An approach towards individualized lower rate settings for pacemakers.

Abstract

Key Findings▪Without evidence-based guidance, the pacemaker lower rate limit is typically left at 60 beats per minute, which is much lower than the average adult resting heart rate of 71–79 beats per minute based on large cohorts.▪While low heart rates are beneficial for patients with systolic dysfunction, pacing at a more physiologic heart rate may be a therapeutic target for patients with diastolic dysfunction or heart failure with a preserved ejection fraction (HFpEF).▪Using data from the Centers for Disease Control and Prevention growth charts, the National Health and Nutrition Examination Survey, and the Health-eHeart Study, we demonstrate a negative linear relationship between height and resting heart rate both during human growth and among healthy adult individuals.▪We derived a simple linear regression equation that defines the relationship between height and resting heart rate, which could be used in future studies to investigate a personalized pacemaker lower rate in patients with diastolic dysfunction or HFpEF. ▪Without evidence-based guidance, the pacemaker lower rate limit is typically left at 60 beats per minute, which is much lower than the average adult resting heart rate of 71–79 beats per minute based on large cohorts.▪While low heart rates are beneficial for patients with systolic dysfunction, pacing at a more physiologic heart rate may be a therapeutic target for patients with diastolic dysfunction or heart failure with a preserved ejection fraction (HFpEF).▪Using data from the Centers for Disease Control and Prevention growth charts, the National Health and Nutrition Examination Survey, and the Health-eHeart Study, we demonstrate a negative linear relationship between height and resting heart rate both during human growth and among healthy adult individuals.▪We derived a simple linear regression equation that defines the relationship between height and resting heart rate, which could be used in future studies to investigate a personalized pacemaker lower rate in patients with diastolic dysfunction or HFpEF. When Walton Lillehei and Earl Bakken pioneered the use of pacemakers for heart block following cardiac surgery, they reasoned that the programmed lower rate limit (LRL) should be set to a heart rate (HR) that the patient would be expected to have if conduction disease was not present.1Lillehei C.W. Gott V.L. Hodges Jr., P.C. et al.Transitor pacemaker for treatment of complete atrioventricular dissociation.J Am Med Assoc. 1960; 172: 2006-2010Crossref PubMed Scopus (57) Google Scholar While the adult resting HR is known to average between 71 and 79 beats per minute (bpm),2Ostchega Y. Porter K.S. Hughes J. et al.Resting pulse rate reference data for children, adolescents, and adults: United States, 1999-2008.Natl Health Stat Report. 2011; : 1-16Google Scholar,3Avram R. Tison G.H. Aschbacher K. et al.Real-world heart rate norms in the Health eHeart study.NPJ Digit Med. 2019; 2: 58Crossref PubMed Google Scholar the expected resting HR for a given individual is not known. Owing to the desire to limit dyssynchronous pacing from conventional pacing sites4Gillis A.M. Russo A.M. Ellenbogen K.A. et al.HRS/ACCF expert consensus statement on pacemaker device and mode selection.Heart Rhythm. 2012; 9: 1344-1365Abstract Full Text Full Text PDF PubMed Scopus (44) Google Scholar and because a method to predict an individual’s resting HR is unknown, the pacemaker LRL is typically left at or near the factory setting of 60 bpm.5Biffi M. Melissano D. Rossi P. et al.The OPTI-MIND study: a prospective, observational study of pacemaker patients according to pacing modality and primary indications.Europace. 2014; 16: 689-697Crossref PubMed Scopus (10) Google Scholar This may not be the ideal backup pacing rate for all pacemaker-reliant patients. The 60-bpm-fits-all approach dates back to an era before conduction system pacing—His bundle, left bundle, fascicular, and Bachmann bundle pacing—existed. With the potential to implant a fully physiologic pacing system, the pacemaker LRL could be customized without pacemaker-mediated dyssynchrony. Recent evidence suggests that backup rates better approximating physiologic resting HRs benefit pacemaker-reliant patients with heart failure and a preserved ejection fraction (HFpEF) (Supplemental Tables 1 and 2).6Meyer M. LeWinter M.M. Heart rate and heart failure with preserved ejection fraction: time to slow beta-blocker use?.Circ Heart Fail. 2019; 