Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms
Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms
复制标题
DOI：
10.5664/jcsm.5476
复制
发表时间：
2016-01-01
影响因子：
4.3
通讯作者：
Bianchi, Matt T.
中科院分区：
文献类型：
--
作者：
Ustun, Berk;Westover, Brandon;Bianchi, Matt T.
研究目的：阻塞性睡眠呼吸暂停（OSA）是一种可治疗的发病率和死亡率。然而，大多数OSA患者仍未确诊。我们使用了一种新的机器学习方法SLIM（Supersparse Linear Models）来检验一个假设，即基于常规可用医疗信息的诊断筛查工具将优于仅基于患者报告的睡眠相关症状的诊断筛查工具上级。方法：我们分析了多导睡眠图（PSG）和我们临床睡眠实验室测试的1，922名患者的自我报告临床信息。我们使用SLIM和7种最先进的分类方法来产生OSA筛查的预测模型，这些模型使用的特征来自：（i）自我报告的症状;（ii）原则上可以从电子健康记录中提取的自我报告的医疗信息（人口统计学、合并症）或（iii）两者。对于诊断OSA，我们发现仅使用病史特征的模型性能上级仅使用症状的模型性能，并且与使用所有特征的模型性能相似。性能与其他广泛使用的工具相似：灵敏度为64.2%，特异性为77%。SLIM的准确性是类似的国家的最先进的分类模型应用于此dataset，但具有完全透明的好处，允许动手预测使用是/否回答少量的临床querions.Conclusion：预测OSA，变量，如年龄，性别，BMI和病史是上级的症状变量，我们检查预测OSA。SLIM产生了一个可操作的临床工具，可以应用于现代电子健康记录中常规可用的数据，这可能有助于自动化，而不是手动，OSA筛查。
Study Objective: Obstructive sleep apnea (OSA) is a treatable contributor to morbidity and mortality. However, most patients with OSA remain undiagnosed. We used a new machine learning method known as SLIM (Supersparse Linear Integer Models) to test the hypothesis that a diagnostic screening tool based on routinely available medical information would be superior to one based solely on patient-reported sleep-related symptoms.Methods: We analyzed polysomnography (PSG) and self-reported clinical information from 1,922 patients tested in our clinical sleep laboratory. We used SLIM and 7 state-of-the-art classification methods to produce predictive models for OSA screening using features from: (i) self-reported symptoms; (ii) self-reported medical information that could, in principle, be extracted from electronic health records (demographics, comorbidities), or (iii) both.Results: For diagnosing OSA, we found that model performance using only medical history features was superior to model performance using symptoms alone, and similar to model performance using all features. Performance was similar to that reported for other widely used tools: sensitivity 64.2% and specificity 77%. SLIM accuracy was similar to state-of-the-art classification models applied to this dataset, but with the benefit of full transparency, allowing for hands-on prediction using yes/no answers to a small number of clinical queries.Conclusion: To predict OSA, variables such as age, sex, BMI, and medical history are superior to the symptom variables we examined for predicting OSA. SLIM produces an actionable clinical tool that can be applied to data that is routinely available in modern electronic health records, which may facilitate automated, rather than manual, OSA screening.Commentary: A commentary on this article appears in this issue on page 159.