Statins for primary prevention: what is the regulator’s role?

2020 ◽  
pp. bmjebm-2019-111321 ◽  
Author(s):  
Tom Jefferson ◽  
Maryanne Demasi ◽  
Peter Doshi

Globally, drug regulators have approved statins for the prevention of cardiovascular disease (CVD), although their use in primary prevention has been controversial. A highly publicised debate has ensued over whether the benefits outweigh the harms. Drug regulators, which are legally required to make independent judgements on drug approvals, have remained silent during the debate. Our aim was to navigate the decision-making processes of European drug regulators and ultimately request the data upon which statins were approved. Our findings revealed a system of fragmented regulation in which many countries licensed statins but did not analyse the data themselves. There is no easily accessible archive containing information about the licensing approval of statins or a central location for holding the trial data. This is an unsustainable model and serves neither the general public, nor researchers.

2021 ◽  
Author(s):  
Gaurav Gulati ◽  
Riley J Brazil ◽  
Jason Nelson ◽  
David van Klaveren ◽  
Christine M. Lundquist ◽  
...  

AbstractBackgroundClinical prediction models (CPMs) are used to inform treatment decisions for the primary prevention of cardiovascular disease. We aimed to assess the performance of such CPMs in fully independent cohorts.Methods and Results63 models predicting outcomes for patients at risk of cardiovascular disease from the Tufts PACE CPM Registry were selected for external validation on publicly available data from up to 4 broadly inclusive primary prevention clinical trials. For each CPM-trial pair, we assessed model discrimination, calibration, and net benefit. Results were stratified based on the relatedness of derivation and validation cohorts, and net benefit was reassessed after updating model intercept, slope, or complete re-estimation. The median c statistic of the CPMs decreased from 0.77 (IQR 0.72-0.78) in the derivation cohorts to 0.63 (IQR 0.58-0.66) when externally validated. The validation c-statistic was higher when derivation and validation cohorts were considered related than when they were distantly related (0.67 vs 0.60, p < 0.001). The calibration slope was also higher in related cohorts than distantly related cohorts (0.69 vs 0.58, p < 0.001). Net benefit analysis suggested substantial likelihood of harm when models were externally applied, but this likelihood decreased after model updating.ConclusionsDiscrimination and calibration decrease significantly when CPMs for primary prevention of cardiovascular disease are tested in external populations, particularly when the population is only distantly related to the derivation population. Poorly calibrated predictions lead to poor decision making. Model updating can reduce the likelihood of harmful decision making, and is needed to realize the full potential of risk-based decision making in new settings.


2009 ◽  
Vol 42 (19) ◽  
pp. 37
Author(s):  
WILLIAM E. GOLDEN ◽  
ROBERT H. HOPKINS

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