目的:是否可以根据复发性妊娠丢失(RPL)患者的危险因素来预测后续妊娠丢失的风险?
结论:列线图,从通过多变量逻辑回归确定的独立危险因素构建,作为预测RPL患者后续妊娠丢失可能性的可靠工具。
背景:大约1-3%的有生育能力的夫妇经历RPL,一半以上缺乏明确的病因。评估RPL患者随后的妊娠损失率并确定高危人群进行早期干预对于妊娠咨询至关重要。以前的预测模型主要集中在无法解释的RPL上,纳入基线特征,如年龄和以前的妊娠损失数量,有限的实验室和超声指标。
方法:回顾性研究涉及3387例RPL患者,这些患者最初在仁济医院生殖免疫学诊所寻求治疗,上海交通大学医学院,2020年1月1日至2022年12月31日。其中,1153例RPL患者符合纳入标准并纳入分析。
方法:RPL定义为妊娠28周前与同一伴侣发生两次或更多次妊娠损失(包括生化妊娠损失)。包含基本人口统计数据的数据,实验室指标(自身抗体,外周免疫凝血,和内分泌因素),子宫和子宫内膜超声检查结果,随后的妊娠结局通过初始问卷从登记患者中收集,怀孕后每两周就诊一次,医学数据检索,以及对失踪病人的电话随访。R软件用于数据清理,根据妊娠成功和妊娠丢失,以7:3的比例将数据分为训练队列(n=808)和验证队列(n=345).通过多变量逻辑回归确定独立预测因子。制作了一个列线图,通过10倍交叉验证进行评估,并与仅包含年龄和先前妊娠损失数量的模型进行比较。使用AUC评估构建的列线图,校正曲线,决策曲线分析(DCA),和临床影响曲线分析(CICA)。然后将患者分为低风险和高风险亚组。
结果:我们包括年龄,先前怀孕失败的数量,狼疮抗凝药,抗心磷脂IgM,抗磷脂酰丝氨酸/凝血酶原复合物IgM,抗双链DNA抗体,花生四烯酸诱导的血小板聚集,列线图中的凝血酶时间和双侧子宫动脉收缩/舒张比率之和。训练队列的列线图AUC为0.808(95%CI:0.770-0.846),验证队列为0.731(95%CI:0.660-0.802)。分别。10倍交叉验证的AUC范围为0.714至0.925,平均AUC为0.795(95%CI:0.750-0.839)。与仅包含年龄和先前妊娠损失数量的模型相比,列线图的AUC更好。校正曲线,DCA,和CICA显示出良好的一致性和临床适用性。在低危组和高危组之间观察到妊娠损失率的显着差异(P<0.001)。
结论:本研究是回顾性的,重点是单个生殖免疫学诊所的患者,缺少外部验证数据。不能排除胚胎染色体异常对妊娠丢失的潜在影响,以及对所有病例的药物管理影响了妊娠丢失的危险因素的调查和模型的预测功效。
结论:这项研究标志着在开发和验证RPL患者随后妊娠丢失的风险预测列线图以有效分层其风险方面的开创性努力。我们已经将列线图集成到用于临床应用的在线网络工具中。
背景:本研究得到了国家自然科学基金(82071725)的支持。所有作者都没有竞争利益可声明。
背景:不适用。
OBJECTIVE: Could the risk of subsequent pregnancy loss be predicted based on the risk factors of recurrent pregnancy loss (RPL) patients?
CONCLUSIONS: A nomogram, constructed from independent risk factors identified through multivariate logistic regression, serves as a reliable tool for predicting the likelihood of subsequent pregnancy loss in RPL patients.
BACKGROUND: Approximately 1-3% of fertile couples experience RPL, with over half lacking a clear etiological factor. Assessing the subsequent pregnancy loss rate in RPL patients and identifying high-risk groups for early intervention is essential for pregnancy counseling. Previous prediction models have mainly focused on unexplained RPL, incorporating baseline characteristics such as age and the number of previous pregnancy losses, with limited inclusion of laboratory and ultrasound indicators.
METHODS: The retrospective study involved 3387 RPL patients who initially sought treatment at the Reproductive Immunology Clinic of Renji Hospital, Shanghai Jiao Tong University School of Medicine, between 1 January 2020 and 31 December 2022. Of these, 1153 RPL patients met the inclusion criteria and were included in the analysis.
METHODS: RPL was defined as two or more pregnancy losses (including biochemical pregnancy loss) with the same partner before 28 weeks of gestation. Data encompassing basic demographics, laboratory indicators (autoantibodies, peripheral immunity coagulation, and endocrine factors), uterine and endometrial ultrasound results, and subsequent pregnancy outcomes were collected from enrolled patients through initial questionnaires, post-pregnancy visits fortnightly, medical data retrieval, and telephone follow-up for lost patients. R software was utilized for data cleaning, dividing the data into a training cohort (n = 808) and a validation cohort (n = 345) in a 7:3 ratio according to pregnancy success and pregnancy loss. Independent predictors were identified through multivariate logistic regression. A nomogram was developed, evaluated by 10-fold cross-validation, and compared with the model incorporating solely age and the number of previous pregnancy losses. The constructed nomogram was evaluated using the AUC, calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA). Patients were then categorized into low- and high-risk subgroups.
RESULTS: We included age, number of previous pregnancy losses, lupus anticoagulant, anticardiolipin IgM, anti-phosphatidylserine/prothrombin complex IgM, anti-double-stranded DNA antibody, arachidonic acid-induced platelet aggregation, thrombin time and the sum of bilateral uterine artery systolic/diastolic ratios in the nomogram. The AUCs of the nomogram were 0.808 (95% CI: 0.770-0.846) in the training cohort and 0.731 (95% CI: 0.660-0.802) in the validation cohort, respectively. The 10-fold cross-validated AUC ranged from 0.714 to 0.925, with a mean AUC of 0.795 (95% CI: 0.750-0.839). The AUC of the nomogram was superior compared to the model incorporating solely age and the number of previous pregnancy losses. Calibration curves, DCAs, and CICAs showed good concordance and clinical applicability. Significant differences in pregnancy loss rates were observed between the low- and high-risk groups (P < 0.001).
CONCLUSIONS: This study was retrospective and focused on patients from a single reproductive immunology clinic, lacking external validation data. The potential impact of embryonic chromosomal abnormalities on pregnancy loss could not be excluded, and the administration of medication to all cases impacted the investigation of risk factors for pregnancy loss and the model\'s predictive efficacy.
CONCLUSIONS: This study signifies a pioneering effort in developing and validating a risk prediction nomogram for subsequent pregnancy loss in RPL patients to effectively stratify their risk. We have integrated the nomogram into an online web tool for clinical applications.
BACKGROUND: This study was supported by the National Natural Science Foundation of
China (82071725). All authors have no competing interests to declare.
BACKGROUND: N/A.