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In clinical practice , initiation and intensification of cardiovascular disease (CVD) treatment is guided by risk prediction algorithms
Vascular Guidelines for Diabetes
In contrast, the European Society of Cardiology (ESC) does not recommend specific CVD risk prediction tools, but instead divides individuals into three categories based on risk factors, including: presence or absence of target organ damage, number of risk factors, duration of diabetes and age
In contrast, the European Society of Cardiology (ESC) does not recommend specific CVD risk prediction tools, but instead divides individuals into three categories based on risk factors, including: presence or absence of target organ damage, number of risk factors, duration of diabetes and age
Assessment scores were determined through a literature review and included regardless of the type of predicted CVD outcome and whether or not patients with type 2 diabetes were included
The researchers assessed 22 scores
Of these, the Systematic Coronary Coronary Risk Assessment (SCORE) CVD rules derived in the general population had a significant impact on CVD (C statistic 0.
A simple recalibration process greatly improved the performance of the fraction, and the CVD calibration rate is now between 0.
10-year cumulative incidence of cardiovascular disease after diagnosis
diagnosis
Scores with more predictors were not superior to scores with fewer predictors: C statistic 0.
Scores specific to patients with diabetes did not discriminate better than those derived in the general population: the UK Prospective Diabetes Study (UKPDS) scores performed significantly worse than SCORE CVD (P value < 0.
The CVD risk prediction score did not accurately identify patients with type 2 diabetes who had CVD events at 10 years of follow-up
references:
Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings.
Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings.
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