{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T16:11:32Z","timestamp":1782749492678,"version":"3.54.5"},"reference-count":66,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T00:00:00Z","timestamp":1775433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Centro Cl\u00ednico Acad\u00e9mico de Lisboa","award":["FF-CCAL.05.2025"],"award-info":[{"award-number":["FF-CCAL.05.2025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Delayed graft function (DGF) remains a common early complication after deceased donor kidney transplantation and is challenging to anticipate using routine pre-implant clinical variables alone. We investigated whether high-throughput Fourier transform infrared (FTIR) spectroscopy of static cold storage preservation fluid (not machine perfusion perfusate) captures biochemical information associated with DGF and warrants further evaluation alongside routine pre-implant clinical predictors. Methods: In this single-center retrospective cohort, we analyzed preservation fluid samples from 56 kidney transplants originating from 49 deceased donors (7 donors contributed two kidneys); DGF occurred in 14\/56 (25.0%). Dried-film FTIR spectra were acquired using a plate-based high-throughput accessory, and analyses focused on the fingerprint region (900\u20131800 cm\u22121) with prespecified preprocessing and quality control. We developed and compared clinical-only, FTIR-only, and combined predictive models and estimated performance using donor-blinded 5-fold StratifiedGroupKFold cross-validation (grouped by donor code) to prevent leakage across paired kidneys. Results: Donor-blinded discrimination (pooled out-of-fold ROC-AUC) was 0.775 for the clinical-only model, 0.814 for the FTIR-only model, and 0.796 for the combined model; probabilistic accuracy (Brier score; lower is better) was 0.162, 0.194, and 0.177, respectively. Calibration intercepts were negative and slopes were &lt;1, indicating overly extreme risk estimates under strict donor-blinded validation and supporting recalibration prior to deployment. Decision curve analysis suggested a positive net benefit for clinically plausible thresholds. Conclusions: These findings support the feasibility of rapid, low-cost FTIR profiling of routinely available preservation fluid as a proof-of-concept approach for exploratory DGF risk stratification, rather than as a clinically deployable prediction tool. Given the small sample size and the instability of subgroup estimates, the main next steps are external validation in larger multicenter cohorts, prospective workflow studies, and model updating\/recalibration.<\/jats:p>","DOI":"10.3390\/jcm15072762","type":"journal-article","created":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T08:09:00Z","timestamp":1775549340000},"page":"2762","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Machine Learning-Assisted FTIR Spectroscopy Analysis of Kidney Preservation Fluids for Delayed Graft Function Risk Stratification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8911-3380","authenticated-orcid":false,"given":"Luis","family":"Ramalhete","sequence":"first","affiliation":[{"name":"Blood and Transplantation Center of Lisbon, Instituto Portugu\u00eas do Sangue e da Transplanta\u00e7\u00e3o, Alameda das Linhas de Torres, n\u25e6 117, 1769-001 Lisbon, Portugal"},{"name":"NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"iNOVA4Health\u2014Advancing Precision Medicine, N\u00facleo de Investiga\u00e7\u00e3o em Doen\u00e7as Renais, NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"LBS\u2014Lisbon Business & Government School, Rua de S\u00e3o Bernardo 34-A, 1200-427 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9369-6486","authenticated-orcid":false,"given":"R\u00faben","family":"Ara\u00fajo","sequence":"additional","affiliation":[{"name":"NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0528-2716","authenticated-orcid":false,"given":"Miguel Bigotte","family":"Vieira","sequence":"additional","affiliation":[{"name":"NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"iNOVA4Health\u2014Advancing Precision Medicine, N\u00facleo de Investiga\u00e7\u00e3o em Doen\u00e7as Renais, NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"Nephrology, Hospital Curry Cabral, Unidade Local de Sa\u00fade de S\u00e3o Jos\u00e9, R. da Benefic\u00eancia 8, 1050-099 