Background Limited ultrasound capacity in low-resource settings makes correct gestational
age (GA) dating difficult. Previous work demonstrated that newborn metabolic profiles
can accurately determine gestational age, but this relationship has not been evaluated
in low-income countries. The objective of this study was to validate and adapt a metabolic
GA dating model developed using newborn blood spots for use in a low-resource setting
in rural Uganda.
Methods A cohort of pregnant women was followed prospectively and heel stick blood
spots were collected from 666 newborns in Busia, Uganda at the time of delivery. They
were dried, frozen, and shipped to the US where they were tested for 47 metabolites.
Metabolic model performance was assessed using early ultrasound determined GA as the
standard. Models tested included previously built multivariable models and models
specifically adapted to the Busia population.
Results The previously built model successfully dated 81.2% of newborns within two
weeks of their ultrasound GA. Only 4.8% of GAs were off by greater than three weeks.
In the model adapted to the local population, 89.2% of GAs matched their corresponding
ultrasound to within two weeks. The model-derived preterm birth rate was 7.2% compared
to 5.9% by ultrasound.
Conclusions These results suggest that metabolic dating is a reliable method to determine
GA in a low-income setting. Metabolic dating offers the potential to better elucidate
preterm birth rates in low-resource settings, which is important for assessing population-level
patterns, tailoring clinical care, and understanding the developmental trajectories
of preterm infants.
In 2015, an estimated 1.055 million children under the age of five died from complications
of preterm birth making it the leading cause of under-5 mortality worldwide ([1]).
For the over 14 million preterm infants born annually [2] who do survive, complications
including infection, neurological damage, sepsis, respiratory distress, necrotizing
enterocolitis, and hearing and vision difficulties are common [3-8]. Moreover, a disproportionate
share of preterm births occur in low-income countries. Specifically, the countries
within Sub-Saharan Africa are estimated to account for 25% of global livebirths but
28.2% of global preterm births [2]. Particularly challenging, is that data from low-income
countries is vulnerable to a high degree of uncertainty due to incomplete or lack
of robust birth surveillance systems, absence of standardized definitions of preterm
birth and viability, and lack of ability to accurately ascertain gestational age [2,9].
The inability to accurately ascertain gestational ages is a barrier to not only describing
the epidemiology of preterm birth, but also to designing, implementing, and monitoring
interventions that aim to improve health outcomes in this vulnerable population. Without
reliable gestational age information, health systems can fail to efficiently utilize
limited resources, and clinicians may miss opportunities for effective therapeutic
interventions targeting issues specific to preterm infants. The current standard of
care to determine gestational age is ultrasound dating early on in pregnancy, but
ultrasound technology isn’t widely available in low-income countries. Alternative
methods include using last menstrual period (LMP), clinical assessments like the new
Ballard score [10] and Dubowitz score [11], and birthweight alone but none of these
methods have been determined to yield an accurate gestational age – particularly when
there is co-morbid growth restriction [9,12-15]. LMP has been shown to lack accuracy
due to irregular menstrual cycles and unreliable recall, which is compounded in low-income
countries by late presentation to antenatal care [9,13,15,16]. Clinical assessments
like those included in the Ballard and Dubowitz scores are often imprecise due to
skill and training discrepancies between evaluators [9,12,14,15], and difficulty when
gauging the gestational age of growth-restricted newborns [9,12].
Recently, the World Health Organization (WHO) prioritized the development and validation
of more reliable methods of gestational age dating in order to more accurately estimate
preterm birth [17]. One such method pioneered by our group and others is to use metabolic
markers from newborn heel-stick blood spots collected during routine newborn screening
to determine gestational age at birth [18-20]. Our group and others have found that
metabolites measured in newborn heel-stick samples can date ≥90% of newborns within
two weeks of dating by early ultrasound – including in babies with intrauterine growth
restriction (IUGR) [18,19].
