Improving preterm newborn identification in low-resource settings with machine learning
Improving preterm newborn identification in low-resource settings with machine learning
Katelyn J. RittenhouseID 0 1 2
Bellington Vwalika 1 2
Alexander KeilID 0 1 2
Jennifer Winston 0 1 2
Marie Stoner 0 1 2
Joan T. Price 0 1 2
Monica Kapasa 1 2
Mulaya Mubambe 1 2
Vanilla Banda 1 2
Whyson Muunga 1 2
Jeffrey S. A. StringerID 0 1 2
0 University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States, 2 University of North Carolina Global Projects Zambia , Lusaka , Zambia , 3 University of Zambia School of Medicine , Lusaka , Zambia
1 Editor: Chelsea Dobbins, University of Queensland , AUSTRALIA
2 Melinda Gates Foundation grant to the Global Alliance to Prevent Prematurity and Stillbirth (Seattle Children's Hospital/GAPPS 13008/ OPP1033514). Additional support was provided by the US National Institutes of Health through the UNC Center for AIDS Research , P30 AI50410
Globally, preterm birth is the leading cause of neonatal death with estimated prevalence and associated mortality highest in low- and middle-income countries (LMICs). Accurate identification of preterm infants is important at the individual level for appropriate clinical intervention as well as at the population level for informed policy decisions and resource allocation. As early prenatal ultrasound is commonly not available in these settings, gestational age (GA) is often estimated using newborn assessment at birth. This approach assumes last menstrual period to be unreliable and birthweight to be unable to distinguish preterm infants from those that are small for gestational age (SGA). We sought to leverage machine learning algorithms incorporating maternal factors associated with SGA to improve accuracy of preterm newborn identification in LMIC settings.
Methods and findings
This study uses data from an ongoing obstetrical cohort in Lusaka, Zambia that uses early
pregnancy ultrasound to estimate GA. Our intent was to identify the best set of parameters
commonly available at delivery to correctly categorize births as either preterm (<37 weeks)
or term, compared to GA assigned by early ultrasound as the gold standard. Trained
midwives conducted a newborn assessment (<72 hours) and collected maternal and neonatal
data at the time of delivery or shortly thereafter. New Ballard Score (NBS), last menstrual
period (LMP), and birth weight were used individually to assign GA at delivery and
categorize each birth as either preterm or term. Additionally, machine learning techniques
incorporated combinations of these measures with several maternal and newborn characteristics
associated with prematurity and SGA to develop GA at delivery and preterm birth prediction
models. The distribution and accuracy of all models were compared to early ultrasound
dating. Within our live-born cohort to date (n = 862), the median GA at delivery by early
ultrasound was 39.4 weeks (IQR: 38.3?40.3). Among assessed newborns with complete data
included in this analysis (n = 468), the median GA by ultrasound was 39.6 weeks (IQR:
38.4?40.3). Using machine learning, we identified a combination of six accessible
trainee / mentor support: T32 HD075731 (JTP),
K01 TW010857 (JTP), and D43 TW009340 (KR).
The funders had no role in study design, data
collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
parameters (LMP, birth weight, twin delivery, maternal height, hypertension in labor, and
HIV serostatus) that can be used by machine learning to outperform current GA prediction
methods. For preterm birth prediction, this combination of covariates correctly classified
>94% of newborns and achieved an area under the curve (AUC) of 0.9796.
We identified a parsimonious list of variables that can be used by machine learning
approaches to improve accuracy of preterm newborn identification. Our best-performing
model included LMP, birth weight, twin delivery, HIV serostatus, and maternal factors
associated with SGA. These variables are all easily collected at delivery, reducing the skill and
time required by the frontline health worker to assess GA.
