Administrative data are not sensitive for the detection of peripheral artery disease in the community
Administrative data are not sensitive for the detection of peripheral artery disease in the community
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DOI：
10.1177/1358863x16631041
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发表时间：
2016-08-01
影响因子：
3.5
通讯作者：
McMurtry, M. Sean
中科院分区：
文献类型：
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作者：
Hong, Yongzhe;Sebastianski, Meghan;McMurtry, M. Sean
我们试图评估使用行政健康数据的病例确定是否是从社区中识别外周动脉疾病（PAD）患者的可行方法。将之前两项前瞻性观察性研究中受试者的踝臂指数（ABI）评分与2002年4月至2012年3月三个行政数据库（包括阿尔伯塔住院患者医院数据库）中的国际疾病分类（ICD）和加拿大干预分类（CCI）代码相关联（ICD-10-CA/CCI）、门诊护理数据库（ICD-10-CA/CCI）和从业者支付数据库（ICD-9-CM）。我们使用ABI评分0.90作为金标准，计算了由单个代码或代码集组成的PAD的推定病例定义的诊断统计学。进行多变量logistic回归以研究PAD的其他预测因素。探索诊断代码和预测因素的不同组合，以找出识别PAD研究队列的最佳算法。共有1459例患者纳入我们的分析。平均年龄为63.5岁，66%为男性，PAD患病率为8.1%。使用至少一个ICD诊断或程序代码的算法获得的最高灵敏度为34.7%，特异性为91.9%，阳性预测值（PPV）为27.5%，阴性预测值（NPV）为94.1%。达到最高PPV 65%的算法是年龄70岁，至少有一个代码在443.9（ICD-9-CM）、I73.9、I79.2（ICD-10-CA/CCI）或所有程序代码内，经ABI < 1.0验证（灵敏度5.56%，特异性99.4%和NPV 84.6%）。总之，与ABI相比，使用行政数据分数确定PAD是不敏感的，限制了社区环境中行政数据的使用。
We sought to evaluate whether case ascertainment using administrative health data would be a feasible way to identify peripheral artery disease (PAD) patients from the community. Subjects' ankle-brachial index (ABI) scores from two previous prospective observational studies were linked with International Classification of Diseases (ICD) and Canadian Classification of Interventions (CCI) codes from three administrative databases from April 2002 to March 2012, including the Alberta Inpatient Hospital Database (ICD-10-CA/CCI), Ambulatory Care Database (ICD-10-CA/CCI), and the Practitioner Payments Database (ICD-9-CM). We calculated diagnostic statistics for putative case definitions of PAD consisting of individual code or sets of codes, using an ABI score 0.90 as the gold standard. Multivariate logistic regression was performed to investigate additional predictive factors for PAD. Different combinations of diagnostic codes and predictive factors were explored to find out the best algorithms for identifying a PAD study cohort. A total of 1459 patients were included in our analysis. The average age was 63.5 years, 66% were male, and the prevalence of PAD was 8.1%. The highest sensitivity of 34.7% was obtained using the algorithm of at least one ICD diagnostic or procedure code, with specificity 91.9%, positive predictive value (PPV) 27.5% and negative predictive value (NPV) 94.1%. The algorithm achieving the highest PPV of 65% was age 70 years and at least one code within 443.9 (ICD-9-CM), I73.9, I79.2 (ICD-10-CA/CCI), or all procedure codes, validated with ABI < 1.0 (sensitivity 5.56%, specificity 99.4% and NPV 84.6%). In conclusion, ascertaining PAD using administrative data scores was insensitive compared with the ABI, limiting the use of administrative data in the community setting.