PROGNOSISPROGNOSIS

Clinical Epidemiology and Evidence-based Medicine

UnitFKUI – RSCM

Introduction - Introduction - Prognosis

• important phase of a disease progression of a disease.

• Patient’s, doctor’s, insurance’s concern • Prognosis: the prediction of the future

course of events following the onset of disease. – can include death, complications,

remission/recurrence, morbidity, disability and social or occupational function.

Introduction - PrognosisIntroduction - Prognosis

• Possible outcomes of a disease and the frequency with which they can be expected to occur.– Natural history: the evolution of

disease without medical intervention. – Clinical course: the evolution of

disease in response to medical intervention.

Natural History StudiesNatural History Studies• Degree to which natural history

can be studied depends on the medical system (Scandinavia) and the type of disease (rare, high risk).

• The natural history of some diseases can be studied because:

• remain unrecognized (i.e., asymptomatic) e.g., anemia, hypertension.

• considered “normal” discomforts e.g., arthritis, mild depression.

Natural History StudiesNatural History Studies• Natural history studies permit

the development of rational strategies for:– early detection of disease

• e.g., Invasive Cervical CA.– treatment of disease

• e.g.Ptyriasis versicolor• Diabetes

PrognosisPatients at risk of target event

Prognosticfactor Time

Suffer targetoutcome

Do not suffertarget outcome

?

?

1.1. Was a defined, representative sample of patients Was a defined, representative sample of patients assembled at a common (usually early) point in assembled at a common (usually early) point in the course of their disease?the course of their disease?

2.2. Was the follow-up of the study patients Was the follow-up of the study patients sufficiently long and complete?sufficiently long and complete?

3.3. Were objective outcome criteria applied in a blind Were objective outcome criteria applied in a blind fashion?fashion?

4.4. If subgroups with different prognoses are If subgroups with different prognoses are identified, was there adjustment for important identified, was there adjustment for important prognostic factors and validation in an prognostic factors and validation in an independent “test set” patients?independent “test set” patients?

A. ARE THE RESULTS OF THIS A. ARE THE RESULTS OF THIS PROGNOSIS STUDY VALID? PROGNOSIS STUDY VALID?

• 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 disease cohort

A.1. Was a defined, A.1. Was a defined, representative sample of representative sample of patients assembled at a common patients assembled at a common (usually early) point in the course (usually early) point in the course of their disease?of their disease?

A.2. Was follow-up sufficiently long and A.2. Was follow-up sufficiently long and complete? complete?

• Ideal follow-up period – Until EVERY patient recovers or has one of the other

outcomes of interest, – Until the elapsed time of observation is of clinical

interest to clinicians or patients. • Short follow up time too few study patients with

outcome of interest little information of use to patient

• Loss to follow up influence the estimate of the risk of the outcome validity?. – Patients are too ill (or too well); Die; Move, etc

• Most journals require at least 80% follow-up for a prognosis study to be considered valid.

• Best and worst case scenario!

A.3. Were objective outcome A.3. Were objective outcome criteria applied in a criteria applied in a blindblind fashion?fashion?• investigators making

judgments about clinical outcomes are kept “blind” to subjects’ clinical characteristics and prognostic factors.

• Minimize measurement bias!

Measurement bias

• Measurement bias can be minimized by:– ensuring observers are blinded to

the exposure status of the patients.– using careful criteria (definitions)

for all outcome events. – apply equally rigorous efforts to

ascertain all events in both exposure groups.

• Prognostic factors: factors associated with a particular outcome among disease subjects. Can predict good or bad outcome

• Need not necessarily cause the outcome, just be associated with them strongly enough to predict their development – 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 A.4. If subgroups with different prognoses are identified, was there prognoses are identified, was there adjustment for important prognostic adjustment for important prognostic factors and validation in an factors and validation in an independent “test set” patients?independent “test set” patients?

• Risk factors – distinct from prognostic factors, – include lifestyle behaviors and

environmental exposures that are associated with the development of a target disorder.

