Development and validation of a prediction model for hypotension after neuraxial anesthesia in preeclamptic parturients: a multicenter retrospective study - Su, Jie; Chen, Qumin; Ye, Xiuhong; Lu, Huan (Frontiers in Medicine, 2025)
ObjectiveTo develop and validate a multivariate prediction model for hypotension following neuraxial anesthesia in preeclamptic parturients.MethodsThis multicenter retrospective study analyzed 1,402 preeclamptic parturients (gestational age ≥28 weeks) from three tertiary centers (2013–2024). After exclusions (n = 569), 833 patients were allocated to training (n = 495), internal validation (n = 213), and external validation (n = 125) cohorts. Multivariable logistic regression identified independent predictors, with subsequent nomogram construction. Model performance was assessed via discrimination (AUC), calibration (Hosmer-Lemeshow), and clinical utility [decision curve analysis (DCA), clinical impact curves (CIC)].ResultsSeven independent predictors were identified: platelet count (OR 0.920, 95%CI 0.876–0.966), sFlt-1/PlGF ratio (OR 1.039, 95%CI 1.002–1.078), baseline perfusion index (OR 0.221, 95%CI 0.101–0.485), T6 anesthesia level (OR 11.353, 95%CI 1.408–29.320), local anesthetic dose (OR 29.391, 95%CI 4.792–38.270), fetal weight (OR 1.004, p = 0.045), and umbilical artery S/D ratio (OR 9.319, p < 0.001). The nomogram demonstrated robust discrimination (training AUC 0.851; internal validation AUC 0.836; external validation AUC 0.810) and calibration (mean absolute errors: 0.013–0.038). DCA confirmed clinical utility at a 45% risk threshold (net benefit 0.62), capturing 85% of events with 32% false positives.ConclusionThis validated prediction model accurately stratifies hypotension risk in preeclamptic parturients receiving neuraxial anesthesia. The nomogram facilitates targeted prophylactic interventions, optimizing resource allocation and maternal hemodynamic stability.