THE NEED FOR ADVANCED CLINICAL DECISION SUPPORT SYSTEMS FOR THE MANAGEMENT OF PATIENTS WITH POLYPHARMACY

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1 THE NEED FOR ADVANCED CLINICAL DECISION SUPPORT SYSTEMS FOR THE MANAGEMENT OF PATIENTS WITH POLYPHARMACY SATURDAY/9:00-10:00AM ACPE UAN: L01-P 0.1 CEU/1.0 hr L01-T 0.1 CEU/1.0 hr Activity Type: Knowledge-Based Learning Objectives for Pharmacists & Pharmacy Technicians: Upon completion of this CPE activity participants should be able to: 1. Review the main risk stratification strategies in patients populations. 2. Demonstrate the value of a stratification strategy based on patients drug regimen. 3. Describe the major risk factors considered in an improved approach to the management of drug therapy. 4. Discuss a clinical decision support system for the rapid assessment of the risk of adverse drug-related effects in a patient. 5. Describe the necessary steps in the appropriate interpretation of information provided by a clinical decision support tool. 6. Generate appropriate interventions and manage patients condition using a clinical decision support system. Speaker: Jacques Turgeon, B.Pharm., Ph.D. Dr. Jacques Turgeon is the Associate Dean of the Lake Nona Campus at the University of Florida College of Pharmacy. Prior to this appointment, he served as the Chief Scientific Officer of Tabula Rasa Healthcare. He has held numerous leadership roles including Chief Executive Officer of the Centre hospitalier de l Université de Montréal, where he also served as Director of the University of Montreal Research Center and professor in the Faculty of Pharmacy. He is recognized internationally for his work on CYP450s and mechanisms of drug-drug interactions and has published more than 100 referred articles and 300 abstracts. He is a member of numerous societies and has been acting on the committees of several granting agencies for several years. He has been the Director of the Quebec Cardiovascular Network of the FRSQ and the research Director of the Quebec Heart Institute, Laval Hospital. He is a Fellow of the Canadian Academy of Sciences and the National Academy of Medicine, France. Speaker Disclosure: Jacques Turgeon reports no actual or potential conflicts of interest in relation to this CPE activity. Off-label use of medications will not be discussed during this presentation.

2 The Need for Advanced Clinical Decision Support Systems for the Management of Patients with Polypharmacy: Development of a New Risk Stratification Strategy and Application to Complex Multi-drug Interactions. Jacques Turgeon, B.Pharm., PhD Associate Dean and Director, Lake Nona Campus College of Pharmacy, University of Florida Professor Emeritus, Université de Montréal Fellow, Canadian Academy of Health Sciences Fellow, Académie nationale de médecine, France Recipient of the 2010 Louis Hébert Award Disclosure - I was the Chief Scientific Officer at Tabula Rasa HealthCare until December 31 st, I was the founder and CEO of InterMed-Rx until its acquisition by Tabula Rasa HealthCare in September I led the development of the Risk Stratification Strategy described in this presentation while being CSO at Tabula Rasa HealthCare. 1

3 Learning Objectives To discuss a clinical decision support system for the rapid assessment of the risk of drug-related adverse effects in a patient To review the main risk stratification strategies in patients population To demonstrate the value of a stratification strategy based on patients drug regimen Describe the major risk factors considered in an improved approach to the management of drug therapy To generate appropriate interventions and manage patients condition using a clinical decision support system To describe the necessary steps in the appropriate interpretation of information provided by a clinical decision support tool Major Research Activities: My Three Pillars Intersubject Variability in Drug Action Pharmacokinetics CYP450s Metabolite Characterization Drug Assays Stereoselectivity Transporters Environment (smoking) Pharmacogenetics Drug-drug interactions Pharmacodynamics Cardiovascular diseases Cardiac electrophysiology Potassium channels Patch-clamp Long QT Syndrome HMG CoA reductase toxicity Drug concentrations in the brain Translational Research Clinical Decision Support Systems Risk Stratification 2

