Development of a predictive model for severe peripartum hemorrhage in placenta accreta spectrum cases under neuraxial anesthesia: a multicenter retrospective analysis

Abstract

Background: The placenta accreta spectrum (PAS) represents a significant risk factor for severe postpartum hemorrhage. Recent studies have demonstrated the safety of neuraxial anesthesia (NA) in cesarean delivery (CD) for patients with PAS.

Objectives: To evaluate the risk of severe peripartum hemorrhage in patients with PAS who underwent CD under NA.

Design: A multicenter retrospective cohort study.

Methods: This study analyzed 214 patients diagnosed with PAS. Logistic regression was used to identify factors increasing the risk of severe peripartum hemorrhage. A total of six machine learning (ML) algorithms were employed for model validation.

Results: The predictive model includes the following risk factors: age at delivery >33 years (p = 0.004), history of CD >1 (p = 0.020), preoperative HGB ⩽ 100 g/L (p = 0.013), placenta previa classification (p = 0.001), vascular lacunae within the placenta (p = 0.015), and labor duration (p = 0.026). The validation of ML algorithms revealed that the model achieved an accuracy ranging from 0.68 to 0.71, with an area under the receiver operating characteristic curve between 0.75 and 0.79. A nomogram list and web-based calculator were constructed for clinical implementation, and a risk stratification system was established based on model scores.

Conclusion: A prenatal risk assessment model was developed to estimate the likelihood of severe peripartum hemorrhage in PAS patients undergoing CD under NA. This model may provide preliminary support for clinicians in tailoring anesthetic management strategies for potentially high-risk cases, but further studies are needed to confirm its clinical utility.

Keywords: cesarean delivery; machine learning; neuraxial anesthesia; placenta accreta spectrum; predictive model.

Figure 1.

Flowchart of the study. PAS, placenta accreta spectrum.

Figure 2.

(a) Calibration curve and (b) DCA curve of the model. DCA, decision curve analysis.

Figure 3.

(a) Nomogram and (b) the web interface using SHINY of the predictive model. HGB, hemoglobin.

Figure 4.

Risk score and risk stratification. (a) The distribution of risk scores and (b) the number of people with and without severe peripartum hemorrhage in the three subgroups.