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Diagnosing Osteomyelitis: A Histology Guide for Pathologists

  • Amelia B. Sybenga
    Correspondence
    Address correspondence to: Amelia B. Sybenga, DO, Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, 2200 Children's Way, Nashville, TN 37232.
    Affiliations
    Clinical Fellow, Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN
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  • Daniel C. Jupiter
    Affiliations
    Associate Professor, Department of Preventive Medicine and Community Health, Department of Orthopaedic Surgery and Rehabilitation, The University of Texas Medical Branch, Galveston, TX
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  • V.O. Speights
    Affiliations
    Professor, Department of Pathology and Laboratory Medicine, Scott & White Medical Center, Baylor Scott and White Health, Texas A&M Health Science Center, Temple, TX
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  • Arundhati Rao
    Affiliations
    Professor, Department of Pathology and Laboratory Medicine, Scott & White Medical Center, Baylor Scott and White Health, Texas A&M Health Science Center, Temple, TX
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Published:November 19, 2019DOI:https://doi.org/10.1053/j.jfas.2019.06.007

      Abstract

      Histopathologic examination of bone specimens coupled with bone culture is considered the gold standard for the diagnosis of osteomyelitis (OM). Despite this, studies have demonstrated interpathologist agreement in the diagnosis of OM as low as 30%, largely stemming from a lack of specific definitions and diagnostic criteria. Review of the literature has provided insight into the lifecycle of OM, illustrating the histologic progression of OM phases from acute to chronic, and provides support for defining subcategories of OM. Using an algorithmic histopathologic tool consisting of 15 criteria, each with an associated score, we defined 5 categories of OM: (1) acute OM, (2) acute and chronic OM, (3) chronic OM, (4) chronic active OM, and (5) chronic inactive OM. We reviewed 462 microscopic slides from 263 patients with suspected OM, and for each slide, we determined an algorithm-derived diagnosis, which was then used to calculate a total histopathologic load score (Jupiter score). Algorithm-derived diagnoses recapitulated original clinical diagnoses and diagnosed cases as OM that had not been originally diagnoses. These novel cases were more likely to have subsequent clinical complications. Finally, pathologic load scores were assessed for association with the category of OM.

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