4 Major Research Themes Intersubject Variability in Drug Action Drug-drug Interactions CYP450s & Transporters Pharmacogenetics Clinical Decision Support Systems Risk Stratification Drug-induced Long QT Syndrome herg (I Kr ) channel blockers CYP3As and block of I Kr Drug-drug interactions Cardiac CYP450s & transporters How can Pharmacists Mitigate Risk Associated with Drug-related Adverse Events? Major Research Themes Intersubject Variability in Drug Action Drug-drug Interactions CYP450s & Transporters Pharmacogenetics Clinical Decision Support Systems Risk Stratification Drug-induced Long QT Syndrome herg (I Kr ) channel blockers CYP3As and block of I Kr Drug-drug interactions Cardiac CYP450s & transporters How can Pharmacists Mitigate Risk Associated with Drug-related Adverse Events? 3

5 Impact of Adverse Drug Events Annually in the United States RIP 100,000 Deaths 1 million Emergency department visits 2 million Affected hospital stays 125,000 Hospitalizations 3.5 million Physician office visits 1.7 to to 4.6 Increased days per affected hospital stay 20.4% of Re-admissions ADEs are due mostly from: Multi-drug Interactions Leading to Unintentional Overdosing Accumulative Side Effects Adverse Drug Events and Pharmacists Performance Dec 15, 2016 Pharmacies miss half of dangerous drug combinations Investigative reporters attempted to fill potentially deadly drug combinations at 255 retail pharmacies in the Midwest over the past two years 52% of the time the prescriptions were filled without mentioning the potentially harmful interaction to the patient 4

6 Adverse Drug Events and Pharmacists Performance J Am Med Inform Assoc 2011;18: Only 18 (28%) of the 64 pharmacies correctly identified eligible interactions and non-interactions. - These study results indicate that many pharmacy clinical decision-support systems perform less than optimally with respect to identifying well-known, clinically relevant interactions. Adverse Drug Events and Pharmacists Performance - Is it the science? - Is it the knowledge? - Is it the workflow / time? - Is it the remuneration model? - Is it the lack of information? - Is it the tool? 5

7 Clinical Decision Support Systems My clinical experience, research and thinking lead me to conclude that: An analysis based on multiple one-drug-to-one-drug comparisons is obsolete and most often, inappropriate. There is no electronic software or tool capable of providing the right answer to a practitioner under conditions of multi-drug interactions in a patient with polypharmacy. Tools to be used must provide the most accurate information but let the practitioner make the correct interpretation and action plan. Clinical Decision Support Systems 6

8 Clinical Decision Support Systems: InterMed-Rx ( ) Several drug interactions are due to impaired drug disposition The CYP450 system: To understand, manage and predict drug interactions Substrate Metabolite 7

9 CYP450 Superfamily: In 1987, 7 isoenzymes! J Turgeon PhD Thesis 17 publications on the characterization of mexiletine metabolism CYP1A2 CYP2D6 CYP2D6 involvement in the metabolism of Na + channel blockers 8

10 CYP2D6 and CYP1A2: Mexiletine and Propafenone Clin Pharmacol Ther 2000;67:44-57 How to take advantage of phenoconversion. To map CYP450 Substrates / Inhibitors / Inducers ( ) CYP Substrates Inhibitors Inducers 1A2 Theophylline, caffeine, imipramine, mexiletine Quinolones 2A6 Coumarin, nicotine Diethyldithiocarbamate 2C9 2C19 NSAID, losartan, irbesartan, S-warfarin, celecoxib Omeprazole, R-warfarin Sulfaphenazole Cigarette smoking Rifampin 2D6 Codeine, antiarrhythmics, b- blockers, anti-h1, SSRIs Quinidine 2E1 Alcohol, chlorzoxazone Alcohol 3A4 CCB, anti-h1 2 nd, BZD, cyclosporin, HMG CoA Macrolides, imidazoles Rifampin, phenytoin 9

11 The CYP450 Superfamily: Affinity ( ) CYP3As Inhibitors Substrates Inducers The CYP450 Superfamily: Metabolic pathways ( ) 50% 35% 5% 10% For 3A4 inhibition C ave = 100 / (100 50) C ave = 2 For CYP2C9 inhibition C ave = 100 / (100 5) C ave =

12 Clinical Decision Support Systems: The Matrix ( ) Clinical Decision Support Systems: The Matrix 1 Within the same isoenzyme, do I have more than one substrate or inhibitor, or inducer? 11

