Vigia Emanuel, Ramalhete Luís, Ribeiro Rita, Barros Inês, Chumbinho Beatriz, Filipe Edite, Pena Ana, Bicho Luís, Nobre Ana, Carrelha Sofia, Sobral Mafalda, Lamelas Jorge, Coelho João Santos, Ferreira Aníbal, Marques Hugo Pinto
Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal.
Nova Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal.
J Pers Med. 2023 Jun 29;13(7):1071. doi: 10.3390/jpm13071071.
Pancreas transplantation is currently the only treatment that can re-establish normal endocrine pancreatic function. Despite all efforts, pancreas allograft survival and rejection remain major clinical problems. The purpose of this study was to identify features that could signal patients at risk of pancreas allograft rejection.
We collected 74 features from 79 patients who underwent simultaneous pancreas-kidney transplantation (SPK) and used two widely-applicable classification methods, the Naive Bayesian Classifier and Support Vector Machine, to build predictive models. We used the area under the receiver operating characteristic curve and classification accuracy to evaluate the predictive performance via leave-one-out cross-validation.
Rejection events were identified in 13 SPK patients (17.8%). In feature selection approach, it was possible to identify 10 features, namely: previous treatment for diabetes mellitus with long-term Insulin (U/I/day), type of dialysis (peritoneal dialysis, hemodialysis, or pre-emptive), de novo DSA, vPRA_Pre-Transplant (%), donor blood glucose, pancreas donor risk index (pDRI), recipient height, dialysis time (days), warm ischemia (minutes), recipient of intensive care (days). The results showed that the Naive Bayes and Support Vector Machine classifiers prediction performed very well, with an AUROC and classification accuracy of 0.97 and 0.87, respectively, in the first model and 0.96 and 0.94 in the second model.
Our results indicated that it is feasible to develop successful classifiers for the prediction of graft rejection. The Naive Bayesian generated nomogram can be used for rejection probability prediction, thus supporting clinical decision making.
胰腺移植是目前唯一能够重建胰腺正常内分泌功能的治疗方法。尽管付出了诸多努力，但胰腺移植的存活率和排斥反应仍是主要的临床问题。本研究的目的是确定能够提示胰腺移植排斥风险患者的特征。
我们收集了79例接受同期胰肾联合移植（SPK）患者的74项特征，并使用两种广泛适用的分类方法，即朴素贝叶斯分类器和支持向量机，来构建预测模型。我们通过留一法交叉验证，使用受试者工作特征曲线下面积和分类准确率来评估预测性能。
在13例SPK患者（17.8%）中发现了排斥事件。在特征选择方法中，可以识别出10项特征，即：既往糖尿病长期胰岛素治疗（单位/国际单位/天）、透析类型（腹膜透析、血液透析或预先透析）、新生供者特异性抗体（DSA）、移植前虚拟群体反应性抗体（vPRA）（%）、供者血糖、胰腺供者风险指数（pDRI）、受者身高、透析时间（天）、热缺血时间（分钟）、重症监护时间（天）。结果表明，朴素贝叶斯和支持向量机分类器的预测性能非常好，在第一个模型中，受试者工作特征曲线下面积（AUROC）和分类准确率分别为0.97和0.87，在第二个模型中分别为0.96和0.94。
我们的结果表明，开发成功的移植排斥预测分类器是可行的。朴素贝叶斯生成的列线图可用于排斥概率预测，从而支持临床决策。
