skip to primary navigationskip to content
 

Optimising cardiovascular risk estimation

last modified Jan 07, 2019 09:51 AM

An international collaborative study led by Dr Lisa Pennells, Dr Stephen Kaptoge and Prof Emanuele Di Angelantonio (Public Health and Primary Care) has demonstrated the importance of re-calibration of risk prediction algorithms for better targeting of primary prevention efforts.

Risk estimation is currently a key component of many cardiovascular disease (CVD) prevention guidelines worldwide, but the guidelines differ in the specific CVD risk algorithms recommended for use. This study compared the performance of four widely-used CVD risk algorithms in >350,000 people, initially without CVD, from 86 studies across 22 countries.

While the algorithms ranked people similarly in estimated risk (i.e. generally put those having CVD events at highest risk) they markedly differed in the level of calculated absolute risk, leading to divergence in treatment allocation and estimated clinical impact. These differences disappeared when the researchers employed a new approach to re-scale the algorithms to predict the appropriate risk levels for each study (a process termed ‘re-calibration’).

Hence, re-calibration equalised the previously divergent clinical performance of four widely-used CVD risk algorithms and improved targeting of CVD preventive action to clinical need, supporting the concept of using regularly re-calibrated risk algorithms in routine clinical practice.

The study was published in the European Heart Journal.

Cambridge Cardiovascular logo - transparent

Logo design by Dr Ana-Mishel Spiroski and Dr Sarah Morgan.

 

We connect cardiovascular researchers in Cambridge.

Please follow us on Twitter for local news about research, events, funding calls, and open positions.

For inquiries about our research or the website, please contact Dr Katja Kivinen.