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4525-9062","authenticated-orcid":false,"given":"Emanuel","family":"Vigia","sequence":"additional","affiliation":[{"name":"NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"iNOVA4Health\u2014Advancing Precision Medicine, N\u00facleo de Investiga\u00e7\u00e3o em Doen\u00e7as Renais, NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"LBS\u2014Lisbon Business & Government School, Rua de S\u00e3o Bernardo 34-A, 1200-427 Lisbon, Portugal"},{"name":"Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Unidade Local de Sa\u00fade de S\u00e3o Jos\u00e9, R. da Benefic\u00eancia 8, 1050-099 Lisbon, Portugal"},{"name":"Centro Cl\u00ednico Acad\u00e9mico de Lisboa, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana","family":"Pena","sequence":"additional","affiliation":[{"name":"Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Unidade Local de Sa\u00fade de S\u00e3o Jos\u00e9, R. da Benefic\u00eancia 8, 1050-099 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sofia","family":"Carrelha","sequence":"additional","affiliation":[{"name":"Hepatobiliopancreatic and Transplantation Center, Curry Cabral Hospital, Unidade Local de Sa\u00fade de S\u00e3o Jos\u00e9, R. da Benefic\u00eancia 8, 1050-099 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristiana","family":"Teixeira","sequence":"additional","affiliation":[{"name":"Blood and Transplantation Center of Lisbon, Instituto Portugu\u00eas do Sangue e da Transplanta\u00e7\u00e3o, Alameda das Linhas de Torres, n\u25e6 117, 1769-001 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anibal","family":"Ferreira","sequence":"additional","affiliation":[{"name":"NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"iNOVA4Health\u2014Advancing Precision Medicine, N\u00facleo de Investiga\u00e7\u00e3o em Doen\u00e7as Renais, NOVA Medical School, Faculdade de Ci\u00eancias M\u00e9dicas, Universidade NOVA de Lisboa, 1169-056 Lisbon, Portugal"},{"name":"Nephrology, Hospital Curry Cabral, Unidade Local de Sa\u00fade de S\u00e3o Jos\u00e9, R. da Benefic\u00eancia 8, 1050-099 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5264-9755","authenticated-orcid":false,"given":"Cecilia R. C.","family":"Calado","sequence":"additional","affiliation":[{"name":"ISEL\u2014Instituto Superior de Engenharia de Lisboa, Instituto Polit\u00e9cnico de Lisboa, Rua Conselheiro Em\u00eddio Navarro 1, 1959-007 Lisbon, Portugal"},{"name":"Institute for Bioengineering and Biosciences (iBB), The Associate Laboratory Institute for Health and Bioeconomy-i4HB, Instituto Superior T\u00e9cnico (IST), Universidade de Lisboa (UL), Av. Rovisco Pais, 1049-001 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2995","DOI":"10.1093\/ndt\/gfn158","article-title":"Marked Variation in the Definition and Diagnosis of Delayed Graft Function: A Systematic Review","volume":"23","author":"Yarlagadda","year":"2008","journal-title":"Nephrol. Dial. Transplant."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1097\/TP.0000000000000450","article-title":"Center-Level Variation in the Development of Delayed Graft Function After Deceased Donor Kidney Transplantation","volume":"99","author":"Orandi","year":"2015","journal-title":"Transplantation"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2279","DOI":"10.1111\/j.1600-6143.2011.03754.x","article-title":"Delayed Graft Function in the Kidney Transplant","volume":"11","author":"Siedlecki","year":"2011","journal-title":"Am. J. Transplant."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1681\/ASN.2009040412","article-title":"Delayed Graft Function and the Risk for Death with a Functioning Graft","volume":"21","author":"Tapiawala","year":"2010","journal-title":"J. Am. Soc. Nephrol. JASN"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, M.T., Ramakrishnan, A., Yu, M., Daniel, E., Sandra, V., Sanichar, N., King, K.L., Stevens, J.S., Husain, S.A., and Mohan, S. (2023). Effects of Delayed Graft Function on Transplant Outcomes: A Meta-Analysis. Transplant. Direct, 9.","DOI":"10.1097\/TXD.0000000000001433"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Budhiraja, P., Reddy, K.S., Butterfield, R.J., Jadlowiec, C.C., Moss, A.A., Khamash, H.A., Kodali, L., Misra, S.S., and Heilman, R.L. (2022). Duration of Delayed Graft Function and Its Impact on Graft Outcomes in Deceased Donor Kidney Transplantation. BMC Nephrol., 23.","DOI":"10.1186\/s12882-022-02777-9"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Warmuzi\u0144ska, N., \u0141uczykowski, K., Stryjak, I., Wojtal, E., Woderska-Jasi\u0144ska, A., Masztalerz, M., W\u0142odarczyk, Z., and