The goal of this study was to validate and adapt a metabolic gestational age dating
model [19] developed using newborn heel stick blood spots for use in a low-resource
setting in rural Uganda. Additionally, we sought to compare the performance of the
heel-stick model to a gestational age dating by metabolic profile using a cord blood
sample collected within three hours of the heel stick sample. The successful development
of novel methods to determine gestational age at birth could be crucial to more accurate
assessments of preterm birth rates in low-income settings and has the potential to
inform clinical care of infants born prematurely in these settings.
METHODS
This prospective cohort study was nested within a double-blind, randomized clinical
trial comparing the efficacy and safety of sulfadoxine-pyrimethamine (SP) vs dihydroartemisinin-piperaquine
(DP) as intermittent preventative treatment of malaria during pregnancy [21]. The
study took place between September 2016 and December 2017 in the Busia District of
southeastern Uganda, which is characterized by intense malaria transmission. Individuals
eligible for the study included women who were at least 16 years of age with a viable
pregnancy between 12 and 20 weeks gestation determined by ultrasound, and who were
HIV-uninfected. Written informed consent covering mothers and prospective infants
was required from each participant along with agreements to: avoid taking medications
that were outside of the study protocol, willingness to deliver at the hospital in
Busia, and come to the clinic for any illness during pregnancy including a febrile
event. Women were excluded if they had a history of antimalarial therapy during the
current pregnancy, had known adverse responses to SP or DP, were in early or active
labor, or had a currently active or chronic medical condition requiring inpatient
evaluation. At enrollment, women underwent an initial standardized routine medical
examination, including pre-natal ultrasound for gestational age dating, and were given
a long-lasting insecticidal bed net. Participants were then randomized to receive
either monthly SP or DP during pregnancy. Routine visits were conducted every four
weeks at the clinic in Busia and any additional medical care was also received at
the clinic, which was open every day. Additional information concerning study randomization
and drug administration has been published previously [21].
The majority of women delivered their babies at the hospital adjacent to the clinic.
Women who delivered at home were seen by study staff at the time of delivery or as
soon as possible after delivery. At the time of delivery, a standardized assessment
including information on congenital conditions, specimens collected, birthweight,
infant sex, mode of delivery, and complications (pre-eclampsia, eclampsia, placental
abruption, uterine rupture, fetal injury, cephalopelvic disproportion, and maternal
hemorrhage) was completed. Preterm birth was defined as being born at less than 37
completed weeks of gestation.
Specimens collected for this study included umbilical cord blood and blood from newborn
infant heel sticks. Cord blood was collected using a syringe inserted into the umbilical
vein. From the syringe, 4-5 blood spots were collected. Newborn heel stick blood spots
were obtained using standardized methodology from routine newborn screening [22].
Cord blood was collected at the time of delivery and blood from heel sticks within
3 hours of delivery in the majority of cases. In the occurrence of a home delivery,
heel-stick blood spots were collected as soon as possible but cord blood was unable
to be collected. Both specimen types were collected onto Whatman 903 protein saver
filter paper cards. After allowing blood spots to dry, the filter paper cards were
individually sealed in bags with desiccant pouches and stored in a liquid nitrogen-charged
cryotank until shipped. Specimens were shipped approximately every two weeks from
Kampala, Uganda to the University of California San Francisco using a liquid nitrogen-charged
cryoshipper. Upon arrival in California, samples were removed, placed on dry ice,
and sent to the State Hygienic Laboratory (SHL) in Ankeny, Iowa. Throughout the process,
the cold-chain was maintained at -20°C or lower in order to prevent the degradation
of acylcarnitines and amino acids. Metabolic markers measured by SHL included 2 enzymes,
1 hormone, 12 amino acids, and 32 acylcarnitines and were measured using either tandem
mass spectrometry, time-resolved fluoroimmunoassay, or semiquantitative enzymatic
assay. In depth methodology of these techniques as performed by SHL has been described
previously [23,24].