13 Clinical Decision Support Systems: The Matrix 2 What are the colors (affinity) of interacting drugs? Clinical Decision Support Systems: The Matrix 3 What is the bioavailability (F), of the drug with the LOWEST affinity? 12

14 Clinical Decision Support Systems: The Matrix Log Plasma Concentration F = 80% A small increase in the Cmax is to be expected on first dose Time Clinical Decision Support Systems: The Matrix 4 What is contribution of this metabolic pathway to the overall clearance? C ave = 100 / (100 65) C ave = 3-fold increase 13

15 The CYP450 Superfamily: Time of Dosing The strategy is to avoid having two substrates of the same isoenzyme being present at high concentrations in the liver/intestine at the same time. Give the substrate with the lowest affinity first. Determine the Tmax of the substrate with the lowest affinity. Give the substrate with the highest affinity with a delay equal or greater than the Tmax of the low affinity substrate. The CYP450 Superfamily: Time of Dosing 14

16 CDSS: Clinical Application AM J Geriatrics PharmacotherVol 9(6), 2011: CDSS: Clinical Application 15

17 The value of the innovative CDSS The Innovative CDSS opened the door for translational researches in Pharmacogenomics. Possibility to analyze: - Drug-Drug Interactions, - Multi-Drug Interactions, - Drug-Gene Interactions, - Drug-Drug-Gene Interactions (Phenoconversion) Machine learning - Complex algorithms - Behavioral analyses The Innovative CDSS opened the door for Pharmacogenomics and Phenoconversion (2017) 2016 Tabula Rasa HealthCare, Inc. All Rights Reserved. 16

18 Application: Pharmacogenomics and Phenoconversion Interpretation for Clopidogrel on CYP2C19 and CYP3A4? 2016 Tabula Rasa HealthCare, Inc. All Rights Reserved. Application: Pharmacogenomics and Phenoconversion Interpretation for Amitriptyline on CYP2C19 and CYP2D6? 2016 Tabula Rasa HealthCare, Inc. All Rights Reserved. 17

19 Application: Pharmacogenomics and Phenoconversion Interpretation for Amitriptyline on CYP2C19*2/* Tabula Rasa HealthCare, Inc. All Rights Reserved. Application: Pharmacogenomics and Phenoconversion Interpretation for Amitriptyline on CYP2D6*1/*1. Due to Phenoconversion 2016 Tabula Rasa HealthCare, Inc. All Rights Reserved. 18

20 Major Research Themes Intersubject Variability in Drug Action Drug-drug Interactions CYP450s & Transporters Pharmacogenetics Clinical Decision Support Systems Risk Stratification Drug-induced Long QT Syndrome herg (I Kr ) channel blockers CYP3As and block of I Kr Drug-drug interactions Cardiac CYP450s & transporters How can Pharmacists Mitigate Risk Associated with Drug-related Adverse Events? What is Risk Stratification? The purpose of our research was to develop a tool that can predict risk of drug-related adverse reactions and events using drug claims as the source of information (PACE, EMTM, self-insured employers,...) Specifically, we are trying to predict: - Drug-related adverse reactions and events - Healthcare Outcomes - Costs But more importantly, the purpose of predicting these variables is to determine which patients should receive interventions to avoid negative outcomes by optimizing drug regimen. 19

21 One can assume that there is a relationship between a Risk Score and Medical Expenditures. Medication Risk Score vs Medical Expenditures Yearly Medical Expenditures (US Dollars) Medication Risk Score One can assume distribution of members Yearly Medical Expenditures (US Dollars) Medication Risk Score vs Medical Expenditures Number of members Medication Risk Score 20

22 One could decide to concentrate efforts on 10-15% of the population Yearly Medical Expenditures (US Dollars) Medication Risk Score vs Medical Expenditures 10-15% Number of members Medication Risk Score Various Risk Stratification Strategies Charlson: - Relies on Medical Coding for accuracy Issues in availability of HCC/ICD codes Procedures only allow one code for billing purposes. Von Korff: - Requires updates whenever new drugs are added - Drugs can have more than one disease indication HARP - Utilizes geriatric interviews to stratify - Has only moderate predictive power LACE: - Retrospective hospitalization data does not necessarily mean future hospitalizations 21

