Objective Our study aimed to develop a simple-to-use nomogram for predicting the occurrence of HELLP and factors that may make HELLP progression within 3 days. Design The unpredictability of HELLP syndrome and the severe adverse outcomes for both mother and child make it especially important for us to seek predictive model. Setting We used electronic data from Women’s Hospital, Zhejiang University School of Medicine, between January 1,2014 and December 31,2023. Population or Sample A total of 808 patients were included in this study, including 607 patients in non-HELLP syndrome group and 201 patients in the HELLP syndrome group. methods Single and multiple-factor logistic regression analyses were applied to screen for independent factor affecting both the occurrence of HELLP syndrome and within-3-day HELLP syndrome by R software. A nomogram of HELLP syndrome and its efficacy were developed by ROC curves. Results We developed a nomogram displayed very high discrimination (C index 0.979, CI: 0.971-0.987). In the multivariate regression analysis, blood urine nitrogen and the ratio of creatinine over blood urea nitrogen were in a good significant in predicting within-3-day HELLP syndrome. The sensitivity was found to be 68% and 65%, specificity to be 74% and 68.6% respectively. Conclusions We established a great nomogram to evaluate the occurrence of HELLP syndrome. And we found blood urine nitrogen and the ratio of creatinine over blood urea nitrogen could be efficient predictors of HELLP syndrome occurring within 3 days.

Objective: To develop and validate a predictive model assessing the risk of cesarean delivery in primiparous women based on the findings of magnetic resonance imaging (MRI) studies. Design: Observational study Setting: University teaching hospital. Population: 168 primiparous women with clinical findings suggestive of cephalopelvic disproportion. Methods: All women underwent MRI measurements prior to the onset of labor. A nomogram model to predict the risk of cesarean delivery was proposed based on the MRI data. The discrimination of the model was calculated by the area under the receiver operating characteristic curve (AUC) and calibration was assessed by calibration plots. The decision curve analysis was applied to evaluate the net clinical benefit. Main Outcome Measures: Cesarean delivery. Results: A total of 88 (58.7%) women achieved vaginal delivery, and 62 (41.3%) required cesarean section caused by obstructed labor. In multivariable modeling, the maternal body mass index before delivery, induction of labor, bilateral femoral head distance, obstetric conjugate, fetal head circumference and fetal abdominal circumference were significantly associated with the likelihood of cesarean delivery. The discrimination calculated as the AUC was 0.845 (95% CI: 0.783-0.908; P < 0.001). The sensitivity and specificity of the nomogram model were 0.918 and 0.629, respectively. The model demonstrated satisfactory calibration. Moreover, the decision curve analysis proved the superior net benefit of the model compared with each factor included. Conclusion: Our study provides a nomogram model that can accurately identify primiparous women at risk of cesarean delivery caused by cephalopelvic disproportion based on the MRI measurements.