Currently Viewing:
The American Journal of Managed Care May 2016
Greater Potential Cost Savings With Biosimilar Use
Benjamin Yu, PharmD
Implementing a Hybrid Approach to Select Patients for Care Management: Variations Across Practices
Christine Vogeli, PhD; Jenna Spirt, MPH; Richard Brand, PhD; John Hsu, MD, MPH; Namita Mohta, MD; Clemens Hong, MD, MPH; Eric Weil, MD; and Timothy G. Ferris, MD, MPH
Medicaid Managed Care Penetration and Drug Utilization for Patients With Serious Mental Illness
Aaron L. Schwartz, PhD; Jacqueline Pesa, PhD, MPH; Dilesh Doshi, PharmD; John Fastenau, PhD, MPH; Seth A. Seabury, PhD; Eric T. Roberts, PhD; and David C. Grabowski, PhD
Clinical Interventions Addressing Nonmedical Health Determinants in Medicaid Managed Care
Laura M. Gottlieb, MD, MPH; Kim Garcia, MPH; Holly Wing, MA; and Rishi Manchanda, MD, MPH
Physician Perceptions of Choosing Wisely and Drivers of Overuse
Carrie H. Colla, PhD; Elizabeth A. Kinsella, BA; Nancy E. Morden, MD, MPH; David J. Meyers, MPH; Meredith B. Rosenthal, PhD; and Thomas D. Sequist, MD, MPH
Currently Reading
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
Breast Cancer Multigene Testing Trends and Impact on Chemotherapy Use
G. Thomas Ray, MBA; Jeanne Mandelblatt, MD; Laurel A. Habel, PhD; Scott Ramsey, MD, PhD; Lawrence H. Kushi, ScD; Yan Li, MD; and Tracy A. Lieu, MD, MPH
A Cost-Effectiveness Analysis of Over-the-Counter Statins
Christopher Stomberg, PhD; Margaret Albaugh, MA; Saul Shiffman, PhD; and Neeraj Sood, PhD
Referring Wisely: Orthopedic Referral Guidelines at an Academic Institution
Maria E. Otto, MD; Carlin Senter, MD; Ralph Gonzales, MD, MSPH; and Nathaniel Gleason, MD

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.
ABSTRACT

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.

Am J Manag Care. 2016;22(5):e161-e168
Take-Away Points
 
Automated risk calculator and guideline decision support tools can move clinical care a step closer to systematically individualizing patient therapy and population healthcare.
  • The proportion of the population for whom sufficient electronic data were available to make risk estimation was good.
  • The risk-based decision tool performed well in discrimination and predictiveness.  
  • The risk-based decision tool identified high-risk individuals missed by traditional guidelines, and low-risk individuals who were recommended treatment based on traditional guidelines.
Clinical practice guidelines traditionally have specified levels of biomarkers to determine whether or not patients should be treated and to specify treatment goals. For example, the Seventh Report of the Joint National Committee (JNC-7) guideline for cholesterol specifies 3 different risk strata based on LDL cholesterol levels.1-3 Traditional guidelines can include other risk factors that are typically secondary to the biomarker target, such as gender or smoking status. A notable exception to the traditional approach is the new American College of Cardiology (ACC)/American Heart Association (AHA) cholesterol guideline. Although not explicitly dropping the use of cholesterol treatment goals, these new guidelines endorse the use of a risk calculator to guide treatment.4,5 Depending on their risk of a cardiovascular event, as estimated by a calculator, patients with high cholesterol levels may not be treated and patients with low cholesterol levels may be treated.

We wished to understand the potential implications of using risk calculators and risk-based decision tools to help risk stratify a population and identify individual patients for treatment. In the first part of our study, we evaluated the population applicability, discrimination, and predictiveness of an independently developed cardiovascular disease (CVD) risk calculator.6 The risk calculator was part of a risk-based decision tool under consideration by Kaiser Permanente Southern California (KPSC) for use in place of standard clinical practice guidelines. The validation of the risk calculator against outcomes was considered an essential first step before comparing the risk-based decision tool to traditional care. In the second part of our study, we tested the potential population impact of the risk-based decision tool by comparing actual outcomes in patients selected for possible treatment by the risk-based decision tool versus those selected by traditional clinical practice guidelines.

METHODS
Design, Overview

We conducted a retrospective cohort study of KPSC members with active membership during 2007 to 2010. The ending time point was chosen because at the time of initial analyses, government mortality data were not available past 2010. In the first part of this study, we determined the proportion of an adult population to which the risk calculator could be applied, then we compared predicted and observed outcomes. In the second part of the study, we determined which patients would be selected for medication therapy by the risk-based decision tool and by traditional guidelines, then we compared observed outcomes in these groups. Archimedes, Inc developed the risk calculator and the risk-based decision tool, and was involved in study planning and interpretation, but not in data collection or analysis. No data from KPSC were used to create the risk calculator or the risk-based decision tool evaluated in this study. The study was approved by the KPSC Institutional Review Board.

Setting, Subjects

KPSC is a prepaid, integrated health system with 3.5 million members of diverse race and ethnicity at 14 medical centers and 197 medical offices throughout southern California. Members have similar coverage benefits and a limited range of co-payments for services and medications. Subjects 35 years or older were selected from among health plan members who were alive on December 31, 2007, with no gap in enrollment of more than 45 days in the year prior to that date.