23 All those tools stratify risk based on disease state Charlson: Relies on Medical Coding for accuracy Issues in availability of HCC/ICD codes Procedures only allow one code for billing purposes. Von Korff: Requires updates whenever new drugs are added Drugs can have more than one disease indication HARP Utilizes geriatric interviews to stratify Has only moderate predictive power LACE: Retrospective hospitalization data does not necessarily mean future hospitalizations Largely Used Diagnostic-based Models Adjusted Clinical Groups (ACGs: Johns Hopkins) Clinical Risk Groups (CRGs: 3M ) Clinically-Detailed Risk Information System for Cost (CD-RISC ) Diagnostic Cost Group Hierarchical Condition Category models (DCG/HCC ) Hospital readmission LACE Index PEARL Score FAM-FACE-SG Score. 22

24 Largely Used Diagnostic-based Models: r 2 Variables Age and Sex Demographic variables only ACG-PM CRG DCG-HCC A&S + Dx A&S + Rx A&S +Dx + Rx A&S + Dx + Rx + cost percentile A&S + Dx + Rx + cost A&S + Dx + Rx + cost + DI A&S = Age and Sex Dx = Diagnoses Rx = Prescriptions DI = Deprivation Index Orueta et al. BMC Health Services Research 2013, Largely Used Diagnostic-based Models: Mean Absolute Prediction Error Variables ACG-PM CRG DCG-HCC Age and Sex % A&S + Dx 92.36% 96.36% 91.26% A&S + Rx 93.57% % A&S +Dx + Rx 88.41% % A&S + Dx + Rx + cost percentile A&S + Dx + Rx + cost + DI 85.99% 87.70% 85.05% 86.02% 87.70% 85.06% A&S = Age and Sex Dx = Diagnoses Rx = Prescriptions DI = Deprivation Index Orueta et al. BMC Health Services Research 2013,

25 TRHC Proprietary Risk Stratification Tool Utilizes medication pharmacological characteristics to predict outcomes. Outcomes include healthcare utilization (i.e. cost), adverse events, and medication-related problems. Primary filter: number of drugs Zakrzewski-Jakubiak H, Doan J, Lamoureux P, Singh D, Turgeon J, Tannenbaum C Detection and prevention of drug-drug interactions in the hospitalized elderly: utility of new cytochrome p450-based software. Am J Geriatr Pharmacother Dec;9(6): Doan J, Zakrzewski-Jakubiak H, Roy J, Turgeon J, Tannenbaum C. Prevalence and risk of potential cytochrome P450- mediated drug-drug interactions in older hospitalized patients with polypharmacy. Ann Pharmacother Mar;47(3): We currently look at 5 factors: 1. Side-effect Relative Odd Ratio 2. Cognitive Impairment 3. Sedation Burden 4. Heart Rhythm Impairment 5. Competitive Inhibition TRHC Risk Factor 1: Side-effect Relative Odd Ratio Uses the FDA Adverse Event Reporting System database 102 categories of side-effects Extract and calculate frequency of side-effect for each drug Combined to calculate a DRUG REGIMEN Odd Ratio for a particular side-effect 24

26 TRHC Risk Factor 2: Cognitive Impairment Cognitive Impairment (anticholinergic cognitive score) Agitation / delirium Cardiac arrhythmias Constipation Urinary retention Dry mouth Blurred vision Falls Pneumonia Risk of hospitalization Gray SL, Anderson ML, Dublin S, Hanlon JT, Hubbard R, Walker R, Yu O, Crane PK, Larson EB. Cumulative use of strong anticholinergics and incident dementia: a prospective cohort study. JAMA Intern Med Mar;175(3): Risacher SL, McDonald BC, Tallman EF, West JD, Farlow MR, Unverzagt FW, Gao S, Boustani M, Crane PK, Petersen RC, Jack CR Jr, Jagust WJ, Aisen PS, Weiner MW, Saykin AJ. Association Between Anticholinergic Medication Use and Cognition, Brain Metabolism, and Brain Atrophy in Cognitively Normal Older Adults. Alzheimer s Disease Neuroimaging Initiative. JAMA Neurol Jun 1;73(6): TRHC Risk factor 2: Cognitive Impairment Cognitive Impairment (Anticholinergic Cognitive Burden) Uses categorical scoring - Possible anticholinergics = score of 1 Score of 1 in vitro data - Definite anticholinergics = score of 2 or 3 Score of 2 clinical anticholinergic effect Score of 3 drug may cause delirium West T, Pruchnicki MC, Porter K, et al., Evaluation of anticholinergic burden of medications in older adults. J Am Pharma Assoc 2013;53:

