Flashcards in Week 9. Evaluating Prognostic Literature Deck (50):

1

## Define prognosis

### examining possible outcomes of a condition or disease and the likelihood they will occur baed on patient "presenting" or baseline characteristics.

2

## Example of patient questions

###
- when can I return to playing sport

- when will I walk? Will I ever walk again?

- Will I get better?

- When can I go back to work?

3

## Clinical importance of accurate prognosis?

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1. Expectation:

helps therapist educate patient about appropriate expectations

2. Defined outcomes:

define expected outcomes from therapy

3. Prevent bad outcomes:

possibly prevent bad outcomes through education and intervention

4

## Caution with prognosis

### we should not divulge prognosis just because we know what they are. Some pt don't want to know their prognosis, especially if it is not good. Know when and how to inform patient of poor prognosis

5

## What is prognostic factor?

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Also known as determinant

A presenting or baseline characteristic that is measurable and associated with patient's eventual outcome.

6

## Example of prognostic factor (determinant)

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- demographics (age, sex)

- disease/condition (e.g. previous injury; pre-injury status)

- Clinical status (severity, level of disability)

- Comorbidity (other medical condition)

It is not an intervention

7

## Why does prognosis precede therapy

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prognosis = observation

therapy = interventional studies

1. identify modifiable characteristics through observation

2. test what happens when characteristics are modified through intervention

8

## Prognostic factors: modifiable vs. non-modifiable

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Non-mod.

- age

- sex

- social-economic

- culture

- past history

- genetics

- comorbidity

Mod.

- pre-condition status

- strength

- ROM

- balance

- belief, emotion, behaviour

- loading demand

- lifestyle

9

## Potential Outcome Measures

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1. Survival

- patient, implant, repair

2. Recovery

- rate and time

3. Recurrence/re-injury

10

## Prognostic factor vs. risk factor

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RISK: associated with development of a disease/condition (before onset)

PROGNOSTIC: associated with recovery form a disease/condition (after onset)

risk --> risk factor --> condition/event --> prognostic factor --> prognosis

11

## Prognostic Study designs: RTC

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cannot do RCT on prognosis because cannot assign or randomize subjects, but data from RCT are used in study prognosis

12

## Prognostic Study design: observation

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- PS are observational because cannot assign or randomize subjects, rather just have to look at different characteristics.

- look at outcomes associated with having or not having a factor at time of study entry

13

## Prognostic Study Design: level 1 evidence for study

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Prospective cohort (follow patients over time) = Longitudinal study

1. Natural course = follow untreated patients

2. Clinical course = follow patients treated in usual way

14

## Longitudinal Design

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A study of the same participant(s) at more than one point in time

- Prospective (enrol --> follow up)

- Retrospective (enrolled --> recall)

15

## Inception cohort Design

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Patients at similar point in their condition

- Incident = new cases (best for prognosis studies)

- Prevalent = any cases (including pts who already have bad outcomes, survivor cohorts)

16

## Levels of Prognostic Study Designs

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Level 1: SR of prospective cohort studies

Level 2: Prospective/inception cohort study

Level 3-4: dependent upon quality

- retrospective cohort

- case-control, case series

17

## Example of level of evidence: "What will happen if we do not add a therapy"

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Level 1: Systematic review of inception cohort studies

Level 2: inception cohort studies

Level 3: Cohort study or control arm of randomized trial

Level 4: case-series or case-control studies, or poor quality prognostic cohort studies

18

## What is the main factor that must be included in prognoses

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Prognosis have to be associated with a time

e.g.

1) 80% chance of pt with grade 2 lateral ankle sprain returning to sport within 1 month

2) 11% 5-year risk of injury contralateral ACL in patient undergoing ACL reconstruction

3) 20% 10-year risk of LT work disability in patients with MS

19

## Search terms:

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'predict'

'prognos*'

'prognostic factor'

'predictive validity'

20

## Building Prognosis Question: what do we need to consider

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1. Population/patient

- how do I describe patient similar to mine

2. prognostic factor

- which predictive factors I using

3. outcome/timeframe

- what disease progression can be expected?

21

##
~38 year old female with history of right shoulder rotator cuff repair

Post-surgical protocol said no skiing or heavy lifting for 1 year

After 1 year the patient wants to return to skiing, but is afraid of re-injury

Asking whether it is ok to return to ski

Research question?

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Does preoperative quads strength affect postoperative function after Total Knee Arthroplasty?

22

## How do we know if question is prognosis or intervention study?

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Can we randomize population group? No, then likely a prognosis.

23

## Example of prognosis vs. intervention question for TKA.

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Prognosis

Does preoperative quads strength affect postoperative function after Total Knee Arthroplasty?

