Department of General Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
International Collaborative Research Center for Medical Metabolomics, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Acta Obstet Gynecol Scand. 2024 Jul;103(7):1444-1456. doi: 10.1111/aogs.14832. Epub 2024 Mar 21.
Unexplained recurrent pregnancy loss (URPL), affecting approximately 1%-5% of women, exhibits a strong association with various maternal factors, particularly immune disorders. However, accurately predicting pregnancy outcomes based on the complex interactions and synergistic effects of various immune parameters without an automated algorithm remains challenging.
In this historical cohort study, we analyzed the medical records of URPL patients treated at Xiangya Hospital, Changsha, China, between January 2020 and October 2022. The primary outcomes included clinical pregnancy and miscarriage. Predictors included complement, autoantibodies, peripheral lymphocytes, immunoglobulins, thromboelastography findings, and serum lipids. Least absolute shrinkage and selection operator (LASSO) analysis and logistic regression analysis was performed for model development. The model's performance, discriminatory, and clinical applicability were assessed using area under the curve (AUC), calibration curve, and decision curve analysis, respectively. Additionally, models were visualized by constructing dynamic and static nomograms.
In total, 502 patients with URPL were enrolled, of whom 291 (58%) achieved clinical pregnancy and 211 (42%) experienced miscarriage. Notable differences in complement, peripheral lymphocytes, and serum lipids were observed between the two outcome groups. Moreover, URPL patients with elevated peripheral NK cells (absolute counts and proportion), decreased complement levels, and dyslipidemia demonstrated a significantly increased risk of miscarriage. Four models were developed in this study, of which Model 2 demonstrated superior performance with only seven predictors, achieving an AUC of 0.96 (95% CI: 0.93-0.99) and an accuracy of 0.92. A web-based platform was established to visually present model 2 and to facilitate its utilization by clinicians in outpatient settings (available from: https://yingrongli.shinyapps.io/liyingrong/).
Our findings suggest that the implementation of such prediction models could serve as valuable tools for providing comprehensive information and facilitating clinicians in their decision-making processes.
不明原因复发性妊娠丢失（URPL）影响约 1%-5%的女性，与多种母体因素密切相关，特别是免疫紊乱。然而，在没有自动化算法的情况下，基于各种免疫参数的复杂相互作用和协同效应准确预测妊娠结局仍然具有挑战性。
在这项历史性队列研究中，我们分析了 2020 年 1 月至 2022 年 10 月期间在中国长沙湘雅医院接受治疗的 URPL 患者的病历。主要结局包括临床妊娠和流产。预测因子包括补体、自身抗体、外周淋巴细胞、免疫球蛋白、血栓弹力图结果和血清脂质。采用最小绝对收缩和选择算子（LASSO）分析和逻辑回归分析进行模型开发。使用曲线下面积（AUC）、校准曲线和决策曲线分析分别评估模型的性能、判别和临床适用性。此外，还通过构建动态和静态列线图来可视化模型。
共纳入 502 例 URPL 患者，其中 291 例（58%）实现临床妊娠，211 例（42%）流产。两组间补体、外周淋巴细胞和血清脂质差异显著。此外，URPL 患者外周 NK 细胞（绝对值和比例）升高、补体水平降低和血脂异常，流产风险显著增加。本研究建立了 4 个模型，其中模型 2 仅包含 7 个预测因子，性能最佳，AUC 为 0.96（95%CI：0.93-0.99），准确率为 0.92。建立了一个基于网络的平台，直观地呈现模型 2，并方便临床医生在门诊环境中使用（可从 https://yingrongli.shinyapps.io/liyingrong/ 获取）。
我们的研究结果表明，实施此类预测模型可以作为提供全面信息的有价值工具，并有助于临床医生做出决策。
