J Am Med Inform Assoc - Billing code algorithms to identify cases of peripheral artery disease from administrative data.

Tópicos

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Resumo

JECTIVE: 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 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.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.

Resumo Limpo

jectiv construct valid bill code algorithm identifi patient peripher arteri diseas padmethod extract encount line item detail includ padrel bill code mayo clinic rochest minnesota juli june patient evalu vascular laboratori divid train valid set multipl logist regress analysi use creat integ code score train dataset test valid set appli modelbas code algorithm patient evalu vascular laboratori compar simpler algorithm presenc least one icd pad code also appli algorithm communitybas sampl n follow manual reviewresult logist regress model perform well train valid dataset c statist patient evalu vascular laboratori modelbas code algorithm provid better negat predict valu simpler algorithm reason accur identif pad status lesser sensit greater specif communitybas sampl sensit vs simpler algorithm much lower wherea specif vs higher modelbas algorithmconclus modelbas bill code algorithm reason accuraci identifi pad case communiti patient refer noninvas vascular laboratori simpler algorithm reason accuraci identif pad patient refer vascular laboratori signific less sensit communitybas sampl

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