Bojko, B. (2024). Metabolomic and Lipidomic Profiling for Pre-Transplant Assessment of Delayed Graft Function Risk Using Chemical Biopsy with Microextraction Probes. Int. J. Mol. Sci., 25.","DOI":"10.21203\/rs.3.rs-5529787\/v1"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nieuwenhuijs-Moeke, G.J., Pischke, S.E., Berger, S.P., Sanders, J.S.F., Pol, R.A., Struys, M.M.R.F., Ploeg, R.J., and Leuvenink, H.G.D. (2020). Ischemia and Reperfusion Injury in Kidney Transplantation: Relevant Mechanisms in Injury and Repair. J. Clin. Med., 9.","DOI":"10.3390\/jcm9010253"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1038\/ki.2015.190","article-title":"Delayed Graft Function and the Risk of Acute Rejection in the Modern Era of Kidney Transplantation","volume":"88","author":"Wu","year":"2015","journal-title":"Kidney Int."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1016\/S0140-6736(04)17406-0","article-title":"Delayed Graft Function in Kidney Transplantation","volume":"364","author":"Perico","year":"2004","journal-title":"Lancet"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ahlmark, A., Sallinen, V., R\u00e4is\u00e4nen-Sokolowski, A., Ahopelto, K., Lempinen, M., Lauronen, J., and Helanter\u00e4, I. (2025). Protocol Biopsies in Delayed Graft Function Kidney Transplants From Brain-Dead Donors. Clin. Transplant., 39.","DOI":"10.1111\/ctr.70228"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1097\/00007890-199504150-00007","article-title":"Delayed Graft Function, Acute Rejection, and Outcome after Cadaver Renal Transplantation. The Multivariate Analysis","volume":"59","author":"Troppmann","year":"1995","journal-title":"Transplantation"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.1111\/ajt.16471","article-title":"Delayed Graft Function and Acute Rejection Following HLA-Incompatible Living Donor Kidney Transplantation","volume":"21","author":"Motter","year":"2021","journal-title":"Am. J. Transplant. Off. J. Am. Soc. Transplant. Am. Soc. Transpl. Surg."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1038\/ki.2014.304","article-title":"Each Additional Hour of Cold Ischemia Time Significantly Increases the Risk of Graft Failure and Mortality Following Renal Transplantation","volume":"87","author":"Debout","year":"2015","journal-title":"Kidney Int."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1056\/NEJMoa0802289","article-title":"Machine Perfusion or Cold Storage in Deceased-Donor Kidney Transplantation","volume":"360","author":"Moers","year":"2009","journal-title":"N. Engl. J. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.1016\/S0140-6736(20)32411-9","article-title":"Oxygenated versus Standard Cold Perfusion Preservation in Kidney Transplantation (COMPARE): A Randomised, Double-Blind, Paired, Phase 3 Trial","volume":"396","author":"Jochmans","year":"2020","journal-title":"Lancet"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1038\/s41591-023-02376-7","article-title":"Normothermic Machine Perfusion versus Static Cold Storage in Donation after Circulatory Death Kidney Transplantation: A Randomized Controlled Trial","volume":"29","author":"Hosgood","year":"2023","journal-title":"Nat. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1111\/tri.13593","article-title":"A Systematic Review to Identify Whether Perfusate Biomarkers Produced during Hypothermic Machine Perfusion Can Predict Graft Outcomes in Kidney Transplantation","volume":"33","author":"Guzzi","year":"2020","journal-title":"Transpl. Int."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Crocetto, F., Amicuzi, U., Musone, M., Magliocchetti, M., Di Lieto, D., Tammaro, S., Pastore, A.L., Fuschi, A., Falabella, R., and Ferro, M. (2025). Liquid Biopsy: Current Advancements in Clinical Practice for Bladder Cancer. J. Liq. Biopsy, 9.","DOI":"10.1016\/j.jlb.2025.100310"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.urolonc.2025.07.034","article-title":"Liquid Biopsy Testing in Urological Cancers: Focus on Urine","volume":"43","author":"Pisapia","year":"2025","journal-title":"Urol. Oncol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1016\/j.kint.2022.11.020","article-title":"Untargeted Metabolomics of Perfusate and Their Association with Hypothermic Machine Perfusion and Allograft Failure","volume":"103","author":"Liu","year":"2023","journal-title":"Kidney Int."