12e006213Crossref PubMed Scopus (26) Google Scholar In patients with HFpEF, atrial pacing to achieve a higher HR reduces cardiac filling pressures, whereas pharmacologic HR lowering increases filling pressures and worsens heart failure symptoms.6Meyer M. LeWinter M.M. Heart rate and heart failure with preserved ejection fraction: time to slow beta-blocker use?.Circ Heart Fail. 2019; 12e006213Crossref PubMed Scopus (26) Google Scholar Increasing the pacemaker LRL from 60 bpm to 80 bpm in patients with diastolic dysfunction and/or HFpEF improves quality of life, functional capacity, and NTproBNP levels, particularly in patients with a paced QRS <150 ms or pacing from the Bachmann and His bundles.7Wahlberg K. Arnold M.E. Lustgarten D. et al.Effects of a higher heart rate on quality of life and functional capacity in patients with left ventricular diastolic dysfunction.Am J Cardiol. 2019; 124: 1069-1075Abstract Full Text Full Text PDF PubMed Scopus (3) Google Scholar Rather than suggest an arbitrary lower rate target, we sought to identify a readily available metric to predict resting HR individualized to each person. Although resting HR is influenced by many variables,2Ostchega Y. Porter K.S. Hughes J. et al.Resting pulse rate reference data for children, adolescents, and adults: United States, 1999-2008.Natl Health Stat Report. 2011; : 1-16Google Scholar,3Avram R. Tison G.H. Aschbacher K. et al.Real-world heart rate norms in the Health eHeart study.NPJ Digit Med. 2019; 2: 58Crossref PubMed Google Scholar we hypothesized that height could serve as a useful predictor and sought to better define the height–HR relationship. Height is a predictor of resting HR during the growing process2Ostchega Y. Porter K.S. Hughes J. et al.Resting pulse rate reference data for children, adolescents, and adults: United States, 1999-2008.Natl Health Stat Report. 2011; : 1-16Google Scholar,8CDC Growth Charts. National Center for Health Statistics and the National Center for Chronic Disease Prevention and Health Promotion. May 30, 2000.https://www.cdc.gov/growthcharts/data/set1clinical/Cj41cs021bw.pdfhttps://www.cdc.gov/growthcharts/data/set1clinical/Cj41cs022bw.pdfGoogle Scholar and may also predict resting HR in adults. As humans grow, the average resting HR falls from about 120 bpm in infants to 70–74 bpm in adult men and 73–79 bpm in adult women.2Ostchega Y. Porter K.S. Hughes J. et al.Resting pulse rate reference data for children, adolescents, and adults: United States, 1999-2008.Natl Health Stat Report. 2011; : 1-16Google Scholar,3Avram R. Tison G.H. Aschbacher K. et al.Real-world heart rate norms in the Health eHeart study.NPJ Digit Med. 2019; 2: 58Crossref PubMed Google Scholar,8CDC Growth Charts. National Center for Health Statistics and the National Center for Chronic Disease Prevention and Health Promotion. May 30, 2000.https://www.cdc.gov/growthcharts/data/set1clinical/Cj41cs021bw.pdfhttps://www.cdc.gov/growthcharts/data/set1clinical/Cj41cs022bw.pdfGoogle Scholar Median height data from published national survey data2Ostchega Y. Porter K.S. Hughes J. et al.Resting pulse rate reference data for children, adolescents, and adults: United States, 1999-2008.Natl Health Stat Report. 2011; : 1-16Google Scholar and CDC growth charts8CDC Growth Charts. National Center for Health Statistics and the National Center for Chronic Disease Prevention and Health Promotion. May 30, 2000.https://www.cdc.gov/growthcharts/data/set1clinical/Cj41cs021bw.pdfhttps://www.cdc.gov/growthcharts/data/set1clinical/Cj41cs022bw.pdfGoogle Scholar were collected to establish the relationship of growth-associated height and HR differences, producing evidence of a linear relationship. For each 1-centimeter increase in height, there was a 0.38 bpm reduction in HR (Figure 1). We validated the height–HR relationship in healthy adults enrolled in the Health-eHeart Study with available sex and self-reported height data (n = 4795; n = 2111 female). Resting HR was obtained using photoplethysmography through a smartphone camera; resting HR was assured by excluding measurements preceded by accelerometer-recorded activity (Supplemental Methods).3Avram R. Tison G.H. Aschbacher K. et al.Real-world heart rate norms in the Health eHeart study.NPJ Digit Med. 2019; 2: 58Crossref PubMed Google Scholar Using linear regression, for every 1-centimeter increase in height, there was a 0.22 ± 0.02 bpm reduction in resting HR (P < .001) (Table 1, Figure 2). The height–HR relationship by sex is shown in Supplemental Figure 1 and Supplemental