Objective: To validate an algorithm that identifies fractures using billing codes from the International Classification of Diseases Ninth Revision (ICD-9) and Tenth Revision (ICD-10) for inpatient, outpatient, and emergency department visits in a population of patients.
Methods: We identified and reviewed a random sample of 543 encounters for adults receiving care within a single Veterans Health Administration healthcare system and had a first fracture episode between 2010 and 2019. To determine if an encounter represented a true incident fracture, we performed chart abstraction and assessed the type of fracture and mechanism. We calculated the positive predictive value (PPV) for the overall algorithm and each component diagnosis code along with 95% confidence intervals. Inverse probabilities of selection sampling weights were used to reflect the underlying study population.
Results: The algorithm had an initial PPV of 73.5% (confidence interval [CI] 69.5, 77.1), with low performance when weighted to reflect the full population (PPV 66.3% [CI 58.8, 73.1]). The modified algorithm was restricted to diagnosis codes with PPVs > 50% and outpatient codes were restricted to the first outpatient position, with the exception of one high performing code. The resulting unweighted PPV improved to 90.1% (CI 86.2, 93.0) and weighted PPV of 91.3% (CI 86.8, 94.4). A confirmation sample demonstrated verified performance with PPV of 87.3% (76.0, 93.7). PPVs by location of care (inpatient, emergency department and outpatient) remained greater than 85% in the modified algorithm.
Conclusions: The modified algorithm, which included primary billing codes for inpatient, outpatient, and emergency department visits, demonstrated excellent PPV for identification of fractures among a cohort of patients within the Veterans Health Administration system.
Keywords: accuracy; fracture; phenotype performance.
Published 2023. This article is a U.S. Government work and is in the public domain in the USA.