{"id":54,"title":"Miscarriage Risk Prediction for Women With Polycystic Ovary Syndrome Who Achieve Clinical Pregnancy After Fresh Embryo Transfer: Multicenter Development, Geographic External Validation, and Prospective Temporal Validation Study","title_zh":"多囊卵巢综合征患者新鲜胚胎移植后临床妊娠的流产风险预测：多中心开发、地理外部验证与前瞻性时间验证研究","url":"https://pubmed.ncbi.nlm.nih.gov/42987309","doi":"10.2196/101829","pmid":"42987309","journal":"J Med Internet Res","kind":"paper","lang":"en","published_at":"2026-11-05T00:00:00+00:00","fetched_at":"2026-11-08T03:43:52+00:00","authors":["Tong J","Zhang Y","Liu Y","Liu L","Shang X","Wu F","Guo H","Zhang S","Zhao K","Tang A","Qu M"],"primary_topic":"art-clinical","topics":[{"slug":"art-clinical","score":12.0,"hits":["clinical pregnancy","fresh embryo transfer","gonadotropin","icsi","in vitro fertilization","intracytoplasmic sperm injection","ivf"]},{"slug":"repro-endo","score":7.0,"hits":["anti-müllerian","luteinizing hormone","pcos","polycystic ovary"]},{"slug":"early-pregnancy","score":5.0,"hits":["miscarriage","miscarriages"]},{"slug":"oocyte","score":4.0,"hits":["ovary"]},{"slug":"male","score":1.0,"hits":["sperm"]},{"slug":"endometrium","score":1.0,"hits":["endometrial"]},{"slug":"ai-compute","score":1.0,"hits":["machine learning"]}],"topic_score":12.0,"preference_score":5.0,"preference_labels":["可转化到 ART 实验室的发现"],"llm_score":78.0,"final_score":83.0,"score_dims":{"novelty":70,"rigor":88,"impact":72,"relevance":95,"translation":70},"recommend_reason":"研究样本量大、完成地理外部与前瞻性时间验证，设计严谨，对PCOS新鲜移植后流产咨询有直接参考价值；但校准漂移明显、阳性预测值有限，模型目前仅能作为风险排序工具，转化尚需本地校准。","keywords":["多囊卵巢综合征","新鲜胚胎移植","流产风险预测","机器学习","XGBoost","外部验证"],"selected":1,"story_id":63,"llm_provider":"deepseek","llm_model":"deepseek-flash","llm_effort":"low","source_key":"pubmed-art","source_name":"PubMed · 辅助生殖临床","source_tier":1,"mention_count":2,"has_interpretation":false,"journal_key":null,"journal_tier":"unknown","journal_if":null,"is_mock":false,"summary_zh":"该多中心研究基于8911个新鲜IVF/ICSI临床妊娠周期，开发并验证用于PCOS患者流产风险的机器学习模型。开发队列7241例（流产973例），外部验证1170例，前瞻性时间验证500例。比较13种算法后锁定XGBoost，内部交叉验证AUC 0.885，外部验证AUC 0.787，前瞻性验证AUC 0.763，存在跨队列判别力衰减与校准漂移。风险四分位分层可稳定富集事件，但阳性预测值有限。SHAP提示AMH、hCG日内膜厚度和基础LH为主要预测因子。模型仅适合风险排序与咨询，不能用于诊断或治疗选择。","summary":"BACKGROUND: Women with polycystic ovary syndrome (PCOS) who achieve clinical pregnancy following fresh in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) carry an elevated miscarriage risk. However, individualized risk stratification after clinical pregnancy confirmation but before outcome determination remains challenging in clinical practice. OBJECTIVE: This study aimed to develop, externally and prospectively validate over time, and interpret a machine learning-based risk stratification model for women with PCOS who had achieved a confirmed clinical pregnancy after fresh IVF/ICSI. METHODS: This multicenter study included 8911 clinical pregnancy IVF/ICSI cycles across development (n=7241; miscarriages: n=973), external validation (n=1170; miscarriages: n=146), and prospective temporal validation (n=500; miscarriages: n=62) cohorts. Prespecified predictors contained no observed missing values; training-fold median imputation was kept only as a leakage-safe fallback. A total of 13 candidate machine learning and traditional statistical algorithms were compared internally using repeated stratified 5-fold cross-validation (5 repeats) within the development cohort; preprocessing and hyperparameter tuning were performed exclusively in training folds. A prelocked Extreme Gradient Boosting (XGBoost) model was carried forward unchanged for external and prospective temporal validation. Cross-fitted logistic recalibration of averaged XGBoost out-of-fold probabilities was evaluated as a secondary development-sample diagnostic analysis. Model performance encompassed discrimination, precision-recall, calibration, threshold-based metrics, decision curve analysis, risk stratification, and Shapley additive explanations-based interpretability. RESULTS: Miscarriage rates were 13.4% (973/7241), 12.5% (146/1170), and 12.4% (62/500) for the development, external validation, and prospective temporal validation cohorts, respectively. In repeated cross-validation, XGBoost (area under the receiver operating characteristic curve [AUC] 0.885, 95% CI 0.874-0.896) and LightGBM (AUC 0.882, 95% CI 0.871-0.893) showed comparable discrimination; the paired XGBoost-LightGBM DeLong test was not significant (Holm-adjusted P=.07). Raw XGBoost out-of-fold probabilities overestimated absolute risk (mean predicted risk 30.3% vs observed risk 13.4%); cross-fitted recalibration within the development cohort improved calibration but was not propagated to validation cohorts. The locked XGBoost model achieved AUCs of 0.787 (95% CI 0.748-0.827; Brier score 0.099) in external validation and 0.763 (95% CI 0.709-0.816; Brier score 0.104) in prospective temporal validation, showing cross-cohort performance attenuation. Risk-quartile stratification using fixed development-cohort cutoffs showed consistent event enrichment across validation cohorts, but positive predictive values remained modest. Shapley additive explanations analysis identified anti-Müllerian hormone, endometrial thickness on the day of human chorionic gonadotropin administration, and basal luteinizing hormone as leading predictors. CONCLUSIONS: The locked XGBoost model using routine preoutcome variables may support miscarriage risk stratification and counseling for women with PCOS with clinical pregnancy after fresh IVF/ICSI. Given cross-cohort discrimination loss and calibration drift, it should serve only as a risk-ranking tool, not for diagnosis or treatment selection. Local calibration monitoring is mandatory before any real-world clinical deployment.","interpretation_basis":null,"fulltext_status":null,"interpretation":null,"fulltext":{"available":false,"status":null,"reason":"尚未抓取全文（未入选、分数未达阈值，或还没跑 fulltext 步骤）","pmcid":null,"license":null,"chars":0,"source":null,"fetched_at":null}}