Prognosis research partnership
The Problem
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.
Our Research
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.
Themes
Impact in research
- Blood pressure and incidence of twelve cardiovascular diseases
- Blood tests for the early identification of Alzheimer's Disease
- Breaking records: Using health records to explore antibiotic prescribing practice
- CALIBER cardiovascular disease prevention projects
- Cognitive workload of health technologies
- Heterogeneity of cognitive decline in dementia: taking into account variable time-zero severity
- Infection Response through Virus genomics (ICONIC)
- International comparisons of 'big' health record data: application to cardiovascular diseases
- Investigating adverse effects of psychiatric drugs through data-mining of electronic health records
- KConnect
- Laboratory-confirmed respiratory infections as vascular triggers
- Large scale omics data integration for biomarker discovery, drug repositioning and screening for new therapeutic targets for Alzheimer's disease
- Lipids and cardiovascular diseases in CALIBER
- 'Nothing about us, without us.' Involvement of Experts by Experience in Homeless and Inclusion Health Research
- Prognosis Research Partnership
- Reactivation of varicella zoster virus and vascular outcomes
- Real-time detection of influenza outbreaks in hospitals: demonstrating infection response through virus genomics (ICONIC)
- The prescription-persistence cascade in cardiovascular disease-an opportunity for big data research
- Tuberculosis in migrants
- Video Observed Therapy for TB – The world’s first randomised controlled trial of smartphone enabled “Video Observed Therapy” to support patients to complete tuberculosis treatment