{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T15:19:38Z","timestamp":1791386378289,"version":"4.3.3"},"reference-count":37,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,8]],"date-time":"2022-06-08T00:00:00Z","timestamp":1654646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background: This study aimed to better characterize morbidly obese kidney transplant recipients, their clinical characteristics, and outcomes by using an unsupervised machine learning approach. Methods: Consensus cluster analysis was applied to OPTN\/UNOS data from 2010 to 2019 based on recipient, donor, and transplant characteristics in kidney transplant recipients with a pre-transplant BMI \u2265 40 kg\/m2. Key cluster characteristics were identified using the standardized mean difference. Post-transplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection, were compared among the clusters. Results: Consensus clustering analysis identified 3204 kidney transplant recipients with a BMI \u2265 40 kg\/m2. In this cohort, five clinically distinct clusters were identified. Cluster 1 recipients were predominantly white and non-sensitized, had a short dialysis time or were preemptive, and were more likely to receive living donor kidney transplants. Cluster 2 recipients were older and diabetic. They were likely to have been on dialysis &gt;3 years and receive a standard KDPI deceased donor kidney. Cluster 3 recipients were young, black, and had kidney disease secondary to hypertension or glomerular disease. Cluster 3 recipients had &gt;3 years of dialysis and received non-ECD, young, deceased donor kidney transplants with a KDPI &lt; 85%. Cluster 4 recipients were diabetic with variable dialysis duration who either received non-ECD standard KDPI kidneys or living donor kidney transplants. Cluster 5 recipients were young retransplants that were sensitized. One-year patient survival in clusters 1, 2, 3, 4, and 5 was 98.0%, 94.4%, 98.5%, 98.7%, and 97%, and one-year death-censored graft survival was 98.1%, 93.0%, 96.1%, 98.8%, and 93.0%, respectively. Cluster 2 had the worst one-year patient survival. Clusters 2 and 5 had the worst one-year death-censored graft survival. Conclusions: With the application of unsupervised machine learning, variable post-transplant outcomes are observed among morbidly obese kidney transplant recipients. Recipients with earlier access to transplant and living donation show superior outcomes. Unexpectedly, reduced graft survival in cluster 3 recipients perhaps underscores socioeconomic access to post-transplant support and minorities being disadvantaged in access to preemptive and living donor transplants. Despite obesity-related concerns, one-year patient and graft survival were favorable in all clusters, and obesity itself should be reconsidered as a hard barrier to kidney transplantation.<\/jats:p>","DOI":"10.3390\/jcm11123288","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T02:25:33Z","timestamp":1654827933000},"page":"3288","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States"],"prefix":"10.3390","volume":"11","author":[{"given":"Charat","family":"Thongprayoon","sequence":"first","affiliation":[{"name":"Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shennen A.","family":"Mao","sequence":"additional","affiliation":[{"name":"Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7860-9519","authenticated-orcid":false,"given":"Caroline C.","family":"Jadlowiec","sequence":"additional","affiliation":[{"name":"Division of Transplant Surgery, Mayo Clinic, Phoenix, AZ 85054, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1814-7003","authenticated-orcid":false,"given":"Michael A.","family":"Mao","sequence":"additional","affiliation":[{"name":"Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Napat","family":"Leeaphorn","sequence":"additional","affiliation":[{"name":"Renal Transplant Program, University of Missouri-Kansas City School of Medicine\/Saint Luke\u2019s Health System, Kansas City, MO 64108, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2920-7235","authenticated-orcid":false,"given":"Wisit","family":"Kaewput","sequence":"additional","affiliation":[{"name":"Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pradeep","family":"Vaitla","sequence":"additional","affiliation":[{"name":"Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6010-0033","authenticated-orcid":false,"given":"Pattharawin","family":"Pattharanitima","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6103-2338","authenticated-orcid":false,"given":"Supawit","family":"Tangpanithandee","sequence":"additional","affiliation":[{"name":"Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2888-881X","authenticated-orcid":false,"given":"Pajaree","family":"Krisanapan","sequence":"additional","affiliation":[{"name":"Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA"},{"name":"Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fawad","family":"Qureshi","sequence":"additional","affiliation":[{"name":"Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pitchaphon","family":"Nissaisorakarn","sequence":"additional","affiliation":[{"name":"Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Cooper","sequence":"additional","affiliation":[{"name":"Medstar Georgetown Transplant Institute, Georgetown University School of Medicine, Washington, DC 