Abstract
Objective The aim of this study was to determine if a universally applied risk score threshold
for severe maternal morbidity (SMM) resulted in different performance characteristics
among subgroups of the population.
Study Design This is a retrospective cohort study of deliveries that occurred between July 1,
2016, and June 30, 2020, in a single health system. We examined the performance of
a validated comorbidity score to stratify SMM risk in our cohort. We considered the
risk score that was associated with the highest decile of predicted risk as a “screen
positive” for morbidity. We then used this same threshold to calculate the sensitivity
and positive predictive value (PPV) of this “highest risk” designation among subgroups
of the overall cohort based on the following characteristics: age, race/ethnicity,
parity, gestational age, and planned mode of delivery.
Results In the overall cohort of 53,982 women, the C-statistic was 0.755 (95% confidence
interval [CI], 0.741–0.769) and calibration plot demonstrated that the risk score
was well calibrated. The model performed less well in the following groups: non-White
or Hispanic (C-statistic, 0.734; 95% CI, 0.712–0.755), nulliparas (C-statistic, 0.735;
95% CI, 0.716–0.754), term deliveries (C-statistic, 0.712; 95% CI, 0.694–0.729), and
planned vaginal delivery (C-statistic, 0.728; 95% CI, 0.709–0.747). There were differences
in the PPVs by gestational age (7.8% term and 29.7% preterm) and by planned mode of
delivery (8.7% vaginal and 17.7% cesarean delivery). Sensitivities were lower in women
who were <35 years (36.6%), non-White or Hispanic (40.7%), nulliparous (38.9%), and
those having a planned vaginal delivery (40.9%) than their counterparts.
Conclusion The performance of a risk score for SMM can vary by population subgroups when using
standard thresholds derived from the overall cohort. If applied without such considerations,
such thresholds may be less likely to identify certain subgroups of the population
that may be at increased risk of SMM.
Key Points
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Predictive risk models are helpful at condensing complex information into an interpretable
output.
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Model performance may vary among different population subgroups.
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Prediction models should be examined for their potential to exacerbate underlying
disparities.
Keywords
maternal morbidity - delivery - prediction - risk - risk stratification
