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.
Using machine learning to predict antibody response to SARS-CoV-2 vaccination in solid organ transplant recipients: the multicentre ORCHESTRA cohort / 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.; Package W.; 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.s.R.g.; Lazzarotto T.; Viale P.; Tacconelli E.; Caroccia N.; Tazza B.; Bonazzetti C.; Fan F.; Di Chiara M.; Giacomini M.E.; Vatamanu O.; Pasquini Z.; Pascale R.; Rinaldi M.; Horna C.S.; Campoli C.; Fornaro G.; Trapani F.; Attard L.; Gramegna A.; Cesari V.; Varani S.; Del Turco E.R.; Tedeschi S.; Scolz K.; La Manna G.; Grandinetti V.; Demetri M.; Barbuto S.; Abenavoli C.; Vitale G.; Turco L.; Ravaioli M.; Cescon M.; Bertuzzo V.; Lombardi A.; Trombi A.; Masetti M.; Prestinenzi P.; Sabatino M.; Giovannini L.; Alessio A.; Russo A.; Scuppa M.F.; Borgese L.; Dolci G.; Paganelli G.; Comai G.; Gabrielli L.; Gamberini C.; Leone M.; Granata S.; Verlato A.; Elia R.; Biagio L.S.; Francica A.; Tropea I.; Mongardi M.; Guedes M.N.P.; Maccarone G.; Sciammarella C.; Mirandola M.; Canziani L.M.; Konishi C.; Perlini C.; Rosini G.; Russo F.; Mongillo M.; Gutiérrez-Campos D.; Mart n-Gutiérrez A.B.. - In: CLINICAL MICROBIOLOGY AND INFECTION. - ISSN 1198-743X. - ELETTRONICO. - 29:8(2023), pp. 1084.e1-1084.e7. [10.1016/j.cmi.2023.04.027]
Using machine learning to predict antibody response to SARS-CoV-2 vaccination in solid organ transplant recipients: the multicentre ORCHESTRA cohort
Giannella M.;Marconi L.;Busutti M.;Potena L.;Lazzarotto T.;Viale P.;Caroccia N.;Tazza B.;Bonazzetti C.;Di Chiara M.;Campoli C.;Fornaro G.;Trapani F.;Attard L.;Tedeschi S.;Scolz K.;La Manna G.;Grandinetti V.;Demetri M.;Barbuto S.;Abenavoli C.;Scuppa M. F.;Borgese L.;Comai G.;
2023
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
