Predictive Risk Model for Severe Preeclampsia Associated With Placental Abruption

Background: To evaluate the feasibility and clinical utility of developing a risk prediction model for placental abruption among patients with severe preeclampsia, incorporating maternal age, baseline systolic blood pressure (SBP), baseline diastolic blood pressure (DBP), retroplacental hematoma width, placental growth factor (PlGF), and the soluble fms-like tyrosine kinase-1/PlGF factor (sFlt-1/PlGF) ratio. Methods: This retrospective study enrolled 260 patients with severe preeclampsia who were admitted to the hospital from January 2022 to October 2024. The cases were randomly divided into a training set (n = 182) and a validation set (n = 78) in a 7:3 ratio. The primary outcome was placental abruption. Clinical data, imaging parameters, and biomarker levels were collected. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. A nomogram was subsequently developed, and its predictive performance was evaluated and validated. Results: The incidence of placental abruption was 35.16% (64/182) in the training set and 33.33% (26/78) in the validation set. Multivariate logistic regression analysis identified age, baseline SBP, baseline DBP, retroplacental hematoma width, PlGF, and the sFlt-1/PlGF ratio as independent risk factors (all p < 0.05). The nomogram demonstrated good discriminative ability, with C-index values of 0.890 and 0.848 in the training and validation sets, respectively. The area under the curve (AUC) was 0.890 (95% confidence interval [CI]: 0.827–0.953) and 0.848 (95% CI: 0.733–0.963), respectively. Sensitivity was 0.766 in the training set and 0.588 in the validation set, whereas specificity was 0.890 and 0.944, respectively. Calibration curves showed excellent agreement between predicted and observed outcomes. The Hosmer-Lemeshow test yielded p-values of 0.583 and 0.290, respectively, suggesting good model fit. Conclusions: The nomogram model, incorporating age, baseline SBP, baseline DBP, retroplacental hematoma width, PlGF, and sFlt-1/PlGF ratio effectively predicted the risk of placental abruption in patients with severe preeclampsia. This model may support early clinical intervention. However, the use of single-center data and lack of external validation limit its generalizability, highlighting the need for further verification through multicenter studies.

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Journal
Clinical and Experimental Obstetrics & Gynecology
Published
2026-01-26
DOI
https://doi.org/10.31083/ceog45511
Primary Topic
Maternal and fetal healthcare
Type
article
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article

Predictive Risk Model for Severe Preeclampsia Associated With Placental Abruption

Gang Huang, Youxin Wang, Lan Shen
Clinical and Experimental Obstetrics & Gynecology
Maternal and fetal healthcare
article

Predictive Risk Model for Severe Preeclampsia Associated With Placental Abruption

Gang Huang, Youxin Wang, Lan Shen
article en

Abstract

Background: To evaluate the feasibility and clinical utility of developing a risk prediction model for placental abruption among patients with severe preeclampsia, incorporating maternal age, baseline systolic blood pressure (SBP), baseline diastolic blood pressure (DBP), retroplacental hematoma width, placental growth factor (PlGF), and the soluble fms-like tyrosine kinase-1/PlGF factor (sFlt-1/PlGF) ratio. Methods: This retrospective study enrolled 260 patients with severe preeclampsia who were admitted to the hospital from January 2022 to October 2024. The cases were randomly divided into a training set (n = 182) and a validation set (n = 78) in a 7:3 ratio. The primary outcome was placental abruption. Clinical data, imaging parameters, and biomarker levels were collected. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors. A nomogram was subsequently developed, and its predictive performance was evaluated and validated. Results: The incidence of placental abruption was 35.16% (64/182) in the training set and 33.33% (26/78) in the validation set. Multivariate logistic regression analysis identified age, baseline SBP, baseline DBP, retroplacental hematoma width, PlGF, and the sFlt-1/PlGF ratio as independent risk factors (all p < 0.05). The nomogram demonstrated good discriminative ability, with C-index values of 0.890 and 0.848 in the training and validation sets, respectively. The area under the curve (AUC) was 0.890 (95% confidence interval [CI]: 0.827–0.953) and 0.848 (95% CI: 0.733–0.963), respectively. Sensitivity was 0.766 in the training set and 0.588 in the validation set, whereas specificity was 0.890 and 0.944, respectively. Calibration curves showed excellent agreement between predicted and observed outcomes. The Hosmer-Lemeshow test yielded p-values of 0.583 and 0.290, respectively, suggesting good model fit. Conclusions: The nomogram model, incorporating age, baseline SBP, baseline DBP, retroplacental hematoma width, PlGF, and sFlt-1/PlGF ratio effectively predicted the risk of placental abruption in patients with severe preeclampsia. This model may support early clinical intervention. However, the use of single-center data and lack of external validation limit its generalizability, highlighting the need for further verification through multicenter studies.

Clinical and Experimental Obstetrics & GynecologyVol. 53(1)
Ezhou Central Hospital (CN)
Reduced inequalities
Openalex Percentile: Top 75%
Maternal and fetal healthcare
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