External Validation of a Multivariable Prediction Model for Placenta Accreta Spectrum

BACKGROUND: Placenta accreta spectrum (PAS) is a disorder of abnormal placentation associated with severe postpartum hemorrhage, maternal morbidity, and mortality. Predelivery prediction of this condition is important to determine appropriate delivery location and multidisciplinary planning for operative management. This study aimed to validate a prediction model for PAS developed by Weiniger et al in 2 cohorts who delivered at 2 different United States tertiary centers. METHODS: Cohort A (Brigham and Women’s Hospital; N = 253) included patients with risk factors (prior cesarean delivery and placenta previa) and/or ultrasound features of PAS presenting to a tertiary-care hospital. Cohort B (Columbia University Irving Medical Center; N = 99) consisted of patients referred to a tertiary-care hospital specifically because of ultrasound features of PAS. Using the outcome variable of surgical and/or pathological diagnosis of PAS, discrimination (via c-statistic), calibration (via intercept, slope, and flexible calibration curve), and clinical usefulness (via decision curve analysis) were determined. RESULTS: The model c-statistics in cohorts A and B were 0.728 (95% confidence interval [CI], 0.662–0.794) and 0.866 (95% CI, 0.754–0.977) signifying acceptable and excellent discrimination, respectively. The calibration intercept (0.537 [95% CI, 0.154–0.980] for cohort A and 3.001 [95% CI, 1.899– 4.335] for B), slopes (0.342 [95% CI, 0.170–0.532] for cohort A and 0.604 [95% CI, −0.166 to 1.221] for B), and flexible calibration curves in each cohort indicated that the model underestimated true PAS risks on average and that there was evidence of overfitting in both validation cohorts. The use of the model compared to a treat-all strategy by decision curve analysis showed a greater net benefit of the model at a threshold probability of >0.25 in cohort A. However, no net benefit of the model over the treat-all strategy was seen in cohort B at any threshold probability. CONCLUSIONS: The performance of the Weiniger model is variable based on the case-mix of the population with regard to PAS clinical risk factors and ultrasound features, highlighting the importance of spectrum bias when applying this PAS prediction model to distinct populations. The model showed benefit for predicting PAS in populations with substantial case-mix heterogeneity at threshold probability of >25%.