Predictive Value of Increased Perioperative Heart Rate for All-Cause Mortality After Cardiac Surgery: A Systematic Review and Meta-Analysis

Abstract

Background: Accumulated studies have revealed that heart rates are associated with all-cause mortality in cardiac surgery patients, but the results remain controversial. This meta-analysis aimed to evaluate the predictive value of increased perioperative heart rate for all-cause mortality after cardiac surgery. Methods: We searched PubMed, Embase, Web of Science, and Cumulative Index of Nursing and Allied Health Literature (CINAHL) databases for studies from inception to October 11, 2021. Two researchers independently screened the studies. Titles, authors, publication years, and hazard ratios were extracted. We used a random-effects model to combine the HRs and 95% confidence intervals. Several subgroup analyses were conducted. Statistical significance was set at p < .05. Results: Eleven studies were included in the meta-analysis of 33,849 patients and 3166 (9.4%) deaths. The HR of higher perioperative heart rates was 2.09 (95% CI 1.53–2.86, p < .001, I² = 81%). The HR with a 10-bpm increase in preoperative heart rate was 1.19 (95% CI 1.11–1.26, p < .001, I² = 51%). Subgroup analysis showed patients with higher preoperative heart rates had an HR of 1.88 (95% CI 1.51–2.34, p < .001, I² = 0%), and patients with a higher postoperative heart rate had an HR of 2.29 (95% CI 1.28– 4.09, p < .0001, I² = 91%) compared to patients with lower postoperative heart rates. Conclusion: Increased perioperative heart rate is associated with all-cause mortality in patients undergoing cardiac surgery.

Keywords

heart rate all-cause mortality cardiac surgery

Despite the remarkable progression of surgical procedure (Chen et al., 2011), self-management (Thomas et al., 2019), and complications management (Vijayan, 2021), all-cause mortality in patients undergoing cardiac surgery remains at 11.2–42.7% (Gaudino et al., 2021; Head et al., 2018). Heart rate, the easiest parameter to obtain in the cardiovascular system, determines myocardial oxygen consumption and metabolic demand. An increasing number of studies have indicated that an elevated heart rate is strongly associated with the morbidity and prognosis of cardiovascular disease (Chen et al., 2018; Mathew et al., 1996; Ozieranski et al., 2016).

Numerous studies have focused on the predictive value of perioperative heart rate on mortality in cardiac surgery patients, but whether perioperative heart rate could predict all-cause mortality remains controversial. Reich et al. (1999) demonstrated that a postoperative heart rate >120 bpm in patients with extracorporeal circulation was associated with in-hospital mortality. Mahaffey et al. (2008) pointed out that, after adjusting for demographic data, medical conditions, and medical history, every 10-bpm increase in preoperative heart rate in patients caused a 1.19-fold increase in 1-year all-cause mortality. Conversely, Frank et al. (2010) stated that the preoperative heart rate is not associated with all-cause mortality in cardiac surgery patients. The predictive value of an elevated heart rate remains controversial. Nonetheless, several studies (Antoni et al., 2011; Jensen et al., 2013; Seronde et al., 2014) have shown an independent association between postoperative heart rate and all-cause mortality in cardiac surgery patients in which a higher postoperative heart rate leads to higher all-cause mortality. However, the generalizability of these studies’ findings was limited due to the high variation between overall follow-up time and data type.

Therefore, we conducted a meta-analysis of prospective and retrospective cohort studies involving patients undergoing cardiac surgery to elucidate the predictive value of elevated perioperative heart rate on all-cause mortality in patients with cardiac surgery in both continuous and categorical variables.

Materials and Methods

According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, this meta-analysis was conducted and has already been registered in PROSPERO (CRD42021211372).