Tables 3 and 4). Additional anthropomorphic variables were assessed as predictors of resting HR (Supplemental Tables 3–10). In univariate analysis, female sex and body mass index were positive predictors of resting HR (P < .001), whereas weight was not a significant predictor of resting HR (Supplemental Tables 5–7). In multivariate analysis adjusting for height, sex, and weight, all 3 variables were independent predictors of resting HR (Table 2).Table 1The relationship between height and resting heart rate from the Health-eHeart Study cohortVariableCoefficientStandard error95% confidence intervalP valueIntercept114.1533.686106.926, 121.380<.001Height-0.2180.021-0.259, -0.176<.001 Open table in a new tab Table 2Multivariate analysis of height, weight, and sex on resting heart rate from the Health-eHeart Study cohortVariableCoefficientStandard error95% confidence intervalP valueIntercept103.0035.36892.479, 113.526<.001Height-0.2060.032-0.268, -0.143<.001Weight0.0940.0120.070, 0.118<.001Female sex3.0240.5971.853, 4.195<.001 Open table in a new tab This project adhered to the guidelines set forth by the Office of Human Research Protection that is supported by the U.S. Department of Health & Human Services. The University of Vermont and the UCSF Institutional Review Board deemed this ancillary analysis using data from the Health-eHeart Study to be exempt from review, as it was a retrospective analysis performed on de-identified data. Enrolled subjects in the Health-eHeart Study provided written informed consent. In this analysis, we demonstrate a negative linear relationship between height and resting HR both during human growth2Ostchega Y. Porter K.S. Hughes J. et al.Resting pulse rate reference data for children, adolescents, and adults: United States, 1999-2008.Natl Health Stat Report. 2011; : 1-16Google Scholar,8CDC Growth Charts. National Center for Health Statistics and the National Center for Chronic Disease Prevention and Health Promotion. May 30, 2000.https://www.cdc.gov/growthcharts/data/set1clinical/Cj41cs021bw.pdfhttps://www.cdc.gov/growthcharts/data/set1clinical/Cj41cs022bw.pdfGoogle Scholar and among healthy adult individuals in an out-of-clinic dataset from the Health-eHeart Study.3Avram R. Tison G.H. Aschbacher K. et al.Real-world heart rate norms in the Health eHeart study.NPJ Digit Med. 2019; 2: 58Crossref PubMed Google Scholar Among animal species of different body sizes, body length is consistently and negatively correlated with HR.9O’Rourke M. Arterial Function in Health and Disease. Churchill-Livingstone, Edinburgh1982: 170-182Google Scholar In humans, height correlates with cardiac stroke volume10Jegier W. Sekelj P. Auld P.A. et al.The relation between cardiac output and body size.Br Heart J. 1963; 25: 425-430Crossref PubMed Scopus (33) Google Scholar and a prior investigation evaluating arterial hemodynamics by stature found height to be a strong predictor of resting HR,11Smulyan H. Marchais S.J. Pannier B. Guerin A.P. Safar M.E. London G.M. Influence of body height on pulsatile arterial hemodynamic data.J Am Coll Cardiol. 1998; 31: 1103-1109Crossref PubMed Scopus (188) Google Scholar consistent with our findings. We derived a simple linear regression equation, which defines the height–resting HR relationship that could be used to predict an individual’s resting HR. For example, the predicted average resting HR for a patient who is 150 cm (4.9 feet) tall is 82 bpm, while the predicted HR for a patient who is 195 cm (6.4 feet) tall is 72 bpm (Figure 2). Our analyses reproduce the positive relationships between female sex, body mass index, weight, and resting HR, which have been previously described.2Ostchega Y. Porter K.S. Hughes J. et al.Resting pulse rate reference data for children, adolescents, and adults: United States, 1999-2008.Natl Health Stat Report. 2011; : 1-16Google Scholar,3Avram R. Tison G.H. Aschbacher K. et al.Real-world heart rate norms in the Health eHeart study.NPJ Digit Med. 2019; 2: 58Crossref PubMed Google Scholar While many variables are associated with resting HR (Supplemental Appendix), height is an easily measured and relatively constant variable that is unique to individuals. Given the relatively linear correlation between height and resting HR, we propose height as a simple and pragmatic variable to serve as a starting point toward individualizing the pacemaker LRL for patients with diastolic dysfunction or HFpEF and conduction system pacing. Despite rapid innovation in cardiac devices, pacing algorithms, and programmable pacemaker features tailored to the individual patient, the pacemaker LRL is rarely changed from the factory setting of 60 bpm.5Biffi M. Melissano D. Rossi P. et al.The OPTI-MIND study: a prospective, observational study of pacemaker patients according to pacing modality and primary indications.Europace. 