EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
-
Upload
junaida-rahmi -
Category
Documents
-
view
221 -
download
0
Transcript of EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
1/29
Appraising
Prognostic Study
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
2/29
Introduction - Prognosis
Important phase of a disease
progressionof a disease.
Patients, doctors, insurances concern
Prognosis: the prediction of the futurecourse of events following the onset ofdisease.
can include death, complications,
remission/recurrence, morbidity, disabilityand social or occupational function.
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
3/29
Introduction - Prognosis
Possible outcomes of a disease and thefrequency with which they can be
expected to occur.
Natural history: the evolution of diseasewithoutmedical intervention.
Clinical course:the evolution of disease in
responseto medical intervention.
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
4/29
Natural History Studies
Degree to which natural history can be studieddepends on the medical system (Scandinavia)and the type of disease (rare, high risk).
The natural history of somediseases can be
studied because: remain unrecognized (i.e., asymptomatic) e.g., anemia,
hypertension.
considered normal discomforts e.g., arthritis, mild
depression.
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
5/29
Natural History Studies
Natural history studies permit the
development of rational strategies for:
early detectionof disease e.g., Invasive Cervical CA.
treatmentof disease
e.g. Diabetes
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
6/29
Prognosis
Patients at riskof target event
Prognosticfactor
Time
Suffer target
outcome
Do not suffer
target outcome
?
?
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
7/29
1. Was a defined, representative sample of patientsassembled at a common (usually early) point in the course
of their disease?
2. Was the follow-up of the study patients sufficiently long
and complete?3. Were objective outcome criteria applied in a blind fashion?
4. If subgroups with different prognoses are identified, was
there adjustment for important prognostic factors and
validation in an independent test set patients?
A. ARE THE RESULTS OF THIS
PROGNOSIS STUDY VALID?
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
8/29
How well defined the individuals in the study
criteria - representative of the underlying
population.
inclusion, exclusion
sampling method
similar, well-defined point in the course of
their diseasecohort
A.1. Was a defined, representative sample of
patients assembled at a common (usually early)
point in the course of their disease?
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
9/29
A.2. Was follow-up sufficiently long and
complete? Ideal follow-up period
Until EVERY patient recovers or has one of the otheroutcomes of interest,
Until the elapsed time of observation is of clinical interest toclinicians or patients.
Short follow up timetoo few study patients with outcome of
interest little information of use to patient Loss to follow upinfluence the estimate of the risk of the
outcomevalidity?.
Patients are too ill (or too well); Die; Move, etc
Most journals require at least 80% follow-up for a prognosisstudy to be considered valid.
Best and worst case scenario (sensitivity analysis)
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
10/29
A.3. Were objective outcome criteria
applied in a blind fashion?
Investigators making judgments
about clinical outcomes are kept
blind to subjects clinicalcharacteristics and prognostic
factors.
Minimize measurement bias!
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
11/29
Measurement bias
Measurement bias can be minimized by: ensuring observers are blindedto the exposurestatus of the patients.
using careful criteria (definitions)for all
outcome events. apply equallyrigorous efforts to ascertain all
eventsin both exposure groups.
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
12/29
Prognostic factors: factors associated with aparticular outcome among disease subjects. Canpredict good or bad outcome
Need not necessarily cause the outcome, just beassociated with them strongly enough to predict theirdevelopment examples includes age, co-morbidities, tumor size, severity
of disease etc.
often different from disease risk factors e.g., BMI and pre-menopausal breast CA.
A.4. If subgroups with different prognoses are
identified, was there adjustment for important
prognostic factors and validation in an independent
test set patients?
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
13/29
Risk factors
distinct from prognostic factors,
include lifestyle behaviors and environmental exposures
that are associated with the development of a targetdisorder.
Ex: smoking = important risk factor for developing lung
cancer, but tumor stage is the most important prognostic
factor in individuals who have lung cancer.
A.4. If subgroups with different prognoses are
identified, was there adjustment for important
prognostic factors and validation in an independent
test set patients?
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
14/29
Bias in Follow-up Studies
A. Selection or Confounding Bias1. Assembly or susceptibility bias: when exposed
and non-exposed groups differother than by the
prognostic factors under study, and the
extraneous factoraffects the outcome of thestudy.
