Shao Cuixiang, Chen Qi, Tang Siwen, Wang Chaowen, Sun Renjuan
Department of Obstetrics, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, 214000, People's Republic of China.
Wuxi Medical College, Jiangnan University, Wuxi, Jiangsu, 214122, People's Republic of China.
J Multidiscip Healthc. 2024 Apr 30;17:1953-1969. doi: 10.2147/JMDH.S457818. eCollection 2024.
This study aimed to create, verify and assess the clinical utility of a prediction model for maternal and neonatal adverse outcomes in pregnant women with hypothyroidism.
A prediction model was developed, and its accuracy was tested using data from a retrospective cohort. The study focused exclusively on female patients diagnosed with hypothyroidism who were admitted to a tertiary hospital. The development and validation cohort comprised individuals who gave birth between 1 October 2020 and 31 December 2022. The primary outcome was a combination of crucial maternal and newborn problems (eg premature births, abortions and neonatal asphyxia). The prediction model was developed using logistic regression. Evaluation of the model's performance was conducted based on its ability to discriminate, calibrate and provide clinical value.
In total, nine variables were chosen to develop the predictive model for adverse maternal and neonatal outcomes during pregnancy with hypothyroidism. The area under the curve of the model for predicting maternal adverse outcomes was 0.845, and that for predicting neonatal adverse outcomes was 0.685. The calibration plots showed good agreement between the nomogram predictions and the actual observations in both the training and validation cohorts. Furthermore, decision curve analysis suggested that the nomograms were clinically useful and had good discriminative power to identify high-risk mother-infant cases.
Two models to predict the risk probability of maternal and neonatal adverse outcomes in pregnant women with hypothyroidism were developed and verified to assist physicians in evaluating maternal and neonatal adverse outcomes throughout pregnancy with hypothyroidism and to facilitate decision-making regarding therapy.
本研究旨在创建、验证并评估甲状腺功能减退孕妇母婴不良结局预测模型的临床实用性。
开发了一个预测模型，并使用回顾性队列数据对其准确性进行测试。该研究仅关注在三级医院就诊的被诊断为甲状腺功能减退的女性患者。开发和验证队列包括在2020年10月1日至2022年12月31日期间分娩的个体。主要结局是关键的母婴问题（如早产、流产和新生儿窒息）的综合情况。使用逻辑回归开发预测模型。基于模型的区分能力、校准能力和提供临床价值的能力对模型性能进行评估。
总共选择了九个变量来开发甲状腺功能减退孕妇孕期母婴不良结局的预测模型。预测母亲不良结局模型的曲线下面积为0.845，预测新生儿不良结局模型的曲线下面积为0.685。校准图显示在训练队列和验证队列中，列线图预测与实际观察结果之间具有良好的一致性。此外，决策曲线分析表明列线图在临床上有用，并且具有良好的区分能力来识别高危母婴病例。
开发并验证了两个预测甲状腺功能减退孕妇母婴不良结局风险概率的模型，以协助医生在甲状腺功能减退孕妇的整个孕期评估母婴不良结局，并促进治疗决策。
