The Impact of Co-Morbid Peripheral Vascular Disease on Mortality in a Racially Balanced Cohort with Congestive Heart Failure

Kezerashvili, Anna; Delaney, Jessica; Schaefer, Michael J.; Janis, Gregory C.; Bello, Ricardo; Spevack, Daniel M.; Li, Xuan; Du, Evelyn; Ostfeld, Robert J.

doi:10.1016/j.cardfail.2008.06.340

Cited by 1 publication

(1 citation statement)

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“…A list of PAD-related ICD-9-CM diagnosis/procedure and CPT-4 procedural codes was compiled through literature review 14–19 and manual searches of the ICD-9-CM/CPT code books. 12 13 All ICD-9-CM diagnosis codes relevant to lower extremity atherosclerotic disease (eg, 440.2×—atherosclerosis of native arteries of the extremities; 440.3×—atherosclerosis of bypass graft of the extremities; 440.8×—atherosclerosis of other specified arteries; 440.9×—generalized and unspecified atherosclerosis, including arteriosclerotic vascular disease not otherwise specified; 443.9×—peripheral vascular disease, unspecified), CPT-4 or ICD-9-CM procedural codes related to PAD (eg, non-invasive lower extremity arterial evaluation, arteriography, duplex ultrasonography, magnetic resonance angiography, and CT angiography of the lower extremity arteries), or surgical/revascularization procedures related to PAD (eg, lower extremity vascular bypass, percutaneous revascularization, amputation, etc) were included (see online supplementary table S1).…”

Section: Methodsmentioning

confidence: 99%

Billing code algorithms to identify cases of peripheral artery disease from administrative data

Fan

Arruda‐Olson

Leibson

et al. 2013

J Am Med Inform Assoc

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ObjectiveTo construct and validate billing code algorithms for identifying patients with peripheral arterial disease (PAD).MethodsWe extracted all encounters and line item details including PAD-related billing codes at Mayo Clinic Rochester, Minnesota, between July 1, 1997 and June 30, 2008; 22 712 patients evaluated in the vascular laboratory were divided into training and validation sets. Multiple logistic regression analysis was used to create an integer code score from the training dataset, and this was tested in the validation set. 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). We also applied both algorithms to a community-based sample (n=4420), followed by a manual review.ResultsThe logistic regression model performed well in both training and validation datasets (c statistic=0.91). In patients evaluated in the vascular laboratory, the model-based code algorithm provided better negative predictive value. The simpler algorithm was reasonably accurate for identification of PAD status, with lesser sensitivity and greater specificity. 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.ConclusionsA 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 had reasonable accuracy for identification of PAD in patients referred to the vascular laboratory but was significantly less sensitive in a community-based sample.

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Section: Methodsmentioning

confidence: 99%