Risk of mortality and recurrent cardiovascular events in patients with acute coronary syndromes on high intensity statin treatment

- 10,092 ACS patients on high dose and 21,174 on no statins were followed 2001-12.
- Royston-Parmar models were implemented to model hazard for new CVD events and death.
- Risk of a new event developed similarly, but was much higher for those not on statins.
- Risk of death increased over time for the no-statin, but not the high statin, group.
- High statin treatment reduced gender effects on risk of mortality and new events.

Several randomized controlled trials have shown a benefit of high-dose intensive statin treatment in reducing risk of death and second cardiovascular disease (CVD) events in patients previously diagnosed with an acute coronary syndrome (ACS). Non-randomized studies in clinical settings support these findings, but large, long-term, observational studies addressing CVD and non-CVD endpoints are lacking. In this retrospective longitudinal study, we followed ACS patients in Sweden during 2001-2012 using national health registry and medical record data. A total of 49,857 patients were identified, of whom 10,092 (20.2%) received high dose statins and 21,174 (42.7%) received no statins. Royston-Parmar parametric time-to-event models were implemented to model hazard for second CVD events and death, stratified by gender and diabetes diagnosis. We found that risk of a second CVD event developed similarly in both treatment groups, but was much higher in the no statin group. Risk of CVD-related death remained relatively constant for the high-statin group, while it increased over time for the no-statin group. Interestingly, males had higher mortality rates in the no-statin group, but not in the high-statin group. All-cause mortality and non-CVD-related death followed similar trends to those observed for CVD-related death. This work provides additional real-world evidence for effect of statins in CVD-related mortality. The hazard functions presented here can provide a basis for future survival modeling and health economic evaluation.