Translating cholesterol guidelines into primary care practice: a multimodal cluster randomized trial

Abstract

PURPOSE We wanted to determine whether an intervention based on patient activation and a physician decision support tool was more effective than usual care for improving adherence to National Cholesterol Education Program guidelines. METHODS A 1-year cluster randomized controlled trial was performed using 30 primary care practices (4,105 patients) in southeastern New England. The main outcome was the percentage of patients screened for hyperlipidemia and treated to their low-density lipoprotein (LDL) and non-high-density lipoprotein (HDL) cholesterol goals. RESULTS After 1 year of intervention, both randomized practice groups improved screening (89% screened), and 74% of patients in both groups were at their LDL and non-HDL cholesterol goals (P <.001). Using intent-to-treat analysis, we found no statistically significant differences between practice groups in screening or percentage of patients who achieved LDL and non-HDL cholesterol goals. Post hoc analysis showed practices who made high use of the patient activation kiosk were more likely to have patients screened (odds ratio [OR] = 2.54; 95% confidence interval [CI], 1.97-3.27) compared with those who made infrequent or no use. Additionally, physicians who made high use of decision support tools were more likely to have their patients at their LDL cholesterol goals (OR = 1.27; 95% CI, 1.07-1.50) and non-HDL goals (OR = 1.23; 95% CI, 1.04-1.46) than low-use or no-use physicians. CONCLUSION This study showed null results with the intent-to-treat analysis regarding the benefits of a patient activation and a decision support tool in improving cholesterol management in primary care practices. Post hoc analysis showed a potential benefit in practices that used the e-health tools more frequently in screening and management of dyslipidemia. Further research on how to incorporate and increase adoption of user-friendly, patient-centered e-health tools to improve screening and management of chronic diseases and their risk factors is warranted.

Trial registration: ClinicalTrials.gov NCT01242319.

Figures

Figure 1

CONSORT 2010 flow diagram for Cholesterol Education Research Trial: HeartAge

Figure 2

Patient-activation tool to calculate HeartAge.

Figure 3

Example of a patient’s calculated HeartAge.

Figure 4

LDL (A) and HDL (B) cholesterol goals over time by risk and treatment group. C=control, CHD=coronary heart disease; HDL=high-density lipoprotein, I=intervention, LDL=low-density lipoprotein. Note: total numbers for the study groups for each risk level are as follows: CHD equivalent risk: control base=368, follow-up=425, and intervention base=405, follow-up=450; high risk: control base=213, follow-up=248, and intervention base=180, follow-up=208; moderate risk: control base = 475, follow-up = 536, and intervention base = 360, follow-up = 448; low risk: control base=1,049, follow-up=896, and intervention base=1,055, follow-up=894. Changes in the numbers from baseline to follow-up are related to the accumulation of additional risk factors over the year of follow-up.

Figure 4

LDL (A) and HDL (B) cholesterol goals over time by risk and treatment group. C=control, CHD=coronary heart disease; HDL=high-density lipoprotein, I=intervention, LDL=low-density lipoprotein. Note: total numbers for the study groups for each risk level are as follows: CHD equivalent risk: control base=368, follow-up=425, and intervention base=405, follow-up=450; high risk: control base=213, follow-up=248, and intervention base=180, follow-up=208; moderate risk: control base = 475, follow-up = 536, and intervention base = 360, follow-up = 448; low risk: control base=1,049, follow-up=896, and intervention base=1,055, follow-up=894. Changes in the numbers from baseline to follow-up are related to the accumulation of additional risk factors over the year of follow-up.