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Thongprayoon C, Tangpanithandee S, Jadlowiec CC, Mao SA, Mao MA, Vaitla P, Acharya PC, Leeaphorn N, Kaewput W, Pattharanitima P, Suppadungsuk S, Krisanapan P, Nissaisorakarn P, Cooper M, Craici IM, Cheungpasitporn W. Characteristics of Kidney Transplant Recipients with Prolonged Pre-Transplant Dialysis Duration as Identified by Machine Learning Consensus Clustering: Pathway to Personalized Care. J Pers Med 2023; 13:1273. [PMID: 37623523 PMCID: PMC10455164 DOI: 10.3390/jpm13081273] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 08/26/2023] Open
Abstract
Longer pre-transplant dialysis duration is known to be associated with worse post-transplant outcomes. Our study aimed to cluster kidney transplant recipients with prolonged dialysis duration before transplant using an unsupervised machine learning approach to better assess heterogeneity within this cohort. We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 5092 kidney transplant recipients who had been on dialysis ≥ 10 years prior to transplant in the OPTN/UNOS database from 2010 to 2019. We characterized each assigned cluster and compared the posttransplant outcomes. Overall, the majority of patients with ≥10 years of dialysis duration were black (52%) or Hispanic (25%), with only a small number (17.6%) being moderately sensitized. Within this cohort, three clinically distinct clusters were identified. Cluster 1 patients were younger, non-diabetic and non-sensitized, had a lower body mass index (BMI) and received a kidney transplant from younger donors. Cluster 2 recipients were older, unsensitized and had a higher BMI; they received kidney transplant from older donors. Cluster 3 recipients were more likely to be female with a higher PRA. Compared to cluster 1, cluster 2 had lower 5-year death-censored graft (HR 1.40; 95% CI 1.16-1.71) and patient survival (HR 2.98; 95% CI 2.43-3.68). Clusters 1 and 3 had comparable death-censored graft and patient survival. Unsupervised machine learning was used to characterize kidney transplant recipients with prolonged pre-transplant dialysis into three clinically distinct clusters with variable but good post-transplant outcomes. Despite a dialysis duration ≥ 10 years, excellent outcomes were observed in most recipients, including those with moderate sensitization. A disproportionate number of minority recipients were observed within this cohort, suggesting multifactorial delays in accessing kidney transplantation.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Caroline C. Jadlowiec
- Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA;
| | - Shennen A. Mao
- Division of Transplant Surgery, Mayo Clinic, Phoenix, AZ 85054, USA;
| | - Michael A. Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Pradeep Vaitla
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Prakrati C. Acharya
- Division of Nephrology, Texas Tech Health Sciences Center El Paso, El Paso, TX 79905, USA;
| | - Napat Leeaphorn
- Renal Transplant Program, University of Missouri-Kansas City School of Medicine/Saint Luke’s Health System, Kansas City, MO 64108, USA;
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Pattharawin Pattharanitima
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine Thammasat University, Pathum Thani 12120, Thailand;
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine Thammasat University, Pathum Thani 12120, Thailand;
- Division of Nephrology, Department of Internal Medicine, Thammasat University Hospital, Pathum Thani 12120, Thailand
| | - Pitchaphon Nissaisorakarn
- Deparment of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA;
| | - Matthew Cooper
- Department of Surgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA;
| | - Iasmina M. Craici
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
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Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:medicina58121831. [PMID: 36557033 PMCID: PMC9783488 DOI: 10.3390/medicina58121831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/03/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022]
Abstract
Background and Objectives: Our study aimed to cluster dual kidney transplant recipients using an unsupervised machine learning approach to characterize donors and recipients better and to compare the survival outcomes across these various clusters. Materials and Methods: We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 2821 dual kidney transplant recipients from 2010 to 2019 in the OPTN/UNOS database. We determined the important characteristics of each assigned cluster and compared the post-transplant outcomes between clusters. Results: Two clinically distinct clusters were identified by consensus cluster analysis. Cluster 1 patients was characterized by younger patients (mean recipient age 49 ± 13 years) who received dual kidney transplant from pediatric (mean donor age 3 ± 8 years) non-expanded criteria deceased donor (100% non-ECD). In contrast, Cluster 2 patients were characterized by older patients (mean recipient age 63 ± 9 years) who received dual kidney transplant from adult (mean donor age 59 ± 11 years) donor with high kidney donor profile index (KDPI) score (59% had KDPI ≥ 85). Cluster 1 had higher patient survival (98.0% vs. 94.6% at 1 year, and 92.1% vs. 76.3% at 5 years), and lower acute rejection (4.2% vs. 6.1% within 1 year), when compared to cluster 2. Death-censored graft survival was comparable between two groups (93.5% vs. 94.9% at 1 year, and 89.2% vs. 84.8% at 5 years). Conclusions: In summary, DKT in the United States remains uncommon. Two clusters, based on specific recipient and donor characteristics, were identified through an unsupervised machine learning approach. Despite varying differences in donor and recipient age between the two clusters, death-censored graft survival was excellent and comparable. Broader utilization of DKT from high KDPI kidneys and pediatric en bloc kidneys should be encouraged to better address the ongoing organ shortage.
