Sleep apnea phenotyping and relationship to disease in a large clinical biobank.
Sleep apnea phenotyping and relationship to disease in a large clinical biobank.
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DOI：
10.1093/jamiaopen/ooab117
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发表时间：
2022-04
期刊：
影响因子：
2.1
通讯作者：
Karlson EW
中科院分区：
文献类型：
--
作者：
Cade BE;Hassan SM;Dashti HS;Kiernan M;Pavlova MK;Redline S;Karlson EW
睡眠呼吸暂停与广泛的病理生理学有关。虽然电子健康记录(EHR)信息有可能揭示睡眠呼吸暂停与相关风险因素和结果之间的关系，但实际挑战阻碍了它的使用。我们的目标是开发一种睡眠呼吸暂停表型算法，利用自然语言处理(NLP)提高EHR病例/控制信息的精确度；在大型临床生物库中识别睡眠呼吸暂停和共病之间的新关联；并使用NLP表型技术调查多导睡眠图统计数据和共病疾病之间的关系。我们对300名被怀疑患有睡眠呼吸暂停的参与者进行了临床图表审查，并应用国际睡眠障碍分类标准对真实病例和非病例进行了分类。我们评估了2种NLP方法和仅使用诊断代码的方法，以最大限度地提高表型精度。Lead算法用于识别睡眠呼吸暂停和常见合并症之间的事件和横断面关联，使用4876例NLP定义的睡眠呼吸暂停病例和3×匹配的对照组。与使用一个诊断代码(≥0.943)相比，优化的NLP表型策略提高了模型精度(≤0.733)。在所测试的疾病中，170种疾病在患者和对照组之间具有显著的发病率优势比(OR)，其中8种疾病通过多导睡眠图(n = 4544)证实，281种疾病在睡眠呼吸暂停患者和对照组之间具有显著的患病率OR，其中41种疾病通过多导睡眠图数据得到确认。NLP信息算法可以提高病例对照睡眠呼吸暂停确定的准确性，从而改善对高度流行的疾病的表型、遗传和其他EHR分析的性能。睡眠呼吸暂停是一种常见疾病，睡眠时呼吸部分或完全停止，导致血液中氧气减少，反复醒来，并增加发展为多种疾病的风险。由于进行夜间睡眠记录的挑战，目前关于睡眠呼吸暂停的研究参与者往往相对较少。电子健康记录(EHR)计费代码对睡眠呼吸暂停的诊断可以改变用途，以增加研究研究的规模，但诊断的准确性会降低。我们开发了一种可重复使用的算法，使用自然语言处理从临床病历中提取信息来提高EHR睡眠呼吸暂停诊断的准确性。作为概念的证明，我们使用该算法识别了数百种疾病，与没有睡眠呼吸暂停的类似患者相比，患有睡眠呼吸暂停的参与者增加了数百种疾病。这些疾病中的许多与睡眠呼吸暂停之间的关系以前没有被认识到。这一改进的算法将有助于加快未来对睡眠呼吸暂停的原因和后果的大规模调查。
Sleep apnea is associated with a broad range of pathophysiology. While electronic health record (EHR) information has the potential for revealing relationships between sleep apnea and associated risk factors and outcomes, practical challenges hinder its use. Our objectives were to develop a sleep apnea phenotyping algorithm that improves the precision of EHR case/control information using natural language processing (NLP); identify novel associations between sleep apnea and comorbidities in a large clinical biobank; and investigate the relationship between polysomnography statistics and comorbid disease using NLP phenotyping. We performed clinical chart reviews on 300 participants putatively diagnosed with sleep apnea and applied International Classification of Sleep Disorders criteria to classify true cases and noncases. We evaluated 2 NLP and diagnosis code-only methods for their abilities to maximize phenotyping precision. The lead algorithm was used to identify incident and cross-sectional associations between sleep apnea and common comorbidities using 4876 NLP-defined sleep apnea cases and 3× matched controls. The optimal NLP phenotyping strategy had improved model precision (≥0.943) compared to the use of one diagnosis code (≤0.733). Of the tested diseases, 170 disorders had significant incidence odds ratios (ORs) between cases and controls, 8 of which were confirmed using polysomnography (n = 4544), and 281 disorders had significant prevalence OR between sleep apnea cases versus controls, 41 of which were confirmed using polysomnography data. An NLP-informed algorithm can improve the accuracy of case-control sleep apnea ascertainment and thus improve the performance of phenome-wide, genetic, and other EHR analyses of a highly prevalent disorder. Sleep apnea is a common disease in which breathing partially or completely pauses during sleep, leading to less oxygen in the blood, repeated awakenings, and increased risk of developing multiple diseases. Current studies of sleep apnea often have relatively few participants due to the challenge of performing overnight sleep recordings. Electronic health record (EHR) billing code diagnoses of sleep apnea could be repurposed to increase the size of research studies, but the accuracy of the diagnoses is reduced. We developed a reusable algorithm that improves the accuracy of EHR sleep apnea diagnoses using natural language processing to extract information from clinical notes. As a proof of concept, we used the algorithm to identify hundreds of diseases that are increased among participants with sleep apnea compared to similar patients without sleep apnea. Many of these disease relationships with sleep apnea have not been previously recognized. This improved algorithm will help to accelerate future large-scale investigations of the causes and consequences of sleep apnea.
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影响因子：
3.7
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Liao KP;Ananthakrishnan AN;Kumar V;Xia Z;Cagan A;Gainer VS;Goryachev S;Chen P;Savova GK;Agniel D;Churchill S;Lee J;Murphy SN;Plenge RM;Szolovits P;Kohane I;Shaw SY;Karlson EW;Cai T
通讯作者：
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通讯作者：
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影响因子：
3.5
作者：
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通讯作者：
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影响因子：
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作者：
Karlson, Elizabeth W.;Boutin, Natalie T.;Allen, Nicole L.
通讯作者：
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