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Record W4312082567 · doi:10.1016/j.xcrm.2022.100842

A robust host-response-based signature distinguishes bacterial and viral infections across diverse global populations

2022· article· en· W4312082567 on OpenAlexaff
Aditya Rao, Stephen J. Popper, Sanjana Gupta, Viengmon Davong, Krista Vaidya, Anisone Chanthongthip, Sabine Dittrich, Matthew T. Robinson, Manivanh Vongsouvath, Mayfong Mayxay, Pruksa Nawtaisong, Biraj Man Karmacharya, Simone A. Thair, Isaac I. Bogoch, Timothy E. Sweeney, Paul N. Newton, Jason R. Andrews, David A. Relman, Purvesh Khatri

Bibliographic record

VenueCell Reports Medicine · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Identification and Susceptibility Testing
Canadian institutionsUniversity of Toronto
FundersNational Institute of Allergy and Infectious DiseasesWellcome Trust
KeywordsBiologyGene signatureTranscriptomeHost responseCoinfectionGeneVirologyMicrobiologyImmunologyGene expressionGeneticsVirusImmune system

Abstract

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Background: Early and accurate diagnosis of acute infection has important consequences for antibiotic stewardship, resource allocation, and clinical outcomes. However, limited sensitivity and specificity of current diagnostics lead to the erroneous prescription of antibiotics. Host-response-based diagnostics have the potential to address these challenges, but accuracy varies widely across heterogeneous global patient populations. Methods: We performed a multi-cohort analysis of 4,200 unique samples across 69 retrospective blood transcriptome datasets from 20 countries. These samples were collected from patients with acute bacterial or viral infections representing a broad spectrum of biological (age, sex, race, ethnicity, pathogen, host genetics), clinical (severity, day of presentation), and technical (Affymetrix, Agilent, Illumina) heterogeneity. We also enrolled patients with infectious diseases in two cohorts from Laos and Nepal. Findings: Current host-response-based gene signatures distinguished intracellular bacterial infection from viral infections with substantially lower accuracy. Using 69 datasets, divided into training and validation datasets, we identified an 8-gene signature that distinguished intracellular or extracellular bacterial infections from viral infections with an area under the receiver operating characteristic curve (AUROC) >0.91 (85.9% specificity, 90.2% sensitivity). In two prospective cohorts from Nepal and Laos, profiled using Fluidigm RT-PCR, the 8-gene classifier distinguished bacterial infections from viral infections with an AUROC of 0.94 (87.9% specificity, 91% sensitivity). Interpretation: The 8-gene signature met the target product profile (90% sensitivity, 80% specificity) proposed by the WHO and others for distinguishing bacterial and viral infections. The 8-gene signature should be considered for further validation and implementation. Funding Information: PK is funded in part by the Bill and Melinda Gates Foundation (OPP1113682); the National Institute of Allergy and Infectious Diseases (NIAID) grants 1U19AI109662, U19AI057229, and 5R01AI125197; Department of Defense contracts W81XWH-18-1-0253 and W81XWH1910235; and the Ralph & Marian Falk Medical Research Trust. DAR is supported by NIH/NIAID U19 AI109761 and the Thomas C. and Joan M. Merigan Endowment at Stanford University. LOMWRU is funded by the Wellcome Trust of Great Britain. AMR is funded by the National Science Foundation Graduate Research Fellowship and the Stanford Graduate Fellowship. This research was funded in whole, or in part, by the Wellcome Trust [Grant number 220211]. Declaration of Interests: AMR, SJP, TES, DAR, and PK are named as inventors on a patent application describing the 8-gene set, which has been licensed to Inflammatix. TES and SAT are employees of Inflammatix, and TES and PK are shareholders in Inflammatix. Inflammatix had no role in the design, funding, or reporting of this work. SD is currently employed by FIND. The other authors declare no conflicts of interest. Ethics Approval Statement: Ethical clearance was granted by the former Faculty of Medical Sciences Ethical Review Committee (now University of Health Sciences Ethics Committee), National University of Laos, the Oxford University Tropical Ethics Committee, and the Stanford University Institutional Review Board.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.286
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations37
Published2022
Admission routes1
Has abstractyes

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