About

Dr. Miklos Z. Molnar is a Professor of Medicine and the Medical Director of the Kidney and Pancreas Transplantation Programs and Living Donor Transplantation Program at the University of Utah Hospital and Clinics. He is also the Director of the Kidney Clinics. Dr. Molnar obtained his M.D. and Ph.D. from Semmelweis University in Budapest, Hungary. After completing his residency and fellowship in Europe, he immigrated to the USA in 2010. He completed his residency in Internal Medicine and received specialized training and faculty experience as a transplant nephrologist at the University of Tennessee Health Science Center in Memphis, TN, following his nephrology fellowship at the University of Toronto, Ontario, Canada. He is board certified in Internal Medicine and Nephrology, with additional certifications in Transplant Medicine from Europe. His primary clinical interests involve all aspects of kidney transplantation, including candidate evaluation for recipients and living donors, organ procurement processes, perioperative care, and long-term management of transplant recipients and donors. His clinical expertise also extends to pancreas transplant evaluation and care, as well as the prevention, diagnosis, and treatment of native kidney diseases and acute kidney injury. Dr. Molnar's primary research interests focus on outcome research in kidney transplant recipients. He is active in clinical research, frequently presents at national and international meetings, and has authored over 300 articles in nephrology and transplant medicine. He serves on editorial boards for several journals, contributing to the advancement of his field.

Research topics

• Medicine
• Internal medicine
• Intensive care medicine
• Virology
• Urology
• Immunology
• Endocrinology
• Gastroenterology
• Pathology
• Family medicine
• Surgery
• Dermatology

Selected publications

• Equitable Donor Assessment Model of Deceased Donor Kidney Quality

  Kidney International Reports · 2026-01-21

  articleOpen access

  Introduction: The Kidney Donor Profile Index (KDPI) guides organ allocation but blends donor and recipient influences, potentially misclassifying organ quality and contributing to inequity. We developed the Equitable Donor Assessment Model (EDAM), a donor-focused index that isolates intrinsic graft-failure risk independent of recipient survival. Methods: = 122,646), we modeled death-censored graft failure with death as a competing event using Fine-Gray regression. Donor coefficients were adjusted for recipient and transplant covariates-including human leukocyte antigen (HLA) mismatch and ischemia time-to derive donor-specific subhazard ratios (SHRs) for the EDAM score. Performance was evaluated using Harrell's C-index and internal-external cross-validation across 5 geography-aligned US super-regions. Results: < 0.05). Nearly half of kidneys classified as moderate-to-high KDPI (≥ 0.20) were reclassified as low-risk by EDAM (< 0.80) and achieved identical 10-year graft survival to conventional KDPI < 0.20 organs. Conclusion: EDAM provides an equitable, donor-centric framework for assessing kidney quality. Although these categorical thresholds were derived in a data-driven manner within the US system and require external validation in international cohorts, EDAM's ability to safely expand the low-risk pool without compromising outcomes suggests it could significantly refine allocation policy and enhance fairness in kidney transplantation.

  PublisherOA PDFDOI

• Qualified prediction system for allograft failure in real world settings: extended validation study

