Soft Computing Methods for Drug Dosing in Renal Anemia

1. Soft Computing Methods for Drug Dosing in Renal Anemia Adam E. Gaweda Kidney Disease Program University of Louisville Louisville, KY http://kdp.louisville.edu

2. Overview • Anemia in End Stage Renal Disease • Soft Computing in Anemia Treatment • Intelligent modeling and control for drug dosing • Perception-based computing in clinical decision support • Summary

3. Mechanism of Red Blood Cell Production Erythropoiesis Hypoxia RBC lifetime = about 120 days Erythropoietin

4. Anemia Management in End Stage Renal Disease Erythropoiesis RBC lifetime = about 60 days hematocrit Vol. % of RBCin blood Erythropoietin

5. Clinical Practice • Dialysis Outcome Quality Initiative (DOQI) guideline: • Maintain hematocrit between 33-36 Vol.% • Protocol driven • Anemia Management Protocol • Frequent dosing changes • Dose adjusted in 53% of patients / month • Mean dose adjustment 1390±2648 units

6. Challenges • Target guidelines very narrow: • Only 30% of healthy population would meet range comparable to DOQI guideline ( ±1.5 Vol. % ) • Erythropoietin administration: • Non-physiological dosing: • Discrete and intermittent • Different routes of administration: SC vs. IV

7. Soft Computing Methods in Anemia Treatment • Intelligent Modeling and Control • Computing with Perceptions

8. Intelligent Modeling and Control • The existence of data records allows for incorporation of data-driven learning: • Neural Networks for dose-response modeling • Direct Inverse Neuro-Control for Erythropoietin administration

9. Control Theoretic Approach to Anemia Treatment Hematocrit level as specified by Dialysis Outcomes Quality Initiative ( 33 - 36 Vol. % ) PATIENT / PLANT PHYSICIAN / DECISION MAKER Erythropoietin Hematocrit

10. HCT(k) b1 b2 tanh x  EPO(k-1) z-1  HCT(k+1) EPO(k) Patient Model HCT HCT EPO

11. Neuro-Controller HCT HCT(k-2) HCT(k-1) HCT(k) EPO(k) z-1 b1 b2 EPO tanh x z-1 EPO EPO(k+1)

12. Development of Plant Model and Controller Treatment Data (1996 – 2000) Levenberg-Marquardt Levenberg-Marquardt PLANT CONTROLLER z-1 tanh x  z-1 tanh x z-1

13. Direct Inverse Neuro-Control Noise S HCT CONTROLLER PLANT z-1 tanh x  z-1 tanh x z-1 EPO

14. Simulation Results – Anemia Management Protocol HCT months EPO

15. Simulation Results –Neuro-Control HCT months EPO

16. Simulation Results – Neuro-Control vs. Protocol

17. Computing with Perceptions • Imprecision exists due to the complexity of human body as well as the quality of data, i.e. laboratory data • Perception-based Fuzzy System • Prediction of approximate response to Erythropoietin treatment

18. Perception-based Fuzzy System Rj: IF x is Aj THEN y is Bj FUZZYFICATION INFERENCE DEFUZZYFICATION y x

19. Computing with Perceptions – Perception as Fuzzy Number Asymmetric Gaussian Membership Function  (x) sr sl m x

20. Computing with Perceptions – Imprecise Input IF x is A THEN y is B A x = A’ x

21. Computing with Perceptions –Mutual Subsethood A A B B A  B A  B

22. Computing with Perceptions –Imprecise Output

23. Perception-based Fuzzy System Example from 166 subjects R1 If HCT is LO and EPO is ME then  HCT is PO R2 If HCT is LO and EPO is LO then  HCT is ZE R3 If HCT is HI and EPO is LO then  HCT is NE R4 If HCT is LO and EPO is HI then  HCT is PO LO HI NE ZE PO LO ME HI

24. Perception-based Fuzzy System Rule Evaluation

25. A Look Into The (Near) Future • By developing this approach further, we will be able to incorporate “non-measurable quantities” such as: • Quality of Life • Morbidity into an intelligent clinical decision support system for treatment of chronic illnesses.

26. Summary • Intelligent Systems and their ability to: • Learn from data • Deal with imprecision have been demonstrated to be a valuable asset for improving the clinical practice in treatment of chronic illnesses.

27. Thank you