1 / 42

Mathematical modeling in chronic kidney disease

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.

kameko-best
Download Presentation

Mathematical modeling in chronic kidney disease

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Mathematical modeling in chronic kidney disease Peter Kotanko, MD Renal Research Institute, New York pkotanko@rriny.com Bangalore, March 2008

  2. 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

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

  4. Hemodialysis Circuit

  5. Hemodialysis Vascular Access by Native Arteriovenous Fistula Ifudu O. N Engl J Med 1998;339:1054-1062

  6. Vascular Access (Shunt)

  7. Hemodialysis: Combination of Diffusive & Convective Transport Forni L and Hilton P. N Engl J Med 1997;336:1303-1309

  8. 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

  9. 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, …)

  10. Pathophysiology of chronic volume overload 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

  11. 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

  12. 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

  13. Concomitant Recording of Relative Blood Volume Change and Calf ECV change Blood volume monitor (BVM) Dry weight monitor

  14. 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.

  15. Goal • Bringing the patient to dry weight, • avoiding the deleterious consequences of overhydration, • reducing the need for uncomfortable measurements

  16. Body composition in dialysis patients: implications for outcomes

  17. 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

  18. BMI and survival in the general and the HD population Kalantar-Zadeh, 2006

  19. Same BMI – Different Body Composition

  20. 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

  21. 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

  22. High Metabolic Rate Compartment and BMI are inversely related Sarkar, Kidney Int 2006

  23. 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

  24. 3-compartment modelof (hydrophilic) uremic toxin kinetics(Cronin-Fine, IJAO 2007) Visceral Organs Extracellular Fluid Muscle Mass

  25. Uremic Toxin Concentration Relates to Body Size (Cronin-Fine, IJAO 2007)

  26. 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)

  27. 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

  28. Relation of Total Organ Mass to Body Weight in 2.004 HD Patients 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

  29. 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

  30. 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

  31. Goal down the road …. • 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)

  32. 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

  33. Systolic Blood Pressure Relates to Mortality AJKD, 2006

  34. Very simple Markov model of SBP evolution predicts survival Kotanko, EDTA 2008

  35. Evolution of pre-HD SBP in surviving HD patients(total N=39.969 HD patients) Follow-up time Kotanko et al, ISN Nexus, 2007

  36. Evolution of pre-HD SBP in non-survivors Follow-up time Kotanko et al, ISN Nexus, 2007

  37. SBP Evolution by Gender & Race

  38. 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.

  39. Thank you for your attention Gracias por su atención Danke für Ihre Aufmerksamkeit Go raibh maith agat Grazie per l´Attenzione Aap saab ka shukriya… Merci pour votre attention شكرا لإنتباهكم

More Related