The promise of machine learning applications in solid organ transplantation.
Journal
NPJ digital medicine
ISSN: 2398-6352
Titre abrégé: NPJ Digit Med
Pays: England
ID NLM: 101731738
Informations de publication
Date de publication:
11 Jul 2022
11 Jul 2022
Historique:
received:
23
11
2021
accepted:
24
06
2022
entrez:
11
7
2022
pubmed:
12
7
2022
medline:
12
7
2022
Statut:
epublish
Résumé
Solid-organ transplantation is a life-saving treatment for end-stage organ disease in highly selected patients. Alongside the tremendous progress in the last several decades, new challenges have emerged. The growing disparity between organ demand and supply requires optimal patient/donor selection and matching. Improvements in long-term graft and patient survival require data-driven diagnosis and management of post-transplant complications. The growing abundance of clinical, genetic, radiologic, and metabolic data in transplantation has led to increasing interest in applying machine-learning (ML) tools that can uncover hidden patterns in large datasets. ML algorithms have been applied in predictive modeling of waitlist mortality, donor-recipient matching, survival prediction, post-transplant complications diagnosis, and prediction, aiming to optimize immunosuppression and management. In this review, we provide insight into the various applications of ML in transplant medicine, why these were used to evaluate a specific clinical question, and the potential of ML to transform the care of transplant recipients. 36 articles were selected after a comprehensive search of the following databases: Ovid MEDLINE; Ovid MEDLINE Epub Ahead of Print and In-Process & Other Non-Indexed Citations; Ovid Embase; Cochrane Database of Systematic Reviews (Ovid); and Cochrane Central Register of Controlled Trials (Ovid). In summary, these studies showed that ML techniques hold great potential to improve the outcome of transplant recipients. Future work is required to improve the interpretability of these algorithms, ensure generalizability through larger-scale external validation, and establishment of infrastructure to permit clinical integration.
Identifiants
pubmed: 35817953
doi: 10.1038/s41746-022-00637-2
pii: 10.1038/s41746-022-00637-2
pmc: PMC9273640
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
89Informations de copyright
© 2022. The Author(s).
Références
Giwa, S. et al. The promise of organ and tissue preservation to transform medicine. Nat. Biotechnol. 35, 530–542 (2017).
pubmed: 28591112
pmcid: 5724041
doi: 10.1038/nbt.3889
Haugen, C. E. et al. National trends in liver transplantation in older adults. J. Am. Geriatrics Soc. 66, 2321–2326 (2018).
doi: 10.1111/jgs.15583
Abecassis, M. et al. Solid‐organ transplantation in older adults: current status and future research. Am. J. Transplant. 12, 2608–2622 (2012).
pubmed: 22958872
pmcid: 3459231
doi: 10.1111/j.1600-6143.2012.04245.x
Mitchell, A. B. & Glanville, A. R. Lung transplantation: a review of the optimal strategies for referral and patient selection. Therapeutic Adv. respiratory Dis. 13, 1753466619880078 (2019).
Schwager, Y. et al. Prediction of three-year mortality after deceased donor kidney transplantation in adults with pre-transplant donor and recipient variables. Ann. Transplant. 24, 273 (2019).
pubmed: 31097680
pmcid: 6540619
doi: 10.12659/AOT.913217
Jadlowiec, C. C. & Taner, T. Liver transplantation: current status and challenges. World J. Gastroenterol. 22, 4438 (2016).
pubmed: 27182155
pmcid: 4858627
doi: 10.3748/wjg.v22.i18.4438
Ortega, F. Organ transplantation in the 21th century. In López-Larrea, C., López-Vázquez, A., Suárez-Álvarez, B (eds) Stem Cell Transplantation 13–26 (Springer, 2012).
Piao, D., Hawxby, A., Wright, H. & Rubin, E. M. Perspective review on solid-organ transplant: needs in point-of-care optical biomarkers. J. Biomed. Opt. 23, 080601 (2018).
