1. COMPUTER - ASSISTED INFUSION OF MUSCLE RELAXANTS Dr Valérie BILLARD

2. NMB - drug ? -dosage ? EFFECT NM blockade NEUROMUSCULAR BLOCKERS (NMB) : EXPECTED EFFECTS Required : Unexpected - early motor testing - respiratory failure - light anaesthesia larynx abdominal orthopedic eye, neuro...

3. NMB : MEASURED EFFECTS T 1 /T initial T 4 /T 1 PTC Visual Force transducer AccelerometryEMG Muscle (AP, OO) NMB - drug ? -dosage ? TwitchTOFTetanosDBS Expected effect ?

4. NMB : Simple closed-loop systems PATIENT MONITOR CONTROLLER INFUSION DEVICE  ANESTHESIOLOGIST

5. Simple closed loop systems : the controller u Properties –Output dependent on the control opération –rapidly achieve a stable control –protected from electrical interference and noise –easy to monitor and to operate u Principle based upon the error (e = measured - target) –Proportional : Rate = K. Weight. e –Proportional Integral: Rate = Kp.weight.e + K i.weight.(  e+P) –Proportional Integral Derivative: Rate = K 1.e+K 2.  e+K 3.de/dt

6. Fuzzy logic control u Control accepting qualitative data as «small»,«big»... –Input = error E and change in the error –Output = controller or change in the controller –Ex. «IF error = 0 and change in error is positive small, THEN output is negative small ». - + setpoint Fuzzifier Fuzzy control Defuziffier dE/dt Process E

7. Closed loop systems : the performances

8. EFFECT (predicted) CONCENTRATION (effect site) PKPD FROM THE DOSE TO THE EFFECT : PK -PD RELATIONSHIP CONCENTRATION(plasma) Ke0 NMB DOSE

9. EFFECT (predicted) CONCENTRATION (effect site) PKPD TARGET THE « MEASURABLE » PREDICTED EFFECT USING PKPD CONCENTRATION(plasma) Ke0 NMB DOSE

10. EFFECT (predicted) CONCENTRATION (effect site) PK PD PK -PD RELATIONSHIP : PERFORMANCES CONCENTRATION(plasma) Ke0 NMB DOSE EFFECT (measured) ERROR

11. PKPD MODEL : ERROR ON THE PK u Wrong drug (rare!) u Wrong model (elimination from central compartment) u PK parameters not adjusted to the current patient –Age »elderly (CL 1  Vdss  ) »infants (CL 1 and Vdss  ) –Obesity (ideal weight vs. real weight) – Renal or liver failure u Variability

12. PKPD MODEL : ERROR ON THE PD u PD model inadapted : –E max vs. others ? –other muscle or measure than in the model u PD parameters not adjusted to the patient –Age : EC 50 lower in infants –Burning –Interactions (volatile +++) u Wrong Ke0 : hypothermia, age u Variability

13. HOW TO DECREASE THE ERROR? u Adjust the PK and PD model to covariates –Clinical research and publications –Library of models u Enter a measured value to adjust the model : Bayesian forecasting –Take globally account of the patient covariates –Could change over time

14. EFFECT(predicted) CONCENTRATION (effect site) PK PD FROM THE DOSE TO THE EFFECT : PK -PD RELATIONSHIP CONCENTRATION(plasma) Ke0 NMB DOSE EFFECT (measured)

15. Bayesian approach u Comes from Bayes description of conditional probability u Combines : –the amount of information given by a population model –with 1 or few pieces of information coming from a patient –to improve the accuracy of the model to describe this patient u Has been used mainly by adding a measured concentration to PK model and applied to antibiotics, lidocaine, theophylline, antineaplasic agents,...

16. Bayesian adaptation using Stanpump u Available for atracurium, vecuronium, rocuronium u Only for target blockade less than 95% PKeffect u Adjust the PK model to a measured value of effect u This value is entered manually (open loop) u Then adjust the target in order to have minimal change

17. CONCLUSION u The effects of muscle relaxants could be measured u This measured effect can – act as input in closed loop system where output is dose –become a target for CCI based on PKPD model –be compared to the target to adapt the model to the patient »PK model : mainly interindividual variability »PD model : mainly intraindividual variability u The relevant clinical effects corresponding to these measures remain to be known