Living Kidney Donors

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Personalized Risk Estimates

The distinction between static and dynamic data presentation in scientific literature is stark: nearly all peer-reviewed clinical and public health research findings rely on static visualizations of pre-determined cohorts. If personalized medicine is to fulfill its promise, dynamic visualization—such as the interactive model above—must become the standard for conveying information to patients, care providers, and public health practitioners.

-- The Truth of Masks 🎭

Introduction

The demographic shift toward older kidney donors necessitates improved characterization of risks to enhance consent processes and outcomes.
— SRTR

The number of older adults (≥50 years) undergoing live donor nephrectomy has quadrupled over the last two decades, a trend expected to persist given demographic shifts, updates in kidney transplant allocation, and changes to Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. Despite this increase, critical knowledge gaps persist in understanding how older donors cope with the physical stress of nephrectomy, particularly the role of age-related kidney changes (glomerular senescence) and the body’s ability to adapt following a 50% nephron mass loss. Approximately 80% of older donors experience a post-donation GFR <60 ml/min/1.73m², meeting the definition of chronic kidney disease (CKD), placing them at increased risk of frailty and hospitalization as they progress toward CKD. However, no studies have identified clinical signatures of resiliency in this unique population, even as nephrectomy directly impacts their renal system and overall health.

Fig. 1 An 84-year-old man became one of the oldest living kidney donors in the United States in May 2019. The consent process for donor candidates of advanced age relies on data extrapolated from younger cohorts, typically with a median age of 40 years. As the demographic profile of living kidney donors continues to age, gaps in both present and missing data become increasingly evident. This highlights the need to systematically characterize existing data and address these gaps to better inform consent processes and optimize outcomes. Frailty, which is strongly associated with a significantly increased risk of perioperative complications, 1 remains uncaptured in most existing databases. The absence of this critical metric creates additional challenges, particularly in consenting older individuals considering donation. Photo Source: Houston Methodist

This thesis focuses on addressing these gaps by prioritizing the patient’s central question, “Can I safely donate my kidney?” over population-level metrics. The scientific aims include: (1) quantifying donation-attributable risks of perioperative death, (2) long-term risks of ESRD and (3) mortality; (4) describing hospitalization prevalence in older donors compared to non-donors; and (5) implementing a risk calculator for perioperative death, long-term ESRD and mortality risks, and sentinel hospitalization in older donors.

This thesis will frame its work in the context of various areas of inquiry, including kidney disease, aging research, and data science. The results of all four aims are delivered in an innovative tool—an app—to enhance provider-patient dialogue about kidney donation risks (aim 5). But the architecture of the app offers so much more that will be discussed in the appendix.

This thesis addresses the critical need to improve understanding of perioperative and long-term risks for older living kidney donors. By identifying resiliency markers and individualizing risk assessment, the study aims to empower older adults and providers with evidence-based tools to make informed decisions in a growing and vulnerable donor population.

Perioperative Mortality

The 90-day risk of death following living kidney donation has been estimated at 3 per 10,000. 2 Notably, some of these deaths were due to homicide, an outcome unlikely to reflect a perioperative risk directly attributable to nephrectomy. 3

To contextualize these risks, we introduce a control population to quantify the baseline 90-day mortality risk faced by healthy eligible donors who do not proceed with donation. Using data from the Organ Procurement and Transplantation Network (OPTN) and the National Health and Nutrition Examination Survey (NHANES), we estimate the 90-day risk of death for both donors and their healthy nondonor counterparts through registry linkages to the National Death Index. Cumulative incidence estimates derived from Cox regression are presented in an accompanying app (see App Features below).

For white female nondonors aged 40 years or younger who self-reported “Very Good or Excellent” health at the time of survey, the 90-day risk of death was estimated at 1.3 per 10,000 (95% CI: 0.5-3.5 per 10,000). Among comparable donors, the 90-day risk of death was 2.2 per 10,000 (95% CI: 1.0-4.7 per 10,000). Among nondonors aged over 50 years, the 90-day risk of death was no higher than that observed in nondonors with similar demographic and health characteristics.

The nephrectomy-attributable risk for a typical young donor is approximately 0.9 per 10,000, substantially lower than the risk currently cited during the informed consent process. For older donors, the nephrectomy-attributable risk may be even lower, though it remains insufficiently quantified due to the limited availability of data.