Search Strategy

The retrieval strategy was the combination of Medical Subject Heading words and free words, which included “heart operation,” “heart surgery,” “cardiac operation,” “cardiac surgery,” “dead,” “death,” “mortality,” “prognosis,” “heart rate,” and “heartbeat.” The search period was from inception to October 11, 2021. The following search strategy was used in PubMed: (cardiac surgery [MESH] OR “cardiac operation” OR cardiosurgery OR “heart surgery” OR “heart operation” OR “open heart” OR “cardiopulmonary bypass” OR “coronary artery bypass grafting” OR CABG OR “valves surgery” OR “valve surgery” OR “valve replacement” OR “congenital heart” OR “aortic dissection” OR aneurysm OR “aortocoronary bypass” OR “coronary surgery” OR “heart surgical procedure” OR “cardiac surgical procedure” OR “cardiovascular surgical procedure”) AND (mortality OR death OR dead OR outcome* OR outcomes OR “follow-up” OR prognosis OR prognos*) AND (heartbeat* OR “cardiac rate*” OR “heart rate*“). In addition, a snowball search for references to relevant studies was also performed.

Selection Criteria

The following criteria were applied in the selection of studies: (1) patients undergoing cardiac surgery aged >18 years, (2) increased perioperative heart rate was the variable of interest, (3) all-cause mortality was the outcome, (4) hazard ratios (HRs) and 95% confidence intervals (CIs) were available, (5) prospective or retrospective studies, and (6) studies published in English. The exclusion criteria were: (1) pregnant patients and (2) conference abstracts. Two researchers separately screened the titles and abstracts according to the inclusion criteria. In addition, two investigators screened the full-text for final inclusion and inconsistencies were resolved by consulting a third investigator.

Data Collection and Quality Assessment

The data included the first author’s name, year of publication, study design, sample size, mean age at baseline, sex ratio, follow-up duration, heart rate and data type (continuous or categorical variable), method of heart rate measurement, all-cause mortality (the longest follow-up time was collected if multiple timepoints of all-cause mortality were reported), covariate in the fully adjusted model, HR, and 95% CI (adjusted HR and 95% CIs were extracted first). The quality assessment used the 9-star Newcastle−Ottawa Scale (NOS; Wells et al., 2015) which comprises three parts: (1) selection (4 items, 4 stars); (2) comparability (1 item, two stars); and (3) outcome (3 items, three stars). A score of 7–9 stars was considered as high quality, 5–6 as moderate quality, and 0–4 as low quality. Two investigators conducted data collection and quality assessment independently, and a third investigator was consulted to settle any inconsistencies.

Statistical Analysis

Review Manager (version 5.3) was applied for all statistical analyses. Heterogeneity was assessed using the Q test and the I² statistic. Low, moderate, and high heterogeneity were noted as I² < 25%, I² between 25% and 75%, and I² > 75%, respectively. A random-effects model was used for a conservative result in case the measured value was not within the normal distribution range of the real value. Subgroup analyses were conducted to explore the source of heterogeneity. The pooled HR was synthesized separately based on the type of heart rate (continuous or categorical variable). The quantitative synthesis was conducted in two steps as previously reported (Qiu et al., 2017), and multiple HRs were reported according to the stratified heart rate. First, all HRs except for the reference were pooled using a fixed-effects model. The pooled HR was used in the final meta-analysis. Sensitivity analysis was carried out in which the pooled HR was recalculated by removing the studies one by one and comparing the results to assess whether the pooled HR was affected by any single study. A funnel plot was drawn to assess publication bias. All analyses were considered statistically significant at p < .05.

Results

Literature Search and Study Characteristics

In total, 8586 studies were identified, with six studies identified in the references of a previous study (Xu et al., 2016). After removing duplicates, the titles and abstracts of 6438 studies were screened. Subsequently, 80 studies were included in the full-text screening. Finally, 11 eligible studies with 33,849 patients and 3166 (9.4%) deaths were included in the meta-analysis (Figure 1). The duration of follow-up ranged from 3 months to 8 years. The heart rate data were continuous variables in three studies (Bemelmans et al., 2013; Mahaffey et al., 2008; Noman et al., 2013) that explored the association between all-cause mortality and preoperative heart rate with a 10-bpm increase. The remaining 8 studies investigated the association between all-cause mortality and perioperative heart rate using categorical variables, of which 4 (Frank et al., 2010; Parodi et al., 2010; Perne et al., 2016; Yang et al., 2020) used preoperative heart rate and another 4 (Antoni et al., 2011; Jensen et al., 2013; Lindman et al., 2015; Seronde et al., 2014) used postoperative heart rates (Table 1). The mean age at baseline, sex ratios, and covariates in the fully adjusted models are shown in the Supplementary Materials. According to the NOS criteria, 7 studies were identified as high quality, 3 as moderate quality, and 1 as low quality (Table 2).