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ramalhete, L., Ara\u00fajo, R., Vieira, M.B., Vigia, E., Pena, A., Carrelha, S., Ferreira, A., and Calado, C.R.C. (2025). Rapid FTIR Spectral Fingerprinting of Kidney Allograft Perfusion Fluids Distinguishes DCD from DBD Donors: A Pilot Machine Learning Study. Metabolites, 15.","DOI":"10.3390\/metabo15110702"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1038\/nprot.2014.110","article-title":"Using Fourier Transform IR Spectroscopy to Analyze Biological Materials","volume":"9","author":"Baker","year":"2014","journal-title":"Nat. Protoc."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ramalhete, L., Ara\u00fajo, R., Vieira, M.B., Vigia, E., Aires, I., Ferreira, A., and Calado, C.R.C. (2025). Integration of FTIR Spectroscopy and Machine Learning for Kidney Allograft Rejection: A Complementary Diagnostic Tool. J. Clin. Med., 14.","DOI":"10.3390\/jcm14030846"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ramalhete, L., Vieira, M.B., Ara\u00fajo, R., Vigia, E., Aires, I., Ferreira, A., and Calado, C.R.C. (2024). Predicting Cellular Rejection of Renal Allograft Based on the Serum Proteomic Fingerprint. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms25073844"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1111\/j.1600-6143.2011.03908.x","article-title":"Preservation Solutions for Static Cold Storage of Kidney Allografts: A Systematic Review and Meta-Analysis","volume":"12","author":"Knight","year":"2012","journal-title":"Am. J. Transplant."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1097\/00007890-200110150-00016","article-title":"Kidney Preservation with University of Wisconsin and Celsior Solution: A Prospective Multicenter Randomized Study","volume":"72","author":"Faenza","year":"2001","journal-title":"Transplantation"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/S0003-4975(02)03675-5","article-title":"Celsior versus Custodiol: Early Postischemic Recovery after Cardioplegia and Ischemia at 5 Degrees C","volume":"74","author":"Ackemann","year":"2002","journal-title":"Ann. Thorac. Surg."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.transproceed.2012.02.031","article-title":"Preservation Solutions for Liver Transplantation in Adults: Celsior versus Custodiol: A Systematic Review and Meta-Analysis with an Indirect Comparison of Randomized Trials","volume":"45","author":"Zuluaga","year":"2013","journal-title":"Transplant. Proc."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rijkse, E., Ceuppens, S., Qi, H., IJzermans, J.N.M., Hesselink, D.A., and Minnee, R.C. (2021). Implementation of Donation after Circulatory Death Kidney Transplantation Can Safely Enlarge the Donor Pool: A Systematic Review and Meta-Analysis. Int. J. Surg., 92.","DOI":"10.1016\/j.ijsu.2021.106021"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"55","DOI":"10.7326\/M14-0697","article-title":"Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement","volume":"162","author":"Collins","year":"2015","journal-title":"Ann. Intern. Med."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Collins, G.S., Moons, K.G.M., Dhiman, P., Riley, R.D., Beam, A.L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J.B., and van Smeden, M. (2024). TRIPOD+AI Statement: Updated Guidance for Reporting Clinical Prediction Models That Use Regression or Machine Learning Methods. BMJ, 385.","DOI":"10.1136\/bmj-2023-078378"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"51","DOI":"10.7326\/M18-1376","article-title":"PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies","volume":"170","author":"Wolff","year":"2019","journal-title":"Ann. Intern. Med."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Moons, K.G.M., Damen, J.A.A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A.L., Van Calster, B., Celi, L.A., and Denaxas, S. (2025). PROBAST+AI: An Updated Quality, Risk of Bias, and Applicability Assessment Tool for Prediction Models Using Regression or Artificial Intelligence Methods. BMJ (Clin. Res. Ed.), 388.","DOI":"10.1136\/bmj-2024-082505"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision Curve Analysis: A Novel Method for Evaluating Prediction Models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med. Decis. Mak. Int. J. Soc. Med. Decis. Mak."