Fig. 2 Static vs. Dynamic Presentation. Authors typically have to commit to what data they will present to colleagues, reviewers, and the public. But clinicians and policy makers typically need to explore a combination of factors to make a personalized decision tailored to their situation. Our platform demonstrates this for the person who is considering donation, but it is applicable to any decision-making situation. We also critically consider the value of a control population for accurate and meaningful inferences

Long-term Risk of ESRD
Donation-attributable risk of end-stage renal disease (ESRD) has been estimated at 26.9 per 10,000 at 15 years. Subgroup estimates for white, black, and Hispanic donors have also been reported. 4 However, personalized risk estimates that take into account age and other associated risk factors remain unknown. 5

We compare the risk of ESRD in kidney donors (using OPTN data) with that of a cohort of nondonors (using NHANES data). See Perioperative Mortality above for more information. Cumulative incidence estimates derived from Cox regression are presented in an accompanying app (see App Features below).

The maximum follow-up period was 15 years, with a median follow-up of 7.6 years for kidney donors (interquartile range [IQR], 3.9–11.5) and 20 years for matched healthy nondonors (IQR, 18–35). The estimated risk of ESRD at 6.5 years for the typical 40 year old white female with SBP 120 mmHg, DBP 80mmHg, BMI 24 kg/\(m^2\), eGFR 95 ml/min/1.73, urine albumin/creatinine ratio of 4mg/g, with a college level education, “Very Good” self-rated health status, no history of diabetes, hypertension, and smoking was significantly higher in kidney donors (5.6 per 10,000; 95% CI, 1.5–20.1) compared to matched healthy nondonors (1.3 per 10,000; 95% CI, 0.8–2.8). Donation by older donors such as the one seen above: 84 year old white male, SBP 140 mmHg, BMI 24/\(m^2\), eGFR 80ml/min/1.73, no history of diabetes or smoking, but has well-controlled hypertension. He is a college graduate. We estimate his risk if his 6.5 year risk at (34.6 per 10,000; 95% CI, 2.2–552.0) compared to the counterfactual scenario in which he had no donated (9.2 per 10,000; 95% CI, 5.2–16.1).

Our personalized donation-attributable risk sets a standard for informed consent among the diverse range of those who seek to donate a kidney. But uncertainty of our estimates for such a rare donor phenotype highlights the challenges that remain.

Show code cell source Hide code cell source

import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network fractal
def define_layers():
    return {
        'Suis': ['Patient Access/Care', 'Hospital Records', 'Disclosure Risk', 'Collaborators', 'Databases', 'Analytic Scripts', ], # Static
        'Voir': ['Information'],  
        'Choisis': ['Baseline', 'Decision'],  
        'Deviens': ['Adverse Event Markers', 'Comorbidity/ICD Codes', 'Temporal Changes'],  
        "M'èléve": ['Mortality Rate', 'Organ Failure',  'Hospitalization', 'Dependency', 'Physical Frailty']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = { # Dynamic
        'yellow': ['Information'],  
        'paleturquoise': ['Analytic Scripts', 'Decision', 'Temporal Changes', 'Physical Frailty'],  
        'lightgreen': ['Databases', 'Comorbidity/ICD Codes', 'Organ Failure', 'Dependency', 'Hospitalization'],  
        'lightsalmon': [
            'Disclosure Risk', 'Collaborators', 'Baseline',  
            'Adverse Event Markers', 'Mortality Rate'
        ],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Calculate positions for nodes
def calculate_positions(layer, x_offset):
    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
    return [(x_offset, y) for y in y_positions]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    G = nx.DiGraph()
    pos = {}
    node_colors = []

    # Add nodes and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(colors.get(node, 'lightgray'))   

    # Add edges (automated for consecutive layers)
    layer_names = list(layers.keys())
    for i in range(len(layer_names) - 1):
        source_layer, target_layer = layer_names[i], layer_names[i + 1]
        for source in layers[source_layer]:
            for target in layers[target_layer]:
                G.add_edge(source, target)

    # Draw the graph
    plt.figure(figsize=(12, 8))
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color='gray',
        node_size=3000, font_size=15, connectionstyle="arc3,rad=0.2"
    )
    plt.title("Ecosystem Integration", fontsize=23)    
    # ✅ Save the actual image *after* drawing it
    plt.savefig("figures/ecosystem-integration.jpeg", dpi=300, bbox_inches='tight')
    # plt.show()

