (March of Dimes, 2012).

Algorithms based on newborn metabolic profiles in combination with clinical covariates such as sex and birthweight have demonstrated the potential to accurately categorize infants across preterm birth categories in high-resource settings (Jelliffe-Pawlowski et al., 2016; Ryckman et al., 2016; Wilson et al., 2016). Data from newborn screening programs in North America have been used to create models capable of estimating gestational age to within 1–2 weeks, but their performance among other infant populations is uncertain. Recent work has focused on refining these models and tailoring them for use across a range of environments and sub-populations, and has suggested that while the models perform well among infants from a variety of backgrounds, ethnicity-specific models may improve the models’ performance (Wilson et al., 2017; Hawken et al., 2017). More recent model iterations have been strenthened by the addition of variables such as newborn hemoglobin peak percentages (calculated from the ratio of fetal to adult hemoglobin levels), which have demonstrated strong associations with gestational age (Wilson et al., 2017). While these algorithms have the potential to provide reliable population estimates of preterm birth burden where prenatal ultrasound data are not available, the models’ generalizability to all infant populations, as well as the feasibility of collecting samples for analysis in low-resource settings, is uncertain. In this paper, we explore the performance of gestational age estimation models in an infant population born in Matlab, Bangladesh. We also comment on the effect of timing of sample collection on newborn metabolic profiles and the feasibility of newborn blood sample collection and analysis in this setting.

Patient characteristics

One cord blood sample was excluded because 100% of analyte values were missing. Imputation was conducted for the remaining samples missing analyte values (n = 28 heel samples and 21 cord samples; no individual sample had more than 5/47 (11%) analyte values missing). The final cohort consisted of 1523 samples from 1069 unique individual newborns. 1036 samples were collected immediately after birth (range: 0 min - 2 hr 1 min) from the umbilical cord, and 487 heel prick samples were collected an average of 14 hr 58 min after birth (range: 25 min - 40 hr 30 min). The majority of samples received (93.2% of heel prick samples; 89.9% of cord blood samples) were from term infants (gestational age ≥37 weeks). 18.1% of heel prick samples and 15.9% of cord blood samples were derived from infants with a birthweight <2500 g. Of the 1069 infants included in the study, 454 contributed both heel and cord blood samples. A summary of participant demographics is provided in Table 1.

Table 1
Characteristics of infants and samples obtained from them.
https://doi.org/10.7554/eLife.42627.003
Heel samples
(n = 487)
Cord samples
(n = 1036)
Paired heel and cord samples
(n = 454 pairs)
Completeness of analyte data, n (%)
No missing analytes459 (94.3%)1015 (98.0%)427 (94.1%)
≥1 analyte missing, missing values imputed28 (5.7%)21 (2.0%)27 (5.9%)
Sex, n (%)
Male246 (50.5%)538 (51.9%)234 (51.5%)
Female241 (49.5%)498 (48.1%)220 (48.5%)
Gestational Age (wks), overall mean (SD)39.1 ± 1.539.0 ± 1.739.2 ± 1.4
Gestational Age Category (wksdays), n (%)
≥37 weeks454 (93.2%)931 (89.9%)425 (93.6%)
320-366 weeks32 (6.6%)102 (9.8%)29 (6.4%)
<320 weeks1 (0.2%)3 (0.3%)0 (0.0%)
Birth Weight (g), mean (SD)
Overall2837.8 ± 433.72862.1 ± 445.92846.8 ± 414.0
Term infants only2879.5 ± 392.92916.5 ± 401.72879.2 ± 389.9
Preterm infants only2264.2 ± 554.82380.3 ± 524.52372.1 ± 470.4
Birth Weight Category, n (%)
≥4000 g3 (0.6%)15 (1.5%)3 (0.7%)
2500 g to < 4000 g396 (81.3%)856 (82.6%)374 (82.4%)
1500 g to < 2500 g84 (17.3%)158 (15.2%)75 (16.5%)
1000 g to < 1500 g4 (0.8%)4 (0.4%)2 (0.4%)
<1000 g0 (0.0%)3 (0.3%)0 (0.0%)
Multiple Birth, n (%)7 (1.4%)19 (1.8%)8 (1.8%)
Newborn age at sample collection (hrs), mean (SD)
Overall14.97 ± 6.540.06 ± 0.2515.06 ± 6.38 (heel)
0.06 ± 0.25 (cord)
Term infants only14.74 ± 6.420.06 ± 0.2514.86 ± 6.22 (heel)
0.06 ± 0.25 (cord)
Preterm infants only18.00 ± 7.500.09 ± 0.2817.97 ± 7.93 (heel)
0.07 ± 0.26 (cord)
  1. Data are presented as mean±standard deviation unless otherwise specified. One cord blood sample was excluded in the data preparation step because 100% of analyte data was missing). All other samples with missing analyte data had no more than 5/47 (11%) missing analyte predictors.

Performance of gestational age estimation models using heel prick data

We determined the performance of previously published metabolic gestational dating algorithms in heel prick-derived data from the Bangladeshi infant cohort. Results of linear regression analyses for heel prick metabolic profiles demonstrated optimal performance among term infants between 38 and 39 completed gestational weeks (Figure 1). Residual plots for each of the three models in both heel and cord samples are provided in Figure 2. In general, all models predicted gestational ages close to full term with the highest accuracy, while tending to overestimate gestational age in preterm infants and underestimate gestational age in post-term infants, in the Bangladesh cohort.

Agreement between algorithmic estimates of gestational age compared to ultrasound-validated gestational age.

(A) Comparison of overall RMSE for heel prick sample and cord blood samples across gestational age models. Performance of gestational age models by infant birthweight for (B) heel prick samples and (C) cord blood samples. Sample sizes are denoted in the graphs. RMSE, root mean square error (average absolute deviation of observed vs. predicted gestational age in weeks). Reported results are the average over 10 imputations.

https://doi.org/10.7554/eLife.42627.004
Residual plots of predicted – observed by observed gestational age.

Heel prick samples: (A) Model 1: Baseline Model, (B) Model 2: Analyte Model, and (C) Model 3: Full Model. Cord blood samples: (D) Model 1: Baseline Model, (E) Model 2: Analyte Model, and (F) Model 3: Full Model.

https://doi.org/10.7554/eLife.42627.005

A baseline model including only clinical covariates (infant sex, birthweight and multiple birth status, Model 1) provided the least accurate estimation of gestational age relative to ultrasound-validated gestational age estimates, RMSE 1.46 weeks. By comparison, a model including analyte covariates (Model 2) had an RMSE of 1.35 weeks. A full model containing all clinical and analyte data (Model 3) demonstrated the lowest RMSE (best performance) of 1.07 weeks and correctly estimated gestational age to within 1 week for 63.9%, and within 2 weeks for 94.3% of all heel prick samples. Among small for gestational age infants, the full heel prick model had an RMSE of 1.12 weeks when growth restriction was defined as birthweight below the 10th percentile for gestational age and an RMSE of 1.30 weeks when defined as birthweight below the 3rd percentile for gestational age. By these definitions, our model accurately estimated gestational age to within 1 week for 62.8% and 53.4% of growth-restricted infants, respectively.

