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import networkx as nx
import matplotlib.pyplot as plt

G = nx.DiGraph()
G.add_node("Sensory", pos=(-2500, 700))
G.add_node("Cognitive", pos=(-4200, 0))
G.add_node("Frailty", pos=(-1000, 0))
G.add_node("Muscle", pos=(-2500, -700))
G.add_node("ADL", pos=(1500, 0))
G.add_node("Outcome", pos=(4000, 0))

G.add_edges_from([("Sensory", "Frailty")])
G.add_edges_from([("Cognitive", "Frailty")])
G.add_edges_from([("Muscle", "Frailty")])
G.add_edges_from([("Frailty", "ADL")])
G.add_edges_from([("ADL", "Outcome")])

pos = nx.get_node_attributes(G, 'pos')
labels = {"Frailty": "Frailty\n(Phenotype)",
          "Sensory": "Sensory\n(Acuity)",
          "Cognitive": "Cognitive\n(Tasks)",
          "Muscle": "Muscle\n(Strength)",
          "ADL": "ADL\n(Independence)",
          "Outcome": "Outcome\n(Hard Endpoints)"}  # Added label for "NDI" node in the labels dictionary

# Update color for the "Scenarios" node
node_colors = ["lightblue","lightblue", "lavender", "lightblue", "lightblue", "lightblue"]
# node_colors = ["lightblue","lavender", "lavender", "lightgreen", "lightpink", "lightpink"]
# Suppress the deprecation warning
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)

plt.figure(figsize=(10, 8))
nx.draw(G, pos, with_labels=False, node_size=20000, node_color=node_colors, linewidths=2, edge_color='black', style='solid')
nx.draw_networkx_labels(G, pos, labels, font_size=14) # , font_weight='bold'
nx.draw_networkx_edges(G, pos, edge_color='black', style='solid', width=2)
plt.xlim(-5000, 5000)
plt.ylim(-1000, 1000)
plt.axis("off")
plt.show()

Background

• Aging and disease are associated with decline in functional reserve capacity

• Earliest manifestations are reduced sensory acuity, cognitive decline, and sarcopenia

• Ultimately these lead to manifestations of the Physical Frailty Phenotype:

  • Energy

    • Self-reported fatigue

    • Low physical activity

  • Mass

    • Unintentional weight loss (Sarcopenia)

    • Weakness (Grip strength)

  • Speed

    • Walking speed over given distance

• Older living kidney donors are healthy, screened individuals

• Screen would exclude those with above symptoms at baseline

• However, nephrectomy is a 50% reduction in structural reserve capacity of the renal system

• Renal system is central to various homeostatic and functional processes:

  • Phosphate excretion Mineral and bone disorder (CKD-MBD)

  • Susceptibility to bone fractures

  • Vascular calcifiction

  • Cardiovascular disease

  • Systemic hypertension

  • Other extra-renal complications of CKD

  • Decreased clearance of prescribed drugs

  • Vulnerability to delirium

  • Adverse reactions to contrast media following diagnostic procedures

• Incidence of Diabetes and Hypertension

  • Higher risk with age and after donation

  • Risk factor for kidney disease

  • May sooner reach ESRD because of low eGFR baseline after nephrectomy

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1. Quantify nephrectomy-attributable risk of ESRD and mortality

   • Can accrutely quantify these hard outcomes in older donors over 30 years and at a national level

   • We do so for both donors and nondonors

2. Describe the prevalence of hospitalizations in older donors vs. older nondonors

   • Upstream of ESRD is CKD and other clinical syndromes that are less robustly captured by national databases

   • Most databases do not capture frailty, its risk factors, and downstream effects on activities of daily living

   • Aging cohort of donors will leave a footprint of present and missing data crucial for planning better studies

3. Implement a risk calculator for ESRD, mortality, and sentinel hospitalization in older donors

   • Seek to make the general public the “end-user”

   • Personalize risk estimation

   • Set to outcomes that matter to older donors