阻塞性睡眠呼吸暂停患者肺部死亡的危险因素：整合Cox回归、随机生存森林和无监督聚类的竞争风险分析

文献信息

中文摘要

引言： 阻塞性睡眠呼吸暂停（OSA）与全因死亡率增加相关，但该人群中肺部死亡的具体决定因素仍鲜有表征。
目的： 在竞争风险框架内，使用互补的统计和机器学习方法，识别OSA患者肺部特异性死亡的独立危险因素。
患者与方法： 我们分析了连续转诊至三级睡眠中心的4368例患者（评估时间为2005年1月至2025年12月），其中2304例为中重度OSA（呼吸暂停低通气指数≥15次/小时）。使用病因特异性Cox回归和Fine-Gray子分布风险模型对肺部死亡（国际疾病分类第十版，J编码）进行建模。随机生存森林（RSF）竞争风险模型量化了变量重要性，Gower距离层次聚类识别了表型。
结果： 在中位（四分位距）随访8.6（6.7-12.5）年（范围0.04-20年）期间，发生101例肺部死亡。在多变量病因特异性Cox模型中，年龄（风险比[HR]，1.11；95% CI，1.09-1.13）、夜间血氧饱和度下降（HR，3.09；95% CI，1.88-5.09）、当前吸烟（HR，2.13；95% CI，1.37-3.30）、体重指数（HR，1.06；95% CI，1.02-1.10）和男性性别（HR，1.61；95% CI，1.01-2.56）是独立预测因素。Fine-Gray模型得出一致结果。RSF的C指数为0.84。聚类识别出4种表型；最高风险聚类的20年累积肺部死亡率接近8%。
结论： 夜间低氧负荷、当前吸烟、年龄、体重指数和心脏代谢合并症是OSA中肺部死亡的主要相关因素。据我们所知，这是首个专门针对OSA肺部死亡的大规模竞争风险分析，在单一队列中整合了病因特异性Cox、Fine-Gray、RSF和聚类。基于机器学习的风险分层识别出临床上不同的表型，可能指导个性化干预；这些发现是假设生成性的，需要前瞻性验证。

英文摘要

INTRODUCTION: Obstructive sleep apnea (OSA) is associated with increased all-cause mortality, yet the specific determinants of pulmonary mortality in this population remain poorly characterized.
OBJECTIVES: To identify independent risk factors for pulmonary-specific death in OSA patients using complementary statistical and machine-learning approaches within a competing risk framework.
PATIENTS AND METHODS: We analyzed 4368 patients consecutively referred to a tertiary sleep center (assessed between January 2005 and December 2025), including 2304 with moderate-to-severe OSA (apnea-hypopnea index ≥15 events/h). Pulmonary mortality (International Classification of Diseases, Tenth Revision, J codes) was modeled using cause-specific Cox regression and Fine-Gray subdistribution hazard models. A random survival forest (RSF) competing risk model quantified variable importance, and Gower-distance hierarchical clustering identified phenotypes.
RESULTS: Over a median (interquartile range) follow-up of 8.6 (6.7-12.5) years (range, 0.04-20 years), 101 pulmonary deaths occurred. In multivariable cause-specific Cox models, age (hazard ratio [HR], 1.11; 95% CI, 1.09-1.13), nocturnal desaturation (HR, 3.09; 95% CI, 1.88-5.09), active smoking (HR, 2.13; 95% CI, 1.37-3.30), body mass index (HR, 1.06; 95% CI, 1.02-1.10), and male sex (HR, 1.61; 95% CI, 1.01-2.56) were independent predictors. Fine-Gray models yielded concordant results. The RSF achieved a C-index of 0.84. Clustering identified 4 phenotypes; the highest-risk cluster reached a 20-year cumulative pulmonary mortality approaching 8%.
CONCLUSIONS: Nocturnal hypoxemic burden, active smoking, age, body mass index, and cardiometabolic comorbidities are the principal correlates of pulmonary mortality in OSA. To our knowledge, this is the first large-scale competing-risk analysis dedicated specifically to pulmonary mortality in OSA, integrating cause-specific Cox, Fine-Gray, RSF, and clustering in a single cohort. Machine-learning-based stratification identifies clinically distinct phenotypes that may guide personalized intervention; these findings are hypothesis-generating and require prospective validation.