Accuracy of the electronic health record's problem list in describing multimorbidity in patients with heart failure in the emergency department.
Accuracy of the electronic health record's problem list in describing multimorbidity in patients with heart failure in the emergency department.
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DOI：
10.1371/journal.pone.0279033
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Casey, Martin F.
中科院分区：
文献类型：
--
作者：
King, Brandon L.;Meyer, Michelle L.;Chari, Srihari, V;Hurka-Richardson, Karen;Bohrmann, Thomas;Chang, Patricia P.;Rodgers, Jo Ellen;Busby-Whitehead, Jan;Casey, Martin F.
心力衰竭（HF）患者经常患有多发性心力衰竭。快速评估多药耐受性对于最大限度地降低有害药物-疾病和药物-药物相互作用的风险非常重要。我们评估了使用电子健康记录（EHR）问题列表识别急诊科（艾德）慢性HF患者共病情况的准确性。对2019年在学术艾德就诊的200例年龄≥65岁、诊断为HF的患者随机样本进行了回顾性病历审查研究。我们评估参与者的慢性病使用：（1）结构化图表审查（金标准）和（2）EHR为基础的算法使用的问题列表。使用医疗保健研究质量机构的Elixhauser Comorgan软件将慢性疾病分为37个疾病领域。对于每个疾病领域，我们报告的灵敏度，特异性，阳性预测值和阴性预测使用EHR为基础的算法。我们计算了类内相关系数（ICC），以评估图表审查和问题列表之间关于Elixhauser域计数的总体一致性。HF患者在病历审查中平均有5.4种慢性疾病（SD 2.1），在基于EHR的问题列表中平均有4.1种慢性疾病（SD 2.1）。最常见的五个领域是单纯性高血压（90%）、肥胖（42%）、慢性肺病（38%）、缺乏性贫血（33%）和糖尿病伴慢性并发症（30.5%）。使用基于EHR的问题列表的阳性预测值和阴性预测值分别为24/37和32/37疾病领域大于90%。基于EHR的问题列表正确识别了每位患者3.7个域，错误分类了每位患者2.0个域。总体而言，比较Elixhauser域计数的ICC为0.77（95% CI：0.71-0.82）。基于EHR的问题列表以中等至良好的准确性捕获ED中HF患者的多项事件。
Patients with heart failure (HF) often suffer from multimorbidity. Rapid assessment of multimorbidity is important for minimizing the risk of harmful drug-disease and drug-drug interactions. We assessed the accuracy of using the electronic health record (EHR) problem list to identify comorbid conditions among patients with chronic HF in the emergency department (ED). A retrospective chart review study was performed on a random sample of 200 patients age ≥65 years with a diagnosis of HF presenting to an academic ED in 2019. We assessed participant chronic conditions using: (1) structured chart review (gold standard) and (2) an EHR-based algorithm using the problem list. Chronic conditions were classified into 37 disease domains using the Agency for Healthcare Research Quality’s Elixhauser Comorbidity Software. For each disease domain, we report the sensitivity, specificity, positive predictive value, and negative predictive of using an EHR-based algorithm. We calculated the intra-class correlation coefficient (ICC) to assess overall agreement on Elixhauser domain count between chart review and problem list. Patients with HF had a mean of 5.4 chronic conditions (SD 2.1) in the chart review and a mean of 4.1 chronic conditions (SD 2.1) in the EHR-based problem list. The five most prevalent domains were uncomplicated hypertension (90%), obesity (42%), chronic pulmonary disease (38%), deficiency anemias (33%), and diabetes with chronic complications (30.5%). The positive predictive value and negative predictive value of using the EHR-based problem list was greater than 90% for 24/37 and 32/37 disease domains, respectively. The EHR-based problem list correctly identified 3.7 domains per patient and misclassified 2.0 domains per patient. Overall, the ICC in comparing Elixhauser domain count was 0.77 (95% CI: 0.71-0.82). The EHR-based problem list captures multimorbidity with moderate-to-good accuracy in patient with HF in the ED.
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