Buciu Victor Bogdan, Novacescu Dorin, Zara Flavia, Șerban Denis Mihai, Tomescu Larisa, Ciurescu Sebastian, Olariu Sebastian, Rakitovan Marina, Armega-Anghelescu Antonia, Cindrea Alexandu Cristian, Ionac Mihai, Chiriac Veronica-Daniela
Doctoral School, "Victor Babes" University of Medicine and Pharmacy Timisoara, E. Murgu Square, No. 2, 300041 Timisoara, Romania.
Department II of Microscopic Morphology, "Victor Babes" University of Medicine and Pharmacy Timisoara, E. Murgu Square, No. 2, 300041 Timisoara, Romania.
J Clin Med. 2025 May 13;14(10):3398. doi: 10.3390/jcm14103398.
Pre-eclampsia is a significant hypertensive disorder affecting 2-8% of pregnancies globally, significantly contributing to maternal/perinatal deaths. Early identification of at-risk patients is crucial for reducing these mortalities, yet first-trimester screening remains inaccessible in many low-resource settings. This study aims to develop a second-trimester risk stratification model based on clinical parameters to assist in managing pre-eclampsia in diverse healthcare contexts. This retrospective cohort study analyzed medical records from 700 pregnancies (350 with preeclampsia, 350 controls) between January 2021 and August 2024 at a tertiary medical center in western Romania. Sample size was calculated to achieve 90% power with α = 0.05 for detecting clinically significant differences between groups. Data analysis focused on clinical variables such as maternal age, hypertension, diabetes, and socioeconomic factors. A scoring model was developed using logistic regression and validated for predictive accuracy using ROC curve analysis, with AUC as the primary metric. Calibration was assessed using the Hosmer-Lemeshow test. The risk stratification model demonstrated an AUC of 0.91 (95% CI: 0.88-0.94), indicating high discriminative capability. The model showed good calibration ( = 0.78). Sensitivity was 74.4%, and specificity reached 97.8%. Patients were categorized into low (0-4 points), moderate (5-7 points), and high-risk (≥8 points) groups based on optimized cut-off values. High-risk patients showed significantly higher rates of adverse outcomes, including eclampsia (12.3% vs. 0% in low-risk, < 0.001) and HELLP syndrome (8.7% vs. 0.5% in low-risk, < 0.001). Neonates born to high-risk mothers had lower birth weight (mean difference: 486 g, < 0.001), smaller head circumference (mean difference: 2.3 cm, < 0.001), and lower APGAR scores (median difference: 2 points, < 0.001). This novel model offers a practical second-trimester risk assessment tool that leverages routine clinical data available after 20 weeks of gestation. It facilitates targeted care and resource allocation, particularly benefiting settings lacking early screening access. Implementation of risk-stratified management protocols could significantly improve maternal and neonatal outcomes in diverse healthcare environments.
子痫前期是一种严重的高血压疾病，影响全球2%-8%的妊娠，是孕产妇/围产期死亡的重要原因。早期识别高危患者对于降低这些死亡率至关重要，但在许多资源匮乏地区，孕早期筛查仍难以实现。本研究旨在基于临床参数开发一种孕中期风险分层模型，以协助在不同医疗环境中管理子痫前期。这项回顾性队列研究分析了罗马尼亚西部一家三级医疗中心2021年1月至2024年8月期间700例妊娠（350例子痫前期患者，350例对照）的病历。计算样本量以在α = 0.05时达到90%的检验效能，用于检测组间具有临床意义的差异。数据分析聚焦于临床变量，如产妇年龄、高血压、糖尿病和社会经济因素。使用逻辑回归建立评分模型，并使用ROC曲线分析验证预测准确性，以AUC作为主要指标。使用Hosmer-Lemeshow检验评估校准情况。风险分层模型的AUC为0.91（95%CI：0.88-0.94），表明具有较高的判别能力。该模型显示出良好的校准（ = 0.78）。敏感性为74.4%，特异性达到97.8%。根据优化的临界值，将患者分为低风险（0-4分）、中度风险（5-7分）和高风险（≥8分）组。高风险患者出现不良结局的发生率显著更高，包括子痫（12.3% vs.低风险组的0%， < 0.001）和HELLP综合征（8.7% vs.低风险组的0.5%， < 0.001）。高风险母亲所生新生儿的出生体重较低（平均差异：486 g， < 0.001），头围较小（平均差异：2.3 cm， < 0.001），阿氏评分较低（中位数差异：2分， < 0.001）。这种新型模型提供了一种实用的孕中期风险评估工具，利用妊娠20周后可获得的常规临床数据。它有助于进行有针对性的护理和资源分配，尤其有利于缺乏早期筛查条件的地区。实施风险分层管理方案可显著改善不同医疗环境中的孕产妇和新生儿结局。
