Valvi Nimish, McFarlane Timothy, Allen Katie S, Gibson P Joseph, Dixon Brian Edward
Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, United States.
Department of Nutrition and Health Science, College of Health, Ball State University, Muncie, IN, United States.
JMIR Form Res. 2023 Dec 27;7:e46413. doi: 10.2196/46413.
Electronic health record (EHR) systems are widely used in the United States to document care delivery and outcomes. Health information exchange (HIE) networks, which integrate EHR data from the various health care providers treating patients, are increasingly used to analyze population-level data. Existing methods for population health surveillance of essential hypertension by public health authorities may be complemented using EHR data from HIE networks to characterize disease burden at the community level.
We aimed to derive and validate computable phenotypes (CPs) to estimate hypertension prevalence for population-based surveillance using an HIE network.
Using existing data available from an HIE network, we developed 6 candidate CPs for essential (primary) hypertension in an adult population from a medium-sized Midwestern metropolitan area in the United States. A total of 2 independent clinician reviewers validated the phenotypes through a manual chart review of 150 randomly selected patient records. We assessed the precision of CPs by calculating sensitivity, specificity, positive predictive value (PPV), F-score, and validity of chart reviews using prevalence-adjusted bias-adjusted κ. We further used the most balanced CP to estimate the prevalence of hypertension in the population.
Among a cohort of 548,232 adults, 6 CPs produced PPVs ranging from 71% (95% CI 64.3%-76.9%) to 95.7% (95% CI 84.9%-98.9%). The F-score ranged from 0.40 to 0.91. The prevalence-adjusted bias-adjusted κ revealed a high percentage agreement of 0.88 for hypertension. Similarly, interrater agreement for individual phenotype determination demonstrated substantial agreement (range 0.70-0.88) for all 6 phenotypes examined. A phenotype based solely on diagnostic codes possessed reasonable performance (F-score=0.63; PPV=95.1%) but was imbalanced with low sensitivity (47.6%). The most balanced phenotype (F-score=0.91; PPV=83.5%) included diagnosis, blood pressure measurements, and medications and identified 210,764 (38.4%) individuals with hypertension during the study period (2014-2015).
We identified several high-performing phenotypes to identify essential hypertension prevalence for local public health surveillance using EHR data. Given the increasing availability of EHR systems in the United States and other nations, leveraging EHR data has the potential to enhance surveillance of chronic disease in health systems and communities. Yet given variability in performance, public health authorities will need to decide whether to seek optimal balance or declare a preference for algorithms that lean toward sensitivity or specificity to estimate population prevalence of disease.
电子健康记录（EHR）系统在美国被广泛用于记录医疗服务提供情况和治疗结果。健康信息交换（HIE）网络整合了治疗患者的各种医疗服务提供者的EHR数据，越来越多地用于分析人群层面的数据。公共卫生当局现有的原发性高血压人群健康监测方法可以通过使用HIE网络的EHR数据来补充，以描述社区层面的疾病负担。
我们旨在推导并验证可计算表型（CP），以使用HIE网络估计基于人群的监测中的高血压患病率。
利用从一个HIE网络获得的现有数据，我们为美国中西部一个中等规模大都市地区的成年人群开发了6种原发性（原发性）高血压候选CP。共有2名独立的临床医生评审员通过对150份随机选择的患者记录进行人工病历审查来验证这些表型。我们通过计算敏感性、特异性、阳性预测值（PPV）、F分数以及使用患病率调整偏差调整κ来评估图表审查的有效性，从而评估CP的精度。我们进一步使用最平衡的CP来估计人群中的高血压患病率。
在548,232名成年人队列中，6种CP产生的PPV范围为71%（95%CI 64.3%-76.9%）至95.7%（95%CI 84.9%-98.9%）。F分数范围为0.40至0.91。患病率调整偏差调整κ显示高血压的一致性百分比高达0.88。同样，个体表型确定的评分者间一致性表明，所检查的所有6种表型均具有高度一致性（范围为0.70-0.88）。仅基于诊断代码的表型具有合理的性能（F分数=0.63；PPV=95.1%），但不平衡，敏感性低（47.6%）。最平衡的表型（F分数=0.91；PPV=83.5%）包括诊断、血压测量和药物治疗，并在研究期间（2014-2015年）识别出210,764名（38.4%）高血压患者。
我们确定了几种高性能的表型，以使用EHR数据识别当地公共卫生监测中的原发性高血压患病率。鉴于美国和其他国家EHR系统的可用性不断提高，利用EHR数据有可能加强卫生系统和社区中慢性病的监测。然而，鉴于性能的变异性，公共卫生当局将需要决定是寻求最佳平衡还是倾向于选择更倾向于敏感性或特异性的算法来估计疾病的人群患病率。