Methods and protocols for the study were approved by the ethics committees of Makerere
University School of Biomedical Sciences (Kampala, Uganda), the Uganda National Council
for Science and Technology (Kampala, Uganda), and the Committee of Human Research
at the University of California San Francisco.
Statistical analyses
A natural log transformation was performed on all metabolites to reduce skewness and
minimize the influence of outliers. Given that our sample size was too small to create
a reliable small for gestational age measurement, two independent alternative methods
were used to determine the 10th percentile birthweight cutoffs by gestational age
and sex. The first method utilized the Intergrowth-21st international standardized
growth curves [25,26]. The second method used a WHO calculator [27] that creates standardized
growth curves based on the mean birthweight of infants born at 40 weeks gestation
in the study population (3178.4g). Continuous variables described using mean and standard
deviation (SD) and categorical variables using frequencies and proportions. Univariable
analyses were performed on metabolites and clinical characteristics comparing term
vs preterm infants using t tests and χ2 or Fischer exact tests (if n ≤5 within a category)
for continuous and categorical variables, respectively.
Models using metabolic data to predict ultrasound dated prematurity were evaluated
in several steps. First, the Ryckman model developed previously to predict gestational
ages of an infant cohort born Iowa was evaluated (validated) for its ability to predict
ultrasound-based gestational age in this cohort (Table S1 in the
Online Supplementary Document
) [19]. Second, a model was built specifically from the Busia data using cross-validated
stepwise multivariable logistic regression. Metabolites with approximately normal
distributions (skew<|1.0|), birthweight, and sex were permitted to enter the model
and a P < 0.05 was required to remain in the model. Overall model performance was
evaluated using area under receiver operating characteristic curve (AUC) and odds
ratios (OR) with 95% confidence intervals (CI) for individual variables. The variables
that formed the final model were then input into a linear regression to obtain specific
predicted gestational ages for each infant. Performance of the linear model was examined
using adjusted R2. Correspondence of the model-determined gestational ages to the
ultrasound-determined gestational ages was also examined specifically in infants born
SGA. The ability of both models to correctly classify preterm and term birth was assessed
using sensitivity, specificity, positive predictive value (PPV), negative predictive
value (NPV), and accuracy. This same model building and evaluating process and the
same comparative analyses were performed using the results from the cord blood specimens
with overall results compared to those from the heel-stick samples.
All analyses were performed using SAS 9.4 (SAS institute, Cary, NC, USA).
RESULTS
Heel-stick blood spots were collected from 666 of the 687 (96.9%) live born infants
in the trial, of those, 39 (5.9%) had been born preterm per ultrasound dating. SGA
rates in this population were 17% and 13.5% by intergrowth and WHO respectively. Infants
born preterm were more likely than their term counterparts to have lower birthweights,
and infants born term were more likely to be singletons than those born preterm. We
found no statistically significant difference in sex, SGA, or age at specimen collection
between infants born term and preterm (
Table 1
). There were 640 infants with blood spots from cord blood, and 36 (5.6%) were born
preterm as determined by ultrasound. Differences between term and preterm infants
within this subset were similar to those above (Table S2 in the
Online Supplementary Document
).
