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Enregistrement W3113562849 · doi:10.1101/2020.12.23.20247379

Prognostic and predictive biomarkers in patients with COVID-19 treated with tocilizumab in a randomised controlled trial

2020· preprint· en· W3113562849 sur OpenAlexaff
Jennifer Tom, Min Bao, Larry Tsai, Aditi Qamra, David Summers, Montserrat Carrasco‐Triguero, Jacqueline McBride, Carrie M. Rosenberger, Celia J. F. Lin, William Stubbings, Kevin G. Blyth, Jordi Carratalà, Bruno François, Thomas Benfield, Derrick S. Haslem, Paolo Bonfanti, Cor H. van der Leest, Nidhi Rohatgi, Lothar Wiese, Charles Edouard Luyt, Farrah Kheradmand, Iván O. Rosas, Fang Cai

Notice bibliographique

RevuemedRxiv · 2020
Typepreprint
Langueen
DomaineMedicine
ThématiqueCOVID-19 Clinical Research Studies
Établissements canadiensRoche (Canada)
Organismes subventionnairesF. Hoffmann-La Roche
Mots-clésTocilizumabMedicineInternal medicinePlaceboPopulationMechanical ventilationFerritinBiomarkerClinical trialC-reactive proteinPneumoniaDiseasePathologyInflammation

Résumé

récupéré en direct d'OpenAlex

Summary Background Retrospective observational studies suggest that interleukin-6 (IL-6), C-reactive protein (CRP), lactate dehydrogenase (LDH), ferritin, lymphocytes, monocytes, neutrophils, D-dimer, and platelets are associated with disease progression, treatment outcomes, or both, in patients with COVID-19 pneumonia. We explored these candidate prognostic and predictive biomarkers with efficacy outcomes after treatment with tocilizumab, an anti–IL-6 receptor antibody using data from the COVACTA trial for patients hospitalised with severe COVID-19 pneumonia. Methods Candidate biomarkers were measured in 295 patients in the tocilizumab arm and 142 patients in the placebo arm. Efficacy outcomes assessed were clinical status on a seven-category ordinal scale (1, discharge; 7, death), mortality, time to hospital discharge, and mechanical ventilation (if not receiving it at randomisation) through day 28. Prognostic and predictive biomarkers were evaluated continuously with proportional odds, binomial or Fine-Gray models, and additional sensitivity analyses. Findings Modelling in the placebo arm showed all candidate biomarkers except LDH and D-dimer were strongly prognostic for day 28 clinical outcomes of mortality, mechanical ventilation, clinical status, and time to hospital discharge. Modelling in the tocilizumab arm showed a predictive value of ferritin for day 28 clinical outcomes of mortality (predictive interaction p=0.03), mechanical ventilation (predictive interaction p=0.01), and clinical status (predictive interaction p=0.02) compared with placebo. Interpretation Multiple biomarkers prognostic for clinical outcomes were confirmed in COVACTA. Ferritin was identified as a predictive biomarker for the effects of tocilizumab in the COVACTA patient population; high ferritin levels were associated with better clinical outcomes for tocilizumab compared with placebo at day 28. RESEARCH IN CONTEXT Evidence before this study The efficacy and safety of the anti–interleukin-6 receptor antibody tocilizumab in the treatment of patients hospitalised with COVID-19 pneumonia was investigated in COVACTA, a double-blind, randomised, placebo-controlled trial. The primary endpoint of improved clinical status on a seven-category ordinal scale (1, discharged/ready for discharge; 7, death) at day 28 was not met in this trial. Among the secondary endpoints, no difference in mortality at day 28 was observed, but time to hospital discharge was shorter in the tocilizumab group. Subgroup analysis suggested there might be a treatment benefit in patients grouped according to their ordinal scale category at baseline. We searched PubMed on September 14, 2020, using the terms “tocilizumab AND (COVID-19 OR coronavirus) AND biomarker” with no language or date restrictions. The search retrieved 18 articles, four of which identified laboratory measures as potential biomarkers in patients who received tocilizumab for the treatment of COVID-19 pneumonia. The biomarkers reported in these studies include interleukin-6, C-reactive protein, ferritin, fibrinogen, liver transaminases, lymphocytes, platelets, and D-dimer. However, these previous studies were single-centre, retrospective, observational studies. Larger, prospective, controlled trials are needed to investigate potential prognostic and predictive biomarkers to assess the outcomes and response to treatments for COVID-19. Added value of this study This exploratory analysis of data from COVACTA demonstrated interleukin-6, C-reactive protein, ferritin, neutrophils (percentage and absolute count), neutrophil-to-lymphocyte ratio, lymphocytes (percentage and absolute count), monocytes (percentage), and platelets as strong prognostic biomarkers in patients hospitalised with severe COVID-19 pneumonia. More important, ferritin showed predictive value for tocilizumab treatment effects on day 28 clinical outcomes of mortality, mechanical ventilation (among the subgroup of patients not receiving mechanical ventilation at randomisation), and clinical status compared with placebo. Implications of all the available evidence In patients with elevated levels of ferritin at baseline, tocilizumab decreased the probability of death, mechanical ventilation, and worsening clinical status at day 28 compared with placebo, suggesting that ferritin might be useful as a predictive biomarker of efficacy outcomes for tocilizumab in patients with severe COVID-19 pneumonia.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,012
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai randomisé · Signal consensuel: Essai randomisé
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,045

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,012
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0050,005
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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.

Tête enseignante Opus0,037
Tête enseignante GPT0,357
Écart entre enseignants0,320 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeEssai randomisé
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2020
Routes d'admission1
Résumé présentoui

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