使用机器学习预测实体器官移植受者对 SARS-CoV-2 疫苗接种的抗体反应：多中心 ORCHESTRA 队列。

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

机构信息

Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy; Infectious Diseases Unit, Department of Integrated Management of Infectious Risk, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Policlinico Sant'Orsola, Bologna, Italy.

Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany; Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.

出版信息

Clin Microbiol Infect. 2023 Aug;29(8):1084.e1-1084.e7. doi: 10.1016/j.cmi.2023.04.027. Epub 2023 May 6.

DOI:10.1016/j.cmi.2023.04.027

PMID:37150358

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10212001/

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 (t), second dose (t), 3 ± 1 month (t), and 1 month after third dose (t). Negative AbR at t 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 t to t. 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.

摘要

目的

本研究旨在评估实体器官移植（SOT）受者在接受 SARS-CoV-2 疫苗加强针后的阴性抗体反应（AbR）的预测因素。

方法

2021 年 3 月至 2022 年 1 月，在意大利和西班牙的六家医院前瞻性招募接受 SARS-CoV-2 疫苗接种的 SOT 受者。在第一剂（t）、第二剂（t）、3±1 个月（t）和第三剂后 1 个月（t）评估 AbR。t 时的阴性 AbR 定义为抗受体结合域滴度<45 BAU/mL。使用年龄、移植类型、移植与接种时间间隔、免疫抑制剂药物、疫苗类型和移植物功能作为协变量，建立机器学习模型来预测个体发生阴性（与阳性）AbR 的风险，随后在验证队列中进行评估。

结果

共有 1615 名 SOT 受者（1072 名[66.3%]为男性；平均年龄±标准差[SD]为 57.85±13.77）入组，其中 1211 名接受了三剂疫苗接种。从 t 到 t，阴性 AbR 率从 93.66%（886/946）降至 21.90%（202/923）。单因素分析显示，老年患者（平均年龄 60.21±11.51 岁比 58.11±13.08 岁）、抗代谢物（57.9%比 35.1%）、类固醇（52.9%比 38.5%）、近期移植（<3 年）（17.8%比 2.3%）以及肾、心或肺移植比肝移植（25%、31.8%、30.4%比 5.5%）更有可能发生阴性 AbR。表现出最佳预测性能的机器学习（ML）算法是逻辑回归（精度-召回曲线-PRAUC 平均值 0.37[95%CI 0.36-0.39]）和 k-最近邻（PRAUC 0.36[0.35-0.37]）。