TY - JOUR
T1 - Billing code algorithms to identify cases of peripheral artery disease from administrative data
AU - Fan, Jin
AU - Arruda-Olson, Adelaide M.
AU - Leibson, Cynthia L.
AU - Smith, Carin
AU - Liu, Guanghui
AU - Bailey, Kent R.
AU - Kullo, Iftikhar J.
PY - 2013
Y1 - 2013
N2 - Objective: To construct and validate billing code algorithms for identifying patients with peripheral arterial disease (PAD). Methods: We 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. Results: The logistic regression model performed well in both training and validation datasets (c statistic=0.91). In patients evaluated in the vascular laboratory, the modelbased 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. Conclusions: 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 had reasonable accuracy for identification of PAD in patients referred to the vascular laboratory but was significantly less sensitive in a community-based sample.
AB - Objective: To construct and validate billing code algorithms for identifying patients with peripheral arterial disease (PAD). Methods: We 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. Results: The logistic regression model performed well in both training and validation datasets (c statistic=0.91). In patients evaluated in the vascular laboratory, the modelbased 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. Conclusions: 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 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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U2 - 10.1136/amiajnl-2013-001827
DO - 10.1136/amiajnl-2013-001827
M3 - Article
C2 - 24166724
AN - SCOPUS:84890486487
SN - 1067-5027
VL - 20
SP - e349-e354
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - E2
ER -