# Run the visualization
visualize_nn()

Fig. 3 The Scientific Enterprise: Ecosystem, Navigation, Decisions, Tracking, Outcomes. The Ecosystem encapsulates the intricate flow of information, decision-making, and outcomes within clinical medicine and public health. It functions as a multi-layered neural network, where patients, students, researchers, and clinicians navigate a complex landscape filled with both opportunities and inefficiencies. At its front-end (yellow node), the web app serves as a transparent, personalized tool, guiding prospective kidney donors through risks, benefits, and post-donation considerations. But beyond this immediate utility lies a deeper infrastructure—a structured backend where information, data, and decision-making processes interconnect dynamically. Students exploring this system may uncover the technical mechanics of data flow, decision nodes, and analytical scripts that underpin clinical research. Meanwhile, principal investigators and research labs act as key agents, shaping the ecosystem through their decisions on disclosure risks, collaboration, and methodological rigor. As stakeholders engage with the platform, they are not merely passive participants but active contributors to the evolution of an adaptive, decentralized, and highly efficient medical knowledge network. This ecosystem is not just a digital tool—it is an epistemic revolution, inviting users at every level to move beyond static knowledge and actively shape the future of patient care, research, and public health decision-making

Hospitalization Risks
Hospitalization may be a sentinel event signaling risk for adverse outcomes, including end-stage renal disease (ESRD) and mortality, among living kidney donors (LKDs). However, only two years of follow-up are mandated for LKDs, limiting long-term risk characterization. To address this, we used a multicenter retrospective cohort study of LKDs with over 40 years of follow-up to identify factors associated with patient-reported all-cause hospitalization.

Patient factors were captured from self-report and supplemented by linkage to the SRTR database. Among an cohort of 3,084 LKDs, 2,251 (73%) donors who donated between May 1968 and December 2019 responded to the survey.

Overall, 1575 (70%) reported any hospitalization a median (interquartile range) of 13 (10-20+) years post-nephrectomy, with surgery/procedure as the most common cause (57%). The cumulative incidence of hospitalization was 47.2% (95%CI: 45.5-48.9) at 20 years post-donation for a 40 year old white female donor with BMI of 25, SBP of 120 mmHg, DBP of 80 mmHg, eGFR of 95 ml/min/1.73 (see App Features above). In a parsimonious model, age at donation (aHR 1.01–1.13–1.27, p=0.04), female sex (aHR 1.07–1.39–1.80, p=0.01), and post-donation diabetes/hypertension (aHR 1.22–1.52–1.88, p<0.01) were associated with hospitalization (see appendix).

While self-reported, the frequency of hospitalization among LKDs beyond two years post-nephrectomy suggests longer follow-up may be warranted for this population. Furthermore, surveillance and follow-up should be emphasized in populations at higher risk of developing diabetes and hypertension after nephrectomy. The absence of a control population with ascertainment of hospitalization remains a critical limitation of this study.

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This thesis invites exploration of dynamic vs. static presentation of results. Traditional tools summarize average risks, but this app allows exploration of personalized scenarios, offering deeper insights. Users can adjust variables like age, GFR, and comorbidities to visualize personalized risks and uncertainties in real time. This app provides an unprecedented ability to explore personalized risks for donors, facilitating informed decision-making and improving the consent process. The evolving role of technology in medicine is hereby aligned with the importance of open science. The supplemental section of this thesis will address the technical aspects of the app and its potential role in the open science and open source movement.

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Using data from the Scientific Registry of Transplant Recipients from 1988 to 2020, we estimated perioperative mortality (death within 90 days of donation), 30-year mortality, and risk of ESRD. Death events were captured through the Organ Procurement and Transplantation Network (OPTN) living donor follow-up reported by transplant programs, the National Technical Information Service Limited Access Death Master File, and deaths made available to the OPTN contractor through an interagency data-sharing agreement between the Centers for Medicare & Medicaid Services (CMS) and the Health Resources and Services Administration. These were secondarily verified by the OPTN contractor. Per OPTN policy, all donor deaths within two years of donation must be reported within 72 hours of the hospital becoming aware of the death. ESRD development was ascertained using Centers for Medicare & Medicaid Services data and defined as the initiation of maintenance dialysis, placement on the transplant waiting list, or receipt of a living or deceased donor kidney transplant, whichever occurred first. The healthy nondonor control population was derived from a cohort of 73,000 participants in the third National Health and Nutrition Examination Survey (NHANES). The National Center for Health Statistics performed linkages to the CMS and National Death Index to to ascertain ESRD and death. Both national datasets provided robust comparisons of donor and nondonor risks. Source: Earlier Presentation