– 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 A.4. If subgroups with different prognoses are identified, was there adjustment for are identified, was there adjustment for important prognostic factors and validation important prognostic factors and validation in an independent “test set” patients?in an independent “test set” patients?

Bias in Follow-up Bias in Follow-up StudiesStudies A. Selection or Confounding BiasA. Selection or Confounding Bias

1. Assembly or susceptibility bias: when exposed and non-exposed groups differ other than by the prognostic factors under study, and the extraneous factor affects the outcome of the study.• Examples:

– differences in starting point of disease (survival cohort)

– differences in stage or extent of disease, co-morbidities, prior treatment, age, gender, or race.

Bias in Follow-up Bias in Follow-up StudiesStudies

A. Selection or Confounding BiasA. Selection or Confounding Bias2. Migration bias:

• patients drop out of the study (lost-to-follow-up). usually subjects drop out because of a valid reason e.g., died, recovery, side effects or disinterest.

• these factors are often related to prognosis. • asses extent of bias by using a best/worst

case analysis. • patients can also cross-over from one

exposure group to another • if cross-over occurs at random = non-

differential misclassification of exposure

Bias in Follow-up Bias in Follow-up StudiesStudies

A. Selection or Confounding BiasA. Selection or Confounding Bias3. Generalizability bias

• related to the selective referral of patients to tertiary (academic) medical centers.

• highly selected patient pool have different clinical spectrum of disease.

• influences generalizability

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.

Survival Cohorts BiasTrue Cohort

Survival Cohort

ObservedImprovement

TrueImprovement

AssembleCohortN=150

Measure OutcomesImproved = 75Not improved = 75

50% 50%

80% 50%Measure OutcomesImproved = 40Not improved = 10

Assemble patients

BeginFollow-upN = 50Not ObservedN = 100

Dropouts:Improved = 35Not improved = 65

II. Bias in Follow-Up Studies

• B. Measurement bias– Measurement (or assessment)

bias occurs 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.

B. Are the results of this B. Are the results of this study important?study important?

1. How likely are the outcomes over time?2. How precise is this prognostic estimate?

B.1. How likely are the outcomes B.1. How likely are the outcomes over time? 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.

Survival Rate1 year survivalA. GoodB. 20%C. 20%D. 20%Median survivalA. ?B. 3 monthsC. 9 monthsD. 7.5 months

B.2 How precise is this prognostic estimate? • Precision 95% confidence

interval– The narrower the confidence interval,

the more precise is the estimate. • If survival over time is the outcome

of interest shorter follow-up periods results in more precision follow up period important to be clinically important

C. CAN WE APPLY THIS VALID, C. CAN WE APPLY THIS VALID, IMPORTANT EVIDENCE ABOUT IMPORTANT EVIDENCE ABOUT PROGNOSIS TO OUR PATIENT? PROGNOSIS TO OUR PATIENT?

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?

Is our patient so different Is our patient so different from those in the study that from those in the study that its results cannot apply?its results cannot apply?• How well do the study results

generalize to the patients in your practice? – Compare patients' important clinical

characteristics, – Read the definitions thoroughly – The closer the match between the patient

before you and those in the study, the more confident you can be in applying the study results to that patient.

• For most differences, the answer to this question is “no”, we can use the study results to inform our prognostic conclusions.

C.2 Will this evidence make a C.2 Will this evidence make a clinically important impact on our clinically important impact on our conclusions about what to offer conclusions about what to offer or tell our patient? 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

the information they want about what the future is likely to hold for them and their illness.

C.2 Will this evidence make a C.2 Will this evidence make a clinically important impact clinically important impact on our conclusions about on our conclusions about what to offer or tell our what to offer or tell our patient? patient? • Communicating to patients

their likely fate• Guiding treatment decisions• Comparing outcomes to make

inferences about quality of care

Conclusion• Prognosis study beneficial

– Communicating to patients their likely fate

– Guiding treatment decisions– Comparing outcomes to make

inferences about quality of care