U of T Department of Family & Community Medicine PEARLS 2014 Presented by Drs. David Kaplan & Noah Ivers at DFCM Conference 2015

1. DFCM Pearls:
The seven research studies that will impact
clinical practice for academic family physicians
Dr. David M. Kaplan MD MSc CCFP
Associate Professor, North York General, Department of Family & Community Medicine
Primary Care Lead, Central Local Health Integration Network
Dr. Noah Ivers MD PhD CCFP
Assistant Professor, Women’s College Hospital, Department of Family & Community
Medicine. Scientist, Women’s College Research Institute
Adjunct scientist, Institute for Clinical Evaluative Studies

2. DISCLOSURE OF COMMERCIAL SUPPORT
• This program has not received financial support

3. FACULTY/PRESENTER DISCLOSURE
• Faculty: Dr. David M. Kaplan
• Relationships with commercial interests: none

4. FACULTY/PRESENTER DISCLOSURE
• Faculty: Dr. Noah Ivers
• Relationships with commercial interests: none

5. The 2014 DFCM Pearls Process

6. DFCM 2014 – Pearl 1
Clinical characteristics associated with
increased risk of adverse events in
patients presenting to the emergency
department with exacerbation of chronic
obstructive pulmonary disease: A
prospective cohort study.
Stiell I, Clement C, Aaron S, Rowe B, Perry J, Brison R, Calder L,
Cagaanan R, Lang E, Borgundvaag B, Forster A, Wells GA

7. The Bottom Line
• 5 variables were independently associated with
adverse events in patients presenting to the ED
with AECOPD:
– prior history of intubation
– initial heart rate ≥ 110/ minute
– being too ill to do a walk test
– hemoglobin < 100 g/L
– urea ≥ 12 mmol/L

8. The Research Question
• Researchers sought to identify clinical
characteristics associated with serious adverse
events in patients with AECOPD
• Why this is important?
– AECOPD is common in clinic and ED
– decision to send a patient to the ED or to
admit is difficult and there is little evidence
to guide management

9. What the Researchers Did
• N=945 patients, of whom 354 (37.5%) were
admitted
• 74 (7.8%) patients with a subsequent serious
adverse event, 36 (49%) had not been
admitted after the initial emergency visit
• Conducted multivariable modeling to find
clinical variables that were independently
associated with adverse events

10. What the Researchers Found

11. What the Researchers Found
• 5 variables that were independently associated with
adverse events:
– prior intubation
– initial heart rate ≥ 110/ minute
– being too ill to do a walk test
– hemoglobin < 100 g/L
– urea ≥ 12 mmol/L
• Using a risk score of 2 or higher as a threshold for
admission would capture all patients with a predicted risk
of adverse events of 7.2% or higher, while only slightly
increasing admission rates, from 37.5% to 43.2%

12. What This Means for Academic and
Clinical Practice
• Once validated, this scale could be used to
reduce mobility and mortality of patient with
AECOPD by slightly increasing the admission
rate for these patients
• Hospitals would need to budget for an
increase of ~6% more COPD admissions (still
lower than reported USA admission rate of
~80%)
• generalization to primary care is uncertain

13. DFCM 2014 – Pearl 2
Feasibility and Validity of the Self-
administered Computerized Assessment
of Mild Cognitive Impairment With Older
Primary Care Patients
Tierney MC, Naglie G, Upshur R, Moineddin R, Charles J,
Jaakkimainen RL

14. The Bottom Line
• It is feasible to use self-
administered computerized
cognitive tests with older primary
care patients and have the results
uploaded to our EMRs

15. The Research Question
• Can the Computerized Assessment of Mild
Cognitive Impairment (CAMCI) be
independently completed by older primary
care patients?
• Why this is important?

