Ms. Soya M. Alone is a 70-year-old woman of Bangladeshi ethnicity with a history of anxiety and depression. She currently lives at home by herself, does not have many friends and family that live nearby, and has had a tough year emotionally after the passing of her husband. She spends most of her time in bed with low daily physical activity and has experienced more weakness and exhaustion over the past year along with loss of muscle mass. Which of the following are potential risk modifiers in this patient when considering her risk for CVD?

A. Bangladeshi ethnicity
B. Psychosocial factors
C. Frailty
D. History of anxiety and depression
E. All of the above

The correct answer is E – All of the above.

Traditional 10-year CVD risk scores do not perform adequately in all ethnicities. Therefore, multiplication of calculated risk by relative risk for specific ethnic subgroups should be considered (Class IIa, LOE B). Individuals from South Asia have higher CVD rates. The ESC guidelines recommend using a correction factor by multiplying the predicted risk by 1.3 for Indians and Bangladeshis, and 1.7 for Pakistanis. These correction factors are derived from data from QRISK3. In the UK, the QRISK calculator algorithm has been derived and validated in 2.3 million people to estimate CVD risk in different ethnic groups and unlike other calculators, it counts South Asian origins as an additional risk factor. The reasons for such differences remain inadequately studied, as do the risks associated with other ethnic backgrounds. Barriers to developing accurate risk prediction tools include the wide heterogeneity amongst the population.

The 2019 ACC/AHA guidelines also list high-risk race/ethnicities such as South Asian ancestry as a risk-enhancing factor. However, there is no separate pooled cohort equation for different ethnicities, and consideration should be given that the pooled cohort equations will underestimate ASCVD risk in South Asians.

Psychosocial stress including loneliness and critical life events are associated, in a dose-response pattern, with the development and progression of ASCVD, with relative risks between 1.2 and 2.0. Conversely, indicators of mental health, such as optimism and a strong sense of purpose, are associated with lower risk. While there is not a specific way proposed by the guidelines for psychosocial factors to improve risk classification, it is important to screen patients with ASCVD for psychological stress, and clinicians should attend to somatic and emotional causes of symptoms as well. The ESC guidelines give a Class IIa (LOE B) recommendation for assessment of stress symptoms and psychosocial stressors.

This patient should also be formally screened for frailty, which is not the same as aging but includes factors such as slowness, weakness, low physical activity, exhaustion and shrinking, and makes her more vulnerable to the effect of stressors and is a risk factor for both high CV and non-CV morbidity and mortality. However, the ability of frailty measures to improve CVD risk prediction has not been formally assessed, so the guidelines do not recommend integrating it into formal CVD risk assessment. Frailty may however, influence treatment as it can help build an individualized care plan.

Mental disorders such as anxiety and depression are associated with the development of CVD as well. Detrimental effects may be potentially caused by unhealthy lifestyle, increased exposure to socioeconomic stressors, and cardiometabolic side-effects of medications. The ESC guidelines give a Class 1 (LOE C) recommendation that mental disorders with either significant functional impairment or decreased use of healthcare systems be considered as influencing total CVD risk.

Main Takeaway

Psychosocial stress and frailty are associated with risk of ASCVD and should be assessed in patients when considering CVD risk. In addition, current risk scores may under-or over-estimate CVD risk in different ethnic minority groups.

Guideline Location

Section 3.3.1, 3.3.2, 3.3.4, 3.4.10, page 3258 – 3259, 3265 – 3266