Introduction
Small for gestational age (SGA) babies are over represented amongst
stillborn infants with a population attributable risk between 23% and
31%. 1 Antenatal identification of SGA infants, along
with optimal management of SGA, has been associated with reduced severe
perinatal morbidity and mortality. 2,3 Therefore,
timely identification of the SGA fetus is a critical component of
antenatal care, 4 and yet less than 25% of SGA
fetuses are usually detected before birth with routine antenatal care.5
The Growth Assessment Protocol (GAP) is a multifaceted educational
programme that incorporates: standardised training in fundal height
measurement; SGA risk selection; specialist review and a schedule of
growth scans for high risk pregnancies; serial fundal height measurement
and plotting fundal height on a customised growth chart; a protocol for
ultrasound scanning in low risk pregnancies according to fundal height
measurements; and an evidence-based guideline for management if SGA is
detected. Implementation of GAP in UK centres has been associated with
increased detection of SGA and a parallel reduction in stillbirth.6 There are few publications reporting implementation
of GAP outside of the UK 7 and few data about how
implementation of GAP impacts on maternal and perinatal morbidity. GAP
is being implemented in several New Zealand District Health Boards but
its effectiveness in the New Zealand setting is currently unknown.
This study aimed to assess the effect of implementation of GAP on
detection of SGA babies and maternal and neonatal outcomes at Counties
Manukau Health (CM Health), a tertiary obstetric facility in South
Auckland, New Zealand. CM Health is the largest provider of maternity
services in New Zealand, serving an ethnically diverse population, with
high rates of obesity and high socio-economic deprivation as well as the
highest perinatal mortality in New Zealand. 8 We
hypothesised that implementation of GAP would result in improved
antenatal detection of pregnancies with SGA babies and that amongst SGA
pregnancies, there would be an increase in induction of labour, no
increase in caesarean section and a reduction in composite adverse
neonatal outcome.
Methods
Core Outcomes
The primary outcome was the proportion of SGA pregnancies that were
detected before birth and secondary outcomes included: induction of
labour, caesarean section, pre-term and post-term birth and composite
adverse neonatal outcome defined as one or more of: neonatal unit
admission for >48 hours, Apgar score <7 at 5
minutes or infant requiring any ventilation.
No funding was obtained to carry out the study and there was no patient
involvement in the planning of this project.
Study population:
Pre-GAP epoch: The Pre-GAP cohort
comprised all mothers who gave birth between January 1st and December
31st, 2012 in CM Health maternity facilities who received care by
hospital employed staff. This was prior to widespread use of GROW charts
and before introduction of the New Zealand Maternal Fetal Medicine SGA
guideline in 2013. 9 Births under the care of
self-employed midwives were excluded as clinical records were not
accessible. Further exclusions comprised booking >20 weeks
and 0 days, no maternal height or weight recorded, multiple pregnancy,
baby born <24 weeks and 0 days or with a major congenital
anomaly (Figure
1).
The data were obtained through the following CM Health databases; (1)
Healthware TM, which is used for recording and storing
clinical data; (2) the patient information management system (PiMSTM), primarily used for tracking, coding and resource
allocation; (3) Concerto, a clinical workstation which allows access to
clinical records including ultrasound scan reports, and (4) the Costpro
system (for information on clinical coding). Data included: maternal
age, ethnicity, New Zealand Deprivation Index, 10parity, date of last menstrual period (LMP), estimated date of delivery
(EDD) by LMP, EDD by ultrasound scan, gestation and weight at booking,
height, smoking, pre-existing hypertension, pre-eclampsia, stillbirth,
induction of labour, mode of birth, date of delivery, gestation at
birth, sex of baby, birth weight, Apgar score at 5 minutes, any neonatal
ventilation, admission to the neonatal unit for >48 hours,
and neonatal death. Missing data were obtained from clinical notes and
data-points that were major outliers were checked by searching the
clinical notes.
The New Zealand bulk birthweight centile calculator version 6.7.8,11 was applied to all births with SGA defined as
birthweight <10th customised centile. The
notes of all women whose babies were SGA at birth were hand checked to
ascertain whether SGA was detected by ultrasound scan. Antenatal
detection of SGA was defined as an ultrasound estimated fetal weight
(EFW) below the tenth customised centile, abdominal circumference
<5th centile, or sequential measurements of
estimated fetal weight or abdominal circumference with slow or no
growth, and/or one or more abnormal Doppler findings. In cases where
detection of SGA was uncertain after review by FJC further review was
undertaken by LMEMcC.
Post-GAP epoch : The post-GAP cohort comprised all mothers who
gave birth between April 1st 2017 and March
31st 2018, in CM Health maternity facilities and who
received care by hospital employed staff. The GAP programme was
introduced in February 2016. Exclusions were as per the pre-GAP cohort
(Figure 1).
