Validation of an automated electronic algorithm and "dashboard" to identify and characterize decompensated heart failure admissions across a medical center
Validation of an automated electronic algorithm and "dashboard" to identify and characterize decompensated heart failure admissions across a medical center
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DOI：
10.1016/j.ahj.2016.10.001
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发表时间：
2017-01-01
影响因子：
4.8
通讯作者：
Lenihan, Daniel J.
中科院分区：
文献类型：
--
作者：
Cox, Zachary L.;Lewis, Connie M.;Lenihan, Daniel J.
背景我们的目的是验证第一个全自动的电子心力衰竭（HF）识别算法的诊断性能，并评估HF仪表板系统的实施，该系统具有2个组成部分：失代偿性HF入院的实时识别和疾病特征和药物治疗的准确表征。B型利钠肽>400 pg/mL;承认HF诊断; HF国际疾病分类，第九次修订版，诊断代码病史;静脉利尿剂给药。我们在2个单独的队列中与设盲的提供者小组相比，验证了HF算法中单独（n = 366）和组合（n = 150）组分的诊断准确性。我们在电子病历中构建了一个HF仪表板，描述了HF算法识别的HF入院的疾病和药物治疗。我们评估了HF仪表板的性能超过26个月的临床use.Results，个别的算法组件显示可变的灵敏度和特异性，分别为：B型利钠肽>400 pg/mL（89%和87%）;利尿剂（80%和92%）;和国际疾病分类，第九次修订版，代码（56%和95%）。HF算法实现了高特异性（95%）、阳性预测值（82%）和阴性预测值（85%），但由于缺失提供者生成的识别数据，实现了有限的灵敏度（56%）。HF仪表板识别和表征3147 HF入院超过26 months.Conclusions自动识别和表征系统可以开发和使用的失代偿性HF的诊断具有很大程度的特异性，虽然灵敏度是有限的临床数据输入。
Background We aim to validate the diagnostic performance of the first fully automatic, electronic heart failure (HF) identification algorithm and evaluate the implementation of an HF Dashboard system with 2 components: real-time identification of decompensated HF admissions and accurate characterization of disease characteristics and medical therapy.Methods We constructed an HF identification algorithm requiring 3 of 4 identifiers: B-type natriuretic peptide >400 pg/mL; admitting HF diagnosis; history of HF International Classification of Disease, Ninth Revision, diagnosis codes; and intravenous diuretic administration. We validated the diagnostic accuracy of the components individually (n = 366) and combined in the HF algorithm (n = 150) compared with a blinded provider panel in 2 separate cohorts. We built an HF Dashboard within the electronic medical record characterizing the disease and medical therapies of HF admissions identified by the HF algorithm. We evaluated the HF Dashboard's performance over 26 months of clinical use.Results Individually, the algorithm components displayed variable sensitivity and specificity, respectively: B-type natriuretic peptide >400 pg/mL (89% and 87%); diuretic (80% and 92%); and International Classification of Disease, Ninth Revision, code (56% and 95%). The HF algorithm achieved a high specificity (95%), positive predictive value (82%), and negative predictive value (85%) but achieved limited sensitivity (56%) secondary to missing provider-generated identification data. The HF Dashboard identified and characterized 3147 HF admissions over 26 months.Conclusions Automated identification and characterization systems can be developed and used with a substantial degree of specificity for the diagnosis of decompensated HF, although sensitivity is limited by clinical data input.