Table 1.

Characteristics of Studies Included in the Meta-Analysis.

Study	Number of Patients	Surgical Treatment	Median Follow-Up	Measurements	Timing of Measurement	Variable Types	Cut-off Value (or Groups) of Heart Rate	Hazard Ratio (95%CI)
Mahaffey 2008	9978	PCI CABG catheterization	1 year	ECG	Preoperative	Continuous	-	1.19 (1.14–1.24)
Bemelmans 2013	4272	CABG Aneurysm surgery	4.4 years	12-Lead ECG	Preoperative	Continuous	-	1.15 (1.08–1.22)
Noman 2013	2130	PCI	1.5 years	12-Lead ECG	Preoperative	Continuous	-	1.59 (1.15–2.20)
Frank 2010	794	CABG	3.2 years	ECG	Preoperative	Category	<60 bpm, 60-74 bpm, 75-89 bpm >90 bpm	3.57 (0.90–14.17)
Yang 2020	820	AVS	5.5 years	Echocar-	Preoperative	Category	≥60	1.74 (1.10–2.75)
Perne 2016	6168	PCI CABG	3 months	ECG	Preoperative	Category	≥90 bpm	1.63 (1.16–2.31)
Parodi 2010	2379	PCI	6 months	ECG	Preoperative	Category	≥80 bpm	2.17 (1.41–3.33)
Jensen 2013	2029	PCI	2 years	ECG	Postoperative	Category	<60 bpm 60-69 bpm, 70-79 bpm, 80-89 bpm, >90 bpm	16.90 (5.20–55.00)
Seronde 2014	3079	PCI surgical revasc	5 years	Not mentioned	Postoperative	Category	>70 bpm	1.23 (1.04–1.45)
Antoni 2012	1453	PCI	4 years	12-Lead ECG	Postoperative	Category	≥70 bpm	2.29 (1.38–3.81)
Lindman 2015	747	TAVR/TAVR-TF/TAVR-nonTF/SAVR	1 year	Not mentioned	Postoperative	Category	>90 bpm	1.99 (1.42–2.80)

Note. PCI = Percutaneous Coronary Intervention; CABG = Coronary Artery Bypass Grafting; ECG = Electrocardiogram; AVS = Aortic Valve Surgery; TAVR = transcatheter Aortic Valve Replacement; TF = Transfemoral; SAVR = Surgical AVR.

Table 2.

The Newcastle–Ottawa Scale of the Included Studies.

	Selection (maximum = 4)	Comparability (maximum = 2)	Outcome (maximum = 3)	Total score< (maximum = 9)
Mahaffey 2008	3	2	2	7
Bemelmans 2013	3	2	3	8
Noman 2013	3	2	2	7
Frank 2010	3	1	2	6
Yang 2020	3	2	2	7
Perne 2016	3	2	1	6
Parodi 2010	3	0	1	4
Jensen 2013	3	2	2	7
Seronde 2014	3	2	2	7
Antoni 2012	3	2	3	8
Lindman 2015	3	0	2	5

Perioperative Heart Rate (Continuous Variables) and All-Cause Mortality

An association between preoperative heart rate and all-cause mortality was reported in three studies (Bemelmans et al., 2013; Mahaffey et al., 2008; Noman et al., 2013). The results showed a 19% increase in all-cause mortality (HR 1.19, 95% CI 1.11–1.26, p < .0001) with every 10-bpm increase in preoperative heart rate with moderate heterogeneity (I² = 51%) when a random-effects model was used (Figure 2).

Perioperative Heart Rate (Categorical Variables) and All-Cause Mortality

Eight studies (Antoni et al., 2011; Frank et al., 2010; Jensen et al., 2013; Lindman et al., 2015; Parodi et al., 2010; Perne et al., 2016; Seronde et al., 2014; Yang et al., 2020) demonstrated an association between perioperative heart rate and all-cause mortality. The pooled results showed that patients with higher perioperative heart rates had a 2.09-fold higher all-cause mortality rate compared to patients with lower perioperative heart rates (HR 2.09, 95% CI 1.53–2.86, p < .0001) with a high level of heterogeneity (I² = 81%) (Figure 3). Therefore, subgroup analyses were performed.

