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The American Journal of Managed Care May 2016
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Potential of Risk-Based Population Guidelines to Reduce Cardiovascular Risk in a Large Integrated Health System
Galina Inzhakova, MPH; Hui Zhou, PhD, MS; Macdonald Morris, PhD; Megan I. Early, MD, MPH; Anny H. Xiang, PhD; Steven J. Jacobsen, MD, PhD; and Stephen F. Derose, MD, MSHS
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Potential of Risk-Based Population Guidelines to Reduce Cardiovascular Risk in a Large Integrated Health System

Galina Inzhakova, MPH; Hui Zhou, PhD, MS; Macdonald Morris, PhD; Megan I. Early, MD, MPH; Anny H. Xiang, PhD; Steven J. Jacobsen, MD, PhD; and Stephen F. Derose, MD, MSHS
The authors evaluated the clinical applicability, accuracy, and implications of using an automated risk calculator and risk-based decision tool in an integrated health system.
For the second part of the study, we compared the characteristics of the “same-sized” populations identified for medication therapy by the risk-based decision tool and traditional guidelines using the t test and χ2 test. Any of 1 or more antihypertensive medications were counted as a single medication recommendation. We determined the outcomes (3-year events/1000 persons) of patients who were identified for possible treatment or not based on the risk-based decision tool, with those identified for treatment or not based on traditional guidelines. We also determined the outcomes in “indeterminate” patients for whom the risk-based decision tool did not calculate a Health Impact score due to insufficient data to calculate risk or to support a treatment recommendation. These indeterminate cases also included patients with medical conditions not modeled (about 5% of all patients): advanced heart failure, late-stage kidney disease, and a CVD event that occurred under age 45. When implementing traditional guidelines, if there was insufficient data to apply the guidelines, patients were assigned to “no treatment” by default since traditional guidelines only specified when to treat, and this approach was consistent with population-based implementation of guidelines.

RESULTS
Risk Calculator Assessment

The study cohort consisted of 1,506,109 patients who met inclusion criteria. Table 1 describes selected characteristics of the study cohort. A risk calculation was possible for 1,082,158 patients (72% of the cohort). The mean (SD) follow-up time for patients in which a calculation was possible was 2.61 (0.86) years.

The risk-based decision tool sorted patients fairly well by their propensity to have a myocardial infarction or stroke: the AROC was 0.774 (95% CI, 0.770-0.779) for myocardial infarction, and 0.805 (95% CI, 0.801-0.808) for stroke. The Hosmer-Lemeshow fit statistic indicated good fit (P <.0001) for each model.

The Figure shows the predictiveness curves of the risk calculator for fatal and nonfatal myocardial infarction (graph A) and stroke (graph B) for 20 different ranked risk groups. The predicted rates were slightly higher than observed rates for myocardial infarctions in the top 15th risk percentile. The predicted rates for strokes were slightly lower than the observed rates in several high-risk groups, although not the highest-risk group.

Treatment Stratification by the Risk-Based Decision Tool and Traditional Guidelines

Table 1 shows the characteristics of 3 subgroups identified for possible new medication treatment by: 1) the risk-based decision tool but not by traditional guidelines, 2) traditional guidelines but not by the risk-based decision tool, and 3) both. For almost all characteristics, there were statistically significant differences (P <.0001) between the groups. The most notable difference was the older age of patients identified for possible treatment by the risk-based decision tool alone versus those identified by traditional guidelines alone. Using the “same size” population identified for treatment by both approaches, more than 50% of patients to whom medications were recommended by the risk-based guidelines only were 70 years or older. The corresponding percentage was less than 5% for those to whom medications were recommended by traditional guidelines only. Compared with traditional guidelines, the risk-based decision tool also identified proportionally more men, more patients with high-normal blood pressures, and more smokers.

Table 2 shows the percentage of the total cohort eligible for new medication treatment based on the risk-based decision tool, as well as the percentage based on traditional guidelines. The risk-based decision tool made statin recommendations in 34.85% of patients (8.73% for, 26.12% against), and antihypertensive recommendations in 18.20% of patients (4.82% for, 13.38% against). Traditional guidelines made statin recommendations in 9.69% of patients and antihypertensive recommendations in 4.94% of patients, with all others not recommended treatment by default. The CVD event rates per 1000 members among those to whom statins were recommended were similar using either approach, and somewhat higher among those to whom antihypertensives were recommended using the risk-based decision tool (72.0 vs 63.1). The event rates per 1000 members identified for treatment by both the risk-based decision tool and traditional guidelines were greater than the event rates identified by either approach alone.

