Publication Date

2018

Abstract

Aims
Recurrent hospitalizations are a major part of the disease burden in heart failure (HF), but conventional analyses consider only the first event. We compared the effect of sacubitril/valsartan vs. enalapril on recurrent events, incorporating all HF hospitalizations and cardiovascular (CV) deaths in PARADIGM‐HF, using a variety of statistical approaches advocated for this type of analysis.
Methods and results
In PARADIGM‐HF, a total of 8399 patients were randomized and followed for a median of 27 months. We applied various recurrent event analyses, including a negative binomial model, the Wei, Lin and Weissfeld (WLW), and Lin, Wei, Ying and Yang (LWYY) methods, and a joint frailty model, all adjusted for treatment and region. Among a total of 3181 primary endpoint events (including 1251 CV deaths) during the trial, only 2031 (63.8%) were first events (836 CV deaths). Among a total of 1195 patients with at least one HF hospitalization, 410 (34%) had at least one further HF hospitalization. Sacubitril/valsartan compared with enalapril reduced the risk of recurrent HF hospitalization using the negative binomial model [rate ratio (RR) 0.77, 95% confidence interval (CI) 0.67–0.89], the WLW method [hazard ratio (HR) 0.79, 95% CI 0.71–0.89], the LWYY method (RR 0.78, 95% CI 0.68–0.90), and the joint frailty model (HR 0.75, 95% CI 0.66–0.86) (all P < 0.001). The effect of sacubitril/valsartan vs. enalapril on recurrent HF hospitalizations/CV death was similar.
Conclusions
In PARADIGM‐HF, approximately one third of patients with a primary endpoint (time‐to‐first) experienced a further event. Compared with enalapril, sacubitril/valsartan reduced both first and recurrent events. The treatment effect size was similar, regardless of the statistical approach applied.

School/Institute

Mary MacKillop Institute for Health Research

Document Type

Open Access Journal Article

Access Rights

Open Access

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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Epidemiology Commons

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