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Clinical validation of an AI-based blood testing device for diagnosis and prognosis of acute infection and sepsis
Journal article   Open access   Peer reviewed

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

Oliver Liesenfeld, Sanjay Arora, Tom P Aufderheide, Casey M Clements, Elizabeth DeVos, Miriam Fischer, Evangelos J Giamarellos-Bourboulis, Stacey House, Roger L Humphries, Jasreen Kaur Gill, …
Nature medicine, Vol.31(12), pp.4044-4054
12/2025
DOI: 10.1038/s41591-025-03933-y
PMCID: PMC12705421
PMID: 41028541
url
https://doi.org/10.1038/s41591-025-03933-yView
Published (Version of record) Open Access

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.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.

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