Heterogeneity Analysis

As shown in Figure 3, a 1.88-fold higher all-cause mortality was associated with higher preoperative heart rates (HR 1.88, 95% CI 1.51–2.34, p < .0001) with a low level of heterogeneity (I² = 0%). Higher postoperative heart rates led to a 2.29-fold higher all-cause mortality rate than lower preoperative heart rates (HR 2.29, 95% CI 1.28–4.09, p < .0001) with a high level of heterogeneity (I² = 91%). The level of heterogeneity between the subgroups was low (I² = 0%) (Figure 3). In addition, we conducted several subgroup analyses based on surgery type, follow-up duration, and heart rate cut-off values (Table 3 and Supplementary Materials).

Table 3.

Subgroup Analysis of Pooled HR for all-Cause Mortality.

Categories	No. of Studies	No. of Patients	Pooled HR (95% CI)		Heterogeneity
Categories	No. of Studies	No. of Patients	Fix/Random	p-Value	I² (%)	p-Value
Timing of Measurement	8	17049	1.56 (1.38, 1.76)	<.001	78	<.001
Preoperative	4	9741	1.88 (1.51, 2.34)	<.001	0	.490
Postoperative	4	7308	2.29 (1.28, 4.09)	<.001	91	<.001
Surgery type	8	17049	1.56 (1.38, 1.76)	< .001	78	< .001
PCI	5	15108	2.15 (1.36, 3.40)	<.001	88	<.001
Others	3	1941	2.00 (1.55, 2.57)	<.001	0	.540
Follow-up duration	8	17049	1.56 (1.38, 1.76)	< .001	78	< .001
≤2 years	4	11323	2.41 (1.55, 3.76)	<.001	79	.003
>2years	4	5726	1.78 (1.20, 2.64)	.004	71	.020
Cut-off value	8	17049	1.56 (1.38, 1.76)	< .001	78	< .001
≥90 bpm	4	9738	2.58 (1.55, 4.28)	.002	80	.002
<90 bpm	4	7311	1.73 (1.22, 2.45)	.002	72	.010

Note. HR= hazard ratio; PCI= percutaneous coronary intervention.

Sensitivity Analysis

The robustness of the two results was tested by removing a single study each time. Sensitivity analysis indicated that all quantitative syntheses had good robustness and were not influenced by any single original study.

Publication Bias

The funnel plot of the quantitative synthesis of HRs for perioperative heart rates (categorical variables) demonstrated the presence of publication bias (Figure 4).

Discussion

This study, which included 33,849 individuals with 3166 deaths in 11 studies, explored the predictive value of perioperative heart rate on all-cause mortality in patients undergoing cardiac surgery based on prospective and retrospective cohort studies. The results demonstrated that a 10-bpm increase in the preoperative heart rate resulted in a 19% increase in all-cause mortality. Furthermore, patients with higher perioperative heart rates had a 2.05-fold increase in all-cause mortality compared to those with lower perioperative heart rates. These results are similar to those of previous studies (Xu et al., 2016). Therefore, ascertaining whether heart rate, one of the most easily obtainable objective parameters, is an independent predictive factor of all-cause mortality was worth pursuing. In this study, the pooled HR displayed a good predictive value for all-cause mortality. One of the strengths of this study was that the pooled HR of the included studies was calculated by adjusting the covariables in the regression model, which indicated that the predictive value of preoperative heart rate in all-cause mortality was independent of other factors. Due to the high level of heterogeneity of the quantified synthesis of categorical variables, we conducted subgroup analysis based on the timing of measurement, surgery type, follow-up duration, and a cut-off value for heart rate, which indicated that the timing of measurement and surgery type could affect heterogeneity.

