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?