Billing code algorithms to identify cases of peripheral artery disease from administrative data.
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ABSTRACT: 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 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.
SUBMITTER: Fan J
PROVIDER: S-EPMC3861931 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundStudies of breast cancer outcomes rely on the identification of second breast cancer events (recurrences and second breast primary tumors). Cancer registries often do not capture recurrences, and chart abstraction can be infeasible or expensive. An alternative is using administrative health-care data to identify second breast cancer events; however, these algorithms must be validated against a gold standard.MethodsWe developed algorithms using data from 3152 women in an integrated health-care system who were diagnosed with stage I or II breast cancer in 1993-2006. Medical record review served as the gold standard for second breast cancer events. Administrative data used in algorithm development included procedures, diagnoses, prescription fills, and cancer registry records. We randomly divided the cohort into training and testing samples and used a classification and regression tree analysis to build algorithms for classifying women as having or not having a second breast cancer event. We created several algorithms for researchers to use based on the relative importance of sensitivity, specificity, and positive predictive value (PPV) in future studies.ResultsThe algorithm with high specificity and PPV had 89% sensitivity (95% confidence interval [CI] = 84% to 92%), 99% specificity (95% CI = 98% to 99%), and 90% PPV (95% CI = 86% to 94%); the high-sensitivity algorithm had 96% sensitivity (95% CI = 93% to 98%), 95% specificity (95% CI = 94% to 96%), and 74% PPV (95% CI = 68% to 78%).ConclusionsAlgorithms based on administrative data can identify second breast cancer events with high sensitivity, specificity, and PPV. The algorithms presented here promote efficient outcomes research, allowing researchers to prioritize sensitivity, specificity, or PPV in identifying second breast cancer events.
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Project description:BackgroundAdministrative records from insurance and hospital discharge data sources are important public health tools to conduct passive surveillance of disease in populations. Identifying rare but catastrophic conditions is a challenge since approaches for maximizing valid case detection are not firmly established.ObjectiveThe purpose of our study was to explore a number of algorithms in which International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and other administrative variables could be used to identify cases of muscular dystrophy (MD).MethodsWe used active surveillance to identify possible cases of MD in medical practices in neurology, genetics, and orthopedics in 5 urban South Carolina counties and to identify the cases that had diagnostic support (ie, true cases). We then developed an algorithm to identify cases based on a combination of ICD-9-CM codes and administrative variables from a public (Medicaid) and private insurer claims-based system and a statewide hospital discharge dataset (passive surveillance). Cases of all types of MD and those with Duchenne or Becker MD (DBMD) that were common to both surveillance systems were examined to identify the most specific administrative variables for ascertainment of true cases.ResultsPassive statewide surveillance identified 3235 possible cases with MD in the state, and active surveillance identified 2057 possible cases in 5 actively surveilled counties that included 2 large metropolitan areas where many people seek medical care. There were 537 common cases found in both the active and passive systems, and 260 (48.4%) were confirmed by active surveillance to be true cases. Of the 260 confirmed cases, 70 (26.9%) were recorded as DBMD.ConclusionsAccuracy of finding a true case in a passive surveillance system was improved substantially when specific diagnosis codes, number of times a code was used, age of the patient, and specialty provider variables were used.
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Project description:RationalePeripheral arterial disease (PAD) is a clinical manifestation of extracoronary atherosclerosis. Despite sharing the same risk factors, only 20% to 30% of patients with coronary artery disease (CAD) develop PAD. Decline in the number of bone marrow-derived circulating progenitor cells (PCs) is thought to contribute to the pathogenesis of atherosclerosis. Whether specific changes in PCs differentiate patients with both PAD and CAD from those with CAD alone is unknown.ObjectiveDetermine whether differences exist in PCs counts of CAD patients with and without known PAD.Methods and results1497 patients (mean age: 65 years; 62% men) with known CAD were identified in the Emory Cardiovascular Biobank. Presence of PAD (n=308) was determined by history, review of medical records, or imaging and was classified as carotid (53%), lower extremity (41%), upper extremity (3%), and aortic disease (33%). Circulating PCs were enumerated by flow cytometry. Patients with CAD and PAD had significantly lower PC counts compared with those with only CAD. In multivariable analysis, a 50% decrease in cluster of differentiation 34 (CD34+) or CD34+/vascular endothelial growth factor receptor-2 (VEGFR2+) counts was associated with a 31% (P=0.032) and 183% (P=0.002) increase in the odds of having PAD, respectively. CD34+ and CD34+/VEGFR2+ counts significantly improved risk prediction metrics for prevalent PAD. Low CD34+/VEGFR2+ counts were associated with a 1.40-fold (95% confidence interval, 1.03-1.91) and a 1.64-fold (95% confidence interval, 1.07-2.50) increases in the risk of mortality and PAD-related events, respectively.ConclusionsPAD is associated with low CD34+ and CD34+/VEGFR2+ PC counts. Whether low PC counts are useful in screening for PAD needs to be investigated.
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