The mechanism underlying the association between preoperative heart rate and all-cause mortality remains unclear. We hypothesize that there are three reasons for the predictive value. First, a higher heart rate indicates higher sympathetic activity and myocardial oxygen consumption, both of which are associated with a worse outcome. Abnormal sympathetic activity could lead to increased platelet aggregation, coronary vasoconstriction, and arrhythmia (Rovere et al., 1998). Higher myocardial oxygen consumption can lead to abnormal myocardial metabolism, ventricular dysfunction, and myocardial infarction (Ahmadi et al., 2020). Second, studies have indicated that a shorter lifespan is associated with elevated heart rate (Zhang & Zhang, 2009). An increased heart rate could cause a subsequent elevation of reactive oxygen species (Luo et al., 2020), inflammatory cytokines, glucose-lipid metabolism disturbance (Saladini & Palatini, 2018), and abnormal activation of the immune system (Moreira et al., 2017). These physiological dysfunctions could easily contribute to the impairment of organ function and lead to an adverse outcome, including death. Third, patients with higher heart rates are more vulnerable to diabetes mellitus (Docherty et al., 2020) and obesity (Shigetoh et al., 2009). In addition, a higher heart rate is also an independent predictor of hypertension (Caetano & Delgado Alves, 2015). Summarily, this association indicates that patients could be more susceptible to these pathological conditions, which could continuously play a role after cardiac surgery and lead to a worse outcome. In addition, the mean follow-up time was insufficient for the occurrence of death, which could have underestimated the negative effect of preoperative heart rate on all-cause mortality. Therefore, larger sample sizes and longer follow-up durations are needed in future cohort studies to elucidate the predictive value of preoperative heart rate on all-cause mortality in patients undergoing cardiac surgery.

Antoni et al. (2011) demonstrated that postoperative heart rate is not only an indicator of clinical impairment in patients undergoing cardiac surgery but may also relate to prognosis. We hypothesized that a higher postoperative heart rate predicted all-cause mortality for three reasons. First, the influence of a high postoperative heart rate may overlap with the preoperative heart rate in terms of increased myocardial oxygen consumption and abnormal activation of the immune system. Second, patients after cardiac surgery usually exhibit a high heart rate (Lindman et al., 2015) due to the operation, pain level, and psychological status. Adverse outcomes frequently occur when heart rate control is not optimal. Postoperative acute kidney injury (Wang & Bellomo, 2017) and low cardiac output syndrome (Massé & Antonacci, 2005) can be induced by an elevated postoperative heart rate, which increases the potential for liver and central nervous system dysfunction (Kumon et al., 1986) and the risk of death. Third, an increased postoperative heart rate is associated with post-traumatic stress disorder (Morris et al., 2016), which could negatively affect the prognosis of patients after cardiac surgery. This could explain why patients with higher postoperative heart rates had higher all-cause mortality rates than those with postoperative heart rates. Similarly, as the follow-up durations were not long enough in the included studies (mean follow-up, 3 years), we considered that the predictive effect of postoperative heart rate was underestimated. Therefore, additional studies are needed to investigate this further.

Our results indicated that heart rate control during the perioperative period could improve the prognosis of patients undergoing cardiac surgery. Current studies of heart rate control have mainly focused on atrial fibrillation and demonstrated that rate control is sufficient for treatment (Dobrev et al., 2019). Few studies have focused on the management of increased heart rate with sinus rhythm. Based on our hypothesis, we consider that medical staff should attach great importance to an abnormally increased heart rate in patients about to undergo or who have undergone cardiac surgery, regardless of the existence of an abnormal rhythm. The cut-off value for heart rate remains controversial. Zhang et al. (2016) considered 80 bpm as the cut-off value in a meta-analysis that focused on the general population and explored the relationship between resting heart rate and all-cause and cardiovascular mortality. However, Woodward et al. (2014) found that a heart rate >65 bpm had a strong independent effect of premature mortality and stroke in 12 cohort studies. Furthermore, essential aspects of heart rate control have been put forward by the American Heart Association, the American College of Cardiology, the European Society of Cardiology, the Canadian Cardiovascular Society, and the Heart Rhythm Society. However, the cut-off value remains controversial between these associations which reduced the clinical guidance (Andrade et al., 2017). Therefore, future research should determine the cut-off value for heart rate considering different factors such as race, gender, and disease status.

