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Screening for asymptomatic peripheral artery disease (aPAD) with the ankle-brachial index (ABI) test is hypothesized to reduce disease progression and cardiovascular (CV) events by identifying individuals who may benefit from early initiation of medical therapy. Using a Markov model, we evaluated the cost effectiveness of initiating medical therapy (e.g. statin and ACE-inhibitor) after a positive ankle-brachial index (ABI) screen in 65-year-old patients. We modeled progression to symptomatic PAD (sPAD) and CV events with and without ABI screening, evaluating differences in costs and quality-adjusted life years (QALYs). The cost of the ABI test, physician visit, new medication, CV events, and interventions for sPAD were incorporated in the model. We performed sensitivity analysis on model variables with uncertainty. Our model found an incremental cost of US $338 and an incremental QALY of 0.00380 with one-time ABI screening, resulting in an incremental cost-effectiveness ratio (ICER) of $88,758/QALY over a 35-year period. The variables with the largest effects in the ICER were aPAD disease prevalence, cost of monthly medication after a positive screen and 2-year medication adherence rates. Screening high-risk populations, such as tobacco users, where the prevalence of PAD may be 2.5 times higher, decreases the ICER to $24,092/QALY. Our analysis indicates the cost effectiveness of one-time screening for aPAD depends on prevalence, medication costs, and adherence to therapies for CV disease risk reduction. Screening in higher-risk populations under favorable assumptions about medication adherence results in the most favorable cost effectiveness, but limitations in the primary data preclude definitive assessment of cost effectiveness.

We compared the associations of diabetes mellitus (DM) and other cardiovascular disease (CVD) risk factors with decline in the ankle-brachial index (ABI) over 4 years in participants with and without peripheral artery disease (PAD). A total of 566 participants, 300 with PAD, were followed prospectively for 4 years. Mean (SD) baseline ABI values were 0.70 (0.13) for participants with both PAD and DM, 0.67 (0.14) for participants with only PAD, 1.10 (0.13) for participants with only DM, and 1.10 (0.10) for participants with neither PAD nor DM. After adjusting for age, sex, and baseline ABI, the corresponding ABI change from baseline to 4-year follow-up were -0.02, -0.04, +0.05, and +0.05, respectively. Compared to participants with neither PAD nor DM, participants with only PAD showed significantly more ABI decline (p

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. [ABSTRACT FROM AUTHOR]

Sedentary behavior is an adverse health risk factor that is independent of physical activity. The relationship between sedentary behavior, exercise activity and the ankle-brachial index (ABI) is not well understood. We analyzed data from the National Health and Nutrition Examination Survey (NHANES) 2003–2004. Accelerometer data were used to quantify exercise and sedentary time for each participant. A low ABI was defined as a value <1.0 (including borderline values). Multi-variable adjusted logistic regression analyses were performed with sedentary and exercise times as independent variables, adjusting for important confounders. There were 1443 asymptomatic participants (mean age 61 years, 49% female, 55% current/prior smokers) with mean daily sedentary and exercise times of 454±144 and 18±20 minutes, respectively. Of the participants, 23% had an ABI <1.0 (8.7% with ABI <0.9). Sedentary time was positively associated with a low ABI (odds ratio [OR] 1.22 per 1 standard deviation [SD], [95% confidence interval (CI), 1.03–1.43]; p=0.02) while exercise time was inversely associated with a low ABI (OR 0.71 per 1 SD, [95% CI, 0.57–0.89]; p=0.003). Sedentary time is associated with low ABI values in the asymptomatic population. This association appears to be independent of exercise time and warrants further investigation. [ABSTRACT FROM AUTHOR]