Performance of gestational age estimation models using cord blood data

As with heel prick data, algorithmic estimates of gestational age most accurate among term infants (Table 2). When applied to cord blood-derived data, the baseline model (Model 1) and model including analytes (Model 2) performed comparably (RMSE of 1.51 weeks and 1.45 weeks, respectively). As with heel prick data, the full model (Model 3) provided the best estimates of gestational age (RMSE of 1.23). Here, gestational age was correctly estimated to within 2 weeks for 90.4% of infants overall (90.7% and 85% for growth-restricted infants with birthweight below the 10th and 3rd percentiles, respectively; 84.2% for infants < 2500 g). A comparison of the two sample types indicated that metabolic dating models using data derived from heel prick samples provided more accurate gestational age estimates than models using cord blood samples.

Table 2
Proportion of samples with gestational age correctly estimated within 1 week, 2 weeks of ultrasound-validated gestational age.
https://doi.org/10.7554/eLife.42627.006
Heel prick samplesCord blood samples
Overall,
n(%)
SGA10,
n(%)
SGA3,
n(%)
<2500 g,
n(%)
Overall,
n(%)
SGA10,
n(%)
SGA3,
n(%)
<2500 g,
n(%)
Model 1:
Baseline Model
RMSE
n(%) within 1 week
n(%) within 2 weeks
1.46
267 (54.8)
408 (83.8)
1.76
103 (44.6)
177 (76.6)
2.32
17 (14.4)
64 (54.2)
2.22
25 (28.4)
54 (61.4)
1.51
549 (53.0)
861 (83.1)
1.82
180 (42.5)
318 (75.0)
2.38
31 (14.4)
111 (51.6)
2.21
61 (37.0)
112 (67.9)
Model 2:
Analyte Model
RMSE
n(%) within 1 week
n(%) within 2 weeks
1.35
279 (57.3)
431 (88.5)
1.40
123 (53.4)
204 (88.1)
1.38
64 (54.6)
104 (88.1)
1.47
38 (43.2)
74 (84.1)
1.45
544 (52.5)
874 (84.4)
1.43
221 (52.0)
362 (85.4)
1.48
113 (52.5)
181 (84.1)
1.94
62 (37.6)
116 (70.3)
Model 3:
Full Model
RMSE
n(%) within 1 week
n(%) within 2 weeks
1.07
311 (63.9)
459 (94.3)
1.12
145 (62.8)
218 (94.3)
1.30
63 (53.4)
108 (91.4)
1.21
52 (59.1)
83 (94.3)
1.23
615 (59.4)
937 (90.4)
1.20
267 (63.1)
385 (90.7)
1.40
116 (54.1)
183 (85.0)
1.44
88 (53.3)
139 (84.2)
  1. Data are presented as the percentage of the number correctly classified within the total of each birthweight category. Counts were based on the average from 10 imputations rounded to the closest integer.

Dichotomous discrimination of gestational age

We evaluated the discrimination of gestational age across a dichotomous preterm birth threshold (≥37 weeks vs <37 weeks gestational age) (Figure 3). Gestational age estimation models performed best when applied to metabolic profiles derived from heel prick samples. For both types of samples, the best performance was achieved by the full model containing all clinical and analyte data (Model 3) (area under the curve [AUC] 0.945 (95% CI 0.890, 0.999) for heel prick profiles and AUC 0.894 (95% CI 0.853, 0.935) for cord blood profiles).

Performance of models to correctly classify infants according to dichotomous preterm birth threshold (37 weeks gestational age).

Receiver operator curves for: (A) Model 1: Heel prick AUC 0.840 (95% CI 0.754, 0.925), Cord blood AUC 0.806 (95% CI 0.755, 0.858); (B) Model 2: Heel prick AUC 0.895 (95% CI 0.823, 0.968), Cord Blood AUC 0.823 (95% CI 0.773, 0.873). (C) Model 4, Heel prick AUC 0.945 (95% CI 0.890, 0.999), Cord Blood AUC 0.894 (95% CI 0.853, 0.935). Receiver operator curves for models applied to a cross-section of Ontario-derived heel prick samples (Wilson et al., 2017) are provided for comparison.

https://doi.org/10.7554/eLife.42627.007
Table 3

Areas under the ROC curve (AUC) for Bangladesh heel prick and cord blood models, and Ontario reference models.

https://doi.org/10.7554/eLife.42627.008
AUC (lower, upper 95% confidence limits),
A) Model 1:
Sex, Multiple Birth Status, Birthweight Model
B) Model 2:
Analytes, Sex, Multiple Birth Status Model
C) Model 3:
Full Model
0.840 (0.754, 0.925)0.895 (0.823, 0.968)0.945 (0.890, 0.999)
Bangladesh Cord0.806 (0.755, 0.858)0.823 (0.773, 0.873)0.894 (0.853, 0.935)
Ontario Reference
(Wilson et al., 2017)
0.915 (0.909, 0.921)0.946 (0.941, 0.952)0.967 (0.963, 0.971)

Discussion

In this paper, we demonstrate that algorithms developed using newborn screening data from Ontario, Canada are effective in deriving estimates of gestational age in infants born in Matlab, Bangladesh that are accurate to within approximately 1 to 2 weeks of ultrasound-validated gestational age. Data derived from newborn heel prick samples consistently yielded more accurate estimates of gestational age than cord blood-derived data, likely reflecting the fact that our models were originally developed from data obtained from this sample type. Indeed, we have shown that the correlation between cord blood and heel prick-derived data varies significantly across analyte subtypes (Appendix 1).

Accurate assessment of gestational age, preterm birth and small for gestational age is a recognized priority area where there is a need to improve program tracking and accountability (March of Dimes, 2012; WHO, 2014). Although birthweight data are collected in most settings, it is an unreliable surrogate for gestational age that is prone to overestimation of preterm birth rates in low- and middle-income settings where a high proportion of infants are born small for gestational age. Commonly-used gestational age assessments applied after birth are hampered by their reliance on complex scoring systems. A recent systematic review and meta-analysis of 18 newborn assessments based on a variety of neuromuscular, physical and other criteria determined that the most popular scoring systems (the Ballard and Dubowitz scores) systematically overestimated gestational age with wide margins of error (Lee et al., 2016). Whereas gold standard first trimester ultrasound scans are accurate to within one week, the accuracy of measurements based on newborn examination varies from 2 to 4 weeks. Furthermore, newborn clinical assessments of gestational age such as Dubowitz and Ballard scoring, and neonatal anthropometrics have been demonstrated to be inaccurate surrogate markers of gestational age, specifically in rural communities of Bangladesh (Lee et al., 2016).

