
Peter P. Reese
VerifiedUniversity of Pennsylvania · Rehabilitation Medicine
Active 1967–2026
About
Peter P. Reese, MD, PhD, is an adjunct professor of medicine specializing in renal-electrolyte and hypertension at the University of Pennsylvania's Perelman School of Medicine. He is a transplant nephrologist and epidemiologist who cares for patients at the Hospital of the University of Pennsylvania and the Philadelphia Veterans Affairs Medical Center. His research focuses on developing effective strategies to increase access to kidney transplantation, improving the process of selecting and caring for live kidney donors, and determining the outcomes of health policies on vulnerable populations with renal disease, including the elderly. Additionally, he tests strategies to enhance health behaviors such as medication adherence and directs Penn’s Center for Quality, Analytics and Research in Transplantation (PQART). Reese also chairs the Ethics Committee for the United Network for Organ Sharing (UNOS), overseeing organ allocation and transplant regulation in the United States. His contributions to transplant research have been recognized with the Presidential Early Career Award for Scientists and Engineers (PECASE). His work encompasses ethical considerations in organ donation, outcomes among older donors, and innovative approaches to organ transplantation and health policy.
Research topics
- Medicine
- Internal medicine
- Nursing
- Physical therapy
- Intensive care medicine
- Environmental health
- Economics
- Pathology
- Family medicine
- Physical medicine and rehabilitation
- Demography
- Surgery
- Psychiatry
Selected publications
A Prediction Model for Risk of Death in Kidney Transplant Recipients
JAMA Network Open · 2026-04-23
articleOpen accessImportance: Accurate prediction of patient mortality after kidney transplant is an unmet need. Objective: To develop and validate an integrative prediction model that predicts short- and long-term patient mortality for kidney recipients. Design, Setting, and Participants: This international cohort study included patients who underwent transplants between 2004 and 2023 from 14 academic medical centers from Europe and the US. The derivation cohort consisted of 1566 consecutive adult kidney recipients in a deeply phenotyped cohort prospectively recruited in 1 French center between 2004 and 2014. External validation cohorts consisted of 10 951 kidney recipients from 7 centers in France, 3 centers in Europe, and 3 centers in North America who underwent kidney transplants between 2006 and 2023. Data were analyzed from January 2023 to June 2025. Main Outcome and Measures: All-cause mortality was assessed, and 121 candidate prognostic factors, comprising demographics and clinical, biological, imaging, and immunological parameters, were collected. Results: Among 12 517 kidney transplant recipients, including 1566 in the derivation cohort (mean [SD] age, 50.05 [14.31] years; 942 male [60.15%]) and 10 951 in validation cohorts (mean [SD] age, 53.32 [13.97] years; 6766 male [61.78%]), 2486 patients (19.9%) died after a median (IQR) follow-up of 5.08 (2.97-7.00) years. Fourteen prognostic factors (including clinical, biological, and imaging risk factors) were independently associated with patient death (eg, patient age: hazard ratio per 1-year increase in age, 1.07 [95% CI, 1.06-1.08]; P < .001) and were combined into a risk prediction model (mBox). The model exhibited accurate calibration and discrimination in the derivation cohort, with C statistics of 0.82 (95% CI, 0.77-0.87) and 0.80 (95% CI, 0.78-0.82) at 1 and 10 years, respectively, after the transplant. Abbreviated models were developed and validated to ensure model generalizability. Performance of abbreviated models was confirmed in external validation cohorts from France (C statistic, 0.76 [95% CI, 0.73-0.78]), Europe (C statistic, 0.74 [95% CI, 0.72-0.76]), the US (C statistic, 0.74 [95% CI, 0.70-0.78]), the Greater Paris University Hospital database (C statistic, 0.79 [95% CI, 0.77-0.81]), and the University of California at San Francisco database (C statistic, 0.70 [95% CI, 0.65-0.74]). The model was also validated in a series of subpopulations (eg, women vs men: C statistic, 0.81 [95% CI, 0.77-0.84] vs 0.79 [95% CI, 0.77-0.82]) and clinical scenarios (eg, before COVID-19 era: C statistic, 0.79 [95% CI, 0.77-0.81]) with good and stable performance. Conclusions and Relevance: In this study, an accurate prediction model for mortality among kidney recipients, computable at the time of transplant, was developed and externally validated. Results suggest that this model may help stratify patient risk of death, allowing for improved medical decisions.
