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Can a Chronic Care Model Collaborative Reduce Heart Disease Risk in Patients with Diabetes?

Journal of General Internal Medicine volume 22, Article number: 215 (2007) Cite this article

Background

There is a need to identify effective practical interventions to decrease cardiovascular disease risk in patients with diabetes.

Objective

We examine the impact of participation in a collaborative implementing the chronic care model (CCM) on the reduction of cardiovascular disease risk in patients with diabetes.

Design

Controlled pre- and postintervention study.

Patients/Participants

Persons with diabetes receiving care at 13 health care organizations exposed to the CCM collaborative and controls receiving care in nonexposed sites.

Measurements and Main Results

Ten-year risk of cardiovascular disease; determined using a modified United Kingdom Prospective Diabetes Study risk engine score. A total number of 613 patients from CCM intervention sites and 557 patients from usual care control sites met the inclusion criteria. The baseline mean 10-year risk of cardiovascular disease was 31% for both the intervention group and the control group. Participants in both groups had improved blood pressure, lipid levels, and HbA1c levels during the observation period. Random intercept hierarchical regression models showed that the intervention group had a 2.1% (95% CI −3.7%, −0.5%) greater reduction in predicted risk for future cardiovascular events when compared to the control group. This would result in a reduced risk of one cardiovascular disease event for every 48 patients exposed to the intervention.

Conclusions

Over a 1-year interval, this collaborative intervention using the CCM lowered the cardiovascular disease risk factors of patients with diabetes who were cared for in the participating organization’s settings. Further work could enhance the impact of this promising multifactorial intervention on cardiovascular disease risk reduction.

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Acknowledgments

We conducted this evaluation independently without input from the staff of Institute for Health Care Improvement (IHI). The funding agency (Robert Wood Johnson Foundation) received periodic reports and participated on conference calls during the study to get updates, but they had no involvement with the data analysis or drafting of the manuscript.

Dr. Vargas is also supported by the Lazar Fund and the National Center on Minority Health and Health Disparities under grant no. P20 MD000148-02.

Dr. Mangione is partially funded by the UCLA Resource Center for Minority Aging Research (RCMAR), NIA grant no. AG21864.

Potential Financial Conflicts of Interest

None disclosed.

Author information

Authors and Affiliations

  1. Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine at UCLA, 911 Broxton Ave., Los Angeles, CA, 90024, USA

    Roberto B. Vargas MD, MPH & Carol M. Mangione MD, MSPH

  2. RAND Health, Santa Monica, CA, USA

    Roberto B. Vargas MD, MPH, Carol M. Mangione MD, MSPH, Steven Asch MD, MSPH, Joan Keesey BA, Mayde Rosen RN, BSN, Matthias Schonlau PhD & Emmett B. Keeler PhD

  3. Division of General Internal Medicine, West Los Angeles Veteran’s Administration Hospital, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Steven Asch MD, MSPH

Authors
  1. Roberto B. Vargas MD, MPH
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  2. Carol M. Mangione MD, MSPH
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  3. Steven Asch MD, MSPH
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  4. Joan Keesey BA
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  5. Mayde Rosen RN, BSN
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  6. Matthias Schonlau PhD
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  7. Emmett B. Keeler PhD
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Corresponding author

Correspondence to Roberto B. Vargas MD, MPH.

Appendices

Appendix

Technical Appendix

Derivation of Predicted Cardiovascular Disease (CVD) Risk

Change in 10-year predicted risk of fatal myocardial infarction, nonfatal myocardial infarction, or sudden death was determined by the UKPDS risk engine (Isis Innovation Ltd 2001©). The UKPDS risk engine formula is based on the data from 4,540 U.K. prospective diabetes study patients followed up for roughly 10 years tracking the natural history of treated diabetes. This equation is a diabetes-specific formula which estimates the risk of new coronary heart disease events in people with type-2 diabetes based on their mutable characteristics of glycosylated hemoglobin, systolic blood pressure, total cholesterol/HDL cholesterol ratio, smoking status, and immutable characteristics of age, sex, race/ethnicity, and time since diagnosis of diabetes as risk factors.32 The exact formula is given in the UKPDS formula section below.

Additional Exclusion Criteria

During our medical record review, patients were excluded from the UKPDS analysis if more than one lab- or examination-based component necessary to calculate the UKPDS risk score was missing from the medical record in either the preintervention period or the postintervention period (577). Patients were excluded if there was no evidence of diabetes (82), they were not in an assigned intervention or control site (39), or were under 25 years old (67). This resulted in 1,170 patients eligible for the risk score calculation during the pre- and postintervention periods. Eligible patients and characteristics of those excluded are shown in Figure 2. (Figure 2 of the manuscript) Participants who agreed to be interviewed were given a telephone survey, which included demographic data, such as race, and information not reliably available from the medical record, such as current smoking status and duration of diabetes. Some 1,011 out of 1,170 eligible patients whose charts were reviewed also completed the telephone survey.

