Study Objectives:

Obstructive sleep apnea (OSA) is considered to be an important risk factor for the development of cardiovascular disease (CVD). This study aimed to develop and evaluate a machine learning approach with a set of features for assessing the 10-year CVD mortality risk of the OSA population.

Methods:

This study included 2464 patients with OSA that met study inclusion criteria and were selected from the Sleep Heart Health Study (SHHS). We evaluated the importance of potential features by mutual information. The top 9 features were selected to develop a random forest model.

Results:

We evaluated the model performance on a test set (n=493) using the area under the receiver operating curve (AUC) with 95% confidence interval (CI) and confusion matrix. A random forest model awarded the highest AUC of 0.84 (95% CI: 0.78-0.89). The specificity and sensitivity were 73.94% and 81.82%, respectively. Sixty-three years old was a threshold for increased risk of 10-year CVD mortality. Persons with severe OSA had higher risk than those with mild OSA.

Conclusions:

This study demonstrated that a random forest model can provide a quick assessment of the risk of 10-year CVD mortality. Our model may be more informative for patients with OSA in determining their future CVD mortality risk.

中文翻译：

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睡眠心脏健康研究中阻塞性睡眠呼吸暂停可预测 10 年心血管疾病相关死亡率：一种机器学习方法

 学习目标：

阻塞性睡眠呼吸暂停（OSA）被认为是心血管疾病（CVD）发展的重要危险因素。本研究旨在开发和评估一种具有一系列特征的机器学习方法，用于评估 OSA 人群 10 年 CVD 死亡风险。

 方法：

这项研究纳入了 2464 名 OSA 患者，这些患者均符合研究纳入标准，并选自睡眠心脏健康研究 (SHHS)。我们通过互信息评估了潜在特征的重要性。选择前 9 个特征来开发随机森林模型。

 结果：

我们使用具有 95% 置信区间 (CI) 的受试者工作曲线下面积 (AUC) 和混淆矩阵评估了测试集 (n=493) 上的模型性能。随机森林模型的 AUC 最高为 0.84（95% CI：0.78-0.89）。特异性和敏感性分别为73.94%和81.82%。 63 岁是 10 年 CVD 死亡风险增加的阈值。患有严重 OSA 的人比患有轻度 OSA 的人有更高的风险。

 结论：

这项研究表明，随机森林模型可以快速评估 10 年 CVD 死亡风险。我们的模型可能为 OSA 患者提供更多信息，帮助他们确定未来的 CVD 死亡风险。