ClinicalTrials.gov Identifier: NCT02738892
Preterm birth affects more than one in ten live births worldwide.[
] It is the single largest
cause of neonatal death and the second leading cause of death in children under the age of 5
] Many babies who survive a preterm birth face life-long morbidity, including
cognitive disability, poor motors skills, behavioral problems, hearing loss, chronic lung disease, and
decreased economic productivity.[
] The greatest burden of preterm birth falls on low-and
middle-income countries (LMICs), where more than 90% of the global 15 million preterm
deliveries occur each year[
] and where preterm infants carry a 7-fold higher risk of neonatal
mortality and a 2.5-fold higher risk of post-neonatal mortality compared to their full-term
] In these settings, preterm infants often go unrecognized due to inaccurate
estimation of gestational age (GA). On the individual level, this can result in missed
opportunities for clinical intervention; on the population level, this can limit the ability to monitor
preterm birth rates and make informed decisions around policy and resource allocation.
Early prenatal ultrasound, widely regarded as the gold standard for GA dating, is
unavailable in many LMIC settings. In its absence, providers must rely on other methods, such as last
menstrual period (LMP), newborn assessment, or birthweight to classify infant GA at delivery.
Each of these approaches has limitations. Reported LMP is subject to patient recall and can be
very unreliable in settings where women present late for care.[
] Newborn assessment,
including the commonly used New Ballard Score (NBS), suffers from poor inter-rater
] and tends to overestimate GA, particularly in LMICs and settings with high
rates of small-for-gestational age (SGA).[
] Finally, birthweight, while an easily obtained
and reliable indicator, does not distinguish between an infant that is preterm and one that is
We sought to develop a machine learning algorithm that can estimate GA at birth from
readily obtained indicators in a setting where early ultrasound is not available. We were
particularly interested in the simple, binary classification of preterm (i.e., <37 weeks) versus term.
We hypothesized that a model combining LMP, individual elements of the NBS, birthweight,
and key pregnancy risk factors associated with SGA, would outperform any individual
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This study was conducted using data from the Zambian Preterm Birth Prevention Study
(ZAPPS; ClinicalTrials.gov identifier: NCT02738892), an ongoing prospective obstetrical
cohort at the Women and Newborn Hospital of the University Teaching Hospital (UTH) in
Lusaka, Zambia. The rationale for our study, its procedures, and cohort characteristics have
been described elsewhere.[
] Briefly, women are enrolled in early pregnancy and followed
through delivery and the postpartum period. Written informed consent is obtained from all
participants prior to study enrollment for collection of maternal and newborn data. GA is
established by ultrasound (Sonosite M-Turbo; Fuji Sonosite, Inc, Bothell, WA) at study
screening using the fetal crown rump length (if <14 weeks gestation)[
] or head circumference and
femur length (if 14 weeks).[
] All fetal biometry measurements are measured twice and
then averaged for gestational age calculations.
The study employs midwives who attend to participants admitted to the labor ward or
postpartum unit at UTH. Their duties include ensuring that relevant clinical information is
captured in the study record, that babies are weighed at birth or shortly thereafter, and that the
NBS is performed within 72 hours of delivery.
Newborns were included in this analysis if they were live-born and they had a complete set
of characteristics and metrics assessed in this study. We defined preterm birth as birth prior to
37 weeks of gestation and SGA as a birthweight less than the 10th percentile for its
] The NBS sums assessments of 5 domains of neuromuscular maturity and 7
domains of physical maturity into a composite score that is used to assign GA at delivery.[
We evaluated both the composite score and its 12 individual components in this study.
In our analyses, we assessed eight models: three single parameter GA dating methods and
five multiple parameter novel machine learning GA dating models (Table 1). We were
primarily interested in classifying preterm birth as a binary outcome (i.e., <37 weeks or not) to
identify newborns at highest risk of complications from preterm delivery, but we also wished to
GA at delivery NBS (individual
(birth weight 50% components)?
