利用离散时间模型增强慢性鼻窦炎内镜鼻窦手术后的长期复发预测
Enhancing Long-Term Recurrence Prediction in Chronic Rhinosinusitis Following Endoscopic Sinus Surgery Using a Discrete-Time Model.
文献信息
| PMID | 42732470 |
|---|---|
| 原文 | 在 PubMed 查看原文 ↗ |
| 发表日期 | 2026 |
| 作者 | Virat Kirtsreesakul |
| 作者单位 | Department of Otolaryngology, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, 90110, Thailand. |
| 期刊 | Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery |
| SCI 分区 | Q1 |
| IF | 2.9 |
| 研究类型 | AI/ML · 临床 |
| 所属专科 | 鼻科 |
中文摘要
目的： 比较离散时间合并逻辑回归（PLR）模型与连续时间Cox比例风险（Cox）模型在预测慢性鼻窦炎（CRS）患者内镜鼻窦手术（ESS）后2年、5年、10年和15年复发风险方面的预后性能。
研究设计： 回顾性队列研究。
研究地点： 单一三级学术中心。
方法： 共纳入543例接受ESS的CRS患者，分析其复发时间。使用LASSO惩罚的PLR和Cox模型评估了十个预测因子，包括年龄、性别、吸烟状况、哮喘、NSAID过敏、症状持续时间、血嗜酸性粒细胞计数（BEC）、鼻息肉评分（NPS）、MLK discharge-edema子评分（MLK-DE子评分）和Lund-Mackay（LM）评分。使用时间依赖性AUROC、Brier评分、校准、1000次自助法内部验证和决策曲线分析（DCA）评估列线图性能。
结果： 复发率为46.8%。LASSO确定年龄、NSAID过敏、哮喘、症状持续时间、BEC、NPS、MLK-DE子评分和LM评分为关键预测因子，吸烟状况仅在PLR模型中保留。PLR模型显示出数值上更高的区分度（AUROC 0.899-0.912 vs 0.879-0.899），Brier评分相似（0.111-0.144 vs 0.128-0.142）。PLR校准随时间保持良好，而Cox校准在较长随访中下降。尽管Cox在早期时间点表现略好，但PLR在内部验证中显示出稳定的区分度（AUROC 0.863-0.892 vs 0.876-0.897）。DCA显示PLR在所有时间点具有更大且稳定的净获益。
结论： 两种模型在预测长期复发方面均表现良好，但LASSO惩罚的PLR列线图在区分度、校准和临床效用方面随时间提供了数值上更高且更一致的表现。
英文摘要
OBJECTIVE: To compare the prognostic performance of a discrete-time pooled logistic regression (PLR) model and a continuous-time Cox proportional hazards (Cox) model for predicting 2-, 5-, 10-, and 15-year recurrence risk after endoscopic sinus surgery (ESS) in chronic rhinosinusitis (CRS).
STUDY DESIGN: Retrospective cohort study.
SETTING: Single tertiary academic center.
METHODS: A total of 543 CRS patients who underwent ESS were included for analysis of time to recurrence. Ten predictors, including age, sex, smoking status, asthma, NSAID hypersensitivity, symptom duration, blood eosinophil count (BEC), nasal polyp score (NPS), MLK Discharge-Edema subscore (MLK-DE subscore), and Lund-Mackay (LM) score, were evaluated using LASSO-penalized PLR and Cox models. Nomogram performance was assessed using time-dependent AUROC, Brier scores, calibration, 1000-bootstrap internal validation, and decision curve analysis (DCA).
RESULTS: Recurrence occurred in 46.8%. LASSO identified age, NSAID hypersensitivity, asthma, symptom duration, BEC, NPS, MLK-DE subscore, and LM score as key predictors, with smoking status retained only in PLR model. PLR model demonstrated numerically higher discrimination (AUROCs 0.899-0.912 vs 0.879-0.899) with similar Brier scores (0.111-0.144 vs 0.128-0.142). PLR calibration remained strong over time, whereas Cox calibration declined over longer follow-up. Although Cox performed slightly better at early time points, PLR demonstrated consistent discrimination during internal validation (AUROCs 0.863-0.892 vs 0.876-0.897). DCA showed greater and stable net benefit for PLR across all time points.
CONCLUSION: Both models performed well in predicting long-term recurrence, but the LASSO-penalized PLR nomogram provided numerically higher and more consistent discrimination, calibration, and clinical utility over time.