Development of Z-score-based models to predict perinatal mortality in pregnancies complicated by fetal growth restriction managed at a tertiary care hospital.

OBJECTIVE

To develop predictive models using fetal weight Z-scores to predict perinatal mortality in pregnancies affected by fetal growth restriction (FGR) in high-risk settings.

STUDY

DESIGN

This retrospective cohort included singleton pregnancies without congenital anomalies, delivered after 30 weeks of gestation in a tertiary hospital between 2019 and 2022. FGR was defined as weight below the 3rd percentile according to three fetal growth charts. Optimal Z-score cutoffs were identified to maximize sensitivity and specificity for perinatal mortality. Logistic regression models incorporated dichotomized Z-scores and significant predictors. Model performance was assessed using ROC curves, calibration plots, and decision curve analysis (DCA).

RESULTS

Among 17 991 singleton pregnancies, 16 177 met the inclusion criteria. FGR was identified in 369 (2.3 %), 157 (1.0 %), and 335 (2.1 %) foetuses using the Hadlock, INTERGROWTH-21st (IG-21), and local customized charts, respectively. Perinatal mortality occurred in 30/369 (8.1 %), 25/157 (15.9 %), and 31/335 (9.3 %) of these groups. Z-score thresholds (-2.61, -3.17, -2.95) showed strong predictive capacity, with adjusted AUCs of 0.878, 0.884, and 0.917. The final model was logit(p) = β0 + β1Z-score - β2 Gestational age - β3 Preeclampsia (preeclampsia excluded in IG-21). Calibration errors averaged 0.02, 0.04, and 0.02; adjusted odds ratios were 18.7, 37.6, and 23.7. The decision curve analysis suggests that integrating these models into clinical protocols may enhance perinatal risk stratification.

CONCLUSION

Three Z-score-based models accurately predicted perinatal mortality in FGR-complicated pregnancies in a high-risk setting. Their integration into clinical protocols may support individualized risk stratification and decision-making.