Although there was overlap in the patients identified for treatment by the risk-based decision tool and traditional guidelines, the risk-based decision tool identified a number of patients who were at high risk, but who were not identified for treatment by traditional guidelines. For statins, the risk-based decision tool identified an additional 3.80% of the population (beyond the 9.69% identified by traditional guidelines) who were not identified for treatment by traditional guidelines but who had a CVD event rate (45.3 events/1000 patients) that was not much less than that of patients to whom treatment was recommended by traditional guidelines (50.1/1000 patients). Similarly, for antihypertensives, the risk-based decision tool identified an additional 3.04% of the population who were not identified for treatment by traditional guidelines, but who had a similar CVD event rate (65.9/1000 patients) to those for whom treatment was recommended by traditional guidelines (63.1/1000 patients).

The risk-based decision tool also made recommendations against therapy for a number of patients for whom treatment was recommended by traditional guidelines. For statins, the risk-based decision tool made recommendations against treatment for 3.80% of the population. These patients had a CVD event rate that was relatively low (15.5/1000 patients), and virtually identical to the risk among patients to whom treatment was not recommended by traditional guidelines (14.3/1000 patients). For antihypertensives, the risk-based decision tool made recommendations against treatment in 2.51% of the population; this group’s CVD event rate (19.9/1000 patients) was actually lower than that of patients to whom treatment was not recommended by traditional guidelines (22.7/1000 patients).

DISCUSSION
We undertook this study to evaluate an alternative way to implement guidelines using an automated risk calculator and decision support tool that captures EHR data to calculate patients’ risk of CVD outcomes, and identifies the impact of potential treatments. Our objectives were to determine its applicability, the usefulness of its predictions, and how patient identification for possible treatment compares with traditional guidelines. In doing so, we hoped to assess the implications of using a risk-based decision tool for both individual and population care.

The risk calculator was widely applicable to the health plan’s general adult population (72% had a risk estimate given 1 year of clinical data) and demonstrated fairly good discrimination and predictiveness. Our results indicate that when tuned to treatment in the same number of patients as traditional guidelines, the risk-based decision tool identifies a group of individuals who are likely to benefit from treatment but who are missed by current guidelines, even when those guidelines include Framingham risk. It also identifies a group of individuals for whom treatment is recommended by current guidelines, but have risks that are sufficiently low enough that they may safely be left untreated.

In a similar type of comparison, the newer ACC/AHA guideline for statin therapy based on a risk calculator was compared with ATP III guidelines with its cholesterol targets supplemented by Framingham risk in an existing community-based cohort.20 This study compared a different risk-based calculator (ACC/AHA) with essentially the same traditional guidelines as used by KPSC. The findings mirror ours in that the calculator had “greater accuracy and efficiency in identifying increased risk of incident CVD and subclinical coronary artery disease”20—similarly, more higher-risk patients were identified. Additionally of note, the risk-based approach of the ACC/AHA guidelines was deemed, in a simulation study, to have an acceptable cost-effectiveness profile.21 In this simulation study, the cost-effectiveness of the ACC/AHA risk-based approach varied with risk cut points.21 In our study, the Health Impact score was used to introduce the variation of risk to the population affected in place of pure risks.

A significant problem facing all risk-based calculators and decision support tools that use EHR data is that they might not be able to perform the desired calculations, either because of missing data or limitations in patient groups to which they are applicable (eg, late-stage kidney disease). Some of the patients who are excluded from risk models are at higher risk due to relatively infrequent medical conditions; in these situations, decisions are left to the discretion of the treating physician. Hopefully, future risk calculators will address this restricted scope of application. Risk-based decision tools also suffer from the same lack of evidence about treatment as do traditional guidelines. This is especially evident when considering that 72% of patients had a risk calculation, but among patients potentially eligible for a medication, 65% (statins) and 82% (antihypertensives) could not be definitively classified into treatment or no treatment groups.

Limitations

Our study has several limitations, including that the KPSC system may differ in patient outcomes from other health delivery models. Our determination of who would be recommended for treatment was based solely on EHR data and automated guideline implementation rather than clinical practice. Use in practice may increase or reduce the identification of high-risk patients and the differences between approaches. Not all potentially relevant characteristics of a patient, or clinical actions, are captured in the EHR. Additionally, prediction accuracy was not adjusted for subsequent changes in risk factors or clinical treatments. Our results may vary somewhat over shorter or longer follow-up time frames. The risk-based decision tool identified some patients at increased risk who were not identified by traditional guidelines; however, we did not assess the separate contributions of imputation, calculator variables, and estimator accuracy in determining this difference in identification.

 

 
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