Conflict of interest declaration and sources of funding

Transcription

1 Potentially Inappropriate Prescribing immediately prior to Long-Term Care admission (PIP in LTC): Validation of tools for their future use across Ontario Bruyère CLRI Webinar March 24 th, 2016 and Bruyère Research Institute

2 Conflict of interest declaration and sources of funding I have no potential conflicts of interest to declare. I do not accept any gifts, funding, honoraria, shares or any other forms of payment from manufacturers of medication or medical devices, or from providers of medical services. My earnings are derived from the clinical practice of medicine (Ministry of Health and Long-Term Care of Ontario) and from academic work (University of Ottawa). The work presented here is funded by the Government of Ontario through the Bruyère Centre for Learning Research and Innovation in LTC and supported by the Institute for Clinical Evaluative Sciences (ICES); the opinions, results and conclusions reported in this presentation are those of the authors and are independent from the funding sources. No endorsement by CIHR, the Ontario MOHLTC or ICES is intended or should be inferred.

3 Acknowledgements Co-investigators: v Roland Halil v Christina Catley v Barbara Farrell v Cristín Ryan v Douglas G. Manuel Staff: v Matt Hogel v Cody D. Black v Margo Williams

4 Inappropriate Prescribing Potentially inappropriate prescribing (PIP): The use of medicines whose potential harms to older adults may outweigh the benefits * à Frequent and associated with morbidity and mortality, particularly in LTC residents * Fick DM, Cooper JW, Wade WE, Waller JL, Maclean JR, Beers MH. Updating the Beers criteria for potentially inappropriate medication use in older adults: results of a US consensus panel of experts.arch Intern Med. 2003;13(22):

5 So what is the problem? Aging population + vulnerability to medication adverse effects with age adverse events, morbidity and mortality health care services use è costs People aged 65 years and older: * è 15% of the Canadian population è 40% of all retail prescription drug sales è 60% of public drug program spending Potentially inappropriate prescribing (PIP) in seniors estimates from clinical data (patients with at least one PIP): # 22% in the primary care setting 35% in the acute care hospital 60% in the nursing home setting * Canadian Institute for Health Information. Drug Use Among Seniors on Public Drug Programs in Canada, Ottawa, ON; # O'Mahony D, Gallagher P, Ryan C, Byrne S, Hamilton H, Barry P, et al. STOPP & START criteria: A new approach to detecting potentially inappropriate prescribing in old age. European Geriatric Medicine 2010;1(1):45-51.

6 Identifying PIP

7 STOPP/START 80 STOPP and 34 START criteria Includes: Ø Drugs to avoid in the elderly Ø Drug-drug interactions Ø Drug-disease interactions Ø Drugs that increase risk of falls Ø Duplicate drug class prescriptions

8 STOPP/START

9 STOPP/START Criteria have been modified for use with health admin data Able to ID PIPs with frequency comparable to other studies using the full criteria Cahir C, Fahey T, Teeling M, Teljeur C, Feely J, Bennett K. Potentially inappropriate prescribing and cost outcomes for older people: a national population study. Brit J Clin Pharmacol 2010;69(5):543-52

10 Beers Criteria First criteria published 91; updated in 97, 03, 12 Criticised based on: v Inclusion of obsolete/unavailable medications v Not sufficiently inclusive of common instances of PIP v Higher scores not associated with ADEs

11 Beers Criteria

12 Starting from the end Transparent evidence, rational use, equitable access Monitoring of prescribing quality and related patient outcomes Development of targeted strategies for CME about common and/or costly PIPs Development of feedback mechanisms for prescribers Point of care access to medication information for all patients (health administrative data) 85% of prescriptions are written by primary care physicians Target for interventions Doing this at the population level requires population-level data

13 How well do these criteria perform in Health Admin Data?

14 The PIP in Long-Term Care (LTC) study: * Bjerre LM, Halil R, Catley C, et al. Potentially inappropriate prescribing (PIP) in long-term care (LTC) patients: validation of the 2014 STOPP-START and 2012 Beers criteria in a LTC population a protocol for a cross-sectional comparison of clinical and health administrative data. BMJ Open 2015;5:e doi: /bmjopen Study goal: To validate medication appropriateness criteria applicable to health administrative data by comparing their performance when applied to clinical data

15 The PIP in Long-Term Care (LTC) study

16 Study Participants Recruiting newly admitted residents to LTC, convalescent, or respite care after June 2014 from 6 LTC homes in Ottawa area v Individuals providing informed consent v Aged 66+ v OHIP-eligible

17 Recruitment to date 209 willingness to be contacted forms received 110 having informed consent conversation Could not be contacted n= 99 Prospective participant is deceased n= 10 Declined to participate n= 8 92 agreeing to participate Final Study Group n= 87 Patient deceased before data could be collected n= 2 Patient was previously in another LTC n= 3

18 Data Collection Clinical Data Charts abstracted by a contracted pharmacist Excel-based data collection template created Prompts entry of relevant patient data Responds to data entry by directing evaluator toward most pertinent PIPs

19 Data collection sheet McLaughlin Centre for Population Health Risk Assessment

20 Snapshot PIP identified via clinical data * Bjerre LM, Halil R, Catley C, et al. Potentially inappropriate prescribing (PIP) in long-term care (LTC) patients: validation of the 2014 STOPP-START and 2012 Beers criteria in a LTC population a protocol for a cross-sectional comparison of clinical and health administrative data. BMJ Open 2015;5:e doi: /bmjopen