Subgroup Analysis

We also conducted a preplanned subgroup analysis to examine if the intervention affected patients of higher or lower baseline predicted cardiovascular disease risk differently. From a cost and quality of care perspective, patients with higher baseline risk may stand to gain the most benefit from an intervention and may subsequently be identified for a more targeted effort. Therefore, we compared the impact of the intervention on change in cardiovascular risk for those in the upper tercile versus the lower two terciles in each collaborative of predicted preintervention cardiovascular risk. We repeated our multiple hierarchical regression analyses to determine the impact of the intervention on these two groups.

Complete Case and other Sensitivity Analyses

The change in risk over time for those exposed to the CCM model is driven by changes in the three mutable characteristics (e.g., systolic blood pressure, HbA1c, and total cholesterol/HDL cholesterol). As described in the text, if the patient had 5 of the 6 values needed for the calculation of UKPDS risk scores in the pre- and postperiods, we imputed the sixth value. HbA1c and total cholesterol/HDL ratio were imputed using the Markov Chain Monte Carlo MCMC method of Proc MI in SAS while we used the mean to impute systolic blood pressure as less than 1% were missing. Smoking status was also imputed using the MCMC method of Proc MI in SAS. In addition to HbA1c, lipid values, blood pressure values, and smoking, we also included indicator variables for the site of care, gender, and age as covariates. We compare the complete case results to the multiply imputed results in Technical Appendix Table 1 below:

Technical Appendix Table 1 Comparison of Multiple Imputation Results to Complete Case Results
Full size table

The nonmutable factors (e.g., race or age or duration, which changes by 1 for all participants over the year) are needed to determine the preintervention-predicted CVD risk for the preplanned subgroup analysis, but these variables have no direct effect on changes in risk over time. These and one other risk engine variable, smoking status, were collected from the patient survey. For the 159 patients who did not complete the survey, we obtained age and sex from the medical record and imputed the smoking and duration of diabetes (because the coefficients for age at diagnosis and for duration are so similar in the UKPDS formula, the survey-given date of diagnosis used to compute duration has little effect on risk). For purposes of calculating the UKPDS risk score, missing smoking status was also imputed using multiple imputation methods.

Because of the possibility that groups differing in race, education, income, living alone, and insurance status might respond differently to the intervention, we tested whether changes in the UKPDS were influenced by those factors. We performed sensitivity analyses on patients with complete survey data comparing the changes in outcomes adjusted for all these survey-based variables, with changes in outcomes not adjusted for survey-based variables. There was no difference in the difference of changes in predicted risk in the intervention versus control sites. In our final models that generated the results in the paper, these survey-based variables were not included.

The UKPDS Formula

The risk of getting heart disease in the next year = 1 − [exp(−qd T)], where T is the duration of disease, d = 1.078 gives the increase in risk for each year of duration, and q is the product of terms of the form b i raised to the x i power. Each b i is taken from Table 3 and the appendix of their publication and was estimated from UKPDS data. A coefficient greater than 1 implies additional risk for increases in the factor.

Technical Appendix Table 2 List of Components of the Model Equation with Their Corresponding Values
Full size table
Technical Appendix Table 3 Characteristics of Intervention and Control Site Pairs
Full size table

If risk factors had stayed the same in the study, risk would have risen by a little less than 1.078 over the year simply because of the increase in duration. To get to the 10-year risk, T is replaced by T + 1, T + 2 ...fs T + 9 in the formula, and the resulting 10-year risk is 1–10-year survival, or \( 1 - \exp {\left( { - q{\kern 1pt} d^{T} {\left[ {1 + d + d^{2} + \ldots d^{9} } \right]}} \right)} \).

Description of Clinical Sites

Because sites came from within the same organization, we were able to match intervention and control sites on region, type of clinical practice, and roughly on size. Differences across overall organizations and their respective pairs are shown below.

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Vargas, R.B., Mangione, C.M., Asch, S. et al. Can a Chronic Care Model Collaborative Reduce Heart Disease Risk in Patients with Diabetes?. J GEN INTERN MED 22, 215 (2007). https://doi.org/10.1007/s11606-006-0072-5

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Key words

  • chronic care
  • diabetes
  • cardiovascular disease
  • collaborative
  • intervention