1. Epidemiology and Global Health

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
https://doi.org/10.7554/eLife.42627
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Cite this article
  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
(2019)
External validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh
eLife 8:e42627.
https://doi.org/10.7554/eLife.42627
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4 figures, 3 tables and 2 additional files

Figures

Figure 1
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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
Figure 2
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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
Figure 3
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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
Figure 4
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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

Tables

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.

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.

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)

Additional files

Source data 1

Numerical data files for Figures 1 and 2.

https://cdn.elifesciences.org/articles/42627/elife-42627-data1-v1.zip
Transparent reporting form
https://doi.org/10.7554/eLife.42627.010

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  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
(2019)
External validation of postnatal gestational age estimation using newborn metabolic profiles in Matlab, Bangladesh
eLife 8:e42627.
https://doi.org/10.7554/eLife.42627