27 TRHC Risk Factor 3: Sedative Burden TRHC Adjusted Sedative Load Model Daytime sleepness Memory impairment Depression Dizziness Dependence Tolerance Taipale H. Sedative load model and adverse events among community-dwelling older people. University of Eastern Finland, Faculty of Health Sciences, Publication of the University of Eastern Finland. Disseratations in Health Sciences. TRHC Risk Factor 4: Heart Rhythm Impairment Drug-induced Long QT Syndrome Long QT-JT Index Long QT-JT Score 26

28 TRHC Risk factor 4: Heart Rhythm Impairment Long QT-JT Index distribution for 155 drugs analysed Long QT-JT Index = K1 [IC 50 herg, C max at Dose test, Dose max daily, % Protein binding, Drug-Drug Interaction Coefficient] + +/- K2 [herg/cav1.2] +/- K3 [herg/nav1.5] - K4 [herg trafficking] Steffen, L.E., Knowlton, C.H., Turgeon, J. Development of a drug-specific Long QT-JT Index for prediction of drug-induced QT Prolongation. Circulation 2016;134:A TRHC Risk Factor 4: Heart Rhythm Impairment Long QT-JT Index distribution for 155 drugs analysed 15 Steffen, L.E., Knowlton, C.H., Turgeon, J. Development of a drug-specific Long QT-JT Index for prediction of drug-induced QT Prolongation. Circulation 2016;134:A

29 TRHC Risk Factor 4: Heart Rhythm Impairment Predisposing factors to Torsade de Pointes Bradycardia, pause Women Hypokalemia Hypomagnesemia Diuretics Class III antiarrhythmics QT prolonging drugs Drug-drug interactions TRHC Risk Factor 4: Heart Rhythm Impairment Patient-specific Long QT-JT Score : Validation study Histogram of Sample Patient Cases More LQTS Score Steffen, L.E., Knowlton, C.H., Turgeon, J. Risk Identification of drug-induced Long QT Syndrome using a Patient- Specific Long QT-JT Risk Score. Circualtion;134:A

30 TRHC Risk Factor 5: Competitive Inhibition TRHC Risk Factor 5: Competitive Inhibition Risk Factor 5: Competitive Inhibition - Basic principles of enzyme kinetics - Substrate affinity - Extent of changes in substrate concentrtion - Time of dosing 29

31 TRHC Proprietary Risk Stratification Tool Once all 5 algorithms were developed, they were combined using one last algorithm. This last algorithm combines all individual risk factor scores into one overall Total Risk Score for each patient in the data feed. Once this is done, a report is generated containing the risk stratification risks of the entire population being examined. These results are then statistically analyzed TRHC Proprietary Risk Stratification Tool Total Risk Score Distribution in 320,000 patients 30

32 TRHC Proprietary Risk Stratification Tool Yearly Part A and B Expenditurre (US Dollars) Total Risk Score vs Expenditure Total Risk Score TRHC Proprietary Risk Stratification Tool Exclusion MPE ± SEM R 2 No exclusions 5.88 ± % quantile 5.44 ± % quantile 5.54 ± % quantile 5.42 ± <5 drugs 7.24 ± % and <5 drugs 6.29 ± MRS < ± MRS < 3 and 1% 5.18 ± MRS < 3 and 2% 5.25 ± MRS < 3 and 5% 4.92 ±

33 Side Effect Burden 2016 Tabula Rasa HealthCare, Inc. All Rights Reserved Tabula Rasa HealthCare, Inc. All Rights Reserved. 32

34 Side Effect Burden 2016 Tabula Rasa HealthCare, Inc. All Rights Reserved Tabula Rasa HealthCare, Inc. All Rights Reserved. 33

35 Conclusions The use of advanced Clinical Decision Support Systems is of great value for the establishment of personalized, more precise and improved drug regimen. Risk stratification tools could help identify the patients at increased risk of drug-related adverse events. These tools, combined, represent the basis of proposed enhanced MTM models currently being applied and tested. How can Pharmacists Mitigate Risk Associated with Drug-related Adverse Events? Questions? 34