Mathematical modeling in chronic kidney disease

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# Mathematical modeling in chronic kidney disease - PowerPoint PPT Presentation

Mathematical modeling in chronic kidney disease. Peter Kotanko, MD Renal Research Institute, New York pkotanko@rriny.com Bangalore, March 2008. Life Expectancy at 45 to 54 and 55 to 64 Years of Age in the U.S. Resident Population and among Persons with Selected Chronic Diseases.

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### Mathematical modeling in chronic kidney disease

Peter Kotanko, MD

Renal Research Institute, New York

pkotanko@rriny.com

Bangalore, March 2008

Life Expectancy at 45 to 54 and 55 to 64 Years of Age in the

U.S. Resident Population and among Persons with Selected Chronic Diseases

Pastan S and Bailey J. N Engl J Med 1998;338:1428-1437

Uremic Solutes

Meyer T and Hostetter T. N Engl J Med 2007;357:1316-1325

Hemodialysis Vascular Access by

Native Arteriovenous Fistula

Ifudu O. N Engl J Med 1998;339:1054-1062

Forni L and Hilton P. N Engl J Med 1997;336:1303-1309

Blood Urea Nitrogen Levels in Two Theoretical Patients Undergoing Conventional Thrice-Weekly Hemodialysis for 3 Hours on Monday, Wednesday, and Friday

Meyer T and Hostetter T. N Engl J Med 2007;357:1316-1325

Overhydration in dialysis patients
• During each dialysis session the amount of fluid taken on in the inter-dialytic period has to be removed (as much as 6 L/4 hrs)
• Chronic overhydration results in cardiovascular disease (high blood pressure, left ventricular hypertrophy, …)

### Pathophysiology of chronic volume overload

Increased blood pressure

End organ damage

Left ventricular hypertrophy

Vascular disease

Arrhythmia; myocardial infarction;

sudden death

Cardiovascular

disease

Cerebro-vascular

disease

TIA; stroke

Removal of Fluid and Solutes by Ultrafiltration with the Goal to Achieve “Dry Weight” (the “Holy Grail” in dialysis)

Capillary

Bed

Interstitial

Fluid

Blood

Compartment

(venous)

Removal of Plasma Water

During Dialysis by Ultrafiltration

But there is are problems …
• There is no uniform definition of “dry weight”
• There is no universally accepted method to determine “dry weight”
• Determination of “dry weight” by bioimpedance (BIA) of the calf is a potential means
• Multifrequency BIA determines the extracellular volume in a given segment

Concomitant Recording of Relative Blood Volume Change

and Calf ECV change

Blood volume monitor (BVM)

Dry weight monitor

Questions: Can the dynamics of interstitial fluid be modeled in order to determine “dry weight” without the need of frequent BIA measurements?

What we know: ultrafiltration rate (HD machine)

relative change in blood volume (BVM)

change in calf ECV (Dry Weight Monitor)

serum albumin level

What we don’t know:

capillary pressure

interstitial protein conc.

Goal
• Bringing the patient to dry weight,
• avoiding the deleterious consequences of overhydration,
• reducing the need for uncomfortable measurements
Background
• There is convincing evidence that in contrast to findings in the general population high body mass index (BMI; weight [kg] / (height [m])2) in dialysis patients is associated with improved survival
• But: BMI does not differentiate between various components of body composition
RRI Hypothesis
• Uremic toxin generation occurs predominantly in the visceral organs (“high metabolic rate compartment”; HMRC). The mass of key uremiogenic viscera (gut, liver) is relative to body weight or BMI larger in small people
• Uremic toxins (both lipophilic and hydrophilic) are taken up by adipose and muscle tissues and metabolized and/or stored
• The amount of in-tissue metabolism of uremic toxins depends on the fat and muscle mass
• Most important: Since dialysis dose is prescribed per urea distribution volume (=total body water), small patients may be at an increased risk of under-dialysis

Levin, Gotch, JASN 2001

Sarkar, KI 2006

Kotanko, Blood Purif 2007

Predictions made by the RRI model
• Concentration of uremic toxins relate inversely to body size
• Production rate of uremic toxins per unit of body mass is higher in small subjects
• Large patients may have better surrogate outcomes
• Small patients experience better outcomes with higher dialysis doses

Sarkar, Semin Dial 2007

Sarkar, Kidney Int 2006

Body size, gut, muscle, fat, and uremic toxins

Large patient

Fat

Muscle

Small patient

Muscle

Fat

Uremic Toxin

Generation

Uremic Toxin

Generation

Visceral

Organs

Sarkar, KI 2006

Kotanko, Blood Purif 2007

3-compartment modelof (hydrophilic) uremic toxin kinetics(Cronin-Fine, IJAO 2007)

Visceral

Organs

Extracellular

Fluid

Muscle

Mass

### The Plasma Concentration of Pentosidine Relates Inversely to BMI

80

70

R = - 0.55

P < 0.001

60

50

Total pentosidine plasma concentration

(pmol/mg protein)

40

30

20

10

14

18

22

26

34

38

42

30

(Slowik-Zylka, 2006)

BMI (kg/m2)

Body size, gut, muscle, fat, and uremic toxins

Large patient

Fat

Muscle

Small patient

Muscle

Fat

Uremic Toxin

Generation

Uremic Toxin

Generation

Visceral

Organs

Sarkar, KI 2006

Kotanko, Blood Purif 2007

Total organ mass was calculated using regression models by

Gallagher et al (Am J Clin Nutr. 2006, 83:1062)

FEMALES

MALES

N=911

N=1.093

HMRO mass [% of Body Weight]

BMI [kg/m2]

BMI [kg/m2]

Kotanko & Levin Int J Artif Organs, 2007

### Survival Stratified by Tertiles of Race- and Sex-Specific Visceral Organ Mass (% of Weight)

N = 2004

P = 0.0001

(log-rank test)

Mean Survival (days)

Low Tertile: 1031

Middle Tertile: 935

High Tertile: 876

Kotanko, IJAO 2007

Question: is it possible to model the dynamics of uremic toxins with a model including estimates of fat and visceral mass?
• What we know: estimates of body composition (fat, muscle, total body water, visceral mass, blood levels of toxins)
• What we don’t know: tissue concentrations of uremic toxins, exchange rates
• Future dialysis prescription may account for aspects of body composition beyond urea distribution volume and thus improve the care independent of body composition (females/males; small/large)

Hypothesis: Low SBP is the Terminal Pathway

of Various Pathological Processes

High Systolic Blood Pressure

Antihypertensive

Therapy

Cardiovascular

Disease

Malnutrition

Inflammation

Infection

Low Systolic Blood Pressure

Evolution of pre-HD SBP in surviving HD patients(total N=39.969 HD patients)

Follow-up time

Kotanko et al, ISN Nexus, 2007

Evolution of pre-HD SBP in non-survivors

Follow-up time

Kotanko et al, ISN Nexus, 2007

Question: what is the best way to model correlated longitudinal SBP data taking covariates into account ?Ultimate goal: development of an automated alarm system to trigger early diagnostic & therapeutic intervention in deteriorating patients.

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Merci pour votre attention

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