Student Case Presentation
Antemortem vs. Postmortem: Creating a Real-time Electronic Dashboard to Evaluate Diagnostic Accuracy in Three Large Commercial Feedyards
| Date/Time: |
8/27/2026 3:15 PM
|
| Presenter: |
Macy Moore |
| Veterinary School: |
IA |
Abstract:
Rapid and accurate diagnosis of disease is critical in commercial feeding operations to minimize death loss and maintain performance. Feedyard antemortem diagnoses rely heavily on clinical signs, with little use of diagnostic testing or lab confirmation. This often results in an inaccurate diagnosis, improper treatment and missed opportunities. The objective of this study was to create an electronic dashboard to evaluate the agreement between antemortem diagnosis and postmortem diagnostic lesions in 3 large commercial feedyards. Three commercial feedyards (22,500 – 39,000 cattle capacity) located in the pacific northwest were utilized for this study. Mortality and treatment data on 2,223 calves from January 1, 2024, through May 20, 2025, were obtained from their animal health software. Morbidity and mortality events were recorded by feedyard personnel and classified based on body system affected. Individual mortalities with missing treatment data and treatments that occurred 30 days or greater prior to the mortality event were excluded from the data set. Mortality and treatment data were compared for their accuracy using Microsoft Power BI™. “True” was defined as an agreement between last treatment diagnosis and mortality diagnosis. “False” was defined as a disagreement between last treatment diagnosis and mortality diagnosis. All feedyards trended toward 50% true diagnostic accuracy (A: 50.4%; B: 49.1%; C: 57.1%). Across diagnostic groupings, respiratory disease contributed the most to mortalities (A: 38.9%; B: 43.3%; C: 46.8%) and yielded the highest diagnostic accuracy (A: 90.7%; B: 80.9%; C: 83.6%). No trends were evident based on days on feed at the time of death. Limitations of this study include data entry inconsistencies complicating organization and consolidation of the data within the program. This tool enhanced real-time interpretation of large datasets by consolidating key metrics into an interactive dashboard, facilitating trend identification, and translating findings into actionable insights.