Metabolic gestational dating approaches emerged in response to the urgent need to improve the epidemiology and surveillance of preterm birth. Circulating newborn metabolites are known to be affected by gestational age and gestational age is routinely considered in the interpretation of newborn screening analysis (Slaughter et al., 2010; Oladipo et al., 2011; Newborn Screening Ontario, 2017). To date, three groups in North America have developed metabolic dating algorithms based on newborn health administrative datasets (Jelliffe-Pawlowski et al., 2016; Ryckman et al., 2016; Wilson et al., 2016). Research has since sought to refine existing models through the addition of analytes known to correlate with gestational age and develop tiered models of varying complexity. Our own group has demonstrated that proportions of fetal and adult hemoglobins are some of the strongest individual predictors of gestational age, (Wilson et al., 2017) and we have also validated our algorithms across ethnic subgroups in Ontario (Hawken et al., 2017). Efforts are currently underway to begin implementing metabolic gestational age dating in low-resource settings to determine the burden of preterm birth and intrauterine growth restriction. The results from our study offer a reason to be optimistic about these efforts. While the intent of metabolic gestational age dating at present is to provide population-based estimates of the burden of preterm birth, it is conceivable that this approach could also be used to guide care for individual newborns who are identified as preterm.

Our study had a number of important strengths and limitations. Strengths of our approach include the use of internationally-derived samples to externally validate our models and using samples from a well-described cohort of infants with gestational age confirmed by first trimester ultrasound. The study design of the PreSSMat cohort in which our study was nested ensured that enrollment was open to a representative selection of women and newborns delivering in the Matlab icddr,b service area. Other strengths include the high quality of samples received for analysis, and the use of paired cord blood and heel prick samples to compare model performance metrics. The primary limitation of this study is the participation bias against very preterm and extremely preterm infants, whose parents expressed reluctance to subjecting their newborn to these collection procedures. As a result, we had a relatively small number of samples collected from very preterm and extremely preterm infants, limiting our ability to comment on model performance in these sub-groups. In this Bangladesh cohort, the gestational ages estimated from our models were most accurate in infants who were confirmed to be close to full-term by first trimester ultrasound. Algorithm-derived gestational ages tended to be overestimated in preterm infants and underestimated in post-term infants. This suggests that calibration in the large (i.e. introducing a calibration slope adjustment (Steyerberg, 2010) to model predictions could improve overall model performance in this external cohort, although this was not conducted in the current study.

Our findings are encouraging for several reasons. First, this work provides early evidence that gestational dating models developed using metabolic data derived from a North American cohort perform well in low-resource populations. The model originally published by our group was developed using data from a Canadian-born cohort of 250,000 infants. In Ontario, the model was able to estimate gestational age to within one week (RMSE 1.06 vs 1.07 for the Bangladeshi cohort) overall and correctly ascertain gestational age to within 2 weeks for 94.9% of infants (vs. 94.3% for the Bangladeshi cohort) (Wilson et al., 2016); estimates that compare favorably against other currently-used postnatal gestational age estimation methods that produce estimates varying in accuracy from 2 to 4 weeks gestational age (Taylor et al., 2010; Spinnato et al., 1984; Robillard et al., 1992; Lee et al., 2016; Alexander et al., 1992). Second, our metabolic models provided significantly improved estimates of gestational age among infants with birthweights < 2500 g, cases where current estimates based on symphysis fundal height and neuromuscular assessments perform poorly (Spinnato et al., 1984; Goto, 2013). Lastly, we are encouraged by the potential utility of cord blood profiles for deriving gestational age estimates. Differences in cord blood and heel prick profiles described in our analysis likely stem from a number of factors related to timing of collection, including early postnatal fluctuations in neonatal TSH levels, (Ryckman et al., 2012; Büyükgebiz, 2013) and infant feeding status prior to collection. Although the performance of the models when applied to cord-blood-derived data was somewhat attenuated relative to heel prick data, development of cord-blood-specific models restricted to analytes less susceptible to fluctuations in the postnatal environment may further improve gestational age estimation.

Ultimately, acceptable levels of error in gestational age measurements will need to be determined by public health and maternal child health officials. Given the acknowledged limitations of existing alternatives to ultrasound estimation, metabolic gestational dating approaches appear to offer reliable estimates that are unencumbered by user variability. As we prepare for the scale-up and implementation of metabolic gestational dating approaches for robust population-level estimates of preterm birth, our findings highlight a number of opportunities and challenges. First, heel prick samples taken for newborn screening are typically collected at least 24 hr after birth to accommodate postpartum fluctuations in analyte levels. In many settings around the world, mother-infant pairs are discharged from healthcare settings within the first 24 hr after delivery (Campbell et al., 2016). As a result, the accuracy of existing metabolic dating algorithms would be compromised by the change in timing of sample collection. Second, newborn screening is not a standard service of practice in low- and middle-income countries, including Bangladesh. It was therefore unsurprising that anecdotal feedback from field nurses assisting with this study indicated that parents were hesitant to consent to heel prick procedures for their infants. Although on-site research staff received extensive training through videos, visual guides and in-person training, a preference for collection of cord blood samples over heel prick amongst research staff may also have affected the number and quality of samples collected. A quality assurance trial was required to improve sample collection and handling techniques. While our current models were originally optimized for application to heel prick data, we highlight an opportunity to optimize these algorithms for use on cord blood data. Transitioning to cord blood-based models would additionally bypass the need to impose discomfort on the child, stress on parents and staff, and also avoid the requirement for extensive training and screening of sample collection techniques. Finally, population-level metabolic screening provides the additional opportunity to provide insight into the prevalence of congenital conditions in participating jurisdictions.

In summary, metabolic gestational age dating approaches offer a novel means for providing accurate population-level gestational age estimates. As we work toward implementing preterm birth surveillance initiatives in a variety of low-income settings (Mundel, 2017), the level of acceptable accuracy of metabolic algorithms should be considered. Application of models to cord blood metabolic profiles is the most feasible option at present, although derivation and optimization of such models are warranted. Utility of other maternal, pregnancy and infant factors that were not available to us in the current analysis for improving existing metabolic dating models may also be of benefit. Where population-level surveillance of preterm birth might be supported through the analysis of a few drops of blood taken shortly after birth, future work should aim to derive models that determine other priority birth outcomes.

