Derivation and Application of a Tool to Estimate Benefits From Multiple Therapies That Reduce Recurrent Stroke Risk

Abstract

Background and Purpose- Lowering blood pressure and cholesterol, antiplatelet/antithrombotic use, and smoking cessation reduce risk of recurrent stroke. However, gaps in risk factor control among stroke survivors warrant development and evaluation of alternative care delivery models that aim to simultaneously improve multiple risk factors. Randomized trials of care delivery models are rarely of sufficient duration or size to be powered for low-frequency outcomes such as observed recurrent stroke. This creates a need for tools to estimate how changes across multiple stroke risk factors reduce risk of recurrent stroke. Methods- We reviewed existing evidence of the efficacy of interventions addressing blood pressure reduction, cholesterol lowering, antiplatelet/antithrombotic use, and smoking cessation and extracted relative risks for each intervention. From this, we developed a tool to estimate reductions in recurrent stroke risk, using bootstrapping and simulation methods. We also calculated a modified Global Outcome Score representing the proportion of potential benefit (relative risk reduction) achieved if all 4 individual risk factors were optimally controlled. We applied the tool to estimate stroke risk reduction among 275 participants with complete 12-month follow-up data from a recently published randomized trial of a healthcare delivery model that targeted multiple stroke risk factors. Results- The recurrent stroke risk tool was feasible to apply, yielding an estimated reduction in the relative risk of ischemic stroke of 0.36 in both the experimental and usual care trial arms. Global Outcome Score results suggest that participants in both arms likely averted, on average, 45% of recurrent stroke events that could possibly have been prevented through maximal implementation of interventions for all 4 individual risk factors. Conclusions- A stroke risk reduction tool facilitates estimation of the combined impact on vascular risk of improvements in multiple stroke risk factors and provides a summary outcome for studies testing alternative care models to prevent recurrent stroke. Registration- URL: https://www.clinicaltrials.gov; Unique identifier: NCT00861081.

Keywords: cardiovascular diseases; health services research; humans; outcome assessment health care; stroke.

Figures

Figure 1.

Reduction in relative risk (RR) and absolute risks of ischemic stroke among SUSTAIN (Systemic Use of Stroke Averting Interventions) experimental and usual-care arms across the 4 cases. Absolute RR=RRR×0.11 based on the risk of recurrent stroke of in the first year after stroke (11%). Solid bars in A represent the relative risk reduction (RRR) for recurrent stroke achieved in experimental and usual-care arms at 12 mo follow-up, compared with baseline, for 4 cases (see text for case definitions). Estimates in B represent the difference in RRR (left axis) and absolute (right axis) risk reductions achieved by experimental and usual-care arms. Error bars display uncertainty intervals produced by the probabilistic simulation approach in 10 000 bootstrap samples.

Figure 2.

Modified Global Outcome Score (GO Score) in SUSTAIN (Systemic Use of Stroke Averting Interventions) experimental and usual-care arms across the 4 cases. Solid bars in A represent the GO Score in experimental and usual-care arms, for 4 cases. The GO Score for ischemic stroke risk represents the proportion of potentially preventable stroke risk achieved with the level of care provided at the end of the trial, given the level of care received at the beginning of the trial. It is calculated as the ratio of the estimated relative risk reduction (RRR) achieved at the end of the trial divided by the estimated ideal RRR when all risk factors are optimally treated for each individual (RRRachieved/RRRideal). Estimates in B represent the difference in GO Score between experimental and usual-care arms. Error bars display uncertainty intervals produced by probabilistic simulation in 10 000 bootstrap samples.