The process of data collection differed from the pre-GAP cohort as by
2017 all maternity records were electronic, after the introduction of
the maternity clinical information system (MCIS) in 2015. All post-GAP
data was retrieved from the MCIS, PiMSTM , Concerto
and Costpro systems, with similar data checking and calculation of
customised birthweight centiles as for the pre-GAP cohort. An
independent audit of detected SGA/FGR was conducted by two final year
medical students who were trained by the lead investigator and reviewed
all SGA pregnancies to determine whether SGA was detected in the
antenatal period or not. In cases where detection of SGA was uncertain
after review by the medical students, further review was undertaken by
FJC, using the same criteria as for the pre-GAP epoch.
Implementation of GAP
The implementation of GAP at CM Health commenced in February 2016 with a
series of workshops. Clinicians were provided with an SGA risk
assessment tool and management algorithm, summarising the major risk
factors for SGA, appropriate care plan depending on risk of SGA, and a
guide to management once SGA is suspected (Supplementary Figure 1).
Education is scenario based and interactive, with a focus on training in
standardised fundal height measurement, accurate use and interpretation
of GROW charts, and evidence-based management of SGA pregnancies,
according to the New Zealand Maternal Fetal Medicine SGA guideline.9 A written test is completed by attendees to assess
learning at completion of the workshop. Additionally, an e-learning
programme is available for consolidation, and it is recommended that
clinicians undertake this annually.
Analyses
Analyses were conducted using SAS 9.4 (SAS Institute INC., Cary, NC,
USA). Maternal characteristics were compared between epochs using
chi-square and t-test, for categorical and continuous data,
respectively. Primary and secondary outcomes were compared between
epochs by logistic regression, with exposure (epoch) effect expressed as
odds ratio and 95% confidence interval (CI). Analyses were adjusted for
potential confounding by factors known to be associated with SGA,
including the New Zealand Deprivation Index, ethnicity, maternal age,
BMI, and cigarette smoking. Two-tailed alpha level was set at 0.05.
Subgroup analysis was undertaken to determine if SGA status influenced
exposure (epoch) effect. Further subgroup analysis was undertaken among
cases of SGA to determine if antenatal detection of SGA influenced
exposure (epoch) effect. Secondary analyses explored if the exposure
(epoch) effect on primary outcomes differed by maternal subgroups (SGA
vs non-SGA and detected SGA versus undetected SGA).
Results
The pre-GAP and post-GAP cohorts included 1105 and 1082 women,
respectively (Table 1). Between epochs there were significant changes in
the characteristics of the study populations with fewer young and older
mothers, and more Asian and fewer Pacific women in the Post-GAP epoch.
There was also a change in BMI distribution with more women with a BMI
of 18.5-24.9 kg/m2 and 25 to 29.9
kg/m2 in the post-GAP epoch. Fewer women smoked in
pregnancy in the post-GAP epoch. There was a small reduction in
gestation at delivery and birthweight between epochs, but no change in
SGA rates (pre-GAP 13.8% [153/1105] vs post-GAP 12.9%
[140/1082]; p=0.53).
Antenatal detection of SGA increased significantly from 22.9% (33/153)
pre-GAP to 57.9% (81/140) after introduction of GAP (aOR=4.8, 95% CI
2.82, 8.18; p<0.0001). Detection of SGA was more pronounced
for Maaori and Pacific Island women (18.9% pre-GAP vs 63.8% post-GAP,
aOR=7.76, 95% CI 3.72, 16.19) compared to women of other ethnicities
(28.6% vs 52.1%, aOR=2.60, 95% CI 1.25, 5.39) (interaction p=0.049)
(Table 2). Detection of SGA was similar amongst smokers and non-smokers,
women living in high and low deprivation, women with and without
preeclampsia and by BMI categories.
Induction of labour and caesarean birth increased between epochs, but
increases were similar among SGA and non-SGA pregnancies and did not
differ by SGA identification status (Table 3). Preterm birth also
increased between epochs in both non-SGA and SGA but there was no
significant increase in preterm birth in detected SGA pregnancies and no
significant interaction between detected and undetected-SGA. In
contrast, there was a reduction in overall post-term birth between
epochs.
There was a significant increase in overall composite adverse neonatal
outcome between
epochs (Table 3). This increase was statistically significant in non-SGA
babies (5.3% pre-GAP vs 9.8% post-GAP; aOR=1.98, 95% CI 1.38, 2.84)
but not amongst SGA babies (16.9% pre-GAP vs 18.2% post-GAP; aOR=1.05,
95% CI 0.55, 1.10), although the evidence for a difference in epoch
effect between SGA versus non-SGA subgroups was not strong (interaction
p=0.09).