Table 1
Univariable analyses of clinical characteristics in infants born term and preterm
with heel-stick blood spots collected
Heel
Total
Term
Preterm
P-value
n = 666
n = 627
n = 39
Gestational age* (weeks), mean (SD)
39.1 (1.7)
39.4 (1.2)
34.4 (2.0)
<0.001
Gestational age category* (completed weeks), n (%):
<0.001
≥37
627 (94.1)
627 (100)
0 (0.0)
32-36
36 (5.4)
0 (0.0)
36 (92.3)
<32
3 (0.5)
0 (0.0)
3 (7.7)
Birthweight (grams), mean (SD)
3036.8 (455.6)
3086.7 (401.7)
2235.1 (523.5)
<0.001
Birthweight category (grams), n (%):
<0.001
≥4000
12 (1.8)
12 (1.9)
0 (0)
3500-3999
96 (14.4)
96 (15.3)
0 (0)
3000-3499
263 (34.5)
261 (41.6)
2 (5.1)
2500-2999
237 (35.6)
225 (35.9)
12 (30.8)
2000-2499
42 (6.3)
30 (4.8)
12 (30.8)
1500-1999
13 (2.0)
3 (0.5)
10 (25.6)
1000-1499
3 (0.5)
0 (0.0)
3 (7.69)
<1000
0 (0.0)
0 (0.0)
0 (0.0)
Age at collection (hours), mean (SD)
1.78 (4.22)
1.7 (4.3)
2.4 (3.5)
0.366
Sex n (%):
0.705
Male
327 (49.1)
309 (49.3)
18 (46.2)
Female
339 (50.9)
318 (50.7)
21 (53.9)
Multiple gestation
25 (3.8)
17 (2.7)
8 (20.5)
<0.001
SGA n (%):
Intergrowth
113 (17.0)
108 (17.2)
5 (12.8)
0.477
Busia specific
90 (13.5)
81 (12.9)
9 (23.1)
0.072
Treatment arm
0.034†
DP
332 (49.9)
319 (50.9)
13 (33.3)
SP
334 (50.1)
308 (49.1)
26 (66.7)
SD – standard deviation, SGA – small for gestational age, DP – dihydroartemisinin-piperaquine,
SP – sulfadoxine-pyrimethamine
*As measured by ultrasound between 12-20 weeks.
†Each infant counted even if part of a multiple gestation (insignificant when calculated
by maternal delivery). Continuous variables described using mean and standard deviation
and categorical variables using frequencies and proportions. t tests and χ2 tests
for continuous and categorical variables respectively were used to compare cases and
control.
Metabolic analyses included 47 routinely measured newborn screening metabolites. Of
these metabolites, 13 were excluded for skewness>|1.0|, leaving 34 metabolites for
univariable analysis. There were 18 metabolites (13 acylcarnitines, 4 amino acids,
and TSH) that differed significantly between term and preterm infants. Those most
strongly associated (P value <0.001) with preterm birth in univariable analysis were
C4, C4-DC, C4-OH, C5, C8, phenylalanine, tyrosine, and TSH (
Table 2
). Among cord blood measurements, 12 metabolites differed significantly between term
and preterm infants (Table S3 in the
Online Supplementary Document
).
Table 2
Univariable analyses of mean levels of metabolic makers in infants born term and preterm
from heel-stick blood spots*
Heel
Term (n = 627)
Preterm (n = 39)
Variable
Mean (SD)
95% CI
Mean (SD)
95% CI
Acylcarnitines:
Free Carnitine
3.02 (0.34) †
2.99 to 3.05
3.13 (0.28) †
3.04 to 3.22
C2
3.08 (0.35)
3.05 to 3.11
3.15 (0.35)
3.03 to 3.26
C3
0.11 (0.39) †
0.08 to 0.14
0.25 (0.43) †
0.11 to 0.39
C4
-1.6 (0.35) ‡
-1.62 to -1.57
