癌症患者的阻塞性睡眠呼吸暂停筛查——一种机器学习方法

Karen A Wong, Ankita Paul, Paige Fuentes, Diane C Lim, Anup Das, Miranda Tan
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引用次数: 0

摘要

背景:阻塞性睡眠呼吸暂停(OSA)是一种非常普遍的睡眠障碍,与白天嗜睡、疲劳和癌症患者全因死亡风险增加有关。现有的OSA筛查工具没有考虑到可能增加OSA风险的癌症相关特征的相互作用。研究设计和方法:本研究是一项回顾性研究,研究对象是在一家三级癌症机构接受家庭睡眠呼吸暂停测试(HSAT)评估OSA的癌症患者。使用无监督机器学习(ML)降维并提取与OSA相关的重要特征。将ML分类器应用于主成分,并使用k-fold交叉验证优化模型超参数。随后,在一组接受HSAT测试的患者中,对OSA的训练模型进行了测试,并与STOP-Bang问卷进行了比较。结果从249例患者的训练数据集中,核主成分分析通过降维提取了8个成分,可以解释OSA的最大方差为98%。OSA的预测因素包括吸烟、哮喘、慢性肾病、STOP-Bang评分、种族、糖尿病、头颈胸放射(RT-HNT)、癌症类型和癌症转移。在ML模型中,PCA+RF具有最高的敏感性(96.8%)、特异性(92.3%)、阴性预测值(92%)、F1评分(0.93)和ROC-AUC评分(0.88)。在前瞻性未见测试集上测试时,PCA+RF筛选算法也比单独使用STOP-Bang问卷表现更好。结论PCA+RF ML模型对OSA的筛查准确率最高。RT-HNT病史、癌症转移和癌症类型被确定为OSA的癌症相关危险因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Screening for Obstructive Sleep Apnea in Patients with Cancer – a Machine Learning Approach
Abstract Background Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder associated with daytime sleepiness, fatigue, and increased all-cause mortality risk in cancer patients. Existing screening tools for OSA do not account for the interaction of cancer-related features that may increase OSA risk. Study Design and Methods This is a retrospective study of cancer patients at a single tertiary cancer institution who underwent home sleep apnea test (HSAT) to evaluate for OSA. Unsupervised machine learning (ML) was used to reduce the dimensions and extract significant features associated with OSA. ML classifiers were applied to principal components and model hyperparameters were optimized using k-fold cross validation. Training models for OSA were subsequently tested and compared with the STOP-Bang questionnaire on a prospective unseen test set of patients who underwent an HSAT. Results From a training dataset of 249 patients, kernel principal component analysis extracted 8 components through dimension reduction to explain the maximum variance with OSA at 98%. Predictors of OSA were smoking, asthma, chronic kidney disease, STOP-Bang score, race, diabetes, radiation to head/neck/thorax (RT-HNT), type of cancer, and cancer metastases. Of the ML models, PCA+RF had the highest sensitivity (96.8%), specificity (92.3%), negative predictive value (92%), F1 score (0.93), and ROC-AUC score (0.88). The PCA+RF screening algorithm also performed better than the STOP-Bang questionnaire alone when tested on a prospective unseen test set. Conclusion The PCA+RF ML model had the highest accuracy in screening for OSA in cancer patients. History of RT-HNT, cancer metastases, and type of cancer were identified as cancer-related risk factors for OSA.
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