In the SGA sub-group, there was some evidence that increased
identification of SGA
post-GAP may be associated with lower composite adverse neonatal outcome
(SGA
identified: 32.4% pre-GAP vs 17.5% post-GAP; aOR=0.44, 95% CI 0.17,
1.15; SGA
non-identified: 12.3% pre-GAP to 19.3% post-GAP; aOR=1.81, 95% CI
0.73, 4.48);
(interaction p=0.03).
Discussion
Main Findings
Our primary hypothesis, that GAP would increase detection of SGA, was
confirmed. A novel feature was that we could explore the efficacy of GAP
on detection of SGA by maternal demographic and clinical
characteristics. While GAP was associated with increased detection of
SGA amongst all ethnic groups, it was encouraging to note the more
pronounced effect amongst women from Maaori and Pacific Island ethnic
backgrounds. Additionally, it was reassuring to note similar SGA
detection in women who smoked compared with non-smokers, and in women
with the highest deprivation compared with other deprivation groups. As
obesity is an independent risk factor for both SGA and stillbirth,12 it was also encouraging to note the high SGA
detection rate (66.7%) amongst women with a BMI >35
kg/m2.
While we hypothesised that GAP would be associated with an increase in
induction of labour amongst women with SGA pregnancies our data did not
demonstrate this effect. A possible explanation could be that the
stratified induction approach recommended by NZ GAP education (9)
includes induction of labour by 38 weeks for SGA pregnancies with
evidence of fetal growth restriction
(EFW<3rd centile or Doppler velocimetry
abnormalities), and expectant management with induction by 40-41 weeks
for SGA pregnancies without evidence of fetal growth restriction.13 A similar stratified approach implemented in
Oxford, United Kingdom, has also been associated with a reduction in
induction of labour. 14
Our hypothesis that there would be no increase in caesarean birth
amongst SGA pregnancies after implementation of GAP was also not
confirmed. Overall, caesarean birth rates rose post-GAP at CM Health,
but there were similar increases in SGA and non-SGA subgroups, and the
magnitude of the effect did not differ between SGA and non-SGA
pregnancies. Importantly, caesarean birth did not increase in the
subgroup of SGA pregnancies identified before birth.
There was a significant increase in composite adverse neonatal outcome
between epochs in non-SGA but not amongst SGA babies. Further in the SGA
subgroup, there was evidence that increased identification of SGA
post-GAP may be associated with lower composite adverse neonatal outcome
and reduced prolonged neonatal unit admission. While we cannot determine
the factors contributing to the better neonatal outcome amongst
identified SGA we speculate that improved antenatal detection of SGA has
resulted in closer monitoring and timely birth that has contributed to
these findings.
Strengths and
Limitations
This is the first New Zealand study to evaluate implementation of GAP.
The New Zealand maternity service is unique in that most women have
continuity of midwifery care. Our study focussed entirely on data from
hospital employed midwives as data on detection of SGA were not
available for self-employed midwives in CM Health. While this may be
considered a limitation, hospital employed community midwives at CM
Health work in a continuity of care model for antenatal care, and as
such, our findings likely represent the New Zealand continuity of care
model of working in partnership with a named midwife for each woman.15 Furthermore, our evaluation was carried out in a
multi-ethnic, high deprivation community with high rates of
co-morbidities, such as obesity.
A strength of this study is that datasets for pre- and post-GAP were
almost 100% complete after extensive checking of handwritten and
electronic records. Multivariable analysis adjusted for confounding
variables which are known risk factors for SGA and an interaction model
was applied to assess the impact of epoch on outcomes.
A limitation is that this study was retrospective with reliance on data
from hospital records. Antenatal detection of SGA in the pre-GAP epoch
was determined after review of the clinical records and ultrasound
reports by the lead investigator who was also the GAP educator. However,
in cases of uncertainty, a final decision was made by the senior
investigator. This could have introduced ascertainment bias favouring
non-detection of SGA in the pre-GAP epoch. Similarly, in the post-GAP
epoch, a final decision on uncertain cases was made by the lead
investigator. Over the four-year period, between the pre- and post-GAP
audits, there were significant demographic and clinical practice
changes, and it is possible that increasing awareness of the use of GROW
charts and the New Zealand Maternal Fetal Medicine Network (NZMFMN) SGA
guideline (first published in 2013) may have resulted in an incremental
increase in SGA detection over the intervening years between epochs.
Our study was underpowered to investigate the impact of GAP on
stillbirth and neonatal death and had limited power to investigate
changes in composite adverse neonatal outcome. We also did not have data
on utilisation of ultrasound scans in the respective study populations
or data on false positive identification of SGA during either epoch.