21 Snapshot PIP identified via clinical data Data Type Clinical Data Health Administrative Data % of patients % of patients % PIP (actual/ with one or % PIP (actual/ with one or Medication Assessment Tools # PIP # PIP/Pt maximum) more PIP # PIP # PIP/Pt maximum) more PIP Full STOPP/START % % Subset of STOPP/START HA Data Subset of STOPP/START clinical data % % Full Beers % % Subset of Beers 2012 HA data Subset of Beers 2012 clinical data % % Bjerre LM, Halil R, Catley C, et al. Potentially inappropriate prescribing (PIP) in long-term care (LTC) patients: validation of the 2014 STOPP-START and 2012 Beers criteria in a LTC population a protocol for a cross-sectional comparison of clinical and health administrative data. BMJ Open 2015;5:e doi: /bmjopen

22 Snapshot Most frequent PIP identified via clinical data Criterion Definition Prevalence START E5 Vitamin D supplement in older people who are housebound or experiencing falls or with osteopenia (Bone Mineral Density T-score is > -1.0 but < -2.5 in multiple sites). 41% START I2 2. Pneumococcal vaccine at least once after age 65 according to national guidelines. 33% Beers Diag B3 Dementia and cognitive impairment --> Anticholinergics (see Table 9 in the original guideline document for full list) Benzodiazepines H2-receptor antagonists 31% Zolpidem Antipsychotics, chronic and as-needed use --> Avoid Beers Caut A4 Antipsychotics Carbamazepine Carboplatin Cisplatin Mirtazapine SNRIs SSRIs TCAs Vincristine --> Use with caution 29%

23 Snapshot Most frequent PIP identified via clinical data Criterion Definition Prevalence START E3 Vitamin D and calcium supplement in patients with known osteoporosis and/or previous fragility fracture(s) and/or (Bone Mineral Density T-scores more than -2.5 in multiple sites). 27% STOPP K2 Neuroleptic drugs (may cause gait dyspraxia, Parkinsonism). 22% START I1 Seasonal trivalent influenza vaccine annually. 22% History of falls or fractures --> Anticonvulsants Antipsychotics Benzodiazepines Beers Diag Nonbenzodiazepine hypnotics B4 Eszopiclone, Zaleplon, Zolpidem 22% TCAs/SSRIs --> Avoid unless safer alternatives are not available; avoid anticonvulsants except for seizure START A6 Angiotensin Converting Enzyme (ACE) inhibitor with systolic heart failure and/or documented coronary artery disease. 19% STOPP A1 Any drug prescribed without an evidence-based clinical indication 16%

24 Data Collection Administrative Data 5 databases accessible to the Institute for Clinical and Evaluative Sciences v ODBD Drug claims v DAD Acute care hospitalizations v NACRS Emergency department visits v OHIP Claims paid by ON health insurance v RPDB Birth and death dates

25 From criteria to SAS code ICD-10 From clinical criteria to SAS code diagnostic codes Section B: Cardiovascular System 3. Beta-blocker in combination with verapamil or diltiazem (risk of heart block). DIN lists SAS code

26 Snapshot of Results Patient Profiles * 11 of 64 linked patients have hospitalizations immediately prior to LTC admission * Mock profile based on patterns commonly seen in data.

27 Snapshot of Results Benzodiazepine (K1) Drug Profiles * * Mock profile based on patterns commonly seen in data.

28 Snapshot of Results Comparing HA and clinical data Health administrative data Clinical data PIP No PIP PIP No PIP Error in self-report by pt or care-giver? Rx dispensed but not taken? Concordant (A) Discordant (C) Discordant (B) Concordant (D) Error in coding? Hospitalization or CCC bed? Total (=A+B+C+D)

29 Snapshot of Results Benzodiazepines The distribution of PIP and non-pip detected from HAD, clinical data, and both HAD and clinical data if PIP were investigated in 100 people for Criterion K1. Concordant = 75%, Discordant=25%

30 Snapshot of Results Neuroleptic (K2) Drug Profiles * * Mock profile based on patterns commonly seen in data.

31 Snapshot of Results Neuroleptics The distribution of PIP and non-pip detected from HAD, clinical data, and both HAD and clinical data if PIP were investigated in 100 people for Criterion K2. Concordant = 67%, Discordant=33%

32 Snapshot of Results PPI > 8 weeks (F2) Drug Profiles * * Mock profile based on patterns commonly seen in data.

33 Snapshot of Results Neuroleptics The distribution of PIP and non-pip detected from HAD, clinical data, and both HAD and clinical data if PIP were investigated in 100 people for Criterion F2. Concordant = 75%, Discordant=25%

34 Next Steps Completion of analyses for all STOPP/START and Beers criteria (coding nearing completion) using both patient clinical data with health administrative data Presentation/dissemination/publication of full results

35 What else? The PIP-STOPP study ( Population-based retrospective cohort study using Ontario s large health administrative and population databases. ( Eligible patients aged 66 years and older who were issued at least one prescription between April 1 st 2003 and March 31st 2014, (approximately 2 million patients) will be included. Goals: To describe the occurrence of PIP in Ontario s older population, and assess the health outcomes and health system costs associated with it.

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37 PIP-STOPP study: Next steps PIP Analyses ED visits hospitalizations death adverse drug events Dissemination Stakeholder engagement

38 Questions? THANK YOU!