ABSTRACT
Objectives: To externally validate the demographic setting of
the online Fetal Medicine Foundation (FMF ) Stillbirth Risk
Calculator based upon maternal medical and obstetric history in a
case-matched cohort.
Design: Retrospective case-control study
Setting: Tertiary referral hospital
Population: 144 fetuses after singleton intrauterine fetal
death (IUFD) and a matched control group of 247 singleton live births
between 2003 and 2019
Methods: Nonparametric receiver operating characteristics (ROC)
analysis was performed to predict the prognostic power of the risk score
and to generate a cut-off value to discriminate best between the events
of stillbirth versus live birth.
Main Outcome Measures: FMF Stillbirth risk score
Results: The IUFD cohort conveyed a significantly higher
overall risk assessment with a median FMF Stillbirth risk score
of 0.45% (0.19-5.70%) compared to live births [0.23% (0.18-1.30%);p <0.001]. Demographic factors mainly contributing to
the increased risk were BMI (p= 0.002), smoking
(p <0.001), chronic hypertension (p= 0.015), APS
(p= 0.017), type 2 diabetes (p <0.001) and need
for insulin (p <0.001). ROC analysis to evaluate the
discriminative ability of the FMF Stillbirth Risk Calculator
showed an area under the curve (AUC) of 0.72 (95% CI 0.67–0.78;p <0.001). The FMF Stillbirth risk score at a
cut-off level of 0.34% (OR 6.22; 95% CI 3.91–9.89;p <0.001) yielded a specificity of 82% and a
sensitivity of 58% in predicting singleton antepartum stillbirths.
Conclusion: The FMF Stillbirth Risk Calculator achieved
a similar performance in our cohort of women as in the reference group.
Funding: This research did not receive any specific grant from
funding agencies in the public, commercial, or not-for-profit sectors.
Keywords: Stillbirth; pregnancy outcome; high-risk pregnancy;
epidemiology, risk prediction; validation study.
Tweetable Abstract The demographic setting of the onlineFMF Stillbirth Risk Calculator helps to identify women at high
risk for antepartum #stillbirth based upon their maternal and obstetric
history #SavingBabies @BJOGTweets @DrDana_Muin @MedUni_Wien