Materials and methods

Objectives

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Our objective was to validate the performance of previously published gestational age estimation models developed in Ontario, Canada (Wilson et al., 2016; Wilson et al., 2017) in a cohort of infants born in Bangladesh. Specifically, we sought to compare estimates of gestational age derived from our algorithms, through the analysis of newborn blood spots, against estimates of gestational age determined by first-trimester ultrasound. A version of the protocol for this study has been published (Murphy et al., 2017). Due to logistical challenges in initiating the study, fewer samples were collected than initially anticipated in our protocol and low numbers of infants with gestational age below 34 weeks. Our methods of sample collection and analysis remained the same.

Newborn screening

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Newborn screening is a public health initiative that screens for rare, treatable conditions that typically produce no symptoms in the neonatal period. Programs vary in scope by jurisdiction, screening for one to over 50 conditions (Therrell et al., 2015). In Ontario, as in many regions, drops of blood are taken by infant heel prick, typically within the first few days after birth, and dried onto filter paper. Dried blood spot samples are then analyzed by a series of assays including tandem mass spectrometry, colorimetric and immunoassays as well as high-performance liquid chromatography for metabolic, genetic and other analyte markers.

Study design

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Sample collection was conducted in the Matlab sub-district of Chandpur, Bangladesh where the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) has been running a Health and Demographic Surveillance System (HDSS) in Matlab since 1966. Based on service provision, the HDSS area is divided into two jurisdictions: 1) the icddr,b service area where women of reproductive age and their children under 5 years of age receive care though icddr,b facilities; and 2) the government service area where individuals receive care from government facilities as in other areas of the country. The present study was conducted in the icddr,b service area, and nested within a cohort study entitled ‘Preterm and Stillbirth Study, Matlab’ (PreSSMat) that was designed to capture data on the biological determinants of adverse pregnancy outcomes, including preterm births. In the PreSSMat cohort, pregnant women were followed prospectively along the pregnancy continuum, with scheduled visits at 11–14 (enrollment and ultrasound), 22–24, and 32 weeks’ gestation, at delivery, and at 6 weeks post-partum to collect socio-demographic and clinical data as well as biological specimens. Preterm births were defined as all births that occurred at <37 weeks’ gestation. ‘Very preterm births’ were those that occurred at <32 weeks, and ‘extremely preterm births’ were those that occurred at <28 weeks. Small for gestational age (SGA10) was defined as cases where birthweight was below the 10th percentile within categories of week of gestational age at delivery and infant sex. The percentiles were calculated and applied based on a North American distribution of birthweight within sex and gestational age categories. We also calculated SGA3, which identifies infants below the 3rd percentile within gestational age and sex categories and is much more likely to reflect infants who suffered intrauterine growth restriction, especially in low and middle-income countries such as Bangladesh where birthweights are lower. Pregnant women were identified by community health workers through monthly home visits. All enrolled women underwent a gestational dating ultrasound at enrollment; otherwise no explicit inclusion or exclusion criteria were applied. All women enrolled in the PreSSMat cohort were eligible for participation in the current study.

Sample collection and analysis

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To examine the effect of timing of sample collection on newborn metabolic profiles, cord blood was collected immediately after birth and spotted on Whatman 903 filter paper. A second dried blood spot sample was also collected via heel prick within 72 hr of delivery or immediately prior to discharge, whichever happened first. The latter reflects the timing of collection for samples used to develop our previously published gestational age estimation models (recommended timing of sample collection for healthy newborns in Ontario, Canada is 24–48 hr after birth). Samples were collected onto filter paper, air-dried and shipped weekly to Newborn Screening Ontario (NSO), the provincial newborn screening facility in Ottawa, Canada. Samples were stored in a temperature and humidity-controlled environment prior to shipment. Eight 3.2 mm diameter samples were punched from each sample for testing of the following analytes: hemoglobin profiles; 17α hydroxyprogesterone (17-OHP); thyroid stimulating hormone (TSH); immunoreactive trypsinogen (IRT); a panel of 12 amino acids and 31 acylcarnitines; t-cell receptor excision circles (TREC); biotinidase activity; and galactose-1-phosphate uridylyltransferase activity. Hemoglobin profiles were determined by high-performance liquid chromatography on a Bio Rad Variant nbs system; neonatal 17-OHP, TSH and IRT were measured using PerkinElmer AutoDELFIA Immunoassays; amino acid and acylcarnitine analysis was performed by electrospray ionization tandem mass spectrometry (Waters TQD); total TREC copy number was measured by quantitative polymerase chain reaction using a ThermoFisher Scientific Viia 7; biotinidase and galactose-1-phosphate uridyltransferase levels were measured using the Astoria-Pacific SPOTCHECK Pro system. Clinical covariates were retrieved from the PreSSMat database to facilitate clinical interpretation of newborn screening data, and also for inclusion as model parameters in this study. Figure 4 summarizes the study design.

Overview of study design.

The current study was nested within the PreSSMat cohort operating in Matlab, Bangladesh. Samples were collected from infants born into the cohort and sent to Ottawa, Canada for analysis at a provincial newborn screening facility.

https://doi.org/10.7554/eLife.42627.009

Newborn screening blood spots are subject to degradation if collected or handled inappropriately. Samples with insufficient good-quality dried blood to complete the full panel of assays were excluded from analysis. Samples with missing analyte values had the missing levels imputed (see Appendix 1 for details). In the process of applying newborn screening procedures for the analysis of samples, results of ‘screen negative’ and ‘screen positive’ were generated for conditions screened for by the NSO program. Management of incidental clinical findings (screen-positive cases) has been reported elsewhere (Murphy et al., 2017).

Statistical analyses

Validation of algorithms

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We sought to compare estimates of gestational age and preterm birth based on our analysis of blood spots against first trimester ultrasound estimates, which are considered the gold standard for gestational age measurement (Committee on Obstetric Practice, the American Institute of Ultrasound in Medicine, and the Society for Maternal-Fetal Medicine, 2017). The performance of the following models was assessed:

  • Model 1: Baseline model containing only the clinical factors of infant sex, birthweight, and multiple birth (yes/no)

  • Model 2: Analytes model including infant sex, multiple birth (yes/no) and newborn screening analytes and pairwise interactions including acylcarnitines, amino acids, endocrine and enzyme markers.

  • Model 3: Full model containing both clinical and analyte data (infant sex, multiple birth (yes/no), birthweight, newborn screening analytes and pairwise interactions)

Statistical modeling approaches are described in Appendix 1. In brief, sample data were scored using multivariable models previously developed using heel prick blood spot samples in a large cohort of infants born in Ontario, Canada (Wilson et al., 2017). The fitted models used for scoring the Bangladesh data included numerous main effects and interaction terms including both analytes and clinical measures (sex, multiple birth, birthweight). However, there was a subset of predictors that were clearly the strongest contributors to the model in terms of independent contribution to explained variance: birthweight (in base and full models) and fetal/adult hemoglobin ratio, TSH, 17OHP, ALA, c5, C4DC and TYR (in the sex +multiple birth +analytes and full models). All analyses were conducted using SAS 9.4 (SAS Institute, 2017) and R 3.3.2 (R core team, 2017).

