Wang Chuan, He Zhihong, Lio Ka U, Shi Haoting, Wang Jieying, Zhang Yu, Zhang Ning
Department of Obstetrics and Gynecology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,
Department of Obstetrics and Gynecology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Gynecol Obstet Invest. 2025;90(2):153-164. doi: 10.1159/000541721. Epub 2024 Oct 5.
The study aimed to identify factors influencing the severity of primary immune thrombocytopenia (ITP) during pregnancy, develop a predictive model for treatment response, and report maternal and neonatal outcomes associated with severe ITP.
A retrospective analysis was conducted on 155 pregnancies with severe ITP between January 2018 and April 2023 at a tertiary critical maternity referral center in Shanghai, China. Participants/Materials: The study included 155 pregnancies diagnosed with severe ITP, divided into groups based on the lowest platelet count (<30 × 109/L vs. 30-50 × 109/L) and first-line treatment response (non-response vs. response).
The study was conducted at Renji Hospital, Shanghai Jiao Tong University School of Medicine, a tertiary critical maternity rescue referral center.
Clinical characteristics and outcomes were compared between groups. A multivariable logistic regression model was used to identify factors associated with the severity of ITP. A prediction model for treatment response was established using LASSO-logistic regression and internally validated.
ITP severity was found to be correlated with low maximum amplitude of thromboelastography (OR 5.43, 95% CI: 1.48-16.00, p = 0.002), bleeding events (OR 4.91, 95% CI: 1.62-14.86, p = 0.005), and low reticulocytes (OR 2.40 × 10-7, 95% CI: 1.06 × 10-13 to 0.55, p = 0.04). Of the 118 patients who received first-line therapy, 52 (44%) responded. The dataset was randomly split into a training (N = 99) and test (N = 23) set with a ratio of 8:2. A predictive nomogram was created and internally validated showing good discrimination. The model yielded an area under receiver operating characteristic curve of 0.78 (0.69-0.87) and 0.85 (0.67-1.00) in the training and validation cohort, respectively. Earlier delivery and high rate of neonatal intensive care unit admission occurred with severe ITP and treatment failure.
The study was limited by a relatively small sample size and the retrospective observational design, which imposed limitations on the assessment of treatment efficacy.
We identified clinical predictors of ITP severity and treatment resistance during pregnancy. A nomogram predicting first-line response was validated. These findings can facilitate clinical decision-making and counseling regarding this challenging pregnancy complication.
The study aimed to identify factors influencing the severity of primary immune thrombocytopenia (ITP) during pregnancy, develop a predictive model for treatment response, and report maternal and neonatal outcomes associated with severe ITP.
A retrospective analysis was conducted on 155 pregnancies with severe ITP between January 2018 and April 2023 at a tertiary critical maternity referral center in Shanghai, China. Participants/Materials: The study included 155 pregnancies diagnosed with severe ITP, divided into groups based on the lowest platelet count (<30 × 109/L vs. 30-50 × 109/L) and first-line treatment response (non-response vs. response).
The study was conducted at Renji Hospital, Shanghai Jiao Tong University School of Medicine, a tertiary critical maternity rescue referral center.
Clinical characteristics and outcomes were compared between groups. A multivariable logistic regression model was used to identify factors associated with the severity of ITP. A prediction model for treatment response was established using LASSO-logistic regression and internally validated.
ITP severity was found to be correlated with low maximum amplitude of thromboelastography (OR 5.43, 95% CI: 1.48-16.00, p = 0.002), bleeding events (OR 4.91, 95% CI: 1.62-14.86, p = 0.005), and low reticulocytes (OR 2.40 × 10-7, 95% CI: 1.06 × 10-13 to 0.55, p = 0.04). Of the 118 patients who received first-line therapy, 52 (44%) responded. The dataset was randomly split into a training (N = 99) and test (N = 23) set with a ratio of 8:2. A predictive nomogram was created and internally validated showing good discrimination. The model yielded an area under receiver operating characteristic curve of 0.78 (0.69-0.87) and 0.85 (0.67-1.00) in the training and validation cohort, respectively. Earlier delivery and high rate of neonatal intensive care unit admission occurred with severe ITP and treatment failure.