Examples:
differences in starting point of disease (survival cohort)
differences in stage or extent of disease, co-morbidities, priortreatment, age, gender, or race.
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
15/29
Bias in Follow-up Studies
A. Selection or Confounding Bias2. Migration bias:
patients drop out of the study (lost-to-follow-up).usually subjects drop out because of a valid reasone.g., died, recovery, side effects or disinterest.
these factors are often related to prognosis. asses extent of bias by using a best/worst caseanalysis.
patients can also cross-overfrom one exposure groupto another
if cross-over occurs at random = non-differentialmisclassification of exposure
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
16/29
Bias in Follow-up StudiesA. Selection or Confounding Bias
3. Generalizability bias
related to the selective referral of
patients to tertiary (academic) medicalcenters.
highly selected patient pool have
different clinical spectrum of disease.
influences generalizability
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
17/29
Survival Cohorts
Survival cohort(or available patient cohort) studies
can be very biased because:
convenience sample of current patients are likely to be at
various stages in the course of their disease. individuals not accounted for have different experiences
from those included e.g., died soon after trt.
Not a true inception cohort e.g., retrospective case
series.
Ob d T
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
18/29
Surviva
lCohorts
B
ias
True Cohort
Survival Cohort
Observed
Improvement
True
Improvement
Assemble
Cohort
N=150
Measure Outcomes
Improved = 75
Not improved = 75
50% 50%
80% 50%Measure OutcomesImproved = 40
Not improved = 10
Assemble
patients
Begin
Follow-up
N = 50
Not
ObservedN = 100
Dropouts:
Improved = 35Not improved = 65
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
19/29
II. Bias in Follow-Up Studies
B. Measurement bias
Measurement (or assessment) biasoccurs when
one group has a higher (or lower) probability of
having their outcome measured or detected.
likely for softer outcomes
side effects, mild disabilities, subclinical disease or
the specific cause of death.
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
20/29
B. Are the results of this study
important?
1. How likely are the outcomes over
time?
2. How precise is this prognostic
estimate?
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
21/29
B.1. How likely are the outcomes over time?
% of outcome of interest at a particular point in
time (1 or 5 year survival rates)
Median time to the outcome (e.g. the length of
follow-up by which 50% of patients have died) Event curves (e.g. survival curves) that
illustrate, at each point in time, the proportion
of the original study sample who have not yet
had a specified outcome.
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
22/29
Survival Rate
1 year survival
A. Good
B. 20%
C. 20%
D. 20%
Median survival
A. ?
B. 3 months
C. 9 monthsD. 7.5 months
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
23/29
B.2 How precise is this prognostic
estimate?
Precision95% confidence interval
The narrower the confidence interval, the more
precise is the estimate.
If survival over time is the outcome of interestshorter follow-up periods results in more
precisionfollow up period important to be
clinically important
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
24/29
C. Can we apply this valid, important
evidence about prognosis to our patients?
1. Is our patient so different from those in
the study that its results cannot apply?
2. Will this evidence make a clinically
important impact on our conclusions
about what to offer or tell our patient?
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
25/29
Is our patient so different from those in
the study that its results cannot apply?
How well do the study results generalize to thepatients in your practice? Compare patients' important clinical characteristics,
Read the definitions thoroughly
The closer the match between the patient beforeyou and those in the study, the more confident youcan be in applying the study results to that patient.
For most differences, the answer to thisquestion is no,we can use the study results
to inform our prognostic conclusions.
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
26/29
C.2 Will this evidence make a clinically
important impact on our conclusions about
what to offer or tell our patient?
Useful for
Initiating or not therapy,
monitoring therapy that has been initiated, deciding which diagnostic tests to order.
providing patients and families with theinformation they want about what the future
is likely to hold for them and their illness.
C 2 Will hi id k li i ll
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
27/29
C.2 Will this evidence make a clinically
important impact on our conclusions
about what to offer or tell our patient?
Communicating to patients their likely
fate
Guiding treatment decisions
Comparing outcomes to make
inferences about quality of care
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
28/29
Conclusion
Prognosis study beneficial
Communicating to patients their likely fate
Guiding treatment decisions
Comparing outcomes to make inferences
about quality of care
-
8/11/2019 EBM Prof Darwin 4. Prognosis EBM_dr Kuntjoro
29/29