There were some limitations to this study. To ensure the reliability of the results, we only incorporated original studies with the same data type in the quantitative synthesis, resulting in a small number of included studies, which may have caused publication bias. In addition, the results of the quantified synthesis showed a high level of heterogeneity. However, the potential factors affecting heterogeneity were explored by subgroup analysis, which indicated that the timing of measurement and surgery type could be potential sources of heterogeneity. Furthermore, although uniform methodological criteria were used in the screening and the studies were all cohort studies, heterogeneity was still inevitable due to the inconsistency of confounding factors when the pooled HR of the original studies was calculated. However, the sensitivity analysis showed that no single study influenced the results, indicating good robustness.

In conclusion, this meta-analysis demonstrated that perioperative heart rate (measured by both continuous and categorical variables) is an independent predictor of all-cause mortality in cardiac surgery patients. However, it is important to note that the small sample size and the heterogeneity warrant cautious interpretation of these results. Multicenter, large-scale cohort studies with longer follow-up durations are needed to further investigate the predictive value of increased perioperative heart rate on all-cause mortality in patients undergoing cardiac surgery.

Supplemental Material

Supplemental Material - Predictive Value of Increased Perioperative Heart Rate for All-Cause Mortality After Cardiac Surgery: A Systematic Review and Meta-Analysis

Supplemental Material for Predictive Value of Increased Perioperative Heart Rate for All-Cause Mortality After Cardiac Surgery: A Systematic Review and Meta-Analysis by Shurong Xu, Yanjuan Lin, Lingyu Lin, Yanchun Peng, and Liangwan Chen in Biological Research For Nursing

Footnotes

Acknowledgments

We would like to thank Editage () for English language editing.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the Fujian Provincial Department of Science and Technology Pilot Project (2021Y0023), the Fujian Provincial Finance Special Project (Grant number: 2020CZ011), and the Fujian Key Laboratory of Cardio-Thoracic Surgery (Fujian Medical University).

ORCID iD

Yanjuan Lin

Supplemental Material

Supplemental material for this article is available online.

References

Ahmadi

Renaud

J. M.

Promislow

, et al. (2020). Increased myocardial oxygen consumption rates are associated with maladaptive right ventricular remodeling and decreased event-free survival in heart failure patients. Journal of Nuclear Cardiology: Official Publication of the American Society of Nuclear Cardiology. 28(6), 2784–2795. https://doi.org/10.1007/s12350-020-02144-x.

Andrade

J. G.

Macle

Nattel

Verma

Cairns

(2017). Contemporary atrial fibrillation management: A Comparison of the Current AHA/ACC/HRS, CCS, and ESC Guidelines. The Canadian Journal of Cardiology, 33(8), 965–976. https://doi.org/10.1016/j.cjca.2017.06.002.

Antoni

M. L.

Boden

Delgado

Boersma

Fox

Schalij

M. J.

Bax

J. J.

(2012). Relationship between discharge heart rate and mortality in patients after acute myocardial infarction treated with primary percutaneous coronary intervention. European Heart Journal, 33(1), 96–102. https://doi.org/10.1093/eurheartj/ehr293.

Bemelmans

R. H.

van der Graaf

Nathoe

H. M.

Wassink

A. M.

Vernooij

J. W.

Spiering

Visseren

F. L.

(2013). The risk of resting heart rate on vascular events and mortality in vascular patients. Int J Cardiol, 168(2), 1410–1415. https://doi.org/10.1016/j.ijcard.2012.12.043.

Caetano

Delgado Alves

(2015). Heart rate and cardiovascular protection. European Journal of Internal Medicine, 26(4), 217–222. https://doi.org/10.1016/j.ejim.2015.02.009.

Chen

Barywani

S. B.

Sigurjonsdottir

(2018). Improved short and long term survival associated with percutaneous coronary intervention in the elderly patients with acute coronary syndrome. BMC Geriatrics, 18(1), 137. https://doi.org/10.1186/s12877-018-0818-z.

Chen

L. W.

X. J.

Zhang

G. C.

Yang

G. F.

Yang

Z. W.

Dong

Cao

Chen

(2011). Total arch repair for acute type A aortic dissection with 2 modified techniques: open single-branched stent graft placement and reinforcement of the dissected arch vessel stump with stent graft. Circulation, 123(22), 2536–2541. https://doi.org/10.1161/CIRCULATIONAHA.110.008656.