Informed consent and ethical approval

Request a detailed protocol

Mothers provided informed consent for their infants to be included in the PreSSMat birth cohort and to have clinical data, cord blood and newborn heel prick samples collected and analysed. The present study was approved by the Research Review and Ethical Review Committees of the International Centre for Diarrhoeal Disease Research, Bangladesh (PR-16039) on July 10, 2016. Approvals were also obtained from the Research Ethics Boards of the Ottawa Health Science Network (20160219–01H) on June 10, 2016, and the Children’s Hospital of Eastern Ontario (16/20E) on June 8, 2016.

Appendix 1

Statistical modelling

Preparation of validation data

For samples with missing analyte values, values were imputed by multiple imputation via predictive mean matching (van Buuren, 2013; Harrell, 2001). This approach borrows information from other study participants with similar patterns for non-missing analytes, using a regression approach to impute missing values using observed values from the other participants. Gestational age (the outcome) and birthweight (the strongest correlate with outcome) were not included as predictors in the predictive mean matching process, to avoid all real or perceived risk of inducing improved predictive performance in the external validation data (Moons et al., 2006). Ten imputations were done to evaluate the variability across imputations, and the range of calculated model validation performance across the ten imputations was calculated.

In preparation for modeling, newborn screening analytes were winsorized using an adapted ‘Tukey Fence’ approach (Tukey, 1977). In brief, the interquartile range for each analyte was calculated, and analyte levels in excess of four interquartile ranges above the third quartile (the upper Tukey fence) or four interquartile ranges below the first quartile (the lower Tukey fence), were assigned either the Tukey fence value, or the smallest/largest observed value in the dataset, whichever was the least extreme. By this approach we preserved the ‘extremeness’ of extreme outliers but reduced the influence of values so extreme that they might disproportionately influence model building and parameter estimation.

Finally, analyte levels and newborn birthweights were standardized to have mean of 0 and standard deviation of 1 by subtracting the mean for each analyte and dividing by the standard deviation in each cohort. This had the effect of normalizing analyte and birthweight results for local factors, while preserving relative covariation of analytes and birthweights across the spectrum of observed gestational age.

Scoring of external validation data and evaluation of model performance

Bangladesh validation data were prepared similarly to Ontario training data and gestational age estimation models previously fit in the Ontario training data were used to score the validation data. Model parameters estimated in the Ontario training data were fixed, and the model regression equation used to calculate an estimated gestational age in the Bangladesh validation data. Model performance was externally validated by comparing the estimated gestational age to the actual ultrasound-validated gestational age of each participating infant. For evaluation of continuous gestational age estimation, root mean square error (measured in weeks) was calculated, as was the percentage of infants with gestational ages correctly estimated within 7, 10 and 14 days of ultrasound-validated gestational age. Performance characteristics for estimating gestational age across dichotomous gestational age thresholds (≥37 vs <37 weeks gestational age) were evaluated using area under the receiver operator curve from a binary logistic regression model.

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Decision letter

  1. Diego Bassani
    Reviewing Editor; SickKids Research Institute, Canada
  2. Eduardo Franco
    Senior Editor; McGill University, Canada
  3. Jessica Duby
    Reviewer; SickKids Hospital Toronto, Canada
  4. Eric Ohuma
    Reviewer; SickKids Hospital Toronto, Canada

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Prabhat Jha as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Jessica Duby (Reviewer #1); Eric Ohuma (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

In this manuscript by Murphy et al., the authors study the accuracy of a metabolic based model to determine gestational age for neonates in Bangladesh when compared to a first trimester ultrasound. The authors make a strong case for the potential impact of a successful algorithm in determining population rates of preterm birth and small-for-gestational age in low-resource settings. This is a potentially important study but the description of the study design and execution is far from complete.

In addition, whilst there is no doubt an accurate estimate of GA is important, there are fundamental problems with this approach that is based on postnatal estimation of GA. First, it helps to perpetuate a standard of care that even WHO has abandoned, namely managing pregnancies without knowing the due date. This is the reason the new WHO guidelines recommend at least one ultrasound scan, in part to estimate gestational age prenatally.

In addition, the authors do not properly address the model's ability to distinguish small-for-gestational age (SGA) neonates, which should be one of the model's primary advantages over using birth weight alone as a surrogate marker for gestational age.

Essential revisions:

1) There are deviations from the cited, published protocol that remain unexplained by the authors. (Murphy et al., 2017)

Major differences between published protocol and current manuscript:

a) Sample size: The protocol's sample size requirements are calculated to be 3,500 participants. The current manuscript details 1523 samples from 1069 newborns with no discussion regarding the achieved sample size. As part of addressing sample size differences, it may benefit the authors to present a figure detailing number of eligible participants, number approached for consent, number consented, etc.

b) Models: The proposed models to test in the protocol are quite different than the models tested in the current manuscript and no justification is provided. For reference, the models listed in the protocol are: "1) birth weight alone; 2) combination of birth weight and fetal/adult haemoglobin levels; 3) combination of birth weight, haemoglobin levels, thyroid-stimulating hormone and 17-OHP (all non-mass-spectrometry-derived analytes); and 4) birth weight and the full panel of newborn screening analytes. Sex and multiple birth (yes, no) will be included in all models." (Murphy et al., 2017) For models used in the reported study, refer to the subsection “Validation of algorithms”.

2) SGA/large-for-gestational age (LGA) babies: The authors need to provide further analysis and discussion regarding the algorithm's performance in SGA/LGA identification. Specifically, the authors need to identify how many SGA/LGA neonates were in their sample and the models' accuracy for these important sub-groups. The authors had listed this analysis in their protocol and have previously performed similar sub-group analysis on the original Ontario cohort which can be used as a guide. (Wilson et al., 2016).

3) While the authors do sub-group analyses by birth weight (<2500g and ≥2500g) in Table 2, weight alone is unimportant when we consider the overarching goal of finding a model that can distinguish preterm birth from SGA and post-term birth from LGA. I would recommend replacing the columns under each of the sample types in Table 2 to be:

1) Overall;

2) SGA;

3) Appropriate-for-gestational age (AGA); and

4) LGA.

4) Imputation of missing values: As only 46 (11%) of observations were imputed, did the authors do a sensitivity analysis of the performance of the model with and without the imputed missing values to evaluate their influence on the results?

5) Prediction score (nomogram) – it would be useful for the authors to also show the relative contribution of each factor in their model. For example, in the baseline model with birthweight, infant sex and multiple births, what is the greatest predictor of GA? A quantification of the relative contribution of each of the factors is useful in understanding their contribution and justification as important variables the prediction of GA. Similarly, this should be done for models 2 and 3 as it allows one to make judgements on how much more improvements in prediction is provided for by including the clinical and analyte data. Also, a comparison of scores helps to discern whether some factors included in the model are correlated and thus are similar in their prediction of GA.

