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. 2022 Feb 11:8:789529.
doi: 10.3389/fmed.2021.789529. eCollection 2021.

Developing and Validating Nomogram to Predict Severe Postpartum Hemorrhage in Women With Placenta Previa Undergoing Cesarean Delivery: A Multicenter Retrospective Case-Control Study

Xiaohe Dang  1 , Li Zhang  2 , Yindi Bao  3 , Jie Xu  4 , Hui Du  5 , Shaoshuai Wang  1 , Yanyan Liu  1 , Dongrui Deng  1 , Suhua Chen  1 , Wanjiang Zeng  1 , Ling Feng  1 , Haiyi Liu  1
Affiliations

Developing and Validating Nomogram to Predict Severe Postpartum Hemorrhage in Women With Placenta Previa Undergoing Cesarean Delivery: A Multicenter Retrospective Case-Control Study

Xiaohe Dang et al. Front Med (Lausanne). .

Abstract

Objective: Developing and validating nomogram to predict severe postpartum hemorrhage (SPPH) in women with placenta previa (PP) undergoing cesarean delivery.

Methods: We conducted a multicenter retrospective case-control study in five hospitals. In this study, 865 patients from January, 2018 to June, 2020 were enrolled in the development cohort, and 307 patients from July, 2020 to June, 2021 were enrolled in the validation cohort. Independent risk factors for SPPH were obtained by using the multivariate logistic regression, and preoperative nomogram and intraoperative nomogram were developed, respectively. We compared the discrimination, calibration, and net benefit of the two nomograms in the development cohort and validation cohort. Then, we tested whether the intraoperative nomogram could be used before operation.

Results: There were 204 patients (23.58%) in development cohort and 80 patients (26.06%) in validation cohort experienced SPPH. In development cohort, the areas under the receiver operating characteristic (ROC) curve (AUC) of the preoperative nomogram and intraoperative nomogram were 0.831 (95% CI, 0.804, 0.855) and 0.880 (95% CI, 0.854, 0.905), respectively. In validation cohort, the AUC of the preoperative nomogram and intraoperative nomogram were 0.825 (95% CI, 0.772, 0.877) and 0.853 (95% CI, 0.808, 0.898), respectively. In the validation cohort, the AUC was 0.839 (95% CI, 0.789, 0.888) when the intraoperative nomogram was used before operation.

Conclusion: We developed the preoperative nomogram and intraoperative nomogram to predict the risk of SPPH in women with PP undergoing cesarean delivery. By comparing the discrimination, calibration, and net benefit of the two nomograms in the development cohort and validation cohort, we think that the intraoperative nomogram performed better. Moreover, application of the intraoperative nomogram before operation can still achieve good prediction effect, which can be improved if the severity of placenta accreta spectrum (PAS) can be accurately distinguished preoperatively. We expect to conduct further prospective external validation studies on the intraoperative nomogram to evaluate its application value.

Keywords: multivariate logistic regression; nomogram; placenta previa; prediction model; severe postpartum hemorrhage.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart for study design. ROC, receiver operating characteristic; DCA, decision curve analysis; Se, sensitivity; Sp, specificity; FNR, false negative rate; FPR, false positive rate; PPV, positive predictive value; NPV, negative predictive value; AUC, area under the receiver operating characteristic curve; YDI, Youden's index.
Figure 2
Figure 2
Nomogram. (A) Preoperative nomogram. (B) Intraoperative nomogram. DGA, delivery gestational age; HGB, hemoglobin; PB, preoperative bleeding; AP, anterior placenta; PPT, placenta previa type; LL, low-lying; PP, placenta previa; BFS, blood flow signal of lower uterine segment; PS, placental sinuses; CDs, number of prior cesarean delivery; VE, vascular engorgement of lower uterine segment; PAS, placenta accrete spectrum; SPPH, severe postpartum hemorrhage.
Figure 3
Figure 3
Calibration cures, ROC cures and DCA cures. (A) Calibration cure of preoperative model in development cohort. (B) Calibration cure of intraoperative model in development cohort. (C) Calibration cure of preoperative model in validation cohort. (D) Calibration cure of intraoperative model in validation cohort. (E) ROC curve in development cohort. (F) ROC curve in validation cohort. (G) DCA curve in development cohort. (H) DCA curve in validation cohort.
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