Dobrev

Aguilar

Heijman

Guichard

J. B.

Nattel

(2019). Postoperative atrial fibrillation: mechanisms, manifestations and management. Nature Reviews Cardiology, 16(7), 417–436. https://doi.org/10.1038/s41569-019-0166-5.

Docherty

K. F.

Shen

Castagno

Petrie

M. C.

Abraham

W. T.

Böhm

Desai

A. S.

Dickstein

Køber

L. V.

Packer

Rouleau

J. L.

Solomon

S. D.

Swedberg

Vazir

Zile

M. R.

Jhund

P. S.

McMurray

JJV

(2020). Relationship between heart rate and outcomes in patients in sinus rhythm or atrial fibrillation with heart failure and reduced ejection fraction. European Journal of Heart Failure, 22(3), 528–538. https://doi.org/10.1002/ejhf.1682.

10.

Frank

Aboyans

Le Guyader

Orsel

Lacroix

Cornu

Laskar

(2010). Usefulness of postoperative heart rate as an independent predictor of mortality after coronary bypass grafting. The American Journal of Cardiology, 106(7), 958–962. https://doi.org/10.1016/j.amjcard.2010.05.026.

11.

Gaudino

Di Franco

Flather

Gerry

Bagiella

Gray

Pearcey

Saw

T. H.

Lees

Benedetto

Fremes

S. E.

Taggart

D. P.

(2021). Association of Age With 10-Year Outcomes After coronary surgery in the arterial revascularization trial. Journal of the American College of Cardiology, 77(1), 18–26. https://doi.org/10.1016/j.jacc.2020.10.047.

12.

Head

S. J.

Milojevic

Daemen

Ahn

J. M.

Boersma

Christiansen

E. H.

Domanski

M. J.

Farkouh

M. E.

Flather

Fuster

Hlatky

M. A.

Holm

N. R.

Hueb

W. A.

Kamalesh

Kim

Y. H.

Mäkikallio

Mohr

F. W.

Papageorgiou

Park

S. J.

Rodriguez

A. E.

Sabik

J. F.

Stables

R. H.

Stone

G. W.

Serruys

P. W.

Kappetein

A. P.

(2018). Mortality after coronary artery bypass grafting versus percutaneous coronary intervention with stenting for coronary artery disease: A pooled analysis of individual patient data. Lancet, 391(10124), 939–948. https://doi.org/10.1016/S0140-6736(18)30423-9.

13.

Jensen

M. T.

Kaiser

Sandsten

K. E.

Alber

Wanitschek

Iversen

Jensen

J. S.

Pedersen

Soerensen

Rickli

Zurek

Fahrni

Bertel

De Servi

Erne

Pfisterer

Galatius

(2013). Heart rate at discharge and long-term prognosis following percutaneous coronary intervention in stable and acute coronary syndromes — results from the BASKET PROVE trial. International Journal of Cardiology, 168(4), 3802–3806. https://doi.org/10.1016/j.ijcard.2013.06.034.

14.

Kumon

Tanaka

Hirata

Naito

Fujita

(1986). Organ failures due to low cardiac output syndrome following open heart surgery. Jpn Circ J, 50(4), 329–335. https://doi.org/10.1253/jcj.50.329.

15.

Lindman

B. R.

Goldstein

J. S.

Nassif

M. E.

Zajarias

Novak

Tibrewala

Vatterott

A. M.

Lawler

Damiano

R. J.

Moon

M. R.

Lawton

J. S.

Lasala

J. M.

Maniar

H. S.

(2015). Systemic inflammatory response syndrome after transcatheter or surgical aortic valve replacement. Heart, 101(7), 537–545. https://doi.org/10.1136/heartjnl-2014-307057.

16.

Luo

Yan

Liu

Zeng

Cheung

C. W.

Liu

Irwin

M. G.

Huang

Xia

(2020). Allopurinol reduces oxidative stress and activates Nrf2/p62 to attenuate diabetic cardiomyopathy in rats. Current Molecular Medicine, 24(2), 1760–1773. https://doi.org/10.1111/jcmm.14870.

17.

