Development and validation of a nomogram for predicting postpartum hemorrhage in women with preeclampsia: A...

This retrospective case-control study aimed to develop a nomogram for predicting postpartum hemorrhage in women with preeclampsia. This study was carried out at the Fujian Maternity and Child Health Hospital, involving 542 preeclampsia patients who underwent vaginal deliveries. The participants were split into 2 groups: a training cohort (85%, n = 460) and a validation cohort (15%, n = 82). Least absolute shrinkage and selection operator regression was applied to pinpoint relevant risk factors by selecting appropriate candidate variables. Subsequently, multivariate logistic regression analysis was conducted on the training set, leading to the creation of a nomogram as a visual risk prediction tool. The model's performance was tested and verified internally and externally by examining receiver operating characteristic curves and calibration curves. The correlation heatmap revealed collinearity among variables, necessitating the use of least absolute shrinkage and selection operator regression to select 4 candidate variables. Multivariate logistic regression analysis identified significant associations with the following outcomes: white blood cell count (odds ratio [OR]: 2.485, 95% confidence interval [CI]: 1.483-4.166), third stage of labor (OR: 1.382, 95% CI: 1.182-1.616), anemia (OR: 9.588, 95% CI: 4.022-22.854), and labor analgesia (OR: 0.187, 95% CI: 0.073-0.477). These variables were utilized to construct the nomogram. The receiver operating characteristic curves demonstrated good predictive performance (area under the curve train = 0.867, area under the curve test = 0.882), and the calibration curve yielded a C-index of 0.867. The nomogram created in this study has good sensitivity and specificity to assess risk and support clinical decision-making for postpartum hemorrhage in women with preeclampsia.