Validating the 8 CPCSSN Case Definitions for Chronic Disease Surveillance in a Primary Care Database of Electronic Health Records
Validating the 8 CPCSSN Case Definitions for Chronic Disease Surveillance in a Primary Care Database of Electronic Health Records
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DOI：
10.1370/afm.1644
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发表时间：
2014-07-01
影响因子：
4.4
通讯作者：
Drummond, Neil
中科院分区：
文献类型：
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作者：
Williamson, Tyler;Green, Michael E.;Drummond, Neil
加拿大初级保健哨点监测网络（CPCSSN）是加拿大第一个基于电子健康记录（EHR）数据的国家慢性病监测系统。本研究的目的是开发和验证用于识别初级保健中8种常见慢性病的病例定义和病例发现算法：方法采用横断面数据验证研究设计，来自不列颠哥伦比亚省、阿尔伯塔（2）、安大略、新斯科舍省和纽芬兰省参与验证EHR病例发现算法。审查了EHR图表的随机样本，对60岁以上的患者以及癫痫或帕金森病患者进行了过度抽样。图表由受过训练的研究助理和对算法诊断不知情的居民进行审查。灵敏度，特异性，阳性和阴性预测值（PPV，NPVs. RESULTS），我们从1,920图表从4个不同的EHR系统（沃尔夫，Med Access，南丁格尔，PS套件）获得的数据。总样本的灵敏度为78%（骨关节炎）超过95%（糖尿病、癫痫和帕金森综合征）;对所有疾病的特异性大于94%; PPV范围为72%至100%。（痴呆）至93%（高血压）;净现值从86%（高血压）超过99%（糖尿病、痴呆、癫痫和帕金森综合征）。结论CPCSSN诊断算法对高血压显示出极好的敏感性和特异性，糖尿病、癫痫和帕金森综合征以及其他疾病的可接受值。CPCSSN数据适用于公共卫生监测、初级保健和卫生服务研究，以及为这些疾病的政策提供信息。
PURPOSE The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is Canada's first national chronic disease surveillance system based on electronic health record (EHR) data. The purpose of this study was to develop and validate case definitions and case-finding algorithms used to identify 8 common chronic conditions in primary care: chronic obstructive pulmonary disease (COPD), dementia, depression, diabetes, hypertension, osteoarthritis, parkinsonism, and epilepsy.METHODS Using a cross-sectional data validation study design, regional and local CPCSSN networks from British Columbia, Alberta (2), Ontario, Nova Scotia, and Newfoundland participated in validating EHR case-finding algorithms. A random sample of EHR charts were reviewed, oversampling for patients older than 60 years and for those with epilepsy or parkinsonism. Charts were reviewed by trained research assistants and residents who were blinded to the algorithmic diagnosis. Sensitivity, specificity, and positive and negative predictive values (PPVs, NPVs) were calculated.RESULTS We obtained data from 1,920 charts from 4 different EHR systems (Wolf, Med Access, Nightingale, and PS Suite). For the total sample, sensitivity ranged from 78% (osteoarthritis) to more than 95% (diabetes, epilepsy, and parkinsonism); specificity was greater than 94% for all diseases; PPV ranged from 72% (dementia) to 93% (hypertension); NPV ranged from 86% (hypertension) to greater than 99% (diabetes, dementia, epilepsy, and parkinsonism).CONCLUSIONS The CPCSSN diagnostic algorithms showed excellent sensitivity and specificity for hypertension, diabetes, epilepsy, and parkinsonism and acceptable values for the other conditions. CPCSSN data are appropriate for use in public health surveillance, primary care, and health services research, as well as to inform policy for these diseases.