A robust host-response-based signature distinguishes bacterial and viral infections across diverse global populations
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».