-1.31 (0.37) ‡
-1.44 to -1.19
C4-DC
-1.79 (0.39) ‡
-1.82 to -1.76
-2.08 (0.31) ‡
-2.18 to -1.98
C4-OH
-2.82 (0.38) ‡
-2.85 to -2.79
-2.56 (0.49) ‡
-2.72 to -2.4
C5
-2.1 (0.38) ‡
-2.13 to -2.07
-1.72 (0.44) ‡
-1.87 to -1.58
C5-OH
-2.34 (0.34)
-2.37 to -2.31
-2.3 (0.35)
-2.41 to -2.19
C6
-3.15 (0.35)
-3.18 to -3.12
-3.05 (0.39)
-3.17 to -2.92
C8
-3.5 (0.45) ‡
-3.54 to -3.46
-3.23 (0.52) ‡
-3.4 to -3.06
C10
-3.26 (0.51) †
-3.3 to -3.22
-3.05 (0.63) †
-3.25 to -2.85
C12
-2.18 (0.69)
-2.23 to -2.13
-2.21 (0.62)
-2.41 to -2.01
C12:1
-3.3 (0.58) †
-3.35 to -3.26
-3.02 (0.82) †
-3.29 to -2.76
C14
-1.7 (0.42) †
-1.73 to -1.66
-1.55 (0.45) †
-1.7 to -1.4
C14:1
-2.59 (0.6) †
-2.63 to -2.54
-2.37 (0.68) †
-2.59 to -2.15
C16
0.97 (0.35)
0.95 to 1
0.97 (0.33)
0.87 to 1.08
C16:1
-2.06 (0.38) †
-2.09 to -2.03
-1.86 (0.46) †
-2.01 to -1.71
C16:1-OH
-2.95 (0.34) †
-2.97 to -2.92
-3.11 (0.33) †
-3.22 to -3.01
C18
0.03 (0.36)
0 to 0.06
-0.03 (0.3)
-0.13 to 0.07
C18:1
-0.09 (0.34)
-0.11 to -0.06
0.01 (0.35)
-0.1 to 0.12
C18:2
-1.76 (0.38)
-1.79 to -1.73
-1.84 (0.29)
-1.94 to -1.75
Amino acids/intermediates:
Alanine
5.53 (0.28) †
5.51 to 5.56
5.42 (0.32) †
5.32 to 5.53
Arginine
2.12 (0.51)
2.08 to 2.16
2.14 (0.49)
1.98 to 2.3
Citrulline
2.45 (0.23)
2.43 to 2.47
2.47 (0.38)
2.35 to 2.59
Glutamate
5.18 (0.26)
5.16 to 5.2
5.17 (0.24)
5.09 to 5.25
Leucine
4.7 (0.22) †
4.68 to 4.72
4.8 (0.25) †
4.72 to 4.89
Methionine
3.04 (0.21)
3.03 to 3.06
3.01 (0.28)
2.92 to 3.11
Ornithine
3.21 (0.24)
3.19 to 3.23
3.14 (0.31)
3.04 to 3.24
Phenylalanine
4.2 (0.21) ‡
4.18 to 4.21
4.34 (0.35) ‡
4.22 to 4.45
Succinylacetone
-0.54 (0.17)
-0.55 to -0.53
-0.52 (0.2)
-0.59 to -0.46
Tyrosine
3.9 (0.24) ‡
3.88 to 3.92
4.12 (0.47) ‡
3.97 to 4.27
Valine
4.66 (0.2)
4.65 to 4.68
4.67 (0.2)
4.6 to 4.73
Hormones:
17-hydroxyprogesterone
3.19 (0.63)
3.14 to 3.24
3.21 (0.74)
2.97 to 3.44
Thyroid stimulating hormone
3.33 (0.71) ‡
3.28 to 3.39
2.81 (0.57) ‡
2.62 to 3
CI – confidence interval, SD – standard deviation
*All variables are natural log transformed.
†P < 0.05.
‡P < 0.001.
The Ryckman model (Table S1 in the
Online Supplementary Document
) identified 48 (7.2%) infants as being born preterm. Using birthweight alone identified
128 (19.2%) as being born preterm. The Ryckman model gestational ages matched ultrasound
gestational ages to within two weeks in 81.2% of infants, and only 4.8% were off by
more than three weeks (
Table 3
). Additionally, the Ryckman model accurately classified 95% of infants as term or
preterm with a sensitivity of 69.2% and a specificity of 96.7% (
Table 4
). When looking at SGA specifically, the Ryckman model underestimated gestational
age by an average of 2.04 and 2.09 weeks, and gestational ages matched ultrasound
gestational ages within two weeks for 43.4% and 36.6% of infants when using intergrowth
and WHO SGA, respectively (
Table 5
).
