Distinct clinical profiles and post-transplant outcomes among kidney transplant recipients with lower education levels: uncovering patterns through machine learning clustering
Charat Thongprayoon,
Jing Miao,
Caroline Jadlowiec,
Shennen A. Mao,
Michael Mao,
Napat Leeaphorn,
Wisit Kaewput,
Pattharawin Pattharanitima,
Oscar A. Garcia Valencia,
Supawit Tangpanithandee,
Pajaree Krisanapan,
Supawadee Suppadungsuk,
Pitchaphon Nissaisorakarn,
Matthew Cooper,
Wisit Cheungpasitporn
Affiliations
Charat Thongprayoon
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
Jing Miao
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
Caroline Jadlowiec
Division of Transplant Surgery, Mayo Clinic, Phoenix, AZ, US
Shennen A. Mao
Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL, USA
Michael Mao
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL, USA
Napat Leeaphorn
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL, USA
Wisit Kaewput
Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand
Pattharawin Pattharanitima
Department of Internal Medicine, Thammasat University, Pathum Thani, Thailand
Oscar A. Garcia Valencia
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
Supawit Tangpanithandee
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
Pajaree Krisanapan
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
Supawadee Suppadungsuk
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
Pitchaphon Nissaisorakarn
Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Matthew Cooper
Division of Transplant Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
Wisit Cheungpasitporn
Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA
Background Educational attainment significantly influences post-transplant outcomes in kidney transplant patients. However, research on specific attributes of lower-educated subgroups remains underexplored. This study utilized unsupervised machine learning to segment kidney transplant recipients based on education, further analyzing the relationship between these segments and post-transplant results.Methods Using the OPTN/UNOS 2017–2019 data, consensus clustering was applied to 20,474 kidney transplant recipients, all below a college/university educational threshold. The analysis concentrated on recipient, donor, and transplant features, aiming to discern pivotal attributes for each cluster and compare post-transplant results.Results Four distinct clusters emerged. Cluster 1 comprised younger, non-diabetic, first-time recipients from non-hypertensive younger donors. Cluster 2 predominantly included white patients receiving their first-time kidney transplant either preemptively or within three years, mainly from living donors. Cluster 3 included younger re-transplant recipients, marked by elevated PRA, fewer HLA mismatches. In contrast, Cluster 4 captured older, diabetic patients transplanted after prolonged dialysis duration, primarily from lower-grade donors. Interestingly, Cluster 2 showcased the most favorable post-transplant outcomes. Conversely, Clusters 1, 3, and 4 revealed heightened risks for graft failure and mortality in comparison.Conclusions Through unsupervised machine learning, this study proficiently categorized kidney recipients with lesser education into four distinct clusters. Notably, the standout performance of Cluster 2 provides invaluable insights, underscoring the necessity for adept risk assessment and tailored transplant strategies, potentially elevating care standards for this patient cohort.