GA: gestational age; NBS: New Ballard Score; LMP: last menstrual period; HTN: hypertension
Composite NBS: Sum of neuromuscular and physical maturity domains
^Birth weight 50%ile: Intergrowth 50th birthweight-for-age centiles used to convert birthweights to GA
?Individual NBS components: 5 Neuromuscular maturity domains and 7 physical maturity domains
?Machine learning models
assess how the models might estimate GA as a continuous outcome. We restricted our models
to maternal and newborn characteristics that are accessible to health workers in
resource-limited settings at the time of delivery, either through direct assessment or review of the medical
The single parameter GA dating methods assessed include 1) LMP, 2) NBS, and 3) birth
weight. GA dating by NBS was calculated from the composite NBS using the formula for GA
conversion, as described by Ballard et al.[
] GA dating by birth weight was calculated under
the na?ve assumption that all infants are born at the 50th birthweight-for-age centile and used
] to convert these birthweights to GA.
The multiple parameter machine learning models assessed include 1) Optimized NBS, 2)
NBS(-)LMP(-), 3) NBS(-)LMP(+), 4) NBS(+)LMP(-), and 5) NBS(+)LMP(+). In all machine
learning models incorporating NBS, including Optimized NBS, all 12 individual NBS
components were included. With the exclusion of Optimized NBS, all machine learning models
included an additional five maternal and newborn parameters with various combinations of
NBS and LMP. Maternal and newborn parameters were identified by stepwise regression and
included birth weight in addition to parameters with an a priori association with preterm birth
(twin delivery, maternal HIV serostatus) and SGA (maternal height, maternal hypertension).
We used hypertension in labor as a surrogate marker for maternal hypertension because,
although imperfect, it is a readily accessible metric at delivery in the maternal delivery case file.
Hypertension in labor was defined as systolic blood pressure 140 and/or diastolic blood
pressure 90 recorded in the maternal delivery case file. For twin deliveries, we included only
baby A (the first baby to be delivered) in our dataset. This approach to twin deliveries reduced
bias toward artificially increased model accuracy by including two newborns with closely
matched characteristics. HIV serostatus was determined by rapid ELISA performed according
to local protocol at first antenatal care visit.[
We used super learner[
] to generate five GA and prematurity prediction models
(components described above). In brief, super learner is a machine learning approach for combining
the strengths of multiple predictive models or learners. Super learner finds the weighted,
convex combination of these algorithms that minimizes the cross-validated mean squared error of
predictions of GA and preterm birth. To reduce concerns about over-fitting the data, we
utilized k-fold cross validation (with 10 folds) to select the combination of learners. K-fold cross
validation ensures that the learner is not fit (trained) to the same data that are used to make
predictions and judge performance. We used super learner computational macro (arXiv:1805.
08058 [stat.ML]) developed in SAS version 9.4 (Cary, North Carolina) along with the SAS
] and GAM?[
] to perform random forest
algorithms, generalized linear and logistic modeling, and generalized additive modeling,
respectively. For each algorithm included in our Super Learner library, the hyper-parameters
were the defaults given by the SAS macro, which were based off defaults from the R super
] For our continuous GA prediction modeling of super learner models, we
combined linear regression, random forest regression, and generalized additive models. For
binary preterm birth classification modeling, we combined logistic regression, random forest
classification, and generalized additive model.
Kernel density plots and Pearson?s correlation coefficients were generated to compare the
predicted GAs from each continuous outcome model to GAs by early ultrasound. For our
primary analysis, we based accuracy of each predictive model on the model fit to the data in
which we had complete data (n = 468). In a subset of births without NBS that were not used to
train predictive models, we subsequently estimated predictive accuracy. We note that, while
super learner utilizes cross-validation to reduce over-fit, the super learner predictions do not,
themselves, estimate cross-validated accuracy; thus, this latter step is necessary to produce fair
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estimates of the out-of-sample accuracy of our approach. Receiver operating curves (ROCs)
were generated and area under the curve (AUC) calculated for the diagnostic accuracy of
preterm birth for each binary classification model. We also calculated the positive predictive
value, negative predictive value, and percent correct classification for the identification of
preterm infants using the best cutoff point for each model, as determined using the Youden
method. The Youden method determines a cutoff point by optimizing the differentiating
ability of a test or model when equal weight is given to sensitivity and specificity. A subsequent
sensitivity analysis including women enrolled in the first trimester (<14 weeks gestation) was
conducted on our best-performing model to further assess stability and validity in women
with the most accurate gestational age dating. All super learner modeling was performed in
SAS as described above; all other analyses were performed using STATA release 14 (College
Station, TX). This study was approved by the University of Zambia Biomedical Research Ethics
Committee and the University of North Carolina Institutional Review Board.