Tonsho, M., Michel, S., Ahmed, Z., Alessandrini, A. & Madsen, J. C. Heart transplantation: challenges facing the field. Cold Spring Harb. Perspect. Med. 4, a015636 (2014).
pubmed: 24789875
pmcid: 3996379
doi: 10.1101/cshperspect.a015636
Mitchell, T. M. Learning M (The McGraw-Hill Companies. Inc, 1997).
Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. N. Engl. J. Med. 380, 1347–1358 (2019).
pubmed: 30943338
doi: 10.1056/NEJMra1814259
Connor, K. L., O’Sullivan, E. D., Marson, L. P., Wigmore, S. J. & Harrison, E. M. The future role of machine learning in clinical transplantation. Transplantation 105, 723–735 (2021).
pubmed: 32826798
doi: 10.1097/TP.0000000000003424
Ishwaran, H., Kogalur, U. B., Blackstone, E. H. & Lauer, M. S. Random survival forests. Ann. Appl. Stat. 2, 841–860. (2008).
doi: 10.1214/08-AOAS169
Hsich, E. M. et al. Variables of importance in the Scientific Registry of Transplant Recipients database predictive of heart transplant waitlist mortality. Am. J. Transplant. 19, 2067–2076 (2019).
pubmed: 30659754
pmcid: 6591021
doi: 10.1111/ajt.15265
Medved, D., Nugues, P. & Nilsson, J. Simulating the outcome of heart allocation policies using deep neural networks. in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 6141–6144 (IEEE, 2018).
Sauthier, N. B. R., Carreir, F. M. & Chassé, M. Detection of Potential Organ Donors; An Automatic Approach on Temporal Data (Critical Care Canada Forum, 2020).
Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016).
Wright, R. E. Logistic Regression. In Grimm, L. G. & Yarnold P. R. (eds), Reading and Understanding Multivariate Statistics (pp. 217–244). Washington DC: American Psychological Association (1995).
Hamouda, E., El-Metwally, S. & Tarek, M. Ant Lion Optimization algorithm for kidney exchanges. PLoS ONE 13, e0196707 (2018).
pubmed: 29723232
pmcid: 5933775
doi: 10.1371/journal.pone.0196707
Briceño, J. et al. Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study. J. Hepatol. 61, 1020–1028 (2014).
pubmed: 24905493
doi: 10.1016/j.jhep.2014.05.039
Ayllón, M. D. et al. Validation of artificial neural networks as a methodology for donor‐recipient matching for liver transplantation. Liver Transplant. 24, 192–203 (2018).
doi: 10.1002/lt.24870
Dorado-Moreno, M. et al. Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem. Artif. Intell. Med. 77, 1–11 (2017).
pubmed: 28545607
doi: 10.1016/j.artmed.2017.02.004
Placona, A. M. et al. Can donor narratives yield insights? A natural language processing proof of concept to facilitate kidney allocation. Am. J. Transplant. 20, 1095–1104 (2020).
pubmed: 31736193
doi: 10.1111/ajt.15705
Marrero, W. J., Lavieri, M. S., Guikema, S. D., Hutton, D. W. & Parikh, N. D. Development of a Predictive Model for Deceased Donor Organ Yield (LWW, 2018).
Medved, D. et al. Improving prediction of heart transplantation outcome using deep learning techniques. Sci. Rep. 8, 1–9 (2018).
doi: 10.1038/s41598-018-21417-7
Yoon, J. et al. Personalized survival predictions via trees of predictors: an application to cardiac transplantation. PLoS ONE 13, e0194985 (2018).
pubmed: 29590219
pmcid: 5874060
doi: 10.1371/journal.pone.0194985
Miller, P. E. et al. Predictive abilities of machine learning techniques may be limited by dataset characteristics: insights from the UNOS database. J. Card. Fail. 25, 479–483 (2019).
pubmed: 30738152
doi: 10.1016/j.cardfail.2019.01.018
Mark, E., Goldsman, D., Gurbaxani, B., Keskinocak, P. & Sokol, J. Using machine learning and an ensemble of methods to predict kidney transplant survival. PLoS ONE 14, e0209068 (2019).
pubmed: 30625130
pmcid: 6326487
doi: 10.1371/journal.pone.0209068
Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc.: Ser. B (Methodol.) 58, 267–288 (1996).
