Identifying and Characterizing a Chronic Cough Cohort Through Electronic Health Records
Identifying and Characterizing a Chronic Cough Cohort Through Electronic Health Records
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DOI：
10.1016/j.chest.2020.12.011
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发表时间：
2021-06-04
期刊：
影响因子：
9.6
通讯作者：
Weaver, Jessica P.
中科院分区：
文献类型：
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作者：
Weiner, Michael;Dexter, Paul R.;Weaver, Jessica P.
背景：慢性咳嗽（CC）持续8周或更长时间影响约10%的成年人，并可能导致昂贵的治疗和生活质量下降。不完整的诊断编码使识别电子健康记录（EHRs）中的CC变得复杂。电子病历文本的自然语言处理（NLP）可以提高检出率。研究问题：NLP是否可以用于识别电子病历中的咳嗽，并确定成人和CC患者的特征？研究设计和方法：中西部EHR系统在2005年至2015年期间确定了年龄在18至85岁之间的患者。NLP用于评估除处方和说明书外的文本注释中提到的咳嗽。两名医生和一名生物统计学家分别审查了12组50例病例，并不断改进，直到咳嗽病例的阳性预测值超过90%。使用NLP （International Classification of Diseases，第十版）或药物诊断咳嗽。三次接触跨越56至120天定义了CC。描述性统计总结了患者和接触，包括转诊。结果：优化NLP需要识别和消除咳嗽否认、指示和历史参考文献。在235,457次咳嗽中，23%的人有相关的诊断代码或药物。将慢性咳嗽诊断应用于CC患者中，确定了23,371例患者（61%为女性），NLP单独确定了其中74%的患者；仅诊断或药物就确定了15%。NLP的阳性预测值为97%。3.0%的患者因咳嗽转诊；最初最常见的是肺部医学（64%的转诊）。局限性：一些诊断代码为咳嗽、就诊间隔大于4个月或多次急性咳嗽发作的患者可能被错误分类。解释：NLP成功地识别出了一大批CC患者，大多数患者是通过NLP单独识别出来的，而不是通过诊断或药物。NLP将患者检出率提高了近7倍，解决了识别和表征CC疾病负担能力方面的差距。几乎所有病例似乎都在初级保健中得到处理。识别这些患者对于确定治疗特征和未满足的需求非常重要。
BACKGROUND: Chronic cough (CC) of 8 weeks or more affects about 10% of adults and may lead to expensive treatments and reduced quality of life. Incomplete diagnostic coding complicates identifying CC in electronic health records (EHRs). Natural language processing (NLP) of EHR text could improve detection.RESEARCH QUESTION: Can NLP be used to identify cough in EHRs, and to characterize adults and encounters with CC?STUDY DESIGN AND METHODS: A Midwestern EHR system identified patients aged 18 to 85 years during 2005 to 2015. NLP was used to evaluate text notes, except prescriptions and instructions, for mentions of cough. Two physicians and a biostatistician reviewed 12 sets of 50 encounters each, with iterative refinements, until the positive predictive value for cough encounters exceeded 90%. NLP, International Classification of Diseases, 10th revision, or medication was used to identify cough. Three encounters spanning 56 to 120 days defined CC. Descriptive statistics summarized patients and encounters, including referrals.RESULTS: Optimizing NLP required identifying and eliminating cough denials, instructions, and historical references. Of 235,457 cough encounters, 23% had a relevant diagnostic code or medication. Applying chronicity to cough encounters identified 23,371 patients (61% women) with CC. NLP alone identified 74% of these patients; diagnoses or medications alone identified 15%. The positive predictive value of NLP in the reviewed sample was 97%. Referrals for cough occurred for 3.0% of patients; pulmonary medicine was most common initially (64% of referrals). LIMITATIONS: Some patients with diagnosis codes for cough, encounters at intervals greater than 4 months, or multiple acute cough episodes may have been misclassified.INTERPRETATION: NLP successfully identified a large cohort with CC. Most patients were identified through NLP alone, rather than diagnoses or medications. NLP improved detection of patients nearly sevenfold, addressing the gap in ability to identify and characterize CC disease burden. Nearly all cases appeared to be managed in primary care. Identifying these patients is important for characterizing treatment and unmet needs.