Table 3
Weeks of difference between heel-stick model determined gestational ages and ultrasound
determined gestational ages
Ryckman heel
Busia heel
Frequency (%)
Cumulative frequency (%)
Frequency (%)
Cumulative frequency (%)
Perfect match*
22 (3.3)
22 (3.3)
43 (6.5)
43 (6.5)
≤1 week
283 (42.5)
305 (45.8)
376 (56.5)
419 (62.9)
≤2 weeks
236 (35.4)
541 (81.2)
175 (26.3)
594 (89.2)
≤3 weeks
94 (14.1)
635 (95.4)
56 (8.4)
650 (97.6)
≤4 weeks
15 (2.3)
650 (97.6)
12 (1.8)
662 (99.4)
≤5 weeks
11 (1.7)
661 (99.3)
3 (0.5)
665 (99.9)
5+ weeks
5 (0.8)
666 (100.0)
1 (0.2)
666 (100.0)
*Perfect match is ±1/2 d.
Table 4
Classification statistics of the heel-stick models used to determine preterm birth
Heel
Ryckman
Busia
Sensitivity
69.2
61.5
Specificity
96.7
98.7
Positive predictive value
56.3
75.0
Negative predictive value
98.1
97.6
Accuracy
95.0
96.5
Table 5
Weeks of difference between heel-stick model determined gestational ages and ultrasound
determined gestational ages in infants born SGA*
Ryckman heel
Busia heel
Intergrowth SGA
Busia specific SGA
Intergrowth SGA
Busia Specific SGA
No SGA (n = 553)
SGA (n = 113)
No SGA (n = 576)
SGA (n = 90)
No SGA (n = 553)
SGA (n = 113)
No SGA (n = 576)
SGA (n = 90)
Perfect match†
22 (4.0)
0 (0.0)
22 (3.8)
0 (0.0)
35 (6.3)
8 (7.1)
38 (6.6)
5 (5.6)
0-1 week
269 (48.6)
14 (12.4)
272 (47.2)
11 (12.2)
334 (60.4)
42 (37.2)
349 (60.6)
27 (30.0)
1-2 weeks
201 (36.4)
35 (31.0)
214 (37.2)
22 (24.4)
140 (25.3)
35 (31.0)
146 (25.4)
29 (32.2)
2-3 weeks
52 (9.4)
42 (37.2)
58 (10.1)
36 (40.0)
32 (5.8)
24 (21.2)
32 (5.6)
24 (26.7)
3-4 weeks
4 (0.7)
11 (9.7)
4 (0.7)
11 (12.2)
9 (1.6)
3 (2.7)
8 (1.4)
4 (4.4)
4-5 weeks
3 (0.5)
8 (7.1)
4 (0.7)
7 (7.8)
3 (0.5)
0 (0.0)
3 (0.5)
0 (0.0)
5+ weeks
2 (0.4)
3 (2.7)
2 (0.4)
3 (3.3)
0 (0.0)
1 (0.9)
0 (0.0)
1 (1.1)
SGA – small for gestational age
*Values are frequency (column %). Busia specific SGA determined using WHO calculator.
†Perfect Match is ±1/2 d.
The Busia specific multivariable model showed the strongest performance identifying
preterm infants (AUC = 0.953 95% CI = 0.921-0.985) and was comprised of 8 variables
including birthweight, TSH, alanine, tyrosine, C4-DC, C5, C10, and C16:1-OH (
Table 6
). This model identified 30 (4.5%) preterm infants, and when used to classify infants
as preterm or term, this model had a sensitivity of 61.5% and a positive predictive
value of 75.0% as compared to the Ryckman model’s values of 69.2% and 56.3%, respectively.
