Clinical validation of an AI-based blood testing device for diagnosis and prognosis of acute infection and sepsis

Abstract Lack of reliable diagnostics for the presence, type and severity of infection in patients presenting to emergency departments with non-specific symptoms poses considerable challenges. We developed TriVerity, which uses isothermal amplification of 29 mRNAs and machine learning algorithms on the Myrna instrument to determine likelihoods of bacterial infection, viral infection and need for critical care interventions within 7 days. To validate TriVerity, the SEPSIS-SHIELD study enrolled 1,222 patients with clinically adjudicated infection status and need for critical care intervention within 7 days as endpoints. The TriVerity Bacterial and Viral scores had higher accuracy than C-reactive protein, procalcitonin or white blood cell count for the diagnosis of bacterial infection with area under the receiver operating characteristic (AUROC) of 0.83, and viral infection (AUROC = 0.91). The TriVerity Severity score had an AUROC of 0.78 for predicting illness severity and allowed reclassification of risk for critical care interventions compared to clinical assessment (quick Sequential Organ Failure Assessment) alone. Each of the three scores had rule-in specificity >92% and rule-out sensitivity >95%. Comparison of antibiotics administration at presentation with post-follow-up adjudication found that TriVerity could potentially reduce false positives and false negatives for inappropriate antibiotics use by 60–70%. Further clinical testing in an interventional setting is needed to prove actionability and clinical benefit of TriVerity.