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Vickers, A.J., Van Calster, B., and Steyerberg, E.W. (2016). Net Benefit Approaches to the Evaluation of Prediction Models, Molecular Markers, and Diagnostic Tests. BMJ, 352.","DOI":"10.1136\/bmj.i6"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1016\/j.eururo.2018.08.038","article-title":"Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators","volume":"74","author":"Wynants","year":"2018","journal-title":"Eur. Urol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"i108","DOI":"10.1093\/bja\/aer357","article-title":"Donation after Circulatory Death","volume":"108","author":"Manara","year":"2012","journal-title":"Br. J. Anaesth."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Burgess, M., Silsby, L., Madden, S., Letra, J.D.S., Thomson, S., Tate, J., Mitchinson, S., Hurley, K., Hurley, R., and Bon, M. (2025). Eligible DBD Donors Proceeding via the DCD Pathway: Incidence, Cause, and Outcomes in the United Kingdom. Transplant. Direct, 11.","DOI":"10.1097\/TXD.0000000000001804"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1038\/ki.2009.377","article-title":"KDIGO Clinical Practice Guideline for the Care of Kidney Transplant Recipients: A Summary","volume":"77","author":"Kasiske","year":"2010","journal-title":"Kidney Int."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez-Dom\u00ednguez, R., Gonz\u00e1lez-Dom\u00ednguez, \u00c1., Sayago, A., and Fern\u00e1ndez-Recamales, \u00c1. (2020). Recommendations and Best Practices for Standardizing the Pre-Analytical Processing of Blood and Urine Samples in Metabolomics. Metabolites, 10.","DOI":"10.3390\/metabo10060229"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Buchanan, J.L., Tormes Vaquerano, J., and Taylor, E.B. (2022). Isolated Effects of Plasma Freezing versus Thawing on Metabolite Stability. Metabolites, 12.","DOI":"10.3390\/metabo12111098"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, R., Ramalhete, L., Ribeiro, E., and Calado, C. (2022). Plasma versus Serum Analysis by FTIR Spectroscopy to Capture the Human Physiological State. BioTech, 11.","DOI":"10.3390\/biotech11040056"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mariani, M., He, S., McHugh, M., Andreoli, M., Pandya, D., Sieber, S., Wu, Z., Fiedler, P., Shahabi, S., and Ferlini, C. (2014). Integrated Multidimensional Analysis Is Required for Accurate Prognostic Biomarkers in Colorectal Cancer. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0101065"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, Z., Ma, R., and Zhong, Y. (2025). Assessing and Improving Reliability of Neighbor Embedding Methods: A Map-Continuity Perspective. Nat. Commun., 16.","DOI":"10.1038\/s41467-025-60434-9"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2279","DOI":"10.1111\/j.1600-6143.2010.03179.x","article-title":"A Risk Prediction Model for Delayed Graft Function in the Current Era of Deceased Donor Renal Transplantation","volume":"10","author":"Irish","year":"2010","journal-title":"Am. J. Transplant."},{"key":"ref_47","unstructured":"Yu, L., and Liu, H. (2003). Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. Proceedings of the Twentieth International Conference on Machine Learning Volume, AAAI Press."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Varma, S., and Simon, R. (2006). Bias in Error Estimation When Using Cross-Validation for Model Selection. BMC Bioinform., 7.","DOI":"10.1186\/1471-2105-7-91"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Saito, T., and Rehmsmeier, M. (2015). The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0118432"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Pettit, R.W., Marlatt, B.B., Miles, T.J., Uzgoren, S., Corr, S.J., Shetty, A., Havelka, J., and Rana, A. (2023). The Utility of Machine Learning for Predicting Donor Discard in Abdominal Transplantation. Clin. Transplant., 37.","DOI":"10.1111\/ctr.14951"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","article-title":"Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures","volume":"21","author":"Steyerberg","year":"2010","journal-title":"Epidemiology"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Theakstone, A.G., Rinaldi, C., Butler, H.J., Cameron, J.M., Confield, L.R., Rutherford, S.H., Sala, A., Sangamnerkar, S., and Baker, M.J. (2021). Fourier-Transform Infrared Spectroscopy of Biofluids: A Practical Approach. Transl. Biophotonics, 3.","DOI":"10.1002\/tbio.202000025"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yan, C. (2025). A Review on Spectral Data Preprocessing Techniques for Machine Learning and Quantitative Analysis. iScience, 28.","DOI":"10.1016\/j.isci.2025.112759"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Van Calster, B., McLernon, D.J., van Smeden, M., Wynants, L., and Steyerberg, E.W. (2019). Calibration: The Achilles Heel of Predictive Analytics. BMC Med., 17.","DOI":"10.1186\/s12916-019-1466-7"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1093\/eurheartj\/ehu207","article-title":"Towards Better Clinical Prediction Models: Seven Steps for Development and an ABCD for Validation","volume":"35","author":"Steyerberg","year":"2014","journal-title":"Eur. Heart J."