Department of Otorhinolaryngology-Head and Neck Surgery, University of Maryland School of Medicine Baltimore, Baltimore, Maryland, U.S.A.
Department of Pediatrics, University of Maryland School of Medicine Baltimore, Baltimore, Maryland, U.S.A.
Laryngoscope. 2022 Jan;132(1):234-241. doi: 10.1002/lary.29852. Epub 2021 Sep 6.
OBJECTIVES/HYPOTHESIS: Obstructive sleep apnea (OSA) is associated with higher risk of morbidity and mortality related to cardiovascular disease (CVD). Due to overlapping clinical risk factors, identifying high-risk patients with OSA who are likely to develop CVD remains challenging. We aimed to identify baseline clinical factors associated with the future development of CVD in patients with OSA.
Retrospective analysis of prospectively collected data.
We performed a retrospective analysis of 967 adults aged 45 to 84 years and enrolled in the Multi-Ethnic Study of Atherosclerosis. Six machine learning models were created using baseline clinical factors initially identified by stepwise variable selection. The performance of these models for the prediction of additional risk of CVD in OSA was calculated. Additionally, these models were evaluated for interpretability using locally interpretable model-agnostic explanations.
Of the 967 adults without baseline OSA or CVD, 116 were diagnosed with OSA and CVD and 851 with OSA alone 10 years after enrollment. The best performing models included random forest (sensitivity 84%, specificity 99%, balanced accuracy 91%) and bootstrap aggregation (sensitivity 84%, specificity 100%, balanced accuracy 92%). The strongest predictors of OSA and CVD versus OSA alone were fasting glucose >91 mg/dL, diastolic pressure >73 mm Hg, and age >59 years.
In the selected study population of adults without OSA or CVD at baseline, the strongest predictors of CVD in patients with OSA include fasting glucose, diastolic pressure, and age. These results may shape a strategy for cardiovascular risk stratification in patients with OSA and early intervention to mitigate CVD-related morbidity.
3 Laryngoscope, 132:234-241, 2022.
目的/假设：阻塞性睡眠呼吸暂停（OSA）与心血管疾病（CVD）相关的发病率和死亡率升高有关。由于临床危险因素重叠，识别可能发生 CVD 的 OSA 高危患者仍然具有挑战性。我们旨在确定与 OSA 患者未来发生 CVD 相关的基线临床因素。
前瞻性收集数据的回顾性分析。
我们对年龄在 45 至 84 岁之间的 967 名成年人进行了回顾性分析，并将其纳入动脉粥样硬化多民族研究。使用逐步变量选择最初确定的基线临床因素，创建了 6 个机器学习模型。计算这些模型对 OSA 患者 CVD 额外风险预测的性能。此外，还使用局部可解释模型不可知解释对这些模型进行了可解释性评估。
在 967 名无基线 OSA 或 CVD 的成年人中，116 人被诊断为 OSA 和 CVD，851 人在入组 10 年后被诊断为 OSA 单独。表现最佳的模型包括随机森林（敏感性 84%，特异性 99%，平衡准确性 91%）和自举聚合（敏感性 84%，特异性 100%，平衡准确性 92%）。预测 OSA 和 CVD 与 OSA 单独的最强预测因子是空腹血糖>91mg/dL、舒张压>73mmHg 和年龄>59 岁。
在基线无 OSA 或 CVD 的选定成年人群中，预测 OSA 患者 CVD 的最强预测因子包括空腹血糖、舒张压和年龄。这些结果可能为 OSA 患者的心血管风险分层和减轻 CVD 相关发病率制定策略提供依据。
3 喉镜，132：234-241，2022 年。