Mahaffey

K. W.

Yang

Pieper

K. S.

Antman

E. M.

White

H. D.

Goodman

S. G.

Cohen

Kleiman

N. S.

Langer

Aylward

P. E.

Col

J. J.

Reist

Ferguson

J. J.

Califf

R. M.

(2008). Prediction of One-Year Survival in High-Risk Patients with Acute Coronary Syndromes: Results from the SYNERGY Trial. Journal of General Internal Medicine, 23(3), 310–316. https://doi.org/10.1007/s11606-007-0498-4.

18.

Massé

Antonacci

(2005). Low cardiac output syndrome: identification and management. Critical Care Nursing Clinics of North America, 17(4), 375. https://doi.org/10.1016/j.ccell.2005.07.005.

19.

Mathew

J. P.

Parks

Savino

J. S.

Friedman

A. S.

Koch

Mangano

D. T.

Browner

W. S.

(1996). Atrial fibrillation following coronary artery bypass graft surgery: predictors, outcomes, and resource utilization. MultiCenter Study of Perioperative Ischemia Research Group. Jsls: Journal of the Society of Laparoendoscopic Surgeons, 276(4), 300–306. https://doi.org/10.1001/jama.276.4.300.

20.

Moreira

H. G.

Lage

R. L.

Martinez

D. G.

Ferreira-Santos

Rondon

M. U. P. B.

Negrão

C. E.

Nicolau

J. C.

(2017). Sympathetic nervous activity in patients with acute coronary syndrome: a comparative study of inflammatory biomarkers. Clin Sci (Lond), 131(9), 883–895. https://doi.org/10.1042/CS20170049.

21.

Morris

M. C.

Hellman

Abelson

J. L.

Rao

(2016). Cortisol, heart rate, and blood pressure as early markers of PTSD risk: A systematic review and meta-analysis. Clinical Psychology Review, 49(11), 79–91. https://doi.org/10.1016/j.cpr.2016.09.001.

22.

Noman

Balasubramaniam

Das

Ang

Kunadian

Ivanauskiene

Zaman

A. G.

(2013). Admission heart rate predicts mortality following primary percutaneous coronary intervention for ST-elevation myocardial infarction: an observational study. Cardiovascular Therapeutics, 31(6), 363–369. https://doi.org/10.1111/1755-5922.12031.

23.

Ozieranski

Kaplon-Cieslicka

Peller

, et al. (2016). Clinical characteristics and predictors of one-year outcome of heart failure patients with atrial fibrillation compared to heart failure patients in sinus rhythm. Kardiologia Polska, 74(3), 251-261. https://doi.org/10.5603/KP.a2015.0180.

24.

Parodi

Bellandi

Valenti

Memisha

Giuliani

Velluzzi

Migliorini

Carrabba

Antoniucci

(2010). Heart rate as an independent prognostic risk factor in patients with acute myocardial infarction undergoing primary percutaneous coronary intervention. Atherosclerosis, 211(1), 255–259. https://doi.org/10.1016/j.atherosclerosis.2010.02.017.

25.

Perne

Schmidt

F. P.

Hochadel

Giannitsis

Darius

Maier

L. S.

Schmitt

Heusch

Voigtländer

Mudra

Gori

Senges

Münzel

(2016). Admission heart rate in relation to presentation and prognosis in patients with acute myocardial infarction. Treatment regimens in German chest pain units. Herz, 41(3), 233–240. https://doi.org/10.1007/s00059-015-4355-7.

26.

Qiu

Cai

Sun

Zuegel

Steinacker

J. M.

Schumann

(2017). Heart rate recovery and risk of cardiovascular events and all-cause mortality: A meta-analysis of prospective cohort studies. [Journal of the American Heart Association Electronic Resource], 6(5). https://doi.org/10.1161/JAHA.117.005505.

27.

Reich

D. L.

Bodian

C. A.

Krol

Kuroda

Osinski

Thys

D. M.

(1999). Intraoperative hemodynamic predictors of mortality, stroke, and myocardial infarction after coronary artery bypass surgery. Anesthesia and Analgesia, 89(4), 814-822. https://doi.org/10.1097/00000539-199910000-00002.

28.

Rovere

M. T. L

Bigger

J. T.

Marcus

F. I.

Mortara

Schwartz

P. J.

(1998). Baroreflex sensitivity and heart-rate variability in prediction of total cardiac mortality after myocardial infarction. Lancet, 351