A robust host-response-based signature distinguishes bacterial and viral infections across diverse global populations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".