PuSH - Publikationsserver des Helmholtz Zentrums München: Using machine learning to predict antibody response to SARS-CoV-2 vaccination in solid organ transplant recipients: The multicentre ORCHESTRA cohort.

Informationen

Hinweise zu Qualitätskriterien von Zeitschriften

Open-Access-Richtlinie der Helmholtz-Gemeinschaft 2016

CC Licencences

Metriken für Publikationen

Navigation

Startseite

English

EVA: Veröffentlichen

Neuer EVA Antrag

Recherche

Erweiterte Suche

Durchblättern nach ...

... HMGU-Autoren/Konsortien

... Organisationsstruktur

... Zeitschriften

... Publikationstypen

... Forschungsdaten

... Arbeitsgruppen

... Erscheinungsjahr

Publikationen im Überblick

Statistik

HGF Fortschrittsbericht

OA Publikationen

Eintragen

Neue Publikation eintragen

Neue Publikation holen aus...

...EVA

Fehlende Publikation melden

Highlights

Suche

Hilfe & Kontakt

Ansprechpartner

Hilfe

Datenschutz

Helmholtz Open Science

Bibliometrische Indikatoren

SHERPA/RoMEO

DOAJ

Export:

Text

Endnote (RIS) BIB

BibTeX

Giannella, M.* ; Huth, M. ; Righi, E.* ; Hasenauer, J. ; Marconi, L.* ; Konnova, A.* ; Gupta, A.* ; Hotterbeekx, A.* ; Berkell, M.* ; Palacios-Baena, Z.R.* ; Morelli, M.C.* ; Tamè, M.* ; Busutti, M.* ; Potena, L.* ; Salvaterra, E.* ; Feltrin, G.* ; Gerosa, G.* ; Furian, L.* ; Burra, P.* ; Piano, S.* ; Cillo, U.* ; Cananzi, M.* ; Loy, M.* ; Zaza, G.* ; Onorati, F.* ; Carraro, A.* ; Gastaldon, F.* ; Nordio, M.* ; Kumar-Singh, S.* ; Baño, J.R.* ; Lazzarotto, T.* ; Viale, P.* ; Tacconelli, E.*

Using machine learning to predict antibody response to SARS-CoV-2 vaccination in solid organ transplant recipients: The multicentre ORCHESTRA cohort.

Clin. Microbiol. Infect. 29, 1084.e1-1084.e7 (2023)

Verlagsversion

DOI

PMC

	Closed

Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.

Abstract
Metriken
Zusatzinfos

OBJECTIVES: The study aim was to assess predictors of negative antibody response (AbR) in solid organ transplant (SOT) recipients after the first booster of SARS-CoV-2 vaccination. METHODS: Solid organ transplant recipients receiving SARS-CoV-2 vaccination were prospectively enrolled (March 2021-January 2022) at six hospitals in Italy and Spain. AbR was assessed at first dose (t₀), second dose (t₁), 3 ± 1 month (t₂), and 1 month after third dose (t₃). Negative AbR at t₃ was defined as an anti-receptor binding domain titre <45 BAU/mL. Machine learning models were developed to predict the individual risk of negative (vs. positive) AbR using age, type of transplant, time between transplant and vaccination, immunosuppressive drugs, type of vaccine, and graft function as covariates, subsequently assessed using a validation cohort. RESULTS: Overall, 1615 SOT recipients (1072 [66.3%] males; mean age±standard deviation [SD], 57.85 ± 13.77) were enrolled, and 1211 received three vaccination doses. Negative AbR rate decreased from 93.66% (886/946) to 21.90% (202/923) from t₀ to t₃. Univariate analysis showed that older patients (mean age, 60.21 ± 11.51 vs. 58.11 ± 13.08), anti-metabolites (57.9% vs. 35.1%), steroids (52.9% vs. 38.5%), recent transplantation (<3 years) (17.8% vs. 2.3%), and kidney, heart, or lung compared with liver transplantation (25%, 31.8%, 30.4% vs. 5.5%) had a higher likelihood of negative AbR. Machine learning (ML) algorithms showing best prediction performance were logistic regression (precision-recall curve-PRAUC mean 0.37 [95%CI 0.36-0.39]) and k-Nearest Neighbours (PRAUC 0.36 [0.35-0.37]). DISCUSSION: Almost a quarter of SOT recipients showed negative AbR after first booster dosage. Unfortunately, clinical information cannot efficiently predict negative AbR even with ML algorithms.

Impact Factor

Scopus SNIP

Web of Science
Times Cited

Scopus
Cited By

Altmetric

14.200

0.000