6) Would also be helpful for the author to show a plot of true GA vs. predicted GA (calibration) as this will evidently show the variability of the prediction as a function of GA as opposed to the aggregated estimates they have presented by GA in Figure 1.

7) The cohort was apparently nested within a PreSSMat study in Matlab but the authors do not provide key information on timing of ultrasound examination, birth examinations and weight and criteria used to categorize infants as either preterm, small for gestational age (SGA) or combinations thereof. Please add the necessary level of detail to the methodology and Results sections.

8) Given the issues with cord blood specificity and the wide range of times in collection of heel prick samples (from a few hours to up to 40 hours), one is left guessing as to the fidelity of design of the gold standard used in this study and early measures. Please explain when were these measurements obtained?

9) Please explain what standards were used for ultrasound based gestational age and add an appropriate justification for adoption of the chosen standards.

10) The overall rates of prematurity in the cohort determined from the study (both cord and heel prick samples) are implausibly low (given what is known from ANISA and AMANHI studies done in Bangladesh). Please explain the reasons for these differences.

11) The authors should comment on what is known about prematurity incidence from prior studies of maternal antenatal care and nutrition supplementation done in Bangladesh on large rural cohorts including Matlab, without that information, it is difficult to place this study in the context of what else is known.

https://doi.org/10.7554/eLife.42627.013

Author response

Summary:

In this manuscript by Murphy et al., the authors study the accuracy of a metabolic based model to determine gestational age for neonates in Bangladesh when compared to a first trimester ultrasound. The authors make a strong case for the potential impact of a successful algorithm in determining population rates of preterm birth and small-for-gestational age in low-resource settings. This is a potentially important study but the description of the study design and execution is far from complete.

In addition, whilst there is no doubt an accurate estimate of GA is important, there are fundamental problems with this approach that is based on postnatal estimation of GA. First, it helps to perpetuate a standard of care that even WHO has abandoned, namely managing pregnancies without knowing the due date. This is the reason the new WHO guidelines recommend at least one ultrasound scan, in part to estimate gestational age prenatally.

Thank you for addressing this point. We agree that managing pregnancies without knowledge of gestational age is not recommended. We therefore do not seek to implement our postnatal estimation models as a clinical tool replace prenatal ultrasound. Rather, we propose that our models could have utility in deriving population-level estimates of preterm birth in geographies or locales where prenatal ultrasound data are frequently available. We have noted in the Introduction that these models are intended for use when ultrasound is not available.

“…these algorithms have the potential to provide reliable population estimates of preterm birth burden when prenatal ultrasound is not available”.

In addition, the authors do not properly address the model's ability to distinguish small-for-gestational age (SGA) neonates which should be one of the model's primary advantages over using birth weight alone as a surrogate marker for gestational age.

Thank you. One of the limitations of our best performing model is its reliance on birthweight as a covariate in estimating gestational age. Including birthweight in the models limits our ability to identify SGA or IUGR infants as the impact of birthweight on gestational age is confounded in these infants.

When birthweight is removed from the model, its performance deteriorates substantially among infants overall, but improves slightly in SGA infants. We have included model performance results for infants with birthweight below the 10th and 3rd percentiles for gestational age and sex as defined by a North American population, however a large proportion of these infants are not truly growth restricted in the context of the local Bangladesh population distribution.

Future work will seek to refine our model that excludes birthweight as a covariate in order to improve its utility in these populations. We discuss the utility of our current models with and without birthweight in SGA cases in greater detail below in response to the other SGA-related reviewer questions.

Essential revisions:

1) There are deviations from the cited, published protocol that remain unexplained by the authors. (Murphy et al., 2017)

Major differences between published protocol and current manuscript:

a) Sample size: The protocol's sample size requirements are calculated to be 3,500 participants. The current manuscript details 1523 samples from 1069 newborns with no discussion regarding the achieved sample size. As part of addressing sample size differences, it may benefit the authors to present a figure detailing number of eligible participants, number approached for consent, number consented, etc.

Thank you for giving us the opportunity to address the differences between our published protocol and our completed study. As the reviewer has noted, we ultimately collected and analysed fewer samples than we initially anticipated. This was due to administrative delays at the outset of the project, particularly while the study was reviewed by three research ethics boards in two countries. By the time the study was initiated, there was less time in which to recruit eligible participants before the end of the study. The sample size calculations presented in our protocol were presented across a range of scenarios, and sample sizes of 1500 and 3500 were projections for Zambia and Bangladesh respectively. The projected sample size of 3500 in Bangladesh was based on Power to detect differences in AUCs for models classifying <34 weeks vs. ≥34 weeks GA. We ended up recruiting fewer total subjects and unfortunately almost no infants <34 weeks, so we have only presented results classifying <37 weeks vs. ≥37 weeks. We still have good statistical power to report c-statistics with good precision. For continuous models, sample size estimates were also driven by the desire to have a minimum number of samples to validate using RMSE estimation (e.g., accurate within ± 1 week) in the smallest subgroups of interest. Thus, we cannot report on validation in <34 weeks. For example, where we only had 3 subjects, but we do have sufficient sample in 34-36 week preterm infants as well as term and post-term infants to report our findings among this group. The following statement has been added to the manuscript:

“Due to logistical challenges in initiating the study, fewer samples were collected than initially anticipated in our protocol, leading to low numbers of infants with gestational age below 34 weeks. Our methods of sample collection and analysis remain the same, recognizing these sampling limitations.”

b) Models: The proposed models to test in the protocol are quite different than the models tested in the current manuscript and no justification is provided. For reference, the models listed in the protocol are: "1) birth weight alone; 2) combination of birth weight and fetal/adult haemoglobin levels; 3) combination of birth weight, haemoglobin levels, thyroid-stimulating hormone and 17-OHP (all non-mass-spectrometry-derived analytes); and 4) birth weight and the full panel of newborn screening analytes. Sex and multiple birth (yes, no) will be included in all models." (Murphy et al., 2017) For models used in the reported study, refer to the subsection “Validation of algorithms”.

Models 1, 2 and 3 were conducted as described in the published protocol. This was an editorial omission. All three models included infant sex and multiple birth (yes/no). Model 1 additionally included birthweight, model 2 excluded birthweight and included analytes and pairwise interactions. Model 3 included birthweight, analytes and pairwise interactions in addition to sex and multiple birth (yes/no). This has been corrected in the Statistical Analysis section of the Materials and methods as follows:

“The performance of the following models was assessed:

Model 1: Baseline model containing only the clinical factors of infant sex, birthweight, and multiple birth (yes/no);

Model 2: Analytes model including infant sex, multiple birth (yes/no) and newborn screening analytes and pairwise interactions including acylcarnitines, amino acids, endocrine and enzyme markers;

Model 3: Full model containing both clinical and analyte data (infant sex, multiple birth (yes/no), birthweight, newborn screening analytes and pairwise interactions)”.