Explanatory Variables: Sources, Measures

The electronic health record (EHR) was used to identify pre-existing conditions using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes assigned during inpatient or outpatient healthcare encounters. We collected data on past cardiovascular risk–related disease history from January 1, 2004 (up to 4 years prior to study outcomes), including diabetes, stroke, myocardial infarction, atrial fibrillation, congestive heart failure, and chronic kidney disease. For 2007, we extracted all the clinical information needed for risk calculation from laboratory and pharmacy data files. Laboratory data included fasting plasma glucose, glycated hemoglobin, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, total cholesterol, triglycerides, serum creatinine, and measures of proteinuria (eg, microalbumin-to-creatinine ratio). We identified medication dispensing dates for antihypertensives (ie, angiotensin converting-enzyme inhibitors and angiotensin receptor blockers, calcium channel-blockers, thiazide diuretics, beta-blockers), cholesterol-lowering drugs (ie, statins and non-statins), and insulin. We identified additional key risk factors from the EHR, including recent smoking, nonprescription aspirin use, body mass index, and blood pressure measurements.

Outcomes: Sources, Measures

We identified fatal and nonfatal myocardial infarction and cerebrovascular (ie, stroke) events from January 1, 2008, to December 31, 2010. These 3-year outcomes were identified from the EHR using ICD-9-CM hospital discharge diagnosis codes (myocardial infarction: 410; and stroke: 346.6x, 430, 431, 432.x, 433.01, 433.11, 433.21, 433.31, 433.81, 433.91, 434.x, 436). Hospitalization discharge diagnosis codes were collected from claims data if the hospitalization occurred at a non-KPSC facility. The date and cause of death were determined from the California State Death Statistical Master Files and the Multiple Cause of Death Files for all subjects, regardless of health plan membership status.

Risk Calculator and Risk-Based Decision Tool

 We predicted CVD risk for all patients using an updated version of a previously developed and described risk calculator.6 The models used in the risk calculator were in the form of a proportional hazards survival model and were independently derived using publicly available data sets from the following studies: Framingham, Framingham Offspring, Atherosclerosis Risk in Communities Study (ARIC), and Cardiovascular Health Study (CHS).2,5-18 The risk calculator was designed to determine the 5-year risk of fatal and nonfatal myocardial infarction, and, as a separate estimate, fatal and nonfatal stroke. Multiple statistical imputation is used to handle missing data when possible. Confidence guardrails prevent presentation of risk information when the data for a particular patient are insufficient for imputation and risk calculation.

The risk-based decision tool is the set of risk calculators and additional models that allow computations of health outcomes and the effects of interventions on risk at a person-specific level. This tool has a software application that utilizes EHR data to create an interactive visual display that quantifies health risks and the benefits of different healthcare interventions for individual patients.6 The risk-based decision tool interface can be used to assist collaborative provider-patient decision making in place of standard clinical practice guidelines. The risk-based decision tool sets a threshold of expected benefits and, for each patient, calculates a Health Impact score—the expected gain in 5-year quality-adjusted life-years, depending on whether or not they follow a treatment recommendation (see eAppendix [available at www.ajmc.com] for details). Information on the expected risks and the outcomes associated with possible therapies is presented to patients and physicians for individualized decision making.6 In a population management context, the risk-based decision tool can be used to prioritize patients by the potential benefit of actionable interventions and rank interventions in order of expected benefit. Some of the models for intervention impact on risk were derived independently, and others were derived from risk equations used in the Archimedes Model, which allows computations of health outcomes and effects of interventions on a person-specific level.6 The model is validated on a continuous basis to remain relevant and comply with new medical guidelines.

All of these models were derived from and validated against data from multiple studies and public sources, but not data from Kaiser Permanente.

Identification of Patients for Medication Therapy

Traditional guidelines and the risk-based decision tool were each used to identify patients for medication therapy. KPSC traditional guidelines were based on the JNC-7 blood pressure guideline and the Adult Treatment Panel III (ATP III) cholesterol guideline, which incorporated use of the Framingham risk calculator for borderline cholesterol values. These traditional guidelines were converted into an automated algorithm that could be applied to the entire population. For the risk-based decision tool, patients were chosen using a Health Impact score that resulted in the same number of patients being selected for treatment as by traditional guidelines (the “same-size” population threshold).

Our focus for this evaluation was therapy with statins (HMG-Co-A reductase inhibitors) and antihypertensives for CVD event prevention. For both traditional clinical practice guidelines and the risk-based decision tool, a patient was not identified for a medication therapy if they were already on it or there was a history of allergy or prior adverse events based on EHR data.

Statistical Analyses

For the first part of this study, we determined the proportion of patients for whom a risk calculation was possible. The calculation was not performed if the data required by the calculator were not present in the EHR and could not be imputed. For comparisons with actual 3-year outcomes, we linearly interpolated the risk calculations from the risk-based decision tool from 5 to 3 years. We used the area under the receiver operating characteristic curve (AROC) to assess how well the risk calculator sorted those who had an outcome from those who did not (ie, discrimination accuracy). We used graphical methods, as described by Pepe et al,19 to assess the risk calculator predictiveness across a range of risk percentiles, which provided a visual calibration and goodness of fit assessment. We used the Hosmer-Lemeshow statistic to test risk calculator model goodness of fit.

 
Copyright AJMC 2006-2019 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
x
Welcome the the new and improved AJMC.com, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up