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Prognostic importance of temporal changes in resting heart rate in heart failure patients: an analysis of the CHARM
Ali Vazir1, Brian Claggett2, Pardeep Jhund3
1Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA Royal Brompton Hospital, Royal Brompton and Harefield NHS Foundation Trust, NIHR Cardiovascular Biomedical Research Unit and Institute of Cardiovascular Medicine and Sciences (ICMS), National Heart and Lung Institute (NHLI), Imperial College London, London, UK.
Insights
Changes in resting heart rate (HR) over time predict adverse outcomes in heart failure (HF) patients. Frequent HR monitoring may identify high-risk individuals for timely intervention.
Area of Science:
- Cardiology
- Clinical Medicine
- Heart Failure Research
Background:
- Resting heart rate (HR) is a known predictor of adverse outcomes in heart failure (HF) patients.
- The prognostic value of HR changes over time in chronic HF is not well understood.
- This study investigates the association between HR variability and patient outcomes.
Purpose of the Study:
- To determine if changes in resting heart rate (HR) over time are associated with adverse outcomes in patients with chronic heart failure (HF).
- To explore the relationship between time-updated HR values and subsequent adverse events in HF patients.
Main Methods:
- Analysis of 7599 patients from the candesartan in heart failure: assessment of reduction in mortality and morbidity program.
- Utilized Cox proportional hazards models to assess the association between preceding HR changes and outcomes.
- Examined the relationship between time-updated HR and subsequent adverse events, adjusting for covariates.
Main Results:
- An increase in HR from a previous visit was linked to a higher risk of all-cause mortality and cardiovascular death or HF hospitalization (aHR 1.06 per 5 b.p.m. increase).
- Decreasing HR was associated with a lower risk of adverse outcomes.
- Time-updated resting HR at each visit also demonstrated a significant association with risk (aHR 1.07 per 5 b.p.m. increase).
Conclusions:
- Changes in resting heart rate (HR) over time serve as a significant predictor of outcomes in chronic heart failure (HF) patients.
- Time-updated HR measurements during follow-up are also predictive of adverse events.
- Suggests that frequent outpatient HR monitoring, potentially using remote technologies, can identify HF patients at increased risk for rehospitalization or death.
Background:
Resting heart rate (HR) is a predictor of adverse outcome in patients with heart failure (HF). Whether changes in HR over time in patients with chronic HF are also associated with adverse outcome is unknown. We explored the relationship between changes in HR from a preceding visit, time-updated HR (i.e. most recent available HR value from a clinic visit) and subsequent outcomes in patients with chronic HF.
Methods And Results:
We studied 7599 patients enrolled in the candesartan in heart failure: assessment of reduction in mortality and morbidity program. We calculated change in HR from the preceding visit and explored its association with outcomes in Cox proportional hazards models, as well the association between time-updated HR and outcome. An increase in HR from preceding visit was associated with a higher risk of all-cause mortality and the composite endpoint of cardiovascular death or hospitalization for HF (adjusted hazard ratio 1.06, 95% confidence intervals, CI: 1.05-1.08, P < 0.001, per 5 b.p.m. higher HR), with lowering of HR being associated with lower risk, adjusting for covariates, including time-updated β-blocker dose and baseline HR. Time-updated resting HR at each visit was also associated with risk (adjusted hazard ratio 1.07, 95% CI: 1.06-1.09; P < 0.001, per 5 b.p.m. higher HR).
Conclusions:
Change in HR over time predicts outcome in patients with chronic HF, as does time-updated HR during follow-up. These data suggest that frequent outpatient monitoring of HR, and identification of changes over time, possibly with remote technologies, may identify patients with HF who may be at increased risk of rehospitalization or death.
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