Between August 2015 and September 2017, 1450 pregnant women were consented and
enrolled into the ZAPPS cohort. To date, 862 (59.4%) participants have had live births with
deliveries captured by a study midwife. A total of 468 (53.1%) of these live births had newborns
assessed at <72 hours of life by a trained nurse midwife and had complete data available to be
included in subsequent preterm birth predictive modeling (Table 2). Among assessed live
births, median ultrasound-based GA was 39.6 weeks (IQR: 38.4?40.3), with preterm birth
prevalence 6.8%. The median birth weight was 3100g (IQR: 2855?3400). The prevalence of
SGA in this population was 14.1%. NBS assessment was the most common missing parameter,
causing study exclusion (n = 300; 76.1% of live births not assessed).
Kernel density plots, with accompanying Pearson correlation coefficients, comparing the
continuous GA distributions of the 8 models evaluated in this study to those calculated by
ultrasound are shown in Fig 1. The models generated by the super learner program for GA as
a continuous outcome clustered estimated GAs around the mean, resulting in a loss of outliers
and less accurate estimation of GA as a continuous outcome (Fig 1D?1H). Despite clustering
p-values calculated by Mann-Whitney test for continuous variables or chi-square test for dichotomous categorical variables
^Hypertension in labor was defined as systolic blood pressure 140 and/or diastolic blood pressure 90 recorded in the maternal delivery case file
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Fig 1. Distribution of gestational age at birth by all continuous models. r = Pearson?s correlation coefficient.
around the mean, the NBS(-)LMP(+) and NBS(+)LMP(+) machine learning models (Fig 1F
and 1H) were found to best approximate the distribution of GA at delivery as compared to
ultrasound dating (Pearson correlation coefficients 0.73 and 0.77, respectively).
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Fig 2. Diagnostic accuracy of binary models to identify preterm newborns. AUC: Area Under Curve.
The accuracy of preterm birth classification by each GA dating method was assessed using
ROCs and associated AUCs (Fig 2). The AUC for Optimized NBS using super learner (0.8684)
was improved compared to the single parameter NBS model (0.7645). The NBS(-)LMP(-)
super learner model incorporating maternal and newborn parameters without LMP and NBS
had an AUC of 0.8664, outperforming the NBS model and performing similarly to Optimized
NBS. Adding LMP to this model, NBS(-)LMP(+), improved the AUC (0.9796) more than
adding NBS or both NBS and LMP (0.9242 and 0.9784, respectively).
Positive predictive value (PPV), negative predictive value (NPV), and correct classification
of all models predicting prematurity are shown in Table 3. In concordance with our ROC
analysis, the NBS(-)LMP(+) super learner model incorporating LMP without NBS had the highest
percent correct classification (94.0%). Additionally, this model had a NPV of 98.9%, similar to
other models, and a PPV of 53.6%, substantially out-performing all other GA dating methods.
In a sensitivity analysis, we tested NBS(-)LMP(+), our best-performing model, on the subset
of women who were enrolled in the first trimester (<14 weeks; n = 204), as their ultrasound
dating is expected to be most accurate. We found a PPV of 73.3%, NPV of 98.8%, correct
classification of 94.6%, and AUC of 0.9679. Additionally, because our best-performing model
excluded NBS?the variable most likely to cause study exclusion due to missingness?we were
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able to test our model in this population. In 245 newborns not included in our initial analysis,
we found a PPV of 71.9%, NPV of 95.7%, correct classification of 90.2%, and AUC of 0.8776.