Intervention

Does quads strengthening prior to Total Knee Arthroplasty improve function?

24

## Evaluate using critical appraisal tool: overarching/essential questions

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- Are the Results Valid? (Review methods)

- What are the Results? (Review & interpret the results)

- Can I apply them to my patient(s)? (Look at inclusion/exclusion for applicability, quality of study, change in outcome associated with a factor)

25

## Prognosis Study Appraisal is similar to?

### appraisal is very similar to appraisal of RCTs

26

## Criteria for determining if the results are valid

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1) Was there representative sampling from a well-defined population?

2) Was there an inception cohort?

3) Was there complete or near complete follow-up?

4) Were objective, unbiased outcome criteria used?

5) Was there adjustment for important prognostic factors and potential cofounder?

27

## Was there representative sampling from a well-defined population?

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- start by looking whether the study recruited "all" patient or "consecutive" cases

If not, study may provided biased estimates of true prognosis

28

## Was there an inception cohort?

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similar point in their condition

- Incident = new cases (best for prognosis)

- Prevalent = any cases (includes patient who may already have bad outcomes, survivor cohort)

29

## Follow-up

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Follow-up long enough for events to occur

- completion rate should be >80% (ideally >85%)

30

## Were objective, unbiased outcome criteria used?

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- measurable

- reproducible

31

## Adjustments for prognostic factors and potential confounders?

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confounder = variable that correlates with both the dependent and independent variables in a way that explains a way some or all of the correlation between these two variable

Example: 3rd factor (being Z) is the potential confounder

Every time Z changes X and Y have the potential to change

If X changes due to this and Z is ignored it would be assumed Y is the changer when really Z is the confounder

32

##
Confounding example: does age affect pain after TKA

- outcome

- prognostic factor

- confounder

###
- outcome = pain

- prognostic factor = age

- confounder = type of implant used (affects pain, implant closed based upon age)

33

##
Example: Recovery of function after thoracic surgery

- outcome

- prognostic factor

- confounder

###
- outcome: function

- prognostic factor: pre-operative health status (age, pain intensity, severity of condition, supportive family)

- confounder: comorbidity

34

## Adjustments for all important prognostic factors/potential confounders

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Sample size:

- typically need 10 patients that had the outcome for every 1 factor in analysis

- large samples can use a lot of potential factors

35

## How likely are the outcomes: mean difference

### compare mean differences (e.g. function) between two groups

36

## How likely are the outcomes: odds/risks ratio

### predict odds or risk of events occurring in the presence of prognostic factors in a single point in time

37

## How likely are the outcomes: hazard ratios

### predict odds of event occurring in the presence/absence of prognostic factors over time

38

## How likely are the outcomes: Survival curve

### Graph of discrete events/non-events occurring over a defined time. In most clinical situations, the chance of an outcome changes with time. Earlier follow-up results are more precise because there are more patients.

39

## Diagnostic 2x2 table a-d

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a = true +

b = false +

c = false -

d = false +

40

##
Formula

odds ratio

risk ratio

###
odds ration = ad/bc

risk ratio = [a/(a+b)] / [c/(c+d)]

41

## Written explanation of OR and RR

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OR = # times the ODDS more likely to

RR = # times more likely to

42

## Interpreting statistics: Odds/Risk/Hazard Ratio

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Odds/Risk/Hazard Ratio

= 1 no increased/decreased risk of the outcome occurring associated with that factor

> 1 = outcome expect to occur more often in exposed relative to non-exposed

< 1 = outcome expected to occur less in exposed relative to non-exposed

43

## Why do we use 95% CI to determine how precise are estimates of likelihood?

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1) CI indicate strength of effect and precision of estimates

2) includes estimate of effect and range (interval) of possible estimate if study were repeated

3) Wide CI = less precise estimate of effect

4) Narrow CI = precise estimate of effect

44

## How to narrow CI

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- increase sample size

- increase number or outcomes

45

## What does precision indicate?

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does not give magnitude of effect, but rather the possible range for magnitude

- precise & significant

- precise & NS

- non-precise & S

- non-precise & NS

46

## Adjustment of 2x2 table: univariable

### considers 1 factor and its association with an outcome (aka univariable or invariable analysis)

47

## Adjustment of 2x2 table: multivariable

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more commonly, we look at impact of multiple factors and their association with the outcome at the same time (aka multivariable analysis)

- looks at the independent effect or a factor after controlling or adjusting for the other factors

48

## Clinical significance: patient type

### were study patients similar to mine (examine inclusion and exclusion criteria)

49

## Clinical significance: patient experience

### will results help me re-assure/inform patients? (does it change what I would tell patients to expect?)

50