■这项研究的重点是接受体外受精(IVF)或卵胞浆内单精子注射(ICSI)的患者早期流产的风险。这些患者通常经历升高的压力水平,并且可能由于与反复失败相关的情绪负担而停止治疗。尽管发现了许多导致早期流产的潜在因素,在将这些因素纳入专门针对IVF/ICSI患者的预测模型方面存在研究空白.这项研究的目的是开发一个用户友好的列线图,其中包含相关的危险因素,以预测IVF/ICSI患者的早期流产。通过内部和外部验证,列线图有助于早期识别高风险患者,支持临床医生做出明智的决定。
对2011年1月至2020年12月在孙逸仙纪念医院进行IVF/ICSI治疗的31,307个周期中的20,322个第一周期进行了回顾性分析。排除不合格循环后,包括6,724个第一新鲜周期并随机分成训练数据集(n=4,516)和内部验证数据集(n=2,208)。使用来自另一家医院的外部数据集(n=1,179)进行验证。Logistic和LASSO回归模型确定了危险因素,和多变量逻辑回归构建了列线图。使用AUC评估模型性能,校正曲线,和决策曲线分析(DCA)。
■确定了早期流产的重要风险因素,包括女性年龄,BMI,自然流产的数量,人工流产和医疗流产的数量,基础FSH水平,hCG日子宫内膜厚度,和质量好的胚胎数量。预测列线图表现出良好的拟合度和判别力,训练的AUC值为0.660、0.640和0.615,内部验证,和外部验证数据集,分别。校准曲线与实际结果具有良好的一致性,和DCA证实了临床有用性。亚组分析显示变异;对于老年人亚组(年龄≥35岁),女性年龄,基础FSH水平,可用胚胎的数量是重要的危险因素,而对于年轻的亚组(年龄<35岁),女性年龄,BMI,自然流产的数量,优质胚胎数量显著。
■我们的研究为一般研究人群和特定年龄亚组早期流产的影响因素提供了有价值的见解。为临床医生提供实用建议。我们考虑到人口差异和地区差异的重要性,确保我们的模型在不同人群中的适应性和相关性。结果的用户友好的可视化和子群分析进一步增强了我们研究的适用性和价值。这些发现对知情决策具有重要意义,考虑到IVF/ICSI患者的个体化治疗策略和结局的优化.
This study focuses on the risk of early miscarriage in patients undergoing in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI). These patients commonly experience heightened stress levels and may discontinue treatment due to emotional burdens associated with repeated failures. Despite the identification of numerous potential factors contributing to early miscarriage, there exists a research gap in integrating these factors into predictive models specifically for IVF/ICSI patients. The objective of this study is to develop a user-friendly nomogram that incorporates relevant risk factors to predict early miscarriage in IVF/ICSI patients. Through internal and external validation, the nomogram facilitates early identification of high-risk patients, supporting clinicians in making informed decisions.
A retrospective analysis was conducted on 20,322 first cycles out of 31,307 for IVF/ICSI treatment at Sun Yat-sen Memorial Hospital between January 2011 and December 2020. After excluding ineligible cycles, 6,724 first fresh cycles were included and randomly divided into a training dataset (n = 4,516) and an internal validation dataset (n = 2,208). An external dataset (n = 1,179) from another hospital was used for validation. Logistic and LASSO regression models identified risk factors, and a multivariable logistic regression constructed the nomogram. Model performance was evaluated using AUC, calibration curves, and decision curve analysis (DCA).
Significant risk factors for early miscarriage were identified, including female age, BMI, number of spontaneous abortions, number of induced abortions and medical abortions, basal FSH levels, endometrial thickness on hCG day, and number of good quality embryos. The predictive nomogram demonstrated good fit and discriminatory power, with AUC values of 0.660, 0.640, and 0.615 for the training, internal validation, and external validation datasets, respectively. Calibration curves showed good consistency with actual outcomes, and DCA confirmed the clinical usefulness. Subgroup analysis revealed variations; for the elder subgroup (age ≥35 years), female age, basal FSH levels, and number of available embryos were significant risk factors, while for the younger subgroup (age <35 years), female age, BMI, number of spontaneous abortions, and number of good quality embryos were significant.
Our study provides valuable insights into the impact factors of early miscarriage in both the general study population and specific age subgroups, offering practical recommendations for clinical practitioners. We have taken into account the significance of population differences and regional variations, ensuring the adaptability and relevance of our model across diverse populations. The user-friendly visualization of results and subgroup analysis further enhance the applicability and value of our research. These findings have significant implications for informed decision-making, allowing for individualized treatment strategies and the optimization of outcomes in IVF/ICSI patients.