Liaw, A. & Wiener, M. Classification and regression by randomForest. R. N. 2, 18–22 (2002).
Yoo, K. D. et al. A machine learning approach using survival statistics to predict graft survival in kidney transplant recipients: a multicenter cohort study. Sci. Rep. 7, 1–12. (2017).
doi: 10.1038/s41598-017-08008-8
Molinari, M. et al. Prediction of perioperative mortality of cadaveric liver transplant recipients during their evaluations. Transplantation 103, e297–e307 (2019).
Ershoff, B. D. et al. Training and validation of deep neural networks for the prediction of 90-day post-liver transplant mortality using Unos registry data. Transplantation Proc. 52, 246–258 (2020).
Khosravi, B., Pourahmad, S., Bahreini, A., Nikeghbalian, S. & Mehrdad, G. Five years survival of patients after liver transplantation and its effective factors by neural network and cox poroportional hazard regression models. Hepat. Mon. 15, e25164 (2015).
Raeisi Shahraki, H., Pourahmad, S. & Ayatollahi, S. M. T. Identifying the prognosis factors in death after liver transplantation via adaptive LASSO in Iran. J. Environ. Public Health 2016, 7620157 (2016).
Kazemi, A., Kazemi, K., Sami, A. & Sharifian, R. Identifying factors that affect patient survival after orthotopic liver transplant using machine-learning techniques. Exp. Clin. Transpl. 17, 775–783 (2019).
doi: 10.6002/ect.2018.0170
Lau, L. et al. Machine-learning algorithms predict graft failure after liver transplantation. Transplantation 101, e125 (2017).
pubmed: 27941428
pmcid: 7228574
doi: 10.1097/TP.0000000000001600
Zare, A. et al. A neural network approach to predict acute allograft rejection in liver transplant recipients using routine laboratory data. Hepatitis Monthly 17, (2017).
Tapak, L., Hamidi, O., Amini, P. & Poorolajal, J. Prediction of kidney graft rejection using artificial neural network. Healthc. Inform. Res. 23, 277–284 (2017).
pubmed: 29181237
pmcid: 5688027
doi: 10.4258/hir.2017.23.4.277
Yabu, J. M., Siebert, J. C. & Maecker, H. T. Immune profiles to predict response to desensitization therapy in highly HLA-sensitized kidney transplant candidates. PLoS ONE 11, e0153355 (2016).
pubmed: 27078882
pmcid: 4831845
doi: 10.1371/journal.pone.0153355
Suykens, J. A. & Vandewalle, J. Least squares support vector machine classifiers. Neural Process. Lett. 9, 293–300 (1999).
doi: 10.1023/A:1018628609742
Abdeltawab, H. et al. A novel CNN-based CAD system for early assessment of transplanted kidney dysfunction. Sci. Rep. 9, 1–11. (2019).
doi: 10.1038/s41598-019-42431-3
Parkes, M. D. et al. An integrated molecular diagnostic report for heart transplant biopsies using an ensemble of diagnostic algorithms. J. Heart Lung Transplant. 38, 636–646 (2019).
pubmed: 30795962
doi: 10.1016/j.healun.2019.01.1318
Halloran, K. M. et al. Molecular assessment of rejection and injury in lung transplant biopsies. J. Heart Lung Transplant. 38, 504–513 (2019).
pubmed: 30773443
doi: 10.1016/j.healun.2019.01.1317
Halloran, K. et al. Molecular phenotyping of rejection‐related changes in mucosal biopsies from lung transplants. Am. J. Transplant. 20, 954–966 (2020).
