Mismatch between self-perceived and calculated cardiometabolic disease risk among participants in a prevention program for cardiometabolic disease: a cross-sectional study

Background

The rising prevalence of cardiometabolic diseases (CMD) calls for effective prevention programs. Self-assessment of CMD risk, for example through an online risk score (ORS), might induce risk reducing behavior. However, the concept of disease risk is often difficult for people to understand. Therefore, the study objective was to assess the impact of communicating an individualized CMD risk score through an ORS on perceived risk and to identify risk factors and demographic characteristics associated with risk perception among high-risk participants of a prevention program for CMD.

Methods

A cross-sectional analysis of baseline data from a randomized controlled trial conducted in a primary care setting. Seven thousand five hundred forty-seven individuals aged 45–70 years without recorded CMD, hypertension or hypercholesterolemia participated. The main outcome measures were: 1) differences in cognitive and affective risk perception between the intervention group - who used an ORS and received an individualized CMD risk score- and the control group who answered questions about CMD risk, but did not receive an individualized CMD risk score; 2) risk factors and demographic characteristics associated with risk perception.

Results

No differences were found in cognitive and affective risk perception between the intervention and control group and risk perception was on average low, even among high-risk participants. A positive family history for diabetes type 2 (β0.56, CI95% 0.39–0.73) and cardiovascular disease (β0.28, CI95% 0.13–0.43), BMI ≥25 (β0.27, CI95% 0.12–0.43), high waist circumference (β0.25, CI95% 0.02–0.48) and physical inactivity (β0.30, CI95% 0.16–0.45) were positively associated with cognitive CMD risk perception in high-risk participants. No other risk factors or demographic characteristics were associated with risk perception.

Conclusions

Communicating an individualized CMD risk score did not affect risk perception. A mismatch was found between calculated risk and self-perceived risk in high-risk participants. Family history and BMI seem to affect the level of CMD risk perception more than risk factors such as sex, age and smoking. A dialogue about personal CMD risk between patients and health care professionals might optimize the effect of the provided risk information.

Trial registration

Dutch trial Register number NTR4277, registered 26th Nov 2013.

P646Effects of personalized therapy-effect predictions on statin treatment decisions by patients and physicians: a three-armed, blinded, randomized controlled trial

Background

Several online tools express an individual's therapy-benefit for various cardiovascular disease (CVD) prevention strategies. The benefit can be expressed in multiple formats, such as absolute 10-year CVD risk reduction or gain in CVD-free life-years. It is increasingly suggested that these estimates can be used in doctor-patient communication to support shared decision-making. However, the actual therapy-benefit to be expected from preventive therapy might be small from the perspective of patients, and it remains unclear how the estimates affect patient and physician decision-making.

Purpose

The primary objective was to determine whether communicating personalized predictions of prognosis and treatment-effects (compared to non-personalized standard practice) leads to lower decisional conflict among patients with stable CVD and prescribed statin medication.

Methods

A hypothesis-blinded, three-armed randomized controlled trial was performed in which 303 patients were randomized in a 1:1:1 ratio to either standard practice (control-group) or to one of two intervention arms. Intervention arms received personalized estimates of prognostic changes associated with both discontinuation of current statin and intensification to the most potent statin type and dose (atorvastatin 80 mg). Intervention arms differed only in the format of the treatment effect estimates: change in personal 10-year absolute CVD risk (iAR-group) or CVD-free life-expectancy (iLE-group). Primary outcome was patient decisional conflict score (DCS) after one-month, which varies from 0 (no conflict) to 100 (high conflict). Secondary outcomes were collected at one or six months: DCS, quality of life, illness perception, patient activation, patient perception of statin efficacy and shared decision-making, self-reported statin adherence, understanding of statin-therapy, post-randomization low-density lipoprotein cholesterol levels, and physician opinion of statin therapy decisions and the intervention. Outcomes are reported as median (25th–75th percentile).

Results

In the iAR group, the change in 10-year absolute CVD-risk was −2.4 (−1.2 to −3.9%) from intensification and +10.2% (+7.7 to +13.5) from discontinuation. In the iLE group, the change in CVD-free life-expectancy was +0.5 years (+0.3 to +0.8) from intensification and −2.0 years (−1.3 to −2.8) from discontinuation. Decisional conflict differed between the intervention arms: median control 27 (20–43), iAR-group 22 (11–30; p-value versus control 0.002), and iLE-group 25 (10–31; p-value versus control 0.02). No differences in secondary outcomes were observed.

Figure 1. Part of the personalized information received by iAR-group (left) and iLE-group (right).

Conclusion

In patients with clinically manifest CVD, providing personalized estimations of treatment-effects lowers decisional conflict associated with statin use. The results support the use of personalized predictions for patient decision making.

Acknowledgement/Funding

Partially funded by a Netherlands Heart Foundation grant (2016T026)