Healthcare encounters are recorded for administrative and reimbursement purposes in the USA. A reliable method for identifying patients with PAD from hospital-wide databases would be useful for genetic and epidemiologic studies of PAD. Ascertainment of cases of PAD based on the ankle-brachial index (ABI) may be biased owing to exclusion of patients diagnosed by other procedures, or those who could not tolerate lower extremity arterial evaluation. 1, 2 Although common and associated with significant mortality and morbidity, PAD is a relatively understudied phenotype of atherosclerotic vascular disease. Lower extremity peripheral artery disease (PAD) is highly prevalent, affecting about eight million people aged ≥40 years in the US population. Peripheral artery disease, billing codes, electronic medical record, informatics Background The simpler algorithm had reasonable accuracy for identification of PAD in patients referred to the vascular laboratory but was significantly less sensitive in a community-based sample. In the community-based sample, the sensitivity (38.7% vs 68.0%) of the simpler algorithm was much lower, whereas the specificity (92.0% vs 87.6%) was higher than the model-based algorithm.Ĭonclusions A model-based billing code algorithm had reasonable accuracy in identifying PAD cases from the community, and in patients referred to the non-invasive vascular laboratory. The simpler algorithm was reasonably accurate for identification of PAD status, with lesser sensitivity and greater specificity. In patients evaluated in the vascular laboratory, the model-based code algorithm provided better negative predictive value. Results The logistic regression model performed well in both training and validation datasets (c statistic=0.91). We also applied both algorithms to a community-based sample (n=4420), followed by a manual review. We applied a model-based code algorithm to patients evaluated in the vascular laboratory and compared this with a simpler algorithm (presence of at least one of the ICD-9 PAD codes 440.20–440.29). Multiple logistic regression analysis was used to create an integer code score from the training dataset, and this was tested in the validation set. Methods We extracted all encounters and line item details including PAD-related billing codes at Mayo Clinic Rochester, Minnesota, between Jand J22 712 patients evaluated in the vascular laboratory were divided into training and validation sets. Objective To construct and validate billing code algorithms for identifying patients with peripheral arterial disease (PAD).
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