pubmed: 31679176
doi: 10.1111/ajt.15685
Williams, K. R. et al. Use of a targeted urine proteome assay (TUPA) to identify protein biomarkers of delayed recovery after kidney transplant. PROTEOMICS–Clin. Appl. 11, 1600132 (2017).
doi: 10.1002/prca.201600132
Costa, S. D. et al. The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis. PLoS ONE 15, e0228597 (2020).
pubmed: 32027717
pmcid: 7004552
doi: 10.1371/journal.pone.0228597
Villeneuve, C. et al. Evolution and determinants of health-related quality-of-life in kidney transplant patients over the first 3 years after transplantation. Transplantation 100, 640–647 (2016).
pubmed: 26569063
doi: 10.1097/TP.0000000000000846
Aubert, O. et al. Archetype analysis identifies distinct profiles in renal transplant recipients with transplant glomerulopathy associated with allograft survival. J. Am. Soc. Nephrol. 30, 625–639 (2019).
pubmed: 30872323
pmcid: 6442337
doi: 10.1681/ASN.2018070777
Moccia, S. et al. Computer-assisted liver graft steatosis assessment via learning-based texture analysis. Int. J. computer Assist. Radiol. Surg. 13, 1357–1367 (2018).
doi: 10.1007/s11548-018-1787-6
Bhat, V., Tazari, M., Watt, K. D. & Bhat, M. New-onset diabetes and preexisting diabetes are associated with comparable reduction in long-term survival after liver transplant: a machine learning approach. Mayo Clinic Proc. 93, 1794–1802 (2018).
Tanaka, T. & Voigt, M. D. Decision tree analysis to stratify risk of de novo non-melanoma skin cancer following liver transplantation. J. Cancer Res. Clin. Oncol. 144, 607–615 (2018).
pubmed: 29362916
doi: 10.1007/s00432-018-2589-5
Lee, B. P. et al. Predicting low risk for sustained alcohol use after early liver transplant for acute alcoholic hepatitis: the sustained alcohol use post–liver transplant score. Hepatology 69, 1477–1487 (2019).
pubmed: 30561766
doi: 10.1002/hep.30478
Lee, H.-C. et al. Prediction of acute kidney injury after liver transplantation: machine learning approaches vs. logistic regression model. J. Clin. Med. 7, 428 (2018).
pmcid: 6262324
doi: 10.3390/jcm7110428
Natekin, A. & Knoll, A. Gradient boosting machines, a tutorial. Front. Neurorobotics 7, 21 (2013).
doi: 10.3389/fnbot.2013.00021
Barbosa, E. J. M. Jr et al. Machine learning algorithms utilizing quantitative CT features may predict eventual onset of bronchiolitis obliterans syndrome after lung transplantation. Academic Radiol. 25, 1201–1212 (2018).
doi: 10.1016/j.acra.2018.01.013
Zhang, Y. et al. An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation. J. Transl. Med. 19, 1–15. (2021).
doi: 10.1186/s12967-021-02990-4
Kampaktsis, P. N. et al. State‐of‐the‐art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: results from the UNOS database. Clin. Transplant. 35, e14388 (2021).
pubmed: 34155697
doi: 10.1111/ctr.14388
Peyster, E. G., Madabhushi, A. & Margulies, K. B. Advanced morphologic analysis for diagnosing allograft rejection: the case of cardiac transplant rejection. Transplantation 102, 1230 (2018).
pubmed: 29570167
pmcid: 6059998
doi: 10.1097/TP.0000000000002189
Ribeiro, M. T., Singh, S. & Guestrin, C. “Why should I trust you?” Explaining the predictions of any classifier. in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1135–1144 (ACM, 2016).
Štrumbelj, E. & Kononenko, I. Explaining prediction models and individual predictions with feature contributions. Knowl. Inf. Syst. 41, 647–665 (2014).
doi: 10.1007/s10115-013-0679-x
Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. in International Conference on Machine Learning. 3319–3328 (PMLR, 2017).