Enhancing cardiovascular disease risk prediction through using longitudinal measures of risk factors in clustered data

WHY IS CARDIOVASCULAR DISEASE IMPORTANT?
Cardiovascular disease (CVD), comprising mostly heart attacks and strokes, is UK's leading cause of death and disability.

WHAT IS CVD RISK ASSESSMENT?
Measurement of "risk factors" that are linked with greater risk of CVD (eg, older age, male sex, smoking, high blood pressure, low and high body mass index, high cholesterol) can identify people who may especially benefit from preventive action, such as lifestyle advice and/or medications. Various risk prediction models are used to summarise levels of several risk markers simultaneously and are used to classify patients into high/medium or low risk groups. All CVD risk prediction models are based on single measures of risk factors.

WHAT IS THE HEALTH SERVICE ALREADY DOING?
In 2008, the UK government announced a national initiative to conduct CVD risk assessment in people aged 40-74 years with no history of CVD. The checks are being done in general practices and pharmacies.

WHY IS RESEARCH NEEDED?
Although experts generally agree that CVD risk assessment has the potential to prevent disease, there is uncertainty about:
- do past measures of risk factors or changes in risk factors yield useful information in addition to current measures of risk factors?
- how can past and current measures of risk factors be incorporated into a statistical model?
- how should information on risk, when incorporating past and current measures of risk factors, best be summarised?

WHAT IS THE PROPOSED RESEARCH?
An interdisciplinary team of internationally recognised statisticians and epidemiologists will:
1. analyse detailed scientific databases with information on CVD risk factors measured more than once in patients with and without heart attacks or strokes in a total of about 4 million participants, thereby reliably determining the use of repeated measures of risk factors for CVD risk assessment;

2. use these databases to identify which CVD risk factors provide useful extra information when measured on more than one occasion for CVD risk assessment;

3. determine suitable statistical models that combine information from past and current measures of risk factors in order to provide up-to-date relevant CVD risk assessments for patients;

4. investigate the use of interactive technology to provide graphical displays of individual CVD risk over time.


WHAT DATA SOURCES ARE AVAILABLE FOR THIS RESEARCH?
We have two large databases for our investigations:

Emerging risk factors collaboration
The Emerging Risk Factors Collaboration has collated individual data on up to 500 characteristics in over 1.8 million participants in over 120 long-term studies. Within these data, approximately 280,000 participants have at least two measurements of the main CVD risk factors.

The Health Improvement Network
The Health Improvement Network (THIN) is a primary care database with anonymised clinical records entered by general practices on their computer computer system for patient management (ViSion). THIN now includes records from around 4 million people with repeat measures of risk factors from over 450 practices across the UK. Patients are included from when they register with a GP and contribute data until death or they leave the practice.

These two data sources, which combine observational studies and electronic health records, have the power and generalisability to help clarify the added value of using repeat measures of risk factors for CVD risk prediction.

HOW WILL HEALTH USERS AND THE PUBLIC BENEFIT?
Findings from this research will help to inform and optimise current (and future) government initiatives in CVD risk assessment. This should lead to use of accurate risk assessment methods. Although the proposed work will focus on predictors for CVD, the methodology and strategies will also be widely applicable for the identification and added value of repeat measures of risk predictors in other diseases.

CURRENT CARDIOVASCULAR DISEASE (CVD) RISK MODELS
Most CVD risk models in clinical use have been constructed from Proportional Hazards models regressed on single measures of selected risk factors. Thus, clinical CVD risk assessments do not take into account repeated measures of risk factors if available.

MODELS FOR LONGITUDINAL MEASURES AND TIME-TO-EVENT DATA
There has been active statistical methodology research into constructing joint models for longitudinal measures and time-to-event data. Most joint models use a set of random effects to induce association between the longitudinal measures and time-to-events, but then require computational intensive approaches for estimation. Consequently, such models are not commonly applied to real problems despite their qualities and abilities to answer key questions. Whilst the relevance of using joint models to assess aetiological associations between longitudinal measures and disease risk is clear, it is less certain how risk predictions from such models can be applied for clinical CVD risk assessment.

WHAT IS THE PROPOSED RESEARCH?
The added value of using longitudinal risk factors for CVD risk prediction in observational and clinical data has not been thoroughly evaluated. We propose to determine statistical methods for developing, assessing and validating CVD risk prediction models using longitudinal measures of risk factors. We will:

1. determine an appropriate CVD risk prediction model that incorporates longitudinal measures of risk factors from multiple studies/centres.
2. determine how to extract and translate CVD risk assessments from a risk prediction model that incorporates longitudinal measures.
3. determine a suitable choice of prognostic measures and graphical and/or tabular presentations for assessing the added value of using longitudinal markers in risk prediction models.
4. quantify the added value of using longitudinal measures of risk factors for CVD risk prediction.

TRANSLATIONAL OBJECTIVE: to develop a statistical methodology to allow for "electronic updating" of cardiovascular disease (CVD) risk assessments by incorporating past and present measures of risk factors into a patient's CVD disease-risk-profile.

1. STRATEGIES FOR CVD SCREENING -> POLICY MAKERS
The planned research has potential to:
* progress the state-of-the-art concerning use of longitudinal measures of risk factors and screening for CVD;
* determine the added value of merging electronic health records with CVD risk algorithms;
* optimize existing individual health records to improve CVD risk assessments leading to reduced costs for national CVD screening;
* increase the effectiveness of CVD screening.

The likely timescale for these impacts are towards the end of the project

2. IMPROVED CLINICAL RISK PREDICTION -> HEALTH CARE PROVIDERS AND USERS
The planned research has potential to:
* lead to provision of improved and more accurate CVD risk assessments;
* lead to innovative CVD risk scores that will make use of all clinical data available in a standard NHS longitudinal clinical record.

The likely timescale for these impacts are at the end of the project.


3. IMPROVED CLINICAL-DECISION MAKING AND CLINICAL OUTCOMES -> HEALTH CARE PROVIDERS AND USERS
The planned research has potential to:
* lead to improved efficiency of targeting preventive action;
* lead to enhanced quality of life and health;
* enhance summaries of CVD risk predictions by incorporating longitudinal measures of risk factors. Summarising risk in this manner may motivate individuals to adopt lifestyle measures or adhere to medication.

The likely timescale for these impacts are after the end of the project.


4. METHODOLOGICAL IMPACT -> BEYOND CARDIOVASCULAR DISEASE
The methodology and strategies will be widely applicable to other chronic diseases strategies for screening and prevention. The likely timescale for these impacts are towards the end of the project.