Other classification metrics were quite similar to the Ryckman model (
Table 4
). The Busia model was able to match ultrasound gestational ages within two weeks
for 89.2% of newborns, and only 2.5% were off by more than three weeks (
Table 3
). It was also more robust to SGA, matching ultrasound within two weeks for 75.3%
and 67.8% of infants but still underestimating gestational age by an average of 1.05
and 1.08 weeks when using intergrowth and WHO SGA, respectively (
Table 5
).
Table 6
Cross validated multivariable logistic heel-stick model built within the Busia cohort
Heel
AUC = 0.953 95% CI = 0.921-0.985
Variable
Parameter estimate
OR (95% CI)
Intercept
8.63
NA
Birthweight (per 100g)
-0.55
0.58 (0.48-0.69)
Thyroid stimulating hormone
-0.72
0.49 (0.25-0.96)
Alanine
-2.53
0.08 (0.01-0.49)
Tyrosine
-0.94
0.39 (0.13-1.13)
C4-DC
-2.13
0.12 (0.02-0.71)
C5
-1.95
0.14 (0.03-0.6)
C10
2.84
17.2 (3.73-79.27)
C16:1-OH
2.96
19.25 (2.51-147.72)
AUC – area under curve, OR – odds ratio, CI – confidence interval
The multivariable model built using cord blood displayed good performance (AUC = 0.935
95% CI = 0.894-0.977) and consisted of 6 variables: birthweight, alanine, C4, C4-DC,
C4-OH, and C16:1-OH (Table S4 in the
Online Supplementary Document
). The cord blood model had lower sensitivity (52.8%) than the heel blood models,
but otherwise, was comparable (Table S5 in the
Online Supplementary Document
). The cord blood model’s gestational ages matched ultrasound gestational ages in
very similar proportions as the heel model for both the entire cohort and the SGA
infants (Table S6 and S7 in the
Online Supplementary Document
).
DISCUSSION
Gathering reliable epidemiologic information concerning preterm birth in low-income
countries is crucial in order to better understand the burden of morbidity and mortality
stemming from being born preterm and in order to allocate resources to alleviate preterm
birth [2,17]. Currently, our best methods to determine gestational age in the absence
of an early antenatal care ultrasound is to depend on LMP, clinical assessments at
birth, or in some cases, birthweight alone all of which can be unreliable [9,12-15].
Our study used newborn screening-based metabolic models to estimate gestational age
at birth, and generated preterm birth rates similar to that based on ultrasound (Ryckman
model = 7.2%, Busia model = 4.5%, Ultrasound = 5.9%). Moreover, the vast majority
of calculated gestational ages were within two weeks of ultrasound determined gestational
age (Ryckman model = 81.2%, Busia model = 89.2%). In comparison to LMP and neonatal
assessments like the Ballard and Dubowitz scores, our models of estimation perform
similarly to those methods used in ideal settings. If, however, there is late entry
to antenatal care for LMP or a lack of time and resources to devote to training and
performing neonatal assessments (both common in low resource settings), then the accuracy
of these alternative methods drastically declines (>±3 weeks) whereas our model should
remain robust [9,12-15]. Overall, our results suggest that heel stick-based metabolic
modeling could be a more scalable and accurate way to measure gestational age compared
to traditional physical exam-based scores in the absence of ultrasound.
The Ryckman model was originally developed within a cohort of infants born in Iowa,
and was one of three models built by research groups working cooperatively in North
America [18-20]. Despite the population in Iowa being remarkably different from our
validation population in Busia, the model still performed reasonably well suggesting
it may be generalizable. Nevertheless, we built a model exclusively within the Busia
population to elucidate any unique metabolic signals between populations. We found
that all seven metabolic variables of importance to the Busia model were also present
in the Ryckman model suggesting that metabolic profiles may only need to be calibrated
to specific populations rather than completely altered.