  BMJ Medicine · 2026-05-01

  articleOpen access

  Objective To perform comprehensive validations of the integrative Box (iBox) system, a prediction model for long term risk of kidney allograft failure, for extension of its context of use in clinical trials as well as for its wider implementation in clinical practice. Design Extended validation study. Setting Paris Transplant Group database (comprising kidney recipients with transplantations between 1 January 2005 and 1 January 2014) and European, North American, and South American hospitals (comprising recipients of kidneys transplanted beween 1 January 2000 and 1 January 2022). Patients were followed until 1 November 2024. Participants 12 683 kidney tranplant recipients from 21 academic centres in Europe, North America, and South America; 4000 patients in the derivation cohort and 8683 in the validation cohorts. Main outcome measures Performance of the iBox, including flexible iBox versions in specific clinical contexts (race-free estimated glomerular filtration rate (eGFR) equations (ie, without including race as a factor in the calculation), in specific clinical contexts (initial nephropathy recurrence, BK virus associated nephropathy, and different immunosuppressive strategies), and over-extended follow-up periods. Predictive performance was assessed by discrimination, calibration, overall fit, and clinical utility. Results 12 683 kidney transplant recipients were included in the study (n=4000 in the derivation cohort and n=8683 in the validation cohorts). Median follow-up time after risk evaluation was 5.78 years (interquartile range (IQR) 3.51-7.00) in the derivation cohort and 4.68 years (2.48-7.00) in the validation cohorts. 549 (13.7%) and 991 (11.4%) patients had graft loss in the derivation and validation cohorts, respectively. All versions of the iBox algorithm maintained good discrimination and overall fit performance in the derivation and validation cohorts (C index range 0.79-0.87, Brier scores 0.08-0.11). Calibration was adequate in some but not all external validation cohorts, with trends toward overestimation or underestimation of predicted risks. Decision curve analysis showed positive and comparable net benefit for all iBox algorithms across decision thresholds up to 40% in the derivation cohort (net benefit 0.07-0.08 at 20% threshold) and validation cohorts (net benefit 0.03-0.11 at 20% threshold). Accounting for the competing risk of death with a functioning graft resulted in similar performance, except for calibration which varied across cohorts, without any model consistently outperforming any other model. The model performed well with different race-free eGFR equations (C index 0.81), in various clinical scenarios, including disease recurrence and BK virus nephropathy, with different immunosuppressive strategies, such as calcineurin inhibitors and mTOR (mechanistic target of rapamycin) inhibitors (C index range 0.74-0.87), and when extending the prediction period to 10 years after risk evaluation (C index 0.79). The iBox predictive performance was not modified when various histological indices were used. The iBox was also superior to eGFR slope (C index 0.81 v 0.62) and circulating anti-HLA donor specific antibodies (C index 0.81 v 0.57) in its predictive ability. Conclusions In this study, the robust predictive performance of the iBox system across diverse real world settings and clinical scenarios was shown. These results highlight the versatility and reliability of the iBox system, and support its use for risk stratification in routine clinical practice and as a surrogate endpoint for clinical trials.

  PublisherDOI

• Early posttransplant rituximab use in kidney transplant recipients with preexisting donor-specific antibodies

  Renal Failure · 2026-01-25 · 1 citations

  articleOpen accessSenior authorCorresponding

  DSA occurred in 31% of those who received rituximab versus in 25% of those who did not. Rituximab administration did not result difference in graft and patient survival or rejection rates or recurrence of preexisting DSA.

  PublisherDOI

• Predicting Simultaneous Heart Kidney Allocation and Posttransplant Adverse Kidney Outcomes

  Kidney International Reports · 2025-10-15

  articleOpen access

  <h2>Abstract:</h2><h3>Background</h3> For individuals with both end-stage heart failure and end-stage kidney disease (ESKD) or persistent acute kidney injury (AKI), simultaneous heart-kidney transplantation (SHKT) emerges as a viable treatment option, potentially yielding superior survival rates compared to heart transplantation (HT) alone. Nevertheless, accurately forecasting kidney recovery following HT in patients with moderate kidney failure poses challenges, complicating the decision-making process for SHKT. <h3>Methods</h3> This study employed a random forest (RF) machine learning algorithm, using 15 variables with the highest feature importance scores in the Organ Procurement and Transplantation Network data in which we analyzed a retrospective cohort of adult HT recipients from 10/18/2018 to 12/31/2020 in the U.S., with a follow-up for at least one year. The algorithm's goal was to predict a composite binary outcome with a calculated probability. An adverse outcome included the need for SHKT or adverse kidney outcomes within the first-year post-transplant (defined as ESKD requiring chronic dialysis, glomerular filtration rate ≤20 ml/min/1.73 m² or listing for re-transplant). The model underwent both internal and external validation. <h3>Results</h3> Of the 6,579 patients in the study cohort, 13.4% received SHKT or experienced adverse kidney outcomes within a year following HT (N=880). The RF model demonstrated a high specificity (0.941-0.955) and negative predictive value (0.940-0.955). However, it exhibited a moderate level of sensitivity (0.605-0.694) and positive predictive value (0.604-0.680). The c-statistics ranged between 0.849 and 0.899, indicating effective class differentiation. <h3>Conclusion</h3> This tool supplements, not replace, clinical judgment in addressing the complexities of SHKT decision-making at the time of waitlisting.