In our urban Zambian cohort with early pregnancy ultrasound dating, we used machine
learning to identify a parsimonious set of maternal and newborn variables associated with
prematurity and small for gestational age that can improve discrimination between preterm and term
newborns as compared to common gestational age dating methods (New Ballard Score, last
menstrual period, and birth weight). This exploratory study demonstrates the promising utility
of machine learning techniques to optimize algorithms for the identification of preterm birth
and other adverse birth outcomes in low-resource settings.
Although a positive correlation between the number of parameters and accuracy of GA
assessment has been established,[
] increasing parameter collection has negative feasibility of
use, particularly in LMIC settings. In sub-Saharan Africa, up to one-half of all deliveries occur
outside of the hospital and have no skilled birth attendant,[
] limiting the utility of GA
dating methods requiring numerous maternal and newborn metrics and characteristics. A
significant strength of our best-performing model is that it incorporates only six maternal and
newborn characteristics and metrics available at delivery: LMP, birth weight, twin gestation,
maternal HIV serostatus, hypertension at delivery, and maternal height.
An interesting finding of our analysis was the strength of LMP as a predictor of GA and
preterm birth. Limitations of LMP as a dating method are well-documented.[
] Women with
lower educational attainment[
] and later presentation to care[
] tend to have less
accurate recall of LMP, and the measure is subject to number preference (e.g. rounding to zero or
five, or preference for 1st or 10th of month) and recall bias.[
] Consequently, GA estimates
by LMP alone suffer imprecision, with some estimates differing by weeks when compared to
] Indeed, data from the Zambia Perinatal Record System, an electronic
system that captured more than 250,000 births over a 6 year period in Lusaka, suggests an
impossibly high preterm birth rate of 35% when LMP is used to determine GA.[
11, 42, 43
these limitations, we demonstrate that LMP is a useful predictor of prematurity and GA at
delivery when incorporated into a model that allows obviously implausible estimates to be
overridden by other parameters.
Further, our best-performing prematurity prediction model excluded NBS. In fact, the
addition of NBS components to our best-performing list of covariates decreased the PPV,
percent correct classification, and AUC of the model. LMP outperformed NBS in prematurity
prediction, both when assessed individually and in combination with other parameters. This
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finding supports a recent systematic review indicating that NBS has lower agreement with
ultrasound dating than LMP.[
] The exclusion of NBS from our best-performing model has
the benefit of omitting lengthy and technical neonatal assessment procedures. Despite only
including one newborn measurement, our model achieves an excellent AUC and correctly
classifies more than 94% of newborns as preterm and term in our well-dated Zambian cohort.
Additionally, when excluding NBS, we were able to apply our model to a cohort of an
additional 245 births with a 20% preterm birth rate, demonstrating the broader applicability of
methods excluding NBS. Implementation of this model using six accessible maternal and
newborn characteristics may increase the accuracy and rapidity of preterm newborn identification
in LMIC settings as well as decrease the time and level of training required by frontline health
workers to assess preterm birth.
The calculated PPVs for all models demonstrate the limitations of our currently utilized,
single parameter methods to correctly identify preterm newborns. Only 32.3% and 28.6% of
newborns predicted to be preterm by LMP and NBS, respectively, were also classified as
preterm by early pregnancy ultrasound dating. Many of our multiple parameter, machine learning
models performed similarly, with comparable PPVs. Only our best-performing model, NBS(-)
LMP(+), had a PPV greater than 50%. All GA dating models demonstrated a propensity for
overestimating preterm birth rates and misclassifying term newborns as preterm; however,
our best-performing model had a decreased tendency to misidentify preterm newborns in this
way. Additionally, this model correctly classified 94% of newborns, including 30 out of 32
preterm newborns. Further, in both our sensitivity analysis of newborns with ultrasound dating
<14 weeks gestation (the most accurate GA dating) and in our external validation cohort of
newborns without NBS, our best-performing model had a PPV >70%. Although far from
perfect as a preterm newborn algorithm, our parsimonious model significantly improves upon
currently available identification methods, allowing clinicians to better direct care and
resources to newborns in need, especially in low-resource, LMIC settings.