18. What the Researchers Did
• pts aged =/>65y (seen consecutively over 2
months by 1 family practice)
• Excludes: pts with dementia dx or previous
work-up for dementia
• N=130 patients with cognitive concerns and a
matched sample of 133 without cognitive
concerns
• CAMCI was individually administered after
instructions to work independently

19. What the Researchers Found
• 259 Pts (98.5%) completed the entire CAMCI
• 241 Pts (91.6%) completed it without any questions
or after simple acknowledgment of their question.
• Lack of computer experience decreased the odds
of independent CAMCI completion

20. What This Means for Academic and
Clinical Practice
• Study supports the feasibility of using self-
administered computerized cognitive tests
with older primary care patients, given the
increasing reliance on computers by people
of all ages.
• Similar tools can be used by patients
independently (regardless of age) with the
results uploaded to our EMRs

21. DFCM 2014 – Pearl 3
Effect of payment incentives on cancer
screening in Ontario primary care
Kiran T, Wilton AS, Moineddin R, Paszat L, Glazier RH.

22. The Bottom Line
• No significant step change in the screening
rate for breast, colon or cervical cancer was
found the year after pay-for-performance
incentives were introduced

23. The Research Question
• To assess whether pay-for-performance
scheme for primary care physicians in
Ontario was associated with increased
cancer screening rates
• Why this is important?
– $109M dollars were spent from 2006-2010 in
Ontario as financial incentives to FPs for
cervical, breast, and colorectal cancer screening
– did they public get value for its money?

24. What the Researchers Did
• Administrative Database
• longitudinal analysis using administrative
data to determine cancer screening rates
• segmented linear regression analysis to
assess whether there was a step change or
change in screening rate trends after
incentives were introduced in 2006/2007

25. What the Researchers Found

26. What This Means for Academic and
Clinical Practice
• pay-for-performance scheme was associated
with little or no improvement
• PFP costs a lot of money
• Policy makers should consider other
strategies for improving rates of cancer
screening

27. DFCM 2014 – Pearl 4
Waiting to see the specialist. Patient and
provider characteristics of wait times
from Primary to Specialty Care.
Jaakkimainen RL, Glazier R, Barnsley J, Salkeld E, Lu H, Tu K.

28. The Bottom Line
• Calculated wait times for a referral
from a FP to seeing a specialist
physician are longer than those
reported by physician surveys

29. The Research Question
• To calculate the wait times from when a
referral is made by a family physician (FP) to
when a patient sees a specialist physician
and examine patient and provider factors
related to these wait times.
• Why this is important?
– Family physicians in TCLHIN and CLHIN have
identified access to specialist care a ‘gap’ issue
for patients

30. What the Researchers Did
• Used an Electronic Medical Record Administrative
data Linked Database
• EMR referral date was linked to the administrative
physician claims date to calculate the wait times
• Patient age, sex, socioeconomic status, comorbidity
and FP continuity of care were examined
• Physician age, sex, practice location, practice size
and participation in a primary care delivery model
were examined

31. What the Researchers Found

32. What the Researchers Found
• median waits time:
– medical specialists ranged from 39-76 days
– surgical specialists from 33-66 days
– patient factors did not seem to be associated with wait
times from primary care to specialty care
– physician factors were not consistently associated with
wait times
• other than for FP practice location and size

33. What This Means for Academic and
Clinical Practice
• Calculated wait times for a referral from a FP
to seeing a specialist physician are longer
than those reported by physician surveys
• Wait times from primary to specialty care
need to be included in the calculation of
surgical and diagnostic wait time
benchmarks in Canada in order to
understand true access to care

34. DFCM 2014 – Pearl 5
Effect of an educational toolkit on quality
of care: a pragmatic cluster randomized
trial.
Shah BR, Bhattacharyya O, Yu CH, Mamdani MM, Parsons JA,
Straus SE, Zwarenstein M.

35. The Bottom Line
• Despite being relatively easy and
inexpensive to implement, a printed
educational toolkit did not improve quality of
care or cardiovascular outcomes in a
population with diabetes.

36. The Research Question
• To evaluate the effectiveness of an
educational toolkit focusing on
cardiovascular disease screening and risk
reduction in people with diabetes.
• Why this is important?
– Printed educational materials for clinician
education are one of the most commonly
used approaches for quality improvement
– Do they make a difference?

