University of Alabama at Birmingham.
Vanderbilt University Medical Center, Nashville, Tennessee.
Arthritis Care Res (Hoboken). 2023 Aug;75(8):1821-1829. doi: 10.1002/acr.25061. Epub 2023 Feb 19.
Patients with acute gout are frequently treated in the emergency department (ED) and represent a typically underresourced and understudied population. A key limitation for gout research in the ED is the timely ability to identify acute gout patients. Our goal was to refine a multicriteria, electronic medical record alert for gout flares and to determine its diagnostic characteristics in the ED.
The gout flare alert used electronic medical record data from ED nursing notes and was triggered by the term 'gout' preceding past medical history in the chief complaint, the term 'gout' and a musculoskeletal problem in the chief complaint, or the term 'gout' in the problem list and a musculoskeletal chief complaint. We validated its diagnostic properties to assess presence/absence of gout through manual medical record review using adjudicated expert consensus as the gold standard.
In January 2020, we analyzed 202 patient records from 2 university-based EDs; from these records, 57 patients were identified by our gout flare alert, and 145 were identified by other means as potentially having an acute gout flare. The gout flare alert's positive predictive value was 47% (95% confidence interval [95% CI] 34-60%), negative predictive value was 94% (95% CI 90-98%), sensitivity was 75% (95% CI 61-89%), and specificity was 82% (95% CI 76-88%). The diagnostic properties were similar at both institutions.
Our multicomponent gout flare alert had reasonable sensitivity and specificity, albeit a modest positive predictive value. An electronic gout flare alert may help enable the conduct of gout research in the ED setting.
急性痛风患者常在急诊科(ED)接受治疗,他们通常属于资源匮乏且研究不足的人群。痛风在 ED 中的研究受到限制的一个关键因素是及时识别急性痛风患者的能力。我们的目标是改进一种多标准的电子病历痛风发作警报,并确定其在 ED 中的诊断特征。
痛风发作警报使用 ED 护理记录中的电子病历数据,通过以下方式触发:在主要投诉中,在既往病史之前出现“痛风”一词;在主要投诉中,出现“痛风”一词和肌肉骨骼问题;或者在问题列表中出现“痛风”一词和肌肉骨骼主要投诉。我们通过使用裁定专家共识作为金标准的病历手动审查来验证其诊断特性,以评估是否存在/不存在痛风。
2020 年 1 月,我们分析了来自 2 家大学附属医院的 202 份病历;在这些记录中,我们的痛风发作警报识别出 57 例患者,而其他方法识别出 145 例可能患有急性痛风发作的患者。痛风发作警报的阳性预测值为 47%(95%置信区间[95%CI]34-60%),阴性预测值为 94%(95%CI 90-98%),敏感度为 75%(95%CI 61-89%),特异性为 82%(95%CI 76-88%)。这两个机构的诊断特性相似。
我们的多成分痛风发作警报具有合理的敏感度和特异性,尽管阳性预测值适中。电子痛风发作警报可能有助于在 ED 环境中开展痛风研究。