Administrative data are not sensitive for the detection of peripheral artery disease in the community.
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.
Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
Yongzhe
Y
Hong
Epidemiology Coordinating and Research (EPICORE) Centre, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
Meghan
M
Sebastianski
Faculty of Pharmacy & Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada.
Mark
M
Makowsky
Department of Medicine, University of Alberta, Edmonton, Alberta, Canada Epidemiology Coordinating and Research (EPICORE) Centre, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
Ross
R
Tsuyuki
Department of Medicine, University of Alberta, Edmonton, Alberta, Canada mcmurtry@ualberta.ca.
M Sean
MS
McMurtry
D000067575:Administrative Claims, Healthcare; D000368:Aged; D000416:Alberta / Q000453:epidemiology; D000465:Algorithms; D055109:Ankle Brachial Index; D057225:Data Mining; D016208:Databases, Factual; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D038801:International Classification of Diseases; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D016017:Odds Ratio; D058729:Peripheral Arterial Disease / Q000145:classification / Q000175:diagnosis* / Q000453:epidemiology*; D011237:Predictive Value of Tests; D015995:Prevalence; D012307:Risk Factors
Canadian Classification of Interventions (CCI); International Classification of Diseases (ICD); administrative data; ankle–brachial index; peripheral artery disease