Howell Daniel, Rogers Linda, Kasarskis Andrew, Twyman Kathryn
Division of Pulmonary and Critical Care, New York University, New York.
Division of Pulmonary and Critical Care, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, New York.
Ann Allergy Asthma Immunol. 2022 Jun;128(6):677-681.e7. doi: 10.1016/j.anai.2022.03.025. Epub 2022 Mar 30.
Asthma is one of the most common chronic health conditions, and to leverage the wealth of data in the electronic medical record (EMR), it is important to be able to accurately identify asthma diagnosis.
We aimed to determine the rule-based algorithm with the most balanced performance for sensitivity and positive predictive value of asthma diagnosis.
We performed a diagnostic accuracy study of multiple rule-based algorithms intended to identify asthma diagnosis in the EMR. Algorithm performance was validated by manual chart review of 795 charts of patients seen in a multisite, tertiary-level, pulmonary specialty clinic using explicit diagnostic criteria to distinguish asthma cases from controls.
An asthma diagnosis anywhere in the medical record had a 97% sensitivity and a 77% specificity for asthma (F-score 80) when tested on a validation set of asthma cases and nonasthma respiratory disease from a pulmonary specialty clinic. The most balanced performance was seen with asthma diagnosis restricted to an encounter, hospital problem, or problem list diagnosis with a sensitivity of 94% and specificity of 85% (F-score 84). High sensitivity was achieved with the modified Health Plan Employer Data and Information Set criteria and high specificity was achieved with the NUgene algorithm, an algorithm developed for identifying asthma cases by EMR for genome-wide association studies.
Asthma diagnosis can be accurately identified for research purposes by restricting to encounter, hospital problem, or problem list diagnosis in a pulmonary specialty clinic. Additional rules lead to steep drop-offs in algorithm sensitivity in our population.
哮喘是最常见的慢性健康问题之一，为了利用电子病历（EMR）中的大量数据，准确识别哮喘诊断非常重要。
我们旨在确定在哮喘诊断的敏感性和阳性预测值方面具有最平衡性能的基于规则的算法。
我们对旨在在EMR中识别哮喘诊断的多种基于规则的算法进行了诊断准确性研究。通过对一家多地点、三级肺专科诊所中795例患者的病历进行人工图表审查，使用明确的诊断标准将哮喘病例与对照区分开来，验证算法性能。
在来自肺专科诊所的哮喘病例和非哮喘呼吸系统疾病的验证集上进行测试时，病历中任何位置的哮喘诊断对哮喘的敏感性为97%，特异性为77%（F值80）。当哮喘诊断仅限于一次就诊、医院问题或问题列表诊断时，性能最为平衡，敏感性为94%，特异性为85%（F值84）。修改后的健康计划雇主数据和信息集标准实现了高敏感性，而NUgene算法实现了高特异性，该算法是为通过EMR识别哮喘病例以进行全基因组关联研究而开发的。
通过在肺专科诊所中将诊断限制为就诊、医院问题或问题列表诊断，可以准确识别用于研究目的的哮喘诊断。在我们的人群中，额外的规则会导致算法敏感性急剧下降。