A significant limitation of this current study is survival bias of assessed newborns, as
demonstrated by the significant differences between our assessed and not assessed populations
(Table 2). Preterm, especially early preterm, newborns were sometimes not assessed by study
midwives because they were deemed too ill for the assessment or because of parental or
neonatal provider objection to the exam. These early preterm newborns would likely have been
identified as preterm by models included in this analysis. Thus, our estimates likely underestimate
the performance of preterm birth identification in all models. Even with continuous staffing of
the labor ward by midwives trained on NBS performance, many newborns were not evaluated
within 72 hours. As many early preterm and critically ill newborns are never assessed,
newborn assessments may not be the most effective measure of GA for these babies. In our cohort,
we would be able to assess significantly more newborns in our model (81% vs. 54%) if we
included newborns on whom NBS was not collected, indicating that GA dating methods
excluding newborn assessment may be more efficacious in LMIC settings.
A further limitation of our current model is that it was developed to optimize the accuracy
the average GA for a given set of covariates, which was then used to infer term ( 37 weeks)
versus preterm (<37 weeks) births. This model results in limited accuracy in the prediction of
the complete distribution of GA. Utilizing super learner capabilities to better model the
distribution of GA, rather than just the mean, as a continuous outcome may be a helpful next step
for neonatal providers desiring to better estimate accurate GA. Additionally, our current
model requires all characteristics and measurements to assess preterm birth status be present
for study inclusion. Consequently, if a woman does not know her LMP, her newborn is
omitted from this model. Future work to assess novel GA and preterm newborn prediction models
using machine learning techniques should include methods to impute missing data. Further,
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as validation of our model was limited to internal k-fold cross validation, preventing
over-fitting the data, in addition to a small model validation cohort of newborns missing NBS,
external validation in a larger dataset should be pursued in future work.
In summary, by leveraging the capacity of cutting-edge machine learning algorithms and
maternal parameters associated with prematurity and SGA newborns, we identified a
parsimonious list of covariates that improves accuracy of preterm newborn identification. Our model
incorporates six accessible maternal and newborn characteristics and metrics, reducing the
skill and time required to assess gestational age. This exploratory study supports the need for
further research into the use of machine learning techniques to improve the accuracy of
gestational age assessment in low resource settings and to assist frontline health workers in
identifying newborns who may require special care.
Conceptualization: Katelyn J. Rittenhouse, Bellington Vwalika, Alexander Keil, Jennifer
Winston, Joan T. Price, Monica Kapasa, Mulaya Mubambe, Vanilla Banda, Whyson Muunga,
Jeffrey S. A. Stringer.
Data curation: Katelyn J. Rittenhouse, Jennifer Winston, Jeffrey S. A. Stringer.
Formal analysis: Katelyn J. Rittenhouse, Alexander Keil, Jennifer Winston, Marie Stoner,
Joan T. Price, Jeffrey S. A. Stringer.
Funding acquisition: Jeffrey S. A. Stringer.
Investigation: Katelyn J. Rittenhouse, Monica Kapasa, Mulaya Mubambe, Vanilla Banda,
Methodology: Katelyn J. Rittenhouse, Bellington Vwalika, Alexander Keil, Jennifer Winston,
Marie Stoner, Jeffrey S. A. Stringer.
Project administration: Katelyn J. Rittenhouse.
Resources: Alexander Keil, Jennifer Winston.
Software: Alexander Keil.
Supervision: Bellington Vwalika, Alexander Keil, Jennifer Winston, Marie Stoner, Joan T.
Price, Jeffrey S. A. Stringer.
Validation: Katelyn J. Rittenhouse.
Writing ? original draft: Katelyn J. Rittenhouse.
Writing ? review & editing: Katelyn J. Rittenhouse, Bellington Vwalika, Alexander Keil,
Jennifer Winston, Marie Stoner, Joan T. Price, Monica Kapasa, Mulaya Mubambe, Vanilla
Banda, Whyson Muunga, Jeffrey S. A. Stringer.
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