Evaluation of a machine learning tool to screen for hypoadrenocorticism in dogs presenting to a teaching hospital.

Abstract

BACKGROUND: Dogs with hypoadrenocorticism (HA) have clinical signs and clinicopathologic abnormalities that can be mistaken as other diseases. In dogs with a differential diagnosis of HA, a machine learning model (MLM) has been validated to discriminate between HA and other diseases. This MLM has not been evaluated as a screening tool for a broader group of dogs. HYPOTHESIS: An MLM can accurately screen dogs for HA. ANIMALS: Dogs (n = 1025) examined at a veterinary hospital. METHODS: Dogs that presented to a tertiary referral hospital that had a CBC and serum chemistry panel were enrolled. A trained MLM was applied to clinicopathologic data and in dogs that were MLM positive for HA, diagnosis was confirmed by measurement of serum cortisol. RESULTS: Twelve dogs were MLM positive for HA and had further cortisol testing. Five had HA confirmed (true positive), 4 of which were treated for mineralocorticoid and glucocorticoid deficiency, and 1 was treated for glucocorticoid deficiency alone. Three MLM positive dogs had baseline cortisol ≤2 μg/dL but were euthanized or administered glucocorticoid treatment without confirming the diagnosis with an ACTH-stimulation test (classified as "undetermined"), and in 4, HA was ruled out (false positives). The positive likelihood ratio of the MLM was 145 to 254. All dogs diagnosed with HA by attending clinicians tested positive by the MLM. CONCLUSIONS AND CLINICAL IMPORTANCE: This MLM can robustly predict HA status when indiscriminately screening all dogs with blood work. In this group of dogs with a low prevalence of HA, the false positive rates were clinically acceptable.