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Baboudjian, M., Gondran-Tellier, B., Boissier, R., Ancel, P., Marjollet, J., Lyonnet, L., Fran\u00e7ois, P., Sabatier, F., Lechevallier, E., and Dutour, A. (2022). An Enhanced Level of VCAM in Transplant Preservation Fluid Is an Independent Predictor of Early Kidney Allograft Dysfunction. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.966951"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"715","DOI":"10.2215\/CJN.03250317","article-title":"Can Preservation Fluid Biomarkers Predict Delayed Graft Function in Transplanted Kidneys?","volume":"12","author":"Hall","year":"2017","journal-title":"Clin. J. Am. Soc. Nephrol. CJASN"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Scurt, F.G., Ernst, A., FischerFr\u00f6hlich, C.-L., Schwarz, A., Becker, J.U., and Chatzikyrkou, C. (2023). Performance of Scores Predicting Adverse Outcomes in Procurement Kidney Biopsies From Deceased Donors with Organs of Lower-Than-Average Quality. Transpl. Int. Off. J. Eur. Soc. Organ Transplant., 36.","DOI":"10.3389\/ti.2023.11399"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1038\/ki.2014.188","article-title":"A Useful Scoring System for the Prediction and Management of Delayed Graft Function Following Kidney Transplantation from Cadaveric Donors","volume":"86","author":"Chapal","year":"2014","journal-title":"Kidney Int."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ahlmark, A., Sallinen, V., Eerola, V., Lempinen, M., and Helanter\u00e4, I. (2024). Characteristics of Delayed Graft Function and Long-Term Outcomes After Kidney Transplantation from Brain-Dead Donors: A Single-Center and Multicenter Registry-Based Retrospective Study. Transpl. Int., 37.","DOI":"10.3389\/ti.2024.12309"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Bary\u0142a, M., Skrzycki, M., Danielewicz, R., Kosieradzki, M., and Struga, M. (2024). Protein Biomarkers in Assessing Kidney Quality before Transplantation-current Status and Future Perspectives (Review). Int. J. Mol. Med., 54.","DOI":"10.3892\/ijmm.2024.5431"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ramalhete, L.M., Ara\u00fajo, R., Ferreira, A., and Calado, C.R.C. (2022). Proteomics for Biomarker Discovery for Diagnosis and Prognosis of Kidney Transplantation Rejection. Proteomes, 10.","DOI":"10.3390\/proteomes10030024"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1308","DOI":"10.1016\/j.transproceed.2024.05.032","article-title":"Diagnostic and Prognostic Value of Machine Perfusion Biomarkers in Kidney Graft Evaluation","volume":"56","author":"Avramidou","year":"2024","journal-title":"Transplant. Proc."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"e202","DOI":"10.1097\/TP.0000000000003679","article-title":"Advances in Kidney Preservation Techniques and Their Application in Clinical Practice","volume":"105","author":"Hosgood","year":"2021","journal-title":"Transplantation"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1177\/0963689719872699","article-title":"Preservation Solutions for Kidney Transplantation: History, Advances and Mechanisms","volume":"28","author":"Chen","year":"2019","journal-title":"Cell Transplant."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Debray, T.P.A., Collins, G.S., Riley, R.D., Snell, K.I.E., Van Calster, B., Reitsma, J.B., and Moons, K.G.M. (2023). Transparent Reporting of Multivariable Prediction Models Developed or Validated Using Clustered Data: TRIPOD-Cluster Checklist. BMJ (Clin. Res. Ed.), 380.","DOI":"10.1136\/bmj-2022-071018"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/7\/2762\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T08:43:09Z","timestamp":1775551389000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/7\/2762"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,6]]},"references-count":66,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["jcm15072762"],"URL":"https:\/\/doi.org\/10.3390\/jcm15072762","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,6]]}}}