The recent development of a comprehensive simulation model that was based on a series of type 1 diabetes-specific equations enables the estimation of a range of end points, including risks of CVD, amputation, hypoglycaemia, hyperglycaemia and death. There is evidence that neither type 2 diabetes-specific nor general CVD risk algorithms adequately predict CVD risk in individuals with type 1 diabetes, and there are currently no life expectancy tables for these patients. The expression of risk-related concepts in natural units such as life expectancy is easy for patients to interpret and has a higher level of recall compared with the relative measure of risk. absolute or relative percentages, frequencies) presentations. The use of visual tools such as colour-coded charts has been shown to be associated with higher levels of patient understanding compared with verbal and numerical (e.g. ĭecision aids that support patients by making their decisions explicit and providing more information about options and benefits/harms have been shown to increase patient preferences for effective CVD risk-reducing strategies and increase the number of patients choosing to start new medications for diabetes. For patients with type 2 diabetes, life expectancies stratified by combinations of risk factor levels have been produced. Recent reviews suggest the importance of discussions on prognosis of patients with serious health conditions. While such risk charts assist in prescribing of medications, patients may also value information on a broader range of health outcome measures including life expectancy. Ĭurrently, risk information predominantly relates to the risk of CVD over a 5 or 10 year period, and includes both risk charts and risk calculators. The delivery of personalised risk information for patients has been shown to be effective at improving the management of modifiable risk factors in individuals with chronic diseases and likely improving patients’ acceptance of doctor-recommended management strategies. Optimal control over risk factors has been shown to reduce the occurrence of diabetes-related adverse events and all-cause mortality. While the gap had been reduced over the second half of the twentieth century, the evidence on the continuation of this improvement in the twenty-first century is mixed.
Non-smokers and women had a higher life expectancy than smokers and men, respectively, with the difference in life expectancy ranging from 0.4 years to 2.7 years between non-smokers and smokers, and from 1.9 years to 5.9 years between women and men, depending on levels of other risk factors.ĭespite increasing gains in life expectancy, individuals with type 1 diabetes have a life expectancy 10–12 years lower than the general population. Individuals with the lowest level (20 kg/m 2) and highest level of BMI (35 kg/m 2) had a lower life expectancy compared with those with a BMI of 25 kg/m 2. The variation in life expectancy was a function of the combination of risk factor values, with HbA 1c and eGFR consistently showing a negative and positive correlation, respectively, with life expectancy at any level combination of other risk factors. In 20-year-old women, this gap was 18.9 years (life expectancy range 35.0–53.9 years). Life expectancy of 20-year-old men varied from 29.3 years to 50.6 years, constituting a gap of 21.3 years between those with worst and best risk factor levels. There was a substantial variation in life expectancy across patients with different risk factor levels.
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