Abstract
BackgroundStudies of the epidemiology and outcomes of avascular necrosis (AVN) require accurate case-finding methods. The aim of this study was to evaluate performance characteristics of a claims-based algorithm designed to identify AVN cases in administrative data.MethodsUsing a centralized patient registry from a US academic medical center, we identified all adults aged ≥18 years who underwent magnetic resonance imaging (MRI) of an upper/lower extremity joint during the 1.5 year study period. A radiologist report confirming AVN on MRI served as the gold standard. We examined the sensitivity, specificity, positive predictive value (PPV) and positive likelihood ratio (LR+) of four algorithms (A-D) using International Classification of Diseases, 9th edition (ICD-9) codes for AVN. The algorithms ranged from least stringent (Algorithm A, requiring ≥1 ICD-9 code for AVN [733.4X]) to most stringent (Algorithm D, requiring ≥3 ICD-9 codes, each at least 30 days apart).ResultsAmong 8200 patients who underwent MRI, 83 (1.0% [95% CI 0.78–1.22]) had AVN by gold standard. Algorithm A yielded the highest sensitivity (81.9%, 95% CI 72.0–89.5), with PPV of 66.0% (95% CI 56.0–75.1). The PPV of algorithm D increased to 82.2% (95% CI 67.9–92.0), although sensitivity decreased to 44.6% (95% CI 33.7–55.9). All four algorithms had specificities >99%.ConclusionAn algorithm that uses a single billing code to screen for AVN among those who had MRI has the highest sensitivity and is best suited for studies in which further medical record review confirming AVN is feasible. Algorithms using multiple billing codes are recommended for use in administrative databases when further AVN validation is not feasible.
Highlights
Studies of the epidemiology and outcomes of avascular necrosis (AVN) require accurate case-finding methods
The use of International Classification of Diseases, Ninth Revision (ICD-9)-based algorithms for the diagnosis of incident AVN has been previously assessed in a Boston Veterans Affair (VA) cohort; this study found low positive predictive values (PPVs) for incident AVN (17–46%) but higher PPVs for prevalent or incident AVN (76–100%) compared to a gold standard of AVN diagnosis by comprehensive medical record review
Study cohort (Table 1): Among 8200 patients who underwent Magnetic resonance imaging (MRI) of the upper and/or lower extremities during the 18-month study period, 83 cases of AVN were identified on MRI, yielding a prevalence of 1.0% [95%CI 0.78–1.22]) The mean age of patients with AVN on MRI was 50.4 years, with 60.2% of these patients being female
Summary
Studies of the epidemiology and outcomes of avascular necrosis (AVN) require accurate case-finding methods. The aim of this study was to evaluate performance characteristics of a claims-based algorithm designed to identify AVN cases in administrative data. Studies related to identifying risk factors, diagnostic tools, and management options for AVN require accurate case-finding methods. Administrative databases may be especially useful to estimate the incidence, prevalence and risk factors for AVN, a relatively rare disease [7]. The use of International Classification of Diseases, Ninth Revision (ICD-9)-based algorithms for the diagnosis of incident AVN has been previously assessed in a Boston Veterans Affair (VA) cohort; this study found low positive predictive values (PPVs) for incident AVN (17–46%) but higher PPVs for prevalent or incident AVN (76–100%) compared to a gold standard of AVN diagnosis by comprehensive medical record review. The quality of the gold standard utilized, the generalizability of these results to non-VA populations, and the applicability of these algorithms to administrative databases where medical records are not available are currently unknown [9]
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