Areas for future work

• Integrating risk vs. benefit scales (e.g. primary prevention treatment vs. bleeding risk)
  • As discussed in Structure of this Wiki, risk assessment scales are generated to assist physicians in decision making to implement interventions that will reduce the risk of an identified illness. However, both sides of the balance scale must be evaluated.
  • “Risk scales” evaluates the potential adverse effects of an intervention while “benefit scales” evaluates the potential beneficial effects of the same intervention.
  • An integration of these two scales, if possible, would be paramount to clinical decision making for the optimal assessment of patient profiles.

• Prediction scales for ethnic minority groups
  • Due to lack of large prospective cohort studies
  • Difficult to generate new scales or recalibrate of existing scales for this population group
  • Lack of prospective studies which validates the use of existing scales for this population group

• Prediction scales for young population group (<35 years)
  • Most scales are generated from an original population group that is older than 35 years old and they tend to underestimate the cardiovascular risk in the younger age group.

• Prediction scales for old population group (>70 years)
  • Ageing population and increasing life expectancy means that the older population will only get larger.
  • Most scales are generated from an original population group that is younger than 70 years old
  • Therefore, the predictive ability of the current scales when applying to an older generation should be investigated.

• Patients on existing treatment/co-morbidities
  • Co-morbidities is common nowadays, it will be interesting to see how certain co-morbidities or treatments arise from these co-morbidities (blood pressure lowering drugs) interacts with the risk of cardiovascular disease

• Individual risk vs. Population risk
  • Scales are generated from prospective population studies to predict individual risk.
  • Validation studies are also prospective studies based on a population different from the original group.
  • It is unclear from the methodology of these validation studies predictive values from individual risk are evaluated against observed values from population risk. (e.g. ratio of a predicted cumulative risks of individuals against observed risks in the population)

• Identification of new modifiable risk factors
  • Modifiable risk factors in the field of cardiovascular medicine have been studied extensively, but more understanding of the underlying pathophysiology could be helpful.
  • These modifiable risk factors are essential in risk prediction scales as they provide motivation for behavioural changes when patients see how their risks for CVD are reduced by adapting a different lifestyle.
  • These modifiable risk factors also enable doctors to provide sound lifestyle advice to patients and be potential targets of drug therapies.