The study was limited by a relatively small sample size and the retrospective observational design, which imposed limitations on the assessment of treatment efficacy.
We identified clinical predictors of ITP severity and treatment resistance during pregnancy. A nomogram predicting first-line response was validated. These findings can facilitate clinical decision-making and counseling regarding this challenging pregnancy complication.
本研究旨在确定影响妊娠期原发性免疫性血小板减少症（ITP）严重程度的因素，建立治疗反应预测模型，并报告与严重ITP相关的母婴结局。
对2018年1月至2023年4月在中国上海一家三级重症产科转诊中心的155例重度ITP妊娠进行回顾性分析。参与者/材料：该研究纳入155例诊断为重度ITP的妊娠，根据最低血小板计数（<30×10⁹/L与30 - 50×10⁹/L）和一线治疗反应（无反应与有反应）分组。
该研究在上海交通大学医学院附属仁济医院进行，这是一家三级重症产科急救转诊中心。
比较各组的临床特征和结局。使用多变量逻辑回归模型确定与ITP严重程度相关的因素。使用LASSO逻辑回归建立治疗反应预测模型并进行内部验证。
发现ITP严重程度与血栓弹力图最大振幅降低（比值比5.43，95%置信区间：1.48 - 16.00，p = 0.002）、出血事件（比值比4.91，95%置信区间：1.62 - 14.86，p = 0.005）和网织红细胞降低（比值比2.40×10⁻⁷，95%置信区间：1.06×10⁻¹³至0.55，p = 0.04）相关。在118例接受一线治疗的患者中，52例（44%）有反应。数据集以8:2的比例随机分为训练集（N = 99）和测试集（N = 23）。创建了一个预测列线图并进行内部验证，显示出良好的区分度。该模型在训练队列和验证队列中的受试者操作特征曲线下面积分别为0.78（0.69 - 0.87）和0.85（0.67 - 1.00）。重度ITP和治疗失败会导致早产和新生儿重症监护病房入住率升高。
本研究受样本量相对较小和回顾性观察设计的限制，这对治疗效果的评估造成了局限性。
我们确定了妊娠期ITP严重程度和治疗抵抗的临床预测因素。验证了预测一线反应的列线图。这些发现有助于针对这种具有挑战性的妊娠并发症进行临床决策和咨询。
本研究旨在确定影响妊娠期原发性免疫性血小板减少症（ITP）严重程度的因素，建立治疗反应预测模型，并报告与严重ITP相关的母婴结局。
对2018年1月至2023年4月在中国上海一家三级重症产科转诊中心的155例重度ITP妊娠进行回顾性分析。参与者/材料：该研究纳入155例诊断为重度ITP的妊娠，根据最低血小板计数（<30×10⁹/L与30 - 50×10⁹/L）和一线治疗反应（无反应与有反应）分组。
该研究在上海交通大学医学院附属仁济医院进行，这是一家三级重症产科急救转诊中心。
比较各组的临床特征和结局。使用多变量逻辑回归模型确定与ITP严重程度相关的因素。使用LASSO逻辑回归建立治疗反应预测模型并进行内部验证。
发现ITP严重程度与血栓弹力图最大振幅降低（比值比5.43，95%置信区间：1.48 - 16.00，p = 0.002）、出血事件（比值比4.91，95%置信区间：1.62 - 14.86，p = 0.005）和网织红细胞降低（比值比2.40×10⁻⁷，95%置信区间：1.06×10⁻¹³至0.55，p = 0.04）相关。在118例接受一线治疗的患者中，52例（44%）有反应。数据集以8:2的比例随机分为训练集（N = 99）和测试集（N = 23）。创建了一个预测列线图并进行内部验证，显示出良好的区分度。该模型在训练队列和验证队列中的受试者操作特征曲线下面积分别为0.78（0.69 - 0.87）和0.85（0.67 - 1.00）。重度ITP和治疗失败会导致早产和新生儿重症监护病房入住率升高。
本研究受样本量相对较小和回顾性观察设计的限制，这对治疗效果的评估造成了局限性。
我们确定了妊娠期ITP严重程度和治疗抵抗的临床预测因素。验证了预测一线反应的列线图。这些发现有助于针对这种具有挑战性的妊娠并发症进行临床决策和咨询。