The intermediate models that limited analytes to fetal/adult hemoglobin ratio, and fetal/adult hemoglobin ratio+17OHP+TSH were not included in the results because, although these subgroups of analytes were more simply and cheaply measurable, the models including only these analytes did not reach the level of predictive accuracy that would make them useful. For the sake of clarity and parsimony we opted not to report the results for those subsets of analytes, which were initially chosen for pragmatic rather than scientific reasons.

2) SGA/large-for-gestational age (LGA) babies: The authors need to provide further analysis and discussion regarding the algorithm's performance in SGA/LGA identification. Specifically, the authors need to identify how many SGA/LGA neonates were in their sample and the models' accuracy for these important sub-groups. The authors had listed this analysis in their protocol and have previously performed similar sub-group analysis on the original Ontario cohort which can be used as a guide. (Wilson et al., 2016).

Thank you. We address this point and point 3 in one response, below.

3) While the authors do sub-group analyses by birth weight (<2500g and ≥2500g) in Table 2, weight alone is unimportant when we consider the overarching goal of finding a model that can distinguish preterm birth from SGA and post-term birth from LGA. I would recommend replacing the columns under each of the sample types in Table 2 to be:

1) Overall;

2) SGA;

3) Appropriate-for-gestational age (AGA); and

4) LGA.

Response to points 2 and 3:

Thank you, we agree that the performance of our models among SGA infants is important. We did not have access to local reference distributions of appropriate weight for gestational age, so we relied on our North American reference distributions, from which we applied cutpoints to define small and large for gestational age (below 3rd percentile, below 10th percentile, and above 90th percentile for given infant sex and GA in weeks. We have added details to the Study Design section of the Materials and methods as follows:

“Small for gestational age was defined as SGA10, for cases where birthweight was below the 10th percentile within categories of week of gestational age at delivery and infant sex. […] We also calculated SGA3, which identifies infants below the 3rd percentile within gestational age and sex categories and is much more likely to reflect infants who suffered intrauterine growth restriction, especially in low and middle income countries such as Bangladesh where birthweights are lower.”

Further, we have added results for SGA10 and SGA3 to Table 2. We have not reported LGA90 since only a very small proportion of infants (3 infants overall) fell into this group.

4) Imputation of missing values: As only 46 (11%) of observations were imputed, did the authors do a sensitivity analysis of the performance of the model with and without the imputed missing values to evaluate their influence on the results?

We did a complete case analysis and investigated the range of results in each of the 10 imputed datasets and determined that the results did not vary substantially in either the complete case analysis, or among the 10 independent imputations. We reported the average performance across the 10 imputed datasets for the validation results.

5) Prediction score (nomogram) – it would be useful for the authors to also show the relative contribution of each factor in their model. For example, in the baseline model with birthweight, infant sex and multiple births, what is the greatest predictor of GA? A quantification of the relative contribution of each of the factors is useful in understanding their contribution and justification as important variables the prediction of GA. Similarly, this should be done for models 2 and 3 as it allows one to make judgements on how much more improvements in prediction is provided for by including the clinical and analyte data. Also, a comparison of scores helps to discern whether some factors included in the model are correlated and thus are similar in their prediction of GA.

Thank you. In the current study, we are using a previously-developed model to score an external cohort for the purposes of external validation and assessment of generalizability. We did not report on the model building and the relative importance of factors in the final model in this study. We did however report on these details in our previously published studies where these models were developed. However, we appreciate the reviewer’s point that this information is important enough to reiterate, so we have added the following to the Statistical Analysis section of the Materials and methods:

“The fitted models used for scoring the Bangladesh data included numerous main effects and interaction terms including both analytes and clinical measures (sex, multiple birth, birthweight). However, there was a subset of predictors that were clearly the strongest contributors to the model in terms of independent contribution to explained variance: birthweight (in base and full models) and fetal/adult hemoglobin ratio, TSH, 17OHP, ALA, c5, C4DC and TYR (in the sex+multiple birtH+analytes and full models).”

6) Would also be helpful for the author to show a plot of true GA vs. predicted GA (calibration) as this will evidently show the variability of the prediction as a function of GA as opposed to the aggregated estimates they have presented by GA in Figure 1.

Thank you. In evaluating our models, we generated residual plots across the range of true gestational ages, but did not include these figures in the manuscript. We agree that these plots are very helpful in understanding the performance of these models above and beyond the aggregate RMSE plots we previously included. We have now included a new figure (Figure 2), which is a panel plot of the three models in both heel prick and cord blood models (6 individual residual plots in all).

7) The cohort was apparently nested within a PreSSMat study in Matlab but the authors do not provide key information on timing of ultrasound examination, birth examinations and weight and criteria used to categorize infants as either preterm, small for gestational age (SGA) or combinations thereof. Please add the necessary level of detail to the methodology and Results sections.

Thank you for this opportunity to provide additional details. Within the PreSSMat study, pregnant women visited the local clinic three times during pregnancy. The first visit to obtain an ultrasound occurred between 11-14 weeks’ gestation. Subsequent visits occurred at 22-24 weeks, 32 weeks, delivery, and six-weeks post-partum. Preterm births were defined as all births that occurred at <37 weeks’ gestation. “Very preterm births” were those that occurred at <32 weeks, and “extremely preterm births” were those that occurred at <28 weeks. As noted, above, we did not have access to local reference distributions of appropriate weight for gestational age, so we relied on our North American reference distributions, from which we applied cutpoints to define small and large for gestational age (below 3rd percentile, below 10th percentile, and above 90th percentile for given infant sex and GA in weeks. This has been added to the Materials and methods section:

“In the PreSSMat cohort, pregnant women were followed prospectively along the pregnancy continuum, with scheduled visits at 11-14 (enrollment and ultrasound), 22-24, and 32 weeks’ gestation, at delivery, and at 6-weeks post-partum to collect socio-demographic and clinical data as well as biological specimens. […] The percentiles were calculated and applied based on a North American distribution of birthweight within sex and gestational age categories.”

8) Given the issues with cord blood specificity and the wide range of times in collection of heel prick samples (from a few hours to up to 40 hours), one is left guessing as to the fidelity of design of the gold standard used in this study and early measures. Please explain when were these measurements obtained?

