As increasing numbers of people worldwide live with one or more health problems, the study of prognosis has never been more important. Prognosis research provides information crucial to understanding, explaining and predicting future clinical outcomes in people with existing disease or health conditions. It provides pivotal evidence to inform outcome prediction, clinical decision making, design and evaluation of stratified medicine (stratified care), and all stages of translational research from molecular biology to health policy.
The PROGRESS Partnership has outlined a framework for prognosis research with four key elements:
- Fundamental prognosis research: This type of research aims to examine the average prognosis of patients, often called their ‘baseline risk’. It provides initial answers to the question, 'What is the prognosis of people with a given disease?', and so quantifies the impact/quality of current care, and motivates & prioritises further inquiry.
- Prognostic factor research: Prognostic factors help define disease at diagnosis, inform clinical and therapeutic decisions, enhance the design & analysis of intervention trials, and help identify targets for new interventions that aim to modify disease course. There are currently major limitations in prognostic factor research, such as publication bias & inadequate replication of initial findings.
- Prognostic model research: Prognostic models utilise multiple prognostic factors in combination to predict the risk of future clinical outcomes in individual patients. Prognostic model research has three main phases: model development, external validation, & investigations of clinical impact.
- Stratified medicine research: Stratified medicine involves tailoring therapeutic decisions for specific, often biologically distinct individuals with the aim of maximising treatment benefit and reducing treatment-related harm. A key part of stratified medicine research is to identify tests (such as biomarker levels or genotypes) that predict an individual’s response to treatment and enables clinicians to identify patients for whom treatment is (most) effective.