38. What the Researchers Did

39. What the Researchers Found
Sub Study 1 (administrative data study)
• death or non-fatal myocardial infarction, occurred in
11,736 (2.5%) patients in the intervention group and
11,536 (2.5%) in the control group (p = 0.77).
Sub Study 2 (clinical data study)
• use of a statin, occurred in 700 (88.1%) patients in
the intervention group and 725 (90.1%) in the control
group (p = 0.26).
• Pre-specified secondary outcomes, including other
clinical events, processes of care, and measures of
risk factor control, were also not improved by the
intervention.

40. What This Means for Academic and
Clinical Practice
• Despite being relatively easy and
inexpensive to implement, printed
educational materials were not effective.
• We need to consider “rigorous and
scientifically based approach to the
development, dissemination, and evaluation
of quality improvement interventions”

41. DFCM 2014 – Pearl 6
Risk of osteoporotic fractures with
Angiotensin II receptor blockers versus
Angiotensin Converting-Enzyme inhibitors
in hypertensive community-dwelling
elderly
Butt D, Mamdani M, Gomes T, Lix L, Lu H, Tu K

42. The Bottom Line
• ARB or an ACE inhibitor have similar effects
on bone health of older patients treated for
hypertension
• These patients also have a decreased risk of
osteoporosis-related fractures with dose
escalation of either drug supporting a
protective effect on bone

43. The Research Question
• Do ACE-I and ARBs have clinically significant
effects on bone health?
• Why this is important?
– ACE inhibitors and ARBs are used to treat
hypertension
– No one has yet examined the risk of
osteoporosis-related fractures in hypertensive
elderly treated with ARBs versus ACE inhibitors

44. What the Researchers Did
• population-based, retrospective cohort study
• cohort of newly treated hypertensive patients
(>66y started on an ACE-I) matched to ARB users
• primary outcome was hip fracture
• secondary outcomes were non-hip major
osteoporotic fractures and other osteoporotic
fractures.
• calculated hazard ratios (HRs) using Cox
proportional hazards model

45. What the Researchers Found
• 87,635 newly treated Ontario hypertensive
elderly
• There were 297 hip fractures, 752 non‐hip
major osteoporotic fractures, and 484 other
osteoporotic fractures that occurred after
starting an ARB or an ACE inhibitor

46. What the Researchers Found
When adjusted for dosage, there was no significant difference between the effects of ARBs and ACE
inhibitors on hip, other major osteoporotic and other osteoporotic fractures

47. What This Means for Academic and
Clinical Practice
• hypertensive older patients treated with an
ARB or an ACE inhibitor have similar effects
on bone
• decreased risk of osteoporosis‐related
fractures with dose escalation of either drug
supporting a protective effect on bone

48. DFCM 2014 – Pearl 7
Increased collision risk among drivers
who report driving after using alcohol and
after using cannabis
Sayer G, Ialomiteanu A, Stoduto G, Wickens CM, Mann RE, Le
Foll B, Brands B

49. The Bottom Line
• Drivers reporting neither driving under the
influence of alcohol nor driving under the
influence of cannabis were significantly less
likely to experience a collision than those
who reported one of these behaviours
• Drivers who reported both behaviours had
the highest collision risk
– three times greater odds of collision than those
who reported only DUIA or DUIC

50. The Research Question
• To assess the self-reported collision risk
among drivers who report DUIA and DUIC in
the Ontario adult population.
• Why this is important?
– consumption of cannabis is common in Ontario
– little research exists on the prevalence of people
who report both DUIA and DUIC and the
collisions experienced by this group

51. What the Researchers Did
• Used the CAMH Monitor (CM)
• N=16,224 (2002 to 2010)
• Past-year self-reported collision involvement
was examined in three groups
– no DUIA or DUIC
– DUIA or DUIC
– DUIA and DUIC)
• Logistic regression analysis

52. What the Researchers Found

53. What This Means for Academic and
Clinical Practice
• Drivers who reported both behaviours had
the highest collision risk - three times
greater odds of collision than those who
reported only DUIA or DUIC
• FPs should counsel patient who admit to
cannabis use using a harm reduction
approach similar to safe EtOH use

54. Questions?
David M. Kaplan
david@davidkaplanmd.com
Noah Ivers
noahivers@gmail.com