2014; 16: 689-697Crossref PubMed Scopus (10) Google Scholar In the systolic heart failure population, pharmacologic HR lowering has well-established benefits.12Swedberg K. Komajda M. Bohm M. et al.Ivabradine and outcomes in chronic heart failure (SHIFT): a randomised placebo-controlled study.Lancet. 2010; 376: 875-885Abstract Full Text Full Text PDF PubMed Scopus (1851) Google Scholar In contrast, among patients with HFpEF low HRs may be detrimental (Supplemental Table 1) by increasing central arterial pressures and left ventricular end-diastolic pressure, both of which contribute to increased wall stress and chronic adverse remodeling.6Meyer M. LeWinter M.M. Heart rate and heart failure with preserved ejection fraction: time to slow beta-blocker use?.Circ Heart Fail. 2019; 12e006213Crossref PubMed Scopus (26) Google Scholar Atrial pacing above 60 bpm improves cardiac filling pressures,6Meyer M. LeWinter M.M. Heart rate and heart failure with preserved ejection fraction: time to slow beta-blocker use?.Circ Heart Fail. 2019; 12e006213Crossref PubMed Scopus (26) Google Scholar symptoms, and functional capacity in HFpEF patients,7Wahlberg K. Arnold M.E. Lustgarten D. et al.Effects of a higher heart rate on quality of life and functional capacity in patients with left ventricular diastolic dysfunction.Am J Cardiol. 2019; 124: 1069-1075Abstract Full Text Full Text PDF PubMed Scopus (3) Google Scholar and reverses concentric left ventricular hypertrophy in animal models13Klein F.J. Bell S. Runte K.E. et al.Heart rate-induced modifications of concentric left ventricular hypertrophy: exploration of a novel therapeutic concept.Am J Physiol Heart Circ Physiol. 2016; 311: H1031-H1039Crossref PubMed Scopus (9) Google Scholar (Supplemental Table 2). We acknowledge several limitations with respect to our analysis of the height–HR relationship. The CDC and national survey data from which the growth-associated height–HR curves were derived use a single resting HR measurement.2Ostchega Y. Porter K.S. Hughes J. et al.Resting pulse rate reference data for children, adolescents, and adults: United States, 1999-2008.Natl Health Stat Report. 2011; : 1-16Google Scholar,8CDC Growth Charts. National Center for Health Statistics and the National Center for Chronic Disease Prevention and Health Promotion. May 30, 2000.https://www.cdc.gov/growthcharts/data/set1clinical/Cj41cs021bw.pdfhttps://www.cdc.gov/growthcharts/data/set1clinical/Cj41cs022bw.pdfGoogle Scholar HR data from the Health-eHeart Study were derived using the geometric mean of all HR values of a participant; however, participants were free to measure their HR at any frequency and time of day.3Avram R. Tison G.H. Aschbacher K. et al.Real-world heart rate norms in the Health eHeart study.NPJ Digit Med. 2019; 2: 58Crossref PubMed Google Scholar These measures underestimate diurnal variations in resting HR and atrioventricular conduction. In addition, most patients with rate-responsive pacemakers do not pace at the programmed LRL during waking hours or activity. However, physical activity levels are reported to be low among HFpEF patients14Shah S.J. Heitner J.F. Sweitzer N.K. et al.Baseline characteristics of patients in the treatment of preserved cardiac function heart failure with an aldosterone antagonist trial.Circ Heart Fail. 2013; 6: 184-192Crossref PubMed Scopus (131) Google Scholar and increased physiologic pacing with a customized LRL at rest or during sleep would be expected to reduce cardiac filling pressures and induce long-term beneficial remodeling. Finally, the proposed height–HR algorithm is a starting point to improve beyond the arbitrary 1-size-fits-all nominal LRL setting of 60 bpm. Further refinements incorporating more variables might be an area for further study. Additional limitations and future directions are detailed in the Supplemental Appendix. In conclusion, conduction system pacing at an increased, individualized backup rate may be an important therapeutic target for patients with HFpEF. In this analysis, we derived a simple linear regression equation that defines the relationship between height and HR and can be used in future studies to investigate a personalized pacemaker lower rate in this population.