  PublisherOA PDFDOI

• “The Final Rule in a Bind: What We Are Learning From Suboptimal Simultaneous Heart-Kidney Outcomes and Potential Solutions to the Problem”: Erratum

  Transplantation · 2025-07-23

  erratum
  PublisherDOI

• Development of BK polyomavirus-associated nephropathy risk prediction in kidney transplant recipients

  Renal Failure · 2025-05-29 · 1 citations

  articleOpen accessSenior authorCorresponding

  BACKGROUND: With the development of potential prevention therapies for BK polyomavirus (BKPyV)-associated nephropathy (BKPyVAN), risk prediction models are needed to identify kidney transplant recipients at high risk for BKPyVAN. METHODS: This single-center retrospective study aimed to develop a risk prediction model and an integer-based risk score for BKPyVAN development, defined as plasma BKPyV-DNA >10,000 copies/mL and/or biopsy-proven BKPyVAN, within 1-year post-transplant, using donor and recipient characteristics at the time of transplantation. We randomly split patients into development and validation cohorts and applied logistic regression with backward selection to identify significant variables. Model performance was evaluated using the area under the receiver-operating characteristic curve (AUC) and calibration plots. RESULTS: This study included 560 patients, of whom 75 (13%) patients had BKPyVAN. Age >50 years, male sex, and prior kidney transplant were selected for the final model. The total integer score ranged from 0 to 4 points, with 1 point assigned for age >50 years and male sex, and 2 points for prior kidney transplant. The AUC was 0.65 in both development and validation cohorts. Calibration plots showed an incremental increase in risk with higher total scores. The integer score indicated that patients with a total score of 2 or higher (i.e. males aged >50 years or those with prior kidney transplants) have a predicted risk of 20% or greater. CONCLUSION: Although the AUC was suboptimal, the results suggest that our model may still be valuable for identifying high-risk patients.

  PublisherDOI

• Management recommendations for kidney transplantation in patients with plasma cell dyscrasia

  Kidney International · 2025-07-28 · 4 citations

  articleOpen access
  PublisherOA PDFDOI

• Equitable Donor Assessment Model (EDAM): A Data-Driven Framework for Stratifying Deceased-Donor Kidney Quality

  Journal of the American Society of Nephrology · 2025-10-01

  article
  PublisherDOI

• Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool

  Renal Failure · 2025-01-21 · 10 citations

  articleOpen access

  Predicting the outcome of a kidney transplant involving a living donor advances donor decision-making donors for clinicians and patients. However, the discriminative or calibration capacity of the currently employed models are limited. We set out to apply artificial intelligence (AI) algorithms to create a highly predictive risk stratification indicator, applicable to the UK's transplant selection process. Pre-transplant characteristics from 12,661 live-donor kidney transplants (performed between 2007 and 2022) from the United Kingdom Transplant Registry database were analyzed. The transplants were randomly divided into training (70%) and validation (30%) sets. Death-censored graft survival was the primary performance indicator. We experimented with four machine learning (ML) models assessed for calibration and discrimination [integrated Brier score (IBS) and Harrell's concordance index]. We assessed the potential clinical utility using decision curve analysis. XGBoost demonstrated the best discriminative performance for survival (area under the curve = 0.73, 0.74, and 0.75 at 3, 7, and 10 years post-transplant, respectively). The concordance index was 0.72. The calibration process was adequate, as evidenced by the IBS score of 0.09. By evaluating possible donor-recipient pairs based on graft survival, the AI-based UK Live-Donor Kidney Transplant Outcome Prediction has the potential to enhance choices for the best live-donor selection. This methodology may improve the outcomes of kidney paired exchange schemes. In general terms we show how the new AI and ML tools can have a role in developing effective and equitable healthcare.

  PublisherDOI

• Abdominal Normothermic Regional Perfusion Kidney Transplant Early Outcomes Are Comparable to Brain Dead Donor Kidneys Despite Higher Kidney Donor Risk Index

  American Journal of Transplantation · 2025-08-01

  article
  PublisherDOI

Recent grants

• NIH Grant R21AG047036

  NIH · $424k · 2017

Frequent coauthors

• Kamyar Kalantar‐Zadeh

  UCLA Medical Center

  1059 shared

• Csaba P. Kövesdy

  University of Tennessee Health Science Center

  881 shared

• Elani Streja

  University of California, Irvine

  392 shared

• István Mucsi

  University Health Network

  383 shared

• Márta Novák

  260 shared

• Manish Talwar

  177 shared

• Masahiko Yazawa

  St. Marianna University School of Medicine

  175 shared

• Vasanthi Balaraman

  171 shared

Education

• M.D.

  Semmelweis University

• Ph.D.

  Semmelweis University

• Other, Transplant Nephrology

  University of Tennessee Health Science Center

• Other, Nephrology

  University of Toronto