电子健康记录中哮喘急性加重的验证：一项系统综述
2026/06/01
摘要
背景：既往研究表明，用于定义哮喘急性加重的算法和编码列表在不同数据源中存在差异，若报告的话。在电子健康记录（EHR）中定义和验证哮喘急性加重，将有助于通过产生更一致和可比较的证据来改进未来利用EHR进行的哮喘研究。
方法：我们系统回顾了文献，以评估在EHR中定义哮喘急性加重并报告哪些算法具有最高有效性的研究。使用为本次综述改编的QUADAS-2工具来评估偏倚风险。
结果：在检索到的研究中，仅有五项符合纳入标准。符合条件的研究使用的算法包含来自国际疾病分类第九版或第十版（ICD-9或ICD-10）及其版本或修改版本的编码，有效性评分各不相同。在算法中使用ICD-9代码493来检测哮喘急性加重时，敏感性评分在44.8%至91.28%之间，特异性>85%。在索赔数据中将ICD-9代码493.xx用作主要和次要诊断时，所有有效性指标均>85%。使用ICD-10代码J45时，敏感性、特异性和阴性预测值的评分也均>85%。
结论：已使用具有不同有效性的算法在EHR中识别哮喘急性加重。包含ICD-9代码493.xx或ICD-10代码J45以检测哮喘急性加重的算法具有较高的有效性评分。然而，这些研究存在偏倚风险，迫切需要使用稳健方法对用于未来EHR研究的定义进行验证。
Validating exacerbations of asthma in electronic health records: a systematic review
Elizabeth Moore, Zakariah Gassasse, Ian Sinha, Daniel B Hawcutt, Jennifer K Quint
Abstract
Background: Previous studies have shown that the algorithms and code lists used to define asthma exacerbations vary across different sources of data, if reported at all. Defining and validating asthma exacerbations in electronic health records (EHR) would help to improve future research on asthma using EHR by leading to more consistent and comparable evidence.
Methods: We systematically reviewed the literature to evaluate studies that define exacerbations of asthma in EHR and report which algorithms have the highest validity. An adapted version of the QUADAS-2 designed for this review was used to assess risk of bias.
Results: Of the studies yielded by the search, only five met the inclusion criteria. Eligible studies used algorithms that contained codes from versions or modifications of either the 9th or 10th revisions of the International Statistical Classification of Diseases and Related Health (ICD-9 or ICD-10), and validity scores varied. Using the ICD-9 code 493 within algorithms to detect asthma exacerbations, sensitivity scores varied from 44.8% to 91.28% and specificity was >85%. Using the ICD-9 code 493.xx as the principal and secondary diagnosis in claims data, validity measures were all >85%. Using the ICD-10 code J45, scores for sensitivity, specificity and negative predictive value were also all >85%.
Conclusions: Algorithms have been used to identify asthma exacerbations in EHR with varying degrees of validity. Algorithms including the ICD-9 code 493.xx or the ICD-10 code J45 to detect asthma exacerbations had high validity scores. However, there was a risk of bias in these studies and urgent work is needed using robust methods to validate definitions for future research using EHR.
背景：既往研究表明，用于定义哮喘急性加重的算法和编码列表在不同数据源中存在差异，若报告的话。在电子健康记录（EHR）中定义和验证哮喘急性加重，将有助于通过产生更一致和可比较的证据来改进未来利用EHR进行的哮喘研究。
方法：我们系统回顾了文献，以评估在EHR中定义哮喘急性加重并报告哪些算法具有最高有效性的研究。使用为本次综述改编的QUADAS-2工具来评估偏倚风险。
结果：在检索到的研究中，仅有五项符合纳入标准。符合条件的研究使用的算法包含来自国际疾病分类第九版或第十版（ICD-9或ICD-10）及其版本或修改版本的编码，有效性评分各不相同。在算法中使用ICD-9代码493来检测哮喘急性加重时，敏感性评分在44.8%至91.28%之间，特异性>85%。在索赔数据中将ICD-9代码493.xx用作主要和次要诊断时，所有有效性指标均>85%。使用ICD-10代码J45时，敏感性、特异性和阴性预测值的评分也均>85%。
结论：已使用具有不同有效性的算法在EHR中识别哮喘急性加重。包含ICD-9代码493.xx或ICD-10代码J45以检测哮喘急性加重的算法具有较高的有效性评分。然而，这些研究存在偏倚风险，迫切需要使用稳健方法对用于未来EHR研究的定义进行验证。
（北京朝阳医院呼吸与危重症医学科 顾宪民 摘译 中日友好医院呼吸与危重症医学科 林江涛 审校）
（J Pediatr Health Care. Jan-Feb 2017;31(1):37-45. doi: 10.1016/j.pedhc.2016.01.005.Eur Respir Rev. 2026 May 27;35(180):260004. doi: 10.1183/16000617.0004-2026. Print 2026 Apr.）
（J Pediatr Health Care. Jan-Feb 2017;31(1):37-45. doi: 10.1016/j.pedhc.2016.01.005.Eur Respir Rev. 2026 May 27;35(180):260004. doi: 10.1183/16000617.0004-2026. Print 2026 Apr.）
Validating exacerbations of asthma in electronic health records: a systematic review
Elizabeth Moore, Zakariah Gassasse, Ian Sinha, Daniel B Hawcutt, Jennifer K Quint
Abstract
Background: Previous studies have shown that the algorithms and code lists used to define asthma exacerbations vary across different sources of data, if reported at all. Defining and validating asthma exacerbations in electronic health records (EHR) would help to improve future research on asthma using EHR by leading to more consistent and comparable evidence.
Methods: We systematically reviewed the literature to evaluate studies that define exacerbations of asthma in EHR and report which algorithms have the highest validity. An adapted version of the QUADAS-2 designed for this review was used to assess risk of bias.
Results: Of the studies yielded by the search, only five met the inclusion criteria. Eligible studies used algorithms that contained codes from versions or modifications of either the 9th or 10th revisions of the International Statistical Classification of Diseases and Related Health (ICD-9 or ICD-10), and validity scores varied. Using the ICD-9 code 493 within algorithms to detect asthma exacerbations, sensitivity scores varied from 44.8% to 91.28% and specificity was >85%. Using the ICD-9 code 493.xx as the principal and secondary diagnosis in claims data, validity measures were all >85%. Using the ICD-10 code J45, scores for sensitivity, specificity and negative predictive value were also all >85%.
Conclusions: Algorithms have been used to identify asthma exacerbations in EHR with varying degrees of validity. Algorithms including the ICD-9 code 493.xx or the ICD-10 code J45 to detect asthma exacerbations had high validity scores. However, there was a risk of bias in these studies and urgent work is needed using robust methods to validate definitions for future research using EHR.
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