potentially inappropriate prescribing immediately prior to

Potentially Inappropriate Prescribing immediately prior to Long-Term Care admission (PIP in LTC): Validation of tools for their future

use across Ontario

Dr. Lise M. Bjerre, MD, PhD

University of Ottawa, Department of Family Medicine

and Bruyère Research Institute

Bruyère CLRI Webinar

March 24th, 2016

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.

Acknowledgements

• Co-investigators: Roland Halil

Christina Catley

Barbara Farrell

Cristín Ryan

Douglas G. Manuel

• Staff: Matt Hogel

Cody D. Black

Margo Williams

Dr. Lise M. Bjerre, MD, PhD [email protected]


Inappropriate Prescribing

Potentially inappropriate prescribing (PIP):

“The use of medicines whose potential harms to older adults may outweigh the benefits”*

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):2716–2724



So what is the problem?

↑ adverse events, morbidity and mortality

↑health care services use ↑costs

Aging population + ↑ vulnerability to medication adverse effects with age



People aged 65 years and older:*

15% of the Canadian population

40% of all retail prescription drug sales

60% of public drug program spending

*Canadian Institute for Health Information. Drug Use Among Seniors on Public Drug Programs in Canada, 2012. Ottawa, ON; 2014.

# 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.

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

Identifying PIP



STOPP/START

• 80 STOPP and 34 START criteria

• Includes:

Drugs to avoid in the elderly

Drug-drug interactions

Drug-disease interactions

Drugs that increase risk of falls

Duplicate drug class prescriptions



STOPP/START



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



Beers Criteria

• First criteria published ‘91; updated in ‘97, ‘03, ‘12

• Criticised based on:

Inclusion of obsolete/unavailable medications

Not sufficiently inclusive of common instances of PIP

Higher scores not associated with ADEs



Beers Criteria



Starting from the end…

85% of prescriptions are written by

primary care physicians → Target for interventions



“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

Point of care access to medication

information for all patients

(health administrative data)

Development of feedback

mechanisms for prescribers

Doing this at the population level …requires population-level data

How well do these criteria perform in Health Admin Data?



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

To validate medication appropriateness criteria

applicable to health administrative data by comparing

their performance when applied to clinical data



Study goal:

*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:e009715. doi:10.1136/bmjopen-

2015-009715

The PIP in Long-Term Care (LTC) study



Study Participants

• Recruiting newly admitted residents to LTC, convalescent, or respite care after June 2014 from 6 LTC homes in Ottawa area

Individuals providing informed consent

Aged 66+

OHIP-eligible



Recruitment to date



110 having informed consent conversation

92 agreeing to participate

Final Study Group

n= 87

Declined to participate

n= 8

Could not be contacted

n= 99

Prospective participant is deceased

n= 10

Patient was previously in another LTC

n= 3

Patient deceased before data could be collected

n= 2

209 willingness to be contacted forms received

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



Data collection sheet

McLaughlin Centre for Population Health Risk Assessment

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:e009715. doi:10.1136/bmjopen-2015-009715

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:e009715. doi:10.1136/bmjopen-2015-009715

Snapshot – PIP identified via clinical data



Data Type Clinical Data Health Administrative Data

Medication Assessment Tools # PIP # PIP/Pt % PIP (actual/

maximum)

% of patients with one or

more PIP # PIP # PIP/Pt % PIP (actual/

maximum)

% of patients with one or

more PIP

Full STOPP/START 2014 312 3.76 3.27 % 20.48 %

Subset of STOPP/START HA Data

Subset of STOPP/START clinical data 165 1.99 3.26 % 19.28 %

Full Beers 2012 157 1.89 3.44 % 14.46 %

Subset of Beers 2012 HA data

Subset of Beers 2012 clinical data 154 1.86 3.71 % 14.46 %

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

Zolpidem Antipsychotics, chronic and as-needed use --> Avoid

31%

Beers Caut A4

Antipsychotics

Carbamazepine

Carboplatin

Cisplatin

Mirtazapine

SNRIs

SSRIs

TCAs Vincristine --> Use with caution

29%

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%

Beers Diag B4

History of falls or fractures --> Anticonvulsants

Antipsychotics

Benzodiazepines

Nonbenzodiazepine hypnotics

Eszopiclone, Zaleplon, Zolpidem

TCAs/SSRIs --> Avoid unless safer alternatives are not available; avoid anticonvulsants except for seizure

22%

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%

Snapshot – Most frequent PIP identified via clinical data



Data Collection – Administrative Data

• 5 databases accessible to the Institute for Clinical and Evaluative Sciences

ODBD – Drug claims

DAD – Acute care hospitalizations

NACRS – Emergency department visits

OHIP – Claims paid by ON health insurance

RPDB – Birth and death dates



• From clinical criteria to SAS code…

From criteria to SAS code…



Section B: Cardiovascular

System

3. Beta-blocker in combination

with verapamil or diltiazem (risk

of heart block).

DIN lists

SAS code

ICD-10

diagnostic codes

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.

*



Snapshot of Results –Benzodiazepine (K1) Drug Profiles


*



Snapshot of Results – Comparing HA and clinical data



Health administrative data

Clinical data PIP No PIP

PIP Concordant

(A)

Discordant

(B)

No PIP Discordant

(C)

Concordant

(D)

Total

(=A+B+C+D)

Error in coding?

Hospitalization or

CCC bed?

Error in self-report

by pt or care-giver?

Rx dispensed but not taken?

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%



Snapshot of Results – Neuroleptic (K2) Drug Profiles


*



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%



Snapshot of Results – PPI > 8 weeks (F2) Drug Profiles


*



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%



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



What else? The PIP-STOPP study



Goals:

To describe the occurrence of PIP in Ontario’s older population, and assess the health outcomes and health system costs associated with it.

• 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 1st 2003 and March 31st 2014,

(approximately 2 million patients) will be included.

PIP

ED visits hospitalizations

death adverse drug events

PIP-STOPP study: Next steps



Analyses Dissemination

Stakeholder engagement

Questions?

THANK YOU!



mailto:[email protected]





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