Thank you. As noted above, the gold-standard against which our GA estimates were compared was first trimester ultrasound (see below our response to the following comment for more information). We have noted as a limitation that heel-prick samples should be collected at least 24 hours after birth, but given the rates of early mother-child discharge from hospital, heel-prick samples were often collected before this time out of necessity. The post-partum analyte fluctuations that occur within the first few days of birth are a limitation of cord and early heel-prick sampling, and we have noted that this may have affected the performance of our model. However, as ultrasound-validated GA estimates were obtained between 11-14 weeks’ gestation, we are confident that the gestational ages against which our model was compared were accurate to within one week.

9) Please explain what standards were used for ultrasound based gestational age and add an appropriate justification for adoption of the chosen standards.

Thank you. Ultrasound-based gestational age was determined via first trimester ultrasound (between 11-14 weeks). The American College of Obstetricians and Gynecologists recommends first-trimester ultrasound (up to 14 weeks) as the most accurate method of establishing and measuring gestational age (ACOG, 2017). In this study, a portable ultrasound machine was used for all participants, and included measurement of crown-rump length, biparietal diameter, occipito-frontal diameter, head and abdominal circumferences, and femur length. We have referenced the ACOG recommendations within our manuscript.

10) The overall rates of prematurity in the cohort determined from the study (both cord and heel prick samples) are implausibly low (given what is known from ANISA and AMANHI studies done in Bangladesh). Please explain the reasons for these differences.

Thank you for this comment. We believe there are two primary reasons for the lower rates of preterm birth seen in this cohort. First, the women in this cohort benefited from being in the service area for icddr,b, the organization implementing the study in Matlab. Compared to individuals outside of this service area, women in the icddr,b service area benefit from a high level of prenatal and other health care. Additionally, participation in this study allowed pregnancies to be monitored and antenatal care to be provided in ways that may not have occurred had these women not participated in the study. Second, many parents of very preterm and extremely preterm infants were reluctant to have their infants subjected to the sample collection procedures given the challenges already experienced by these infants, and thus did not consent to participate in the study. This was highlighted previously in our Discussion section, but we have made this limitation more explicit. As our objective was not to determine rates of preterm birth in this area however, we have not expanded further as to why our study contains relatively few preterm infants.

“The primary limitation of this study is the participation bias against very preterm and extremely preterm infants, whose parents expressed reluctance to subjecting their newborn to these collection procedures. As a result, we had a relatively small number of samples collected from very preterm and extremely preterm infants, limiting our ability to validate model performance in these sub-groups.”

11) The authors should comment on what is known about prematurity incidence from prior studies of maternal antenatal care and nutrition supplementation done in Bangladesh on large rural cohorts including Matlab, without that information, it is difficult to place this study in the context of what else is known.

Thank you. We believe that this falls outside the scope of our study, as our aim was not to estimate the incidence of preterm birth in Matlab, but rather to comment on our model’s ability to estimate GA in this context. The next step in the implementation of our model will be to use the model to estimate the incidence of preterm birth, but that was not our goal at this time.

https://doi.org/10.7554/eLife.42627.014

Article and author information

Author details

  1. Malia SQ Murphy

    Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
    Contribution
    Writing—original draft, Project administration, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4566-4957
  2. Steven Hawken

    1. Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
    2. Department of Epidemiology and Community Health, University of Ottawa, Ottawa, Canada
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3341-9022
  3. Wei Cheng

    Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
    Contribution
    Data curation, Formal analysis, Visualization, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1475-4079
  4. Lindsay A Wilson

    Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
    Contribution
    Project administration, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9910-3338
  5. Monica Lamoureux

    Newborn Screening Ontario, Children’s Hospital of Eastern Ontario, Ottawa, Canada
    Contribution
    Project administration, Writing—review and editing
    Competing interests
    No competing interests declared
  6. Matthew Henderson

    Newborn Screening Ontario, Children’s Hospital of Eastern Ontario, Ottawa, Canada
    Contribution
    Formal analysis, Writing—review and editing
    Competing interests
    No competing interests declared
  7. Jesmin Pervin

    International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
    Contribution
    Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
  8. Azad Chowdhury

    Dhaka Shishu (Children) Hospital, Dhaka, Bangladesh
    Contribution
    Writing—review and editing
    Competing interests
    No competing interests declared
  9. Courtney Gravett

    Global Alliance to Prevent Prematurity and Stillbirth, Lynnwood, United Stares
    Contribution
    Project administration, Writing—review and editing
    Competing interests
    No competing interests declared
  10. Eve Lackritz

    Global Alliance to Prevent Prematurity and Stillbirth, Lynnwood, United Stares
    Contribution
    Writing—review and editing
    Competing interests
    No competing interests declared
  11. Beth K Potter

    Department of Epidemiology and Community Health, University of Ottawa, Ottawa, Canada
    Contribution
    Writing—review and editing
    Competing interests
    No competing interests declared
  12. Mark Walker

    Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
    Contribution
    Writing—review and editing
    Competing interests
    No competing interests declared
  13. Julian Little

    Department of Epidemiology and Community Health, University of Ottawa, Ottawa, Canada
    Contribution
    Writing—review and editing
    Competing interests
    No competing interests declared
  14. Anisur Rahman

    International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh
    Contribution
    Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
  15. Pranesh Chakraborty

    Newborn Screening Ontario, Children’s Hospital of Eastern Ontario, Ottawa, Canada
    Contribution
    Conceptualization, Investigation, Writing—review and editing
    Competing interests
    No competing interests declared
  16. Kumanan Wilson

    1. Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
    2. Department of Epidemiology and Community Health, University of Ottawa, Ottawa, Canada
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing—review and editing
    For correspondence
    kwilson@ohri.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1741-7705

Funding

Bill and Melinda Gates Foundation (OPP1141535)

  • Kumanan Wilson

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

The authors acknowledge the analytical, lab and other support personnel at Newborn Screening Ontario and the research and clinical staff at icddr,b for their contributions to this project. The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. JL holds a tier 1 Canada Research Chair in Human Genome Epidemiology.

Ethics

Human subjects: Mothers provided informed consent for their infants to be included in the PreSSMat birth cohort and to have clinical data, cord blood and newborn heel prick samples collected and analysed. The study was approved by the Research Review and Ethical Review Committees of the International Centre for Diarrhoeal Disease Research, Bangladesh (PR-16039) on July 10, 2016. Approvals were also obtained from the Research Ethics Boards of the Ottawa Health Science Network (20160219-01H) on June 10, 2016, and the Children's Hospital of Eastern Ontario (16/20E) on June 8, 2016.

Senior Editor

  1. Eduardo Franco, McGill University, Canada

Reviewing Editor

  1. Diego Bassani, SickKids Research Institute, Canada

Reviewers

  1. Jessica Duby, SickKids Hospital Toronto, Canada
  2. Eric Ohuma, SickKids Hospital Toronto, Canada

Publication history

  1. Received: October 5, 2018
  2. Accepted: February 8, 2019
  3. Version of Record published: March 19, 2019 (version 1)

Copyright

© 2019, Murphy et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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