A Data-Driven Method to Discriminate Limb Salvage from Other Combat-Related Extremity Trauma

Introduction: The aim of this study was to address and enhance our ability to study the clinical outcome of limb salvage (LS), a commonly referenced but ill-defined clinical care pathway, by developing a data-driven approach for the identification of LS cases using existing medical code data to iden...

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Bibliographic Details
Published in:Journal of clinical medicine 2023-10, Vol.12 (19), p.6357
Main Authors: Goldman, Stephen M, Eskridge, Susan L, Franco, Sarah R, Souza, Jason M, Tintle, Scott M, Dowd, Thomas C, Alderete, Joseph, Potter, Benjamin K, Dearth, Christopher L
Format: Article
Language:English
Subjects:
Amputation
Causes of
Clinical medicine
Clinical outcomes
Codes
Demographic aspects
Discriminant analysis
Injuries
Limb salvage
Medical coding
Patient outcomes
Trauma
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Summary:Introduction: The aim of this study was to address and enhance our ability to study the clinical outcome of limb salvage (LS), a commonly referenced but ill-defined clinical care pathway, by developing a data-driven approach for the identification of LS cases using existing medical code data to identify characteristic diagnoses and procedures, and to use that information to describe a cohort of US Service members (SMs) for further study. Methods: Diagnosis code families and inpatient procedure codes were compiled and analyzed to identify medical codes that are disparately associated with a LS surrogate population of SMs who underwent secondary amputation within a broader cohort of 3390 SMs with lower extremity trauma (AIS > 1). Subsequently, the identified codes were used to define a cohort of all SMs who underwent lower extremity LS which was compared with the opinion of a panel of military trauma surgeons. Results: The data-driven approach identified a population of n = 2018 SMs who underwent LS, representing 59.5% of the combat-related lower extremity (LE) trauma population. Validation analysis revealed 70% agreement between the data-driven approach and gold standard SME panel for the test cases studied. The Kappa statistic (κ = 0.55) indicates a moderate agreement between the data-driven approach and the expert opinion of the SME panel. The sensitivity and specificity were identified as 55.6% (expert range of 51.8–66.7%) and 87% (expert range of 73.9–91.3%), respectively. Conclusions: This approach for identifying LS cases can be utilized to enable future high-throughput retrospective analyses for studying both short- and long-term outcomes of this underserved patient population.
ISSN:2077-0383
2077-0383
DOI:10.3390/jcm12196357