It Its only a ly a case e rep eport and nd related ed n - - PowerPoint PPT Presentation

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It Its only a ly a case e rep eport and nd related ed n - - PowerPoint PPT Presentation

Mar 12, 2023 175 likes •931 views

It Its only a ly a case e rep eport and nd related ed n nonsen ense David Juurlink University of Toronto CSIM 2018 Oblig igatory D Disclosure S e Slide Personal income Clinical billings Salary support UofT,

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“It “It’s only a ly a case e rep eport” ”

and nd related ed n nonsen ense

David Juurlink

University of Toronto CSIM 2018

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Oblig igatory D Disclosure S e Slide

Personal income

Clinical billings Salary support UofT, Sunnybrook DOM, ICES, Ontario Poison Centre The Medical Letter Medicolegal

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Oblig igatory D Disclosure S e Slide

Personal income

Clinical billings Salary support UofT, Sunnybrook DOM, ICES, Ontario Poison Centre The Medical Letter Medicolegal

No dealings with industry

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Stronger evidence More believable “Only way to show cause-effect”

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Weaker evidence Bias / confounding “Can’t prove cause-effect”

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“That’s nice.”

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Effects of interventions Prognosis Role of agents (or characteristics) in health and disease

We’ e’re t e trying to to a answer er ques estions

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“Evidence-based medicine is the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients.”

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RCTs Ts

Intervention No intervention

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Randomized (!) Conceptually simple Tailored Measures of effect

Relative Absolute

Cost / duration May be unethical May be impossible Quasi-ideal setting

RCTs Ts

Selected patients

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  • Ben Goldacre, Bad Pharm a

“Drugs a are t tested by the people w who m manufacture t them... on hopele lessly ly s small l numbers of weir ird, u unrepresentativ ive p patie ients... i in such a a way y that at t they ey e exagger erate t e the benefits of trea eatmen ents. Unsurpris isin ingly gly, these t trials ls t tend to produce r results t that f favour t the

  • manufacturer. When t

trials ls y yield ld result lts t that c companie ies don’t l like, they ey are e perfec ectly y entitled t to hide e them... s so we o

  • nly ev

ever er s see e a distorted p picture e of any d drug’s t true ef e effec ects.”

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“Always note and record the unusual. Publish it. Place it on permanent record as a short, concise note. Such communications are always

  • f value.”
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“But remember: It’s only a case report so it probably won’t get published anywhere good.”

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Sirianni Ann Emerg Med 2008

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Sirianni Ann Emerg Med 2008

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Sirianni Ann Emerg Med 2008

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Sirianni Ann Emerg Med 2008

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McLaughlin Annals of EM 2000

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POPULATION

With attribute Without attribute

Cross s section ional s l studie ies

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RALES

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RALES

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RALES

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RALES

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RALES

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RALES

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RALES

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RALES

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RALES

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What RALES said vs. What we heard

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500 1000 1500 2000 2500

RALES 1999 NEJM 2004

Epilogu gue

Spironolactone + ACEI (> 66 y with CHF)

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CMAJ 2009

5-fold ld ↑ stro rong

  • pioids

ids

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Opioid deaths, Ontario 1991 - 2015

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2000

% of all deaths involving an opioid 0-14 15-24 25-34 35-44 45-54 55-64 65+

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2000 2005

0-14 15-24 25-34 35-44 45-54 55-64 65+ % of all deaths involving an opioid

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2000 2005 2010

0-14 15-24 25-34 35-44 45-54 55-64 65+ % of all deaths involving an opioid

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2000 2005 2010 2015

0-14 15-24 25-34 35-44 45-54 55-64 65+ % of all deaths involving an opioid

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Cases Controls Exposure

Case se-con

  • ntrol s
  • l studie

ies

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Cases Controls Exposure

Case se-con

  • ntrol s
  • l studie

ies

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Cases Controls Exposure

Case se-con

  • ntrol s
  • l studie

ies

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Rare diseases Long latency Fast, inexpensive Multiple exposures Can’t estimate incidence Biases

  • Subject identification
  • Exposure assessment

Few ethical issues Confounding

  • “Association ≠ causation”

Case se-con

  • ntrol s
  • l studie

ies

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Gatifloxacin Ciprofloxacin

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Happe Ann Int Med 2004

51 mM 81 mM

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~200,000 women ≥66 years initiating a a bispho phospho honate

716 ‘at atypic ical’ l’ f frac acture; 3 3580 c controls ls

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“Typ ypic ical” l” f frac actures

N=9723 723

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+

OUTCOME

+++

Cohort s studies es

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Cohort s studies es

Can n establi lish i h incid idenc ence Clinic nically lly l logical l Expo posur ure n e not b biased ed by by outcome Can n study udy m mult ltiple o iple outcomes es Inefficien ient Expen pensiv ive e Dela layed ed finding dings Biase ses s

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Estimati ting i g inciden ence e

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7 per 100,000 p-y 278 per 100,000 p-y

adjusted HR ~42

JAMA Psych 2015

N=65,784

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Recurrent poisoning aHR 2.85 Male aHR 1.87 Saw a psychiatrist aHR 1.65 Advancing age

Finkelstein JAMA Psych 2015

Pred edictors o

  • f suicide
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Best way to evaluate causality / determine if an intervention can work

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Best way to evaluate causality / determine if an intervention can work Real-world insights not

  • btainable any other way
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Best way to evaluate causality / determine if an intervention can work Real-world insights not

  • btainable any other way

Sometimes awesome

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“That thing I said about case reports? Still solid 120 years later.”

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Self-matched designs

Subject is his or her own control Example: The case-crossover design

Identify event (case) Look back for exposure at different intervals

RISK Interval CONTROL Interval

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7-day risk interval 7-day control interval

21-day

“washout”

interval

Hospitalized with hypotension CCB therapy

Erythromycin OR 5.8 (2.3 to 15.0) Clarithromycin OR 3.7 (2.3 to 6.1) Azithromycin OR 1.5 (0.8 to 2.8)

Macrolide-CCB interaction

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Kline et al. Cardiovasc Res 1997

LV Efficiency LD100

Hi High gh-dos

  • se i

insulin lin i in verapam amil il pois ison

  • nin

ing