Of particular importance within low-income countries where rates of infants born SGA
has been estimated to reach 27% [28], is the impact of growth restriction on the ability
to determine gestational age. Given the unique risks for mortality and morbidity associated
with SGA [29], it is important for a measure of gestational age to differentiate between
an infant born preterm, SGA, or both. Clinical assessments including those by Ballard
and Dubowitz tend to be prone to the underestimation of gestational age in infants
born SGA as they are more likely to exhibit less mature physical characteristics and
behavior [9], and using birthweight alone will also underestimate gestational age.
In our study, the Ryckman model underestimated gestational age in infants born SGA
by an average of about 2 weeks, but the Busia model reduced this to an average underestimation
of a little over 1 week. While underestimation remains a problem, our results suggest
that tuning models to the local population may help in the discrimination of infants
born SGA.
The models from heel-stick blood spots and cord blood were compared to determine if
both methods could be used interchangeably. Having multiple methodologies that are
reliable would prove useful in situations where heel-sticks aren’t ideal – cord blood
is readily available – or where cultural customs make one collection type preferential.
We found, however, that models built from heel-stick blood spots are better by most
metrics including correspondence to ultrasound gestational ages, classification measures,
and performance within SGA infants. This boosted performance is likely the result
of the heel-stick models relying solely on samples taken directly from the infant,
which may be more representative of the infant’s physiology as compared to cord specimens.
The estimated preterm birth rate in Sub-Saharan Africa is approximately 12.0% (CI
8.6%-16.7%) though it varies greatly between countries and studies based on a number
of factors including access to health care, gestational age measurement tool, and
differing definitions of viability and preterm birth [2]. In our study, both the ultrasound
determined preterm birth rate and model determined rates were much lower than would
be expected for the region. One possible explanation for this discrepancy is that
the women in our trial were healthier and had better access to prenatal care than
general population. Women enrolled in the clinical trial received extra antenatal
care visits, antibiotics and other medications, prenatal supplements, and malaria
chemoprevention that a majority of women in low-income regions have limited access
to. It is also possible that the difference reflects some overestimation in the global
estimates, since they often include birthweight as surrogate for prematurity.
Strengths and limitations
This study has important strengths and limitations to consider. A major strength of
the study is the ability to compare our model determined gestational ages to the gold
standard early pregnancy ultrasounds, which are relatively rare in low-income countries.
Additionally, our study was facilitated by a research infrastructure that could acquire
blood spots and maintain dry cold-chain throughout storage and shipping making the
mass spectrometry analyses more reliable as we know metabolite concentrations can
be affected by a number of environmental factors [23]. Likewise, this necessary infrastructure
is a significant obstacle to scale up this particular method of gestational age determination.
Another potential shortcoming of our study is that despite participant selection being
population based, the women in the study received differential treatment from the
rest of the population possibly limiting the generalizability of our results. Finally,
the Busia specific models relied on a small sample, which restricted the model to
using fewer variables and constrained our ability to independently validate the model
in a subset of the population.
While metabolic gestational age dating may provide an immediate impact by improving
preterm birth epidemiology, there are current limitations to the utility of using
metabolic profiles to inform treatment. Currently, with sufficient infrastructure,
the turnaround time for newborn screening is within 1-2 days. To our knowledge, there
is not currently an available facility equipped to handle high throughput mass spectrometry
of newborn samples within Uganda or East Africa, which necessitated shipment of samples
out of country resulting in significant delays. Future efforts should focus on capacity
development in-region not only to expedite gestational age dating, but to facilitate
potentially life-saving diagnoses that accompany newborn screening [24].
CONCLUSIONS
Our findings support the notion that newborn screening metabolic profiles from heel-stick
blood spots can reliably determine gestational age at birth with the additional utility
of accurately estimating preterm birth rates. Utilizing this novel tool in a more
widespread effort can improve preterm birth surveillance and epidemiology. In the
future, if developed, metabolic profiles may help to inform treatment and clinical
management shortly after delivery.
Additional material
Online Supplementary Document