In addition to their profound impact on quality of life, seven of the top ten leading causes of death in the United States in 2010 were chronic conditions, and 86% of health care expenditures were for patients with one or more chronic diseases. Cardio-cerebrovascular Disease (CVD), of which is a chronic disease, is the leading cause of both morbidity and mortality in VHA, the nation, and worldwide while remaining a leading cause of ethnic and socioeconomic status (SES) mortality disparities. A common feature of most chronic disease care is that decision-making is not just a matter of whether to intervene, but when the optimal time to intervene is and which of the available treatments should be tried first. This task becomes even more difficult when there are multiple competing treatments directed at multiple different target outcomes. Furthermore, the risk factors and treatment effects on heart attacks, stroke, congestive heart failure, and renal disease vary substantially, yet guidelines remain simplistic, without integration of blood pressure, lipid, and American Heart Association (AHA)/American Stroke Association (ASA) guidelines.
Our project has the following aims: Aim 1: Examine the degree to which longitudinal baseline patient data improves prediction of overall CVD risk-the key determinant of statin's and blood pressure medication's absolute risk reduction; Aim 2: Develop and validate methods for adjusting estimates of effect sizes, model calibration, and model discrimination for measurement error in electronic health record (EHR)-derived predictor and outcome variables; and, Aim 3: Estimate how the timing, order, and intensity of treatment impact CVD absolute risk reduction within an integrated CVD prevention framework.
This 4-year study is designed to substantively improve primary CVD treatment choices, by dramatically advancing how we use existing historical clinical data and integrating the alternative treatment options by analyzing their strengths, weaknesses, and their differential impact on various CVD outcomes.
In Aim 1 we will analyze 13-years of longitudinal EHR data on Veterans age 45 to 80 using data from national VA datasets, the National Death Index, VA/CMS data, and focused chart reviews. We will test a series of hypotheses trying to understand the relationships of risk factors to different CVD risks and to improve patients' risk stratification, a key factor for estimating absolute risk reduction.
Aim 2 will test the validity and possibility for improvement of the findings of Aim 1. Extensive chart reviews will help estimate the sensitivity and specificity of using EHR diagnosis codes for identifying hard CVD events, and the calibration of the risk prediction tool.
In Aim 3 we will build a Markov Decision Process model to evaluate an integrated optimal approach to considering anti-hypertensive, lipid-lowering and anti-platelet therapy simultaneously based on expected absolute risk reduction from treatment.
We will model the progression of metabolic factors using a Markov Chain model, using a multi-way probabilistic sensitivity analysis to evaluate the effects of uncertainty in model input parameters. Our fully developed model of this Integrated Preventive Cardiology Initiative (IPCI) will be able to examine numerous clinically important questions and hypotheses, informing current policies, shared decision-making and areas important for future research.
No results to date.
If this research is successful, we will improve our ability to estimate an individual's chance of benefiting from advancement of primary CVD preventive treatments by better utilization of historical risk factor data and better integration of alternative treatment options and their differential impact on various CVD outcomes-with heart disease and stroke in particular. These improved estimates may greatly reduce premature mortality and decrease polypharmacy due to unneeded treatment.
None at this time.
Prevention, TRL - Applied/Translational
Cardiovascular Disease, Gap Analysis, Models of Care