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Thongprayoon C, Dumancas CY, Nissaisorakarn V, Keddis MT, Kattah AG, Pattharanitima P, Petnak T, Vallabhajosyula S, Garovic VD, Mao MA, Dillon JJ, Erickson SB, Cheungpasitporn W. Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements. J Clin Med 2021; 10:4441. [PMID: 34640457 PMCID: PMC8509302 DOI: 10.3390/jcm10194441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 09/18/2021] [Accepted: 09/25/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The goal of this study was to categorize patients with abnormal serum phosphate upon hospital admission into distinct clusters utilizing an unsupervised machine learning approach, and to assess the mortality risk associated with these clusters. METHODS We utilized the consensus clustering approach on demographic information, comorbidities, principal diagnoses, and laboratory data of hypophosphatemia (serum phosphate ≤ 2.4 mg/dL) and hyperphosphatemia cohorts (serum phosphate ≥ 4.6 mg/dL). The standardized mean difference was applied to determine each cluster's key features. We assessed the association of the clusters with mortality. RESULTS In the hypophosphatemia cohort (n = 3113), the consensus cluster analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; a higher comorbidity burden, particularly hypertension; diabetes mellitus; coronary artery disease; lower eGFR; and more acute kidney injury (AKI) at admission. Cluster 2 had a comparable hospital mortality (3.7% vs. 2.9%; p = 0.17), but a higher one-year mortality (26.8% vs. 14.0%; p < 0.001), and five-year mortality (20.2% vs. 44.3%; p < 0.001), compared to Cluster 1. In the hyperphosphatemia cohort (n = 7252), the analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age; more primary admission for kidney disease; more history of hypertension; more end-stage kidney disease; more AKI at admission; and higher admission potassium, magnesium, and phosphate. Cluster 2 had a higher hospital (8.9% vs. 2.4%; p < 0.001) one-year mortality (32.9% vs. 14.8%; p < 0.001), and five-year mortality (24.5% vs. 51.1%; p < 0.001), compared with Cluster 1. CONCLUSION Our cluster analysis classified clinically distinct phenotypes with different mortality risks among hospitalized patients with serum phosphate derangements. Age, comorbidities, and kidney function were the key features that differentiated the phenotypes.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 59005, USA; (C.Y.D.); (A.G.K.); (V.D.G.); (J.J.D.); (S.B.E.)
| | - Carissa Y. Dumancas
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 59005, USA; (C.Y.D.); (A.G.K.); (V.D.G.); (J.J.D.); (S.B.E.)
| | - Voravech Nissaisorakarn
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA;
| | - Mira T. Keddis
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Phoenix, AZ 85054, USA;
| | - Andrea G. Kattah
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 59005, USA; (C.Y.D.); (A.G.K.); (V.D.G.); (J.J.D.); (S.B.E.)
| | - Pattharawin Pattharanitima
- Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani 12120, Thailand
| | - Tananchai Petnak
- Division of Pulmonary and Pulmonary Critical Care Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand;
| | - Saraschandra Vallabhajosyula
- Section of Cardiovascular Medicine, Department of Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, USA;
| | - Vesna D. Garovic
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 59005, USA; (C.Y.D.); (A.G.K.); (V.D.G.); (J.J.D.); (S.B.E.)
| | - Michael A. Mao
- Division of Nephrology and Hypertension, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - John J. Dillon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 59005, USA; (C.Y.D.); (A.G.K.); (V.D.G.); (J.J.D.); (S.B.E.)
| | - Stephen B. Erickson
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 59005, USA; (C.Y.D.); (A.G.K.); (V.D.G.); (J.J.D.); (S.B.E.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 59005, USA; (C.Y.D.); (A.G.K.); (V.D.G.); (J.J.D.); (S.B.E.)
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