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Vigia E, Ramalhete L, Ribeiro R, Barros I, Chumbinho B, Filipe E, Pena A, Bicho L, Nobre A, Carrelha S, Sobral M, Lamelas J, Coelho JS, Ferreira A, Marques HP. Pancreas Rejection in the Artificial Intelligence Era: New Tool for Signal Patients at Risk. J Pers Med 2023; 13:1071. [PMID: 37511684 PMCID: PMC10381793 DOI: 10.3390/jpm13071071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 06/21/2023] [Accepted: 06/28/2023] [Indexed: 07/30/2023] Open
Abstract
INTRODUCTION 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. METHODS 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. RESULTS 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. CONCLUSION 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.
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Affiliation(s)
- Emanuel Vigia
- 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
| | - Luís Ramalhete
- Nova Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal
- Blood and Transplantation Center of Lisbon, Instituto Português do Sangue e da Transplantação, Alameda das Linhas de Torres, n 117, 1769-001 Lisbon, Portugal
- iNOVA4Health, Advancing Precision Medicine, RG11, Reno-Vascular Diseases Group, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal
| | - Rita Ribeiro
- Nova Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal
| | - Inês Barros
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Beatriz Chumbinho
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Edite Filipe
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Ana Pena
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Luís Bicho
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Ana Nobre
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Sofia Carrelha
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Mafalda Sobral
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Jorge Lamelas
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - João Santos Coelho
- Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Aníbal Ferreira
- iNOVA4Health, Advancing Precision Medicine, RG11, Reno-Vascular Diseases Group, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal
- Nephrology, Hospital Curry Cabral, Centro Hospitalar Universitário de Lisboa Central, R. da Beneficência 8, 1050-099 Lisbon, Portugal
| | - Hugo Pinto Marques
- 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
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