Potential of risk-based population guidelines to reduce cardiovascular risk in a large integrated health system.

OBJECTIVES: We evaluated an alternative way to implement guidelines using an automated risk calculator and risk-based decision tool to calculate patients' risk of cardiovascular disease (CVD) and recommend therapies. We compared such an approach with traditional guidelines. STUDY DESIGN: A retrospective cohort study of 1,506,109 Kaiser Permanente Southern California members 35 years or older. METHODS: We estimated 3-year risks of fatal and nonfatal myocardial infarction and stroke using an independently developed risk calculator, then graphically compared risks with observed outcomes. We used the area under the receiver operating characteristics curve to assess discrimination, and the Hosmer-Lemeshow statistic to test fit. We compared the characteristics and outcomes of populations identified for medication therapy by the risk-based decision tool and traditional guidelines using bivariate statistics. RESULTS: A risk score was obtained in 72% (1,082,158) of members. The risk calculator was fairly good in discrimination: the area under the curve was 0.774 (95% CI, 0.770-0.779) for myocardial infarction and 0.805 (95% CI, 0.801-0.808) for stroke. Predictiveness and fit was good based on graphical analysis and Hosmer-Lemeshow P < .0001. The risk-based decision tool identified high-risk patients for treatment who were not identified by traditional guidelines (3.80% of all those identified for statins, 3.04% for antihypertensives), as well as low-risk patients who were identified by guidelines (3.80% for statins, 2.51% for antihypertensives). CONCLUSIONS: The risk calculator provided risk estimates in most patients and demonstrated fairly good discrimination and predictiveness. The risk-based decision tool identified high-risk patients for treatment not identified by traditional guidelines, as well as low-risk patients for whom treatment may be unnecessary.

The 'Global Outcomes Score': a quality measure, based on health outcomes, that compares current care to a target level of care.

The quality of health care is measured today using performance measures that calculate the percentage of people whose health conditions are managed according to specified processes or who meet specified treatment goals. This approach has several limitations. For instance, each measure looks at a particular process, risk factor, or biomarker one by one, and each uses sharp thresholds for defining "success" versus "failure." We describe a new measure of quality called the Global Outcomes Score (GO Score), which represents the proportion of adverse outcomes expected to be prevented in a population under current levels of care compared to a target level of care, such as 100 percent performance on certain clinical guidelines. We illustrate the use of the GO Score to measure blood pressure and cholesterol care in a longitudinal study of people at risk of atherosclerotic diseases, or hardening of the arteries. In that population the baseline GO Score was 40 percent, which indicates that the care being delivered was 40 percent as effective in preventing myocardial infarctions and strokes as our target level of care. The GO Score can be used to assess the potential effectiveness of different interventions such as prevention activities, tests, and treatments.

Individualized guidelines: the potential for increasing quality and reducing costs.

BACKGROUND: Current guidelines focus on a particular risk factor and specify criteria for categorizing persons into a small number of treatment groups. OBJECTIVE: To compare current guidelines with individualized guidelines (that use readily available characteristics from each person to calculate the risk reduction expected from treatment and to identify persons for treatment in ranked order of decreasing expected benefit), in the context of blood pressure management. DESIGN: Analysis of person-specific, longitudinal data. SETTING: The ARIC (Atherosclerosis Risk in Communities) Study. PARTICIPANTS: Persons aged 45 to 64 years without preexisting cardiovascular disease who currently do not receive antihypertensive treatment. INTERVENTION: Treatment according to the criteria of the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7 guidelines); individualized guidelines, or treatment in decreasing order of expected benefit; and random care, or treatment of persons selected at random. MEASUREMENTS: Number of myocardial infarctions (MIs) and strokes and medical costs. RESULTS: Compared with treating people according to random care, individualized guidelines could prevent the same number of MIs and strokes as JNC 7 guidelines at savings that are 67% greater than using JNC 7 guidelines, or it could prevent 43% more MIs and strokes for the same cost as treatment according to JNC 7 guidelines. The superiority of individualized guidelines was not sensitive to a wide range of assumptions about costs, treatment effectiveness, level of risk for cardiovascular disease in the population, or effects on workflow. The degree of superiority was sensitive to the accuracy of the method used to rank patients and to its span (the proportion of the population for whom all of the outcomes of interest can be calculated). LIMITATIONS: Specific results apply to the effects of blood pressure management on MI and stroke in the ARIC Study population. The methods for calculating individual benefits require quantitative evidence about the relationships among risk factors, long-term outcomes, and treatment effects. CONCLUSION: Use of individualized guidelines can help to increase the quality and reduce the cost of care.

Focused metabolomic profiling in the drug development process: advances from lipid profiling.

The highly parallel analytical technologies comprising 'omics promised to dramatically improve drug development efficiency by increasing knowledge and improving decision-making capabilities. On this point, the 'omics have largely been a disappointment. The major reason genomics, transcriptomics and proteomics fail to improve decision making capabilities is that they produce so many false positive results that it is difficult to be sure that findings are valid. Metabolomics is not immune to this problem but, when practiced effectively, the technology can reliably produce knowledge to aid in decision making. In particular, focused metabolomics platforms - those that restrict their target analytes to those measured well by the technology - can produce data with properties that maximize sensitivity and minimize the false discovery problem. The most developed focused metabolomics area is lipid profiling.