Rao Suchitra, Bozio Catherine, Butterfield Kristen, Reynolds Sue, Reese Sarah E, Ball Sarah, Steffens Andrea, Demarco Maria, McEvoy Charlene, Thompson Mark, Rowley Elizabeth, Porter Rachael M, Fink Rebecca V, Irving Stephanie A, Naleway Allison
Department of Pediatrics, Hospital Medicine and Infectious Diseases, University of Colorado School of Medicine, Aurora, CO, United States.
Centers for Disease Control and Prevention, Atlanta, GA, United States.
JMIR Form Res. 2023 Jan 17;7:e39231. doi: 10.2196/39231.
Electronic health record (EHR) data provide a unique opportunity to study the epidemiology of COVID-19, clinical outcomes of the infection, comparative effectiveness of therapies, and vaccine effectiveness but require a well-defined computable phenotype of COVID-19-like illness (CLI).
The objective of this study was to evaluate the performance of pathogen-specific and other acute respiratory illness (ARI) International Statistical Classification of Diseases-9 and -10 codes in identifying COVID-19 cases in emergency department (ED) or urgent care (UC) and inpatient settings.
We conducted a retrospective observational cohort study using EHR, claims, and laboratory information system data of ED or UC and inpatient encounters from 4 health systems in the United States. Patients who were aged ≥18 years, had an ED or UC or inpatient encounter for an ARI, and underwent a SARS-CoV-2 polymerase chain reaction test between March 1, 2020, and March 31, 2021, were included. We evaluated various CLI definitions using combinations of International Statistical Classification of Diseases-10 codes as follows: COVID-19-specific codes; CLI definition used in VISION network studies; ARI signs, symptoms, and diagnosis codes only; signs and symptoms of ARI only; and random forest model definitions. We evaluated the sensitivity, specificity, positive predictive value, and negative predictive value of each CLI definition using a positive SARS-CoV-2 polymerase chain reaction test as the reference standard. We evaluated the performance of each CLI definition for distinct hospitalization and ED or UC cohorts.
Among 90,952 hospitalizations and 137,067 ED or UC visits, 5627 (6.19%) and 9866 (7.20%) were positive for SARS-CoV-2, respectively. COVID-19-specific codes had high sensitivity (91.6%) and specificity (99.6%) in identifying patients with SARS-CoV-2 positivity among hospitalized patients. The VISION CLI definition maintained high sensitivity (95.8%) but lowered specificity (45.5%). By contrast, signs and symptoms of ARI had low sensitivity and positive predictive value (28.9% and 11.8%, respectively) but higher specificity and negative predictive value (85.3% and 94.7%, respectively). ARI diagnoses, signs, and symptoms alone had low predictive performance. All CLI definitions had lower sensitivity for ED or UC encounters. Random forest approaches identified distinct CLI definitions with high performance for hospital encounters and moderate performance for ED or UC encounters.
COVID-19-specific codes have high sensitivity and specificity in identifying adults with positive SARS-CoV-2 test results. Separate combinations of COVID-19-specific codes and ARI codes enhance the utility of CLI definitions in studies using EHR data in hospital and ED or UC settings.
电子健康记录（EHR）数据为研究新冠病毒病（COVID-19）的流行病学、感染的临床结局、治疗的比较有效性以及疫苗有效性提供了独特的机会，但需要一个明确的类COVID-19疾病（CLI）的可计算表型。
本研究的目的是评估病原体特异性和其他急性呼吸道疾病（ARI）的国际疾病分类第9版和第10版编码在识别急诊科（ED）或紧急护理（UC）以及住院环境中的COVID-19病例方面的性能。
我们使用美国4个医疗系统的ED或UC以及住院患者的EHR、理赔和实验室信息系统数据进行了一项回顾性观察队列研究。纳入年龄≥18岁、因ARI在ED或UC或住院就诊且在2020年3月1日至2021年3月31日期间接受了严重急性呼吸综合征冠状病毒2（SARS-CoV-2）聚合酶链反应检测的患者。我们使用国际疾病分类第10版编码的组合评估了各种CLI定义，如下：COVID-19特异性编码；VISION网络研究中使用的CLI定义；仅ARI体征、症状和诊断编码；仅ARI的体征和症状；以及随机森林模型定义。我们以SARS-CoV-2聚合酶链反应检测阳性作为参考标准，评估了每个CLI定义的敏感性、特异性、阳性预测值和阴性预测值。我们评估了每个CLI定义在不同住院患者队列以及ED或UC队列中的性能。
在90952例住院病例和137067次ED或UC就诊中，SARS-CoV-2检测阳性的分别有5627例（6.19%）和9866例（7.20%）。COVID-19特异性编码在识别住院患者中SARS-CoV-2阳性患者方面具有较高的敏感性（91.6%）和特异性（99.6%）。VISION CLI定义保持了较高的敏感性（95.8%），但特异性降低（45.5%）。相比之下，ARI的体征和症状敏感性和阳性预测值较低（分别为28.9%和11.8%），但特异性和阴性预测值较高（分别为85.3%和94.7%）。仅ARI诊断、体征和症状的预测性能较低。所有CLI定义对ED或UC就诊的敏感性较低。随机森林方法确定了不同的CLI定义，其在住院患者中的性能较高，在ED或UC就诊中的性能中等。
COVID-19特异性编码在识别SARS-CoV-2检测结果阳性的成年人方面具有较高的敏感性和特异性。COVID-19特异性编码和ARI编码的单独组合提高了CLI定义在使用医院以及ED或UC环境中的EHR数据进行的研究中的效用。
