This historical cohort study was approved by the institutional review board of the Mayo Clinic. We developed and validated billing algorithms to ascertain PAD status at Mayo Clinic Rochester, Minnesota, using administrative data over an 11-year period. We investigated whether billing codes, which include International Classification of Diseases, 9th revision, clinical modification (ICD-9-CM), and Current Procedural Terminology, 4th revision (CPT-4) codes, could be used for reliable ascertainment of PAD cases from administrative databases. Whether billing codes are useful in ascertaining cases of PAD is unknown. 5– 10 For example, the positive predictive value (PPV) of billing codes for acute myocardial infarction was reported to be 96.9%, 6 whereas the PPV for pelvic inflammatory disease was only 18.1%. The accuracy of billing codes for ascertaining disease conditions varies. Moreover, the growing use of the electronic medical records (EMRs) and the development and validation of informatics approaches to access diverse phenotypes from the EMR 4 may facilitate rapid phenotyping. Given these advantages, such data are increasingly being used in medical research. 3 Administrative data are readily available, often for large populations, are inexpensive to acquire, and are computer readable. 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.
Kullo iftikhar mayo clinic code#
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).
Kullo iftikhar mayo clinic manual#
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).
0 kommentar(er)