20007, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9954-9711","authenticated-orcid":false,"given":"Wisit","family":"Cheungpasitporn","sequence":"additional","affiliation":[{"name":"Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55902, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1097\/00007890-199002000-00032","article-title":"Obesity as a risk factor following cadaveric renal transplantation","volume":"49","author":"Holley","year":"1990","journal-title":"Transplantation"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ercole, P.M., Buchanan, P.M., Lentine, K.L., Burroughs, T.E., Schnitzler, M.A., and Modanlou, K.A. (2012). Costs and Outcomes of Privately-Insured Kidney Transplant Recipients by Body Mass Index. J. Nephrol. Ther.","DOI":"10.4172\/2161-0959.S4-003"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1097\/SLA.0b013e3181b4ee9a","article-title":"Obesity, surgical site infection, and outcome following renal transplantation","volume":"250","author":"Lynch","year":"2009","journal-title":"Ann. Surg."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1111\/j.1600-6143.2005.01198.x","article-title":"Obesity and outcome following renal transplantation","volume":"6","author":"Gore","year":"2006","journal-title":"Am. J. Transplant."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1097\/00007890-200201150-00013","article-title":"The impact of body mass index on renal transplant outcomes: A significant independent risk factor for graft failure and patient death","volume":"73","author":"Arndorfer","year":"2002","journal-title":"Transplantation"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lafranca, J.A., Ijermans, J.N.M., Betjes, M.G., and Dor, F.J. (2015). Body mass index and outcome in renal transplant recipients: A systematic review and meta-analysis. BMC Med., 13.","DOI":"10.1186\/s12916-015-0340-5"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1093\/ndt\/gfv214","article-title":"Recipient obesity and outcomes after kidney transplantation: A systematic review and meta-analysis","volume":"30","author":"Hill","year":"2015","journal-title":"Nephrol. Dial. Transplant."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1111\/ajt.16196","article-title":"Effects of body mass index on kidney transplant outcomes are significantly modified by patient characteristics","volume":"21","author":"Schold","year":"2021","journal-title":"Am. J. Transplant."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1159\/000345476","article-title":"Obesity and kidney transplant candidates: How big is too big for transplantation?","volume":"36","author":"Lentine","year":"2012","journal-title":"Am. J. Nephrol."},{"key":"ref_10","first-page":"3","article-title":"The evaluation of renal transplantation candidates: Clinical practice guidelines","volume":"1","author":"Kasiske","year":"2001","journal-title":"Am. J. Transplant."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2644","DOI":"10.1111\/j.1600-6143.2010.03330.x","article-title":"Body mass index, waist circumference and mortality in kidney transplant recipients","volume":"10","author":"Kovesdy","year":"2010","journal-title":"Am. J. Transplant."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.1097\/TP.0000000000000983","article-title":"The Impact of Obesity on Allograft Failure After Kidney Transplantation: A Competing Risks Analysis","volume":"100","author":"Naik","year":"2016","journal-title":"Transplantation"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e14388","DOI":"10.1111\/ctr.14388","article-title":"State-of-the-art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: Results from the UNOS database","volume":"35","author":"Kampaktsis","year":"2021","journal-title":"Clin. Transplant."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"ooab008","DOI":"10.1093\/jamiaopen\/ooab008","article-title":"Machine learning-based prediction of health outcomes in pediatric organ transplantation recipients","volume":"4","author":"Killian","year":"2021","journal-title":"JAMIA Open"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.transproceed.2019.10.019","article-title":"Training and Validation of Deep Neural Networks for the Prediction of 90-Day Post-Liver Transplant Mortality Using UNOS Registry Data","volume":"52","author":"Ershoff","year":"2020","journal-title":"Transplant. Proc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e13554","DOI":"10.1111\/petr.13554","article-title":"Predicting ideal outcome after pediatric liver transplantation: An exploratory study using machine learning analyses to leverage Studies of Pediatric Liver Transplantation Data","volume":"23","author":"Wadhwani","year":"2019","journal-title":"Pediatr. Transplant."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Schwantes, I.R., and Axelrod, D.A. (2021). Technology-Enabled Care and Artificial Intelligence in Kidney Transplantation. Curr. Transplant. Rep., 1\u20136.","DOI":"10.1007\/s40472-021-00336-z"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1097\/TP.0000000000003424","article-title":"The Future Role of Machine Learning in Clinical Transplantation","volume":"105","author":"Connor","year":"2021","journal-title":"Transplantation"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Thongprayoon, C., Vaitla, P., Jadlowiec, C.C., Leeaphorn, N., Mao, S.A., Mao, M.A., Pattharanitima, P., Bruminhent, J., Khoury, N.J., and Garovic, V.D. (2022). Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Black Kidney Transplant Recipients and Associated Outcomes. JAMA Surg., e221286.","DOI":"10.1001\/jamasurg.2022.1286"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1139\/gen-2020-0131","article-title":"Machine learning for precision medicine","volume":"64","author":"MacEachern","year":"2021","journal-title":"Genome"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1007\/s41666-018-0029-6","article-title":"Nearest Consensus Clustering Classification to Identify Subclasses and Predict Disease","volume":"2","author":"Alyousef","year":"2018","journal-title":"J. Healthc. Inform. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1681\/ASN.2020030239","article-title":"Subtyping CKD Patients by Consensus Clustering: The Chronic Renal Insufficiency Cohort (CRIC) Study","volume":"32","author":"Zheng","year":"2021","journal-title":"J. Am. Soc. Nephrol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Thongprayoon, C., Kattah, A.G., Mao, M.A., Keddis, M.T., Pattharanitima, P., Vallabhajosyula, S., Nissaisorakarn, V., Erickson, S.B., Dillon, J.J., and Garovic, V.D. (2021). Distinct Phenotypes of Hospitalized Patients with Hyperkalemia by Machine Learning Consensus Clustering and Associated Mortality Risks. QJM, online ahead of print.","DOI":"10.1093\/qjmed\/hcab194"},{"key":"ref_24","first-page":"1","article-title":"Mice: Multivariate imputation by chained equations in R","volume":"45","year":"2011","journal-title":"J. Stat. Softw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/A:1023949509487","article-title":"Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data","volume":"52","author":"Monti","year":"2003","journal-title":"Mach. Learn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1093\/bioinformatics\/btq170","article-title":"ConsensusClusterPlus: A class discovery tool with confidence assessments and item tracking","volume":"26","author":"Wilkerson","year":"2010","journal-title":"Bioinformatics"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6207","DOI":"10.1038\/srep06207","article-title":"Critical limitations of consensus clustering in class discovery","volume":"4","author":"Michailidis","year":"2014","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1097\/TP.0000000000000891","article-title":"Developing statistical models to assess transplant outcomes using national registries: The process in the United States","volume":"100","author":"Snyder","year":"2016","journal-title":"Transplantation"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e343","DOI":"10.1097\/TXD.0000000000000782","article-title":"Assessing Consensus Between UK Renal Clinicians on Listing for Kidney Transplantation: A Modified Delphi Study","volume":"4","author":"Pruthi","year":"2018","journal-title":"Transplant. Direct"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"S1","DOI":"10.1503\/cmaj.1041588","article-title":"Canadian Society of Transplantation: Consensus guidelines on eligibility for kidney transplantation","volume":"173","author":"Knoll","year":"2005","journal-title":"CMAJ"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1111\/iju.13135","article-title":"Association between body mass index and outcomes in Japanese living kidney transplant recipients: The role of sex differences","volume":"23","author":"Okumi","year":"2016","journal-title":"Int. J. Urol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1111\/ajt.16502","article-title":"OPTN\/SRTR 2019 Annual Data Report: Kidney","volume":"21","author":"Hart","year":"2021","journal-title":"Am. J. Transplant."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1111\/j.1523-1755.2002.kid582.x","article-title":"Patient survival after renal transplantation: IV. Impact of post-transplant diabetes","volume":"62","author":"Cosio","year":"2002","journal-title":"Kidney Int."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1111\/j.1600-6143.2007.02101.x","article-title":"Patient survival and cardiovascular risk after kidney transplantation: The challenge of diabetes","volume":"8","author":"Cosio","year":"2008","journal-title":"Am. J. Transplant"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.semnephrol.2009.10.009","article-title":"Disparities in kidney transplant outcomes: A review","volume":"30","author":"Gordon","year":"2010","journal-title":"Semin. Nephrol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"262","DOI":"10.2215\/CJN.04860420","article-title":"Social Determinants of Health and Race Disparities in Kidney Transplant","volume":"16","author":"Wesselman","year":"2021","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1016\/j.jclinepi.2006.01.014","article-title":"A gentle introduction to imputation of missing values","volume":"59","author":"Donders","year":"2006","journal-title":"J. Clin. Epidemiol."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/11\/12\/3288\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:26:20Z","timestamp":1760138780000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/11\/12\/3288"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,8]]},"references-count":37,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["jcm11123288"],"URL":"https:\/\/doi.org\/10.3390\/jcm11123288","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,8]]}}}