Geographic Accessibility of Deceased Organ Donor Care Units
JAMA Network Open · 2026-03-13
articleOpen accessCorrespondingImportance: Transfers of deceased organ donors from acute care hospitals to specialized donor care units (DCUs) offer operational and outcome advantages; however, current access to DCUs is limited and geographically uneven. Expanding access to DCUs may improve donation system efficiency. Objective: To evaluate the geographic distribution of operating DCUs relative to acute care hospitals and explore how to most efficiently operationalize recommendations that a DCU operate in every donation region. Design, Setting, and Participants: This retrospective cohort study analyzed deceased organ donor and hospital data captured in the Organ Procurement and Transplantation Network and American Hospital Association survey databases from January 1, 2018, to December 31, 2023. Acute-care hospitals and DCUs operating in the continental US and adult (aged ≥18 years) organ donors with brain death managed in acute care hospitals located in 2203 zip codes were included. The data analysis was performed between October 1, 2024, and December 1, 2025. Exposures: Geographic location of organ donor hospitals. Main Outcomes and Measures: The main outcome was the optimal number of DCUs required to enable transportation of all cohort donors from acute care hospitals to DCUs via ambulance (within a 180-minute drive). The number of additional DCUs needed to operationalize recommendations of a DCU in every donation region was quantified with and without consideration for donation service area boundaries using location-allocation modeling. Results: Between 2018 and 2023, 53 093 deceased donors met the inclusion criteria (mean [SD] age, 44.3 years [15.0]; 60.0% male). Among the cohort, 61.9% of donors were managed in acute care hospitals within driving distance of 34 operating DCUs. In the current system with distinct donation service area boundaries, an additional 38 DCUs were estimated to provide plausible access to 92.7% of donors. If donation service area boundaries were ignored, 22 new DCUs were estimated to provide a referral facility for a larger proportion of donors (96.5%). Conclusions and Relevance: This cohort study found that despite their reported advantages and consensus endorsement, heterogeneous adoption of DCUs has left a substantial proportion of deceased donors after brain death more than a 180-minute drive from a DCU. Given inefficiencies introduced by donation service area boundaries, opening additional DCUs in acute care hospitals and donor transport across these existing boundaries may be 2 potential approaches to improve system efficiency and donation outcomes.
The Lancet · 2026-03-01 · 4 citations
articleOpen accessNature Medicine · 2026-02-09 · 6 citations
articleKidney Medicine · 2025-08-21
articleOpen accessRationale & Objective: Most living kidney donors are members of the recipient's social network. Our study aims to characterize patient and social network factors associated with recruiting a living kidney donor. Study Design: A cross-sectional study. Setting & Participants: A total of 106 patients receiving hemodialysis (mean age 60 ± 13 years, 45% female, 75% Black) identified 508 network members (106 who offered to donate and 38 who received a donation request). Predictors: Demographic and network factors (eg instrumental support, strength of relationships between the patients and members). Outcomes: Whether the member offered to donate and whether the member's offer was accepted. Patients' qualitative reasoning regarding decision-making for recruiting a living kidney donor. Analytic Approach: We performed a mixed-methods egocentric network analysis using multilevel logistic regression models as well as qualitative analysis of open-ended survey questions. Results: = 0.02) were more likely to be evaluated at a transplant center. Concern and guilt for the member was the most common reason that patients declined offers (41%) or did not make a living donor request (28%), while members' insistence on donating was a common reason for accepting an offer. Limitations: Small sample of only patients receiving hemodialysis. Conclusions: For many patients, the barrier to living kidney donation was not a lack of potential donors within their network but reticence to recruit a donor due to concern and guilt for the donor. Future living donor kidney transplantation interventions should engage patients and their networks to facilitate communication, address patient-specific concerns and emphasize donor retention.
The Lancet · 2025-07-01 · 12 citations
reviewDonor-Derived Epstein-Barr Virus Infection Among Pediatric Kidney Transplant Recipients
JAMA · 2025-09-17 · 1 citations
articleOpen accessThis study uses US national data to examine Epstein-Barr virus–seronegative donor kidney use among Epstein-Barr virus–seronegative children.
American Journal of Transplantation · 2025-08-01
articleImproving the Prediction of Deceased Donor Allograft Function with Donor Urinary Biomarkers
American Journal of Transplantation · 2025-08-01
articleOpen accessAmerican Journal of Transplantation · 2025-08-01
articleOpen access
Recent grants
NIH · $400k · 2014
Comparative effectiveness of process and outcomes incentives for lipid management
NIH · $3.9M · 2014–2020
NIH · $658k · 2012
NIH · $82k · 2007
Harnessing Behavior to Decrease Urinary Stone Disease Morbidity Research Project
NIH · $5.3M · 2016–2026
Frequent coauthors
- 146 shared
Dorry L. Segev
New York University
- 129 shared
Amit X. Garg
Lawson Health Research Institute
- 129 shared
Greg Knoll
University of Ottawa
- 128 shared
Krista L. Lentine
Saint Louis University Hospital
- 125 shared
Ngan N. Lam
University of Florida Health
- 124 shared
Leroy Storsley
University of Manitoba
- 124 shared
Christine Dipchand
Queen Elizabeth II Health Sciences Centre
- 124 shared
Eric McArthur
Institute for Clinical Evaluative Sciences
Labs
Reese LabPI
Awards & honors
- Presidential Early Career Award for Scientists and Engineers…
- T. Franklin Williams Award in Geriatric Research
- Resume-aware match score
- Save to shortlist
- AI-drafted outreach
See your match with Peter P. Reese
PhdFit ranks faculty by your research interests, methods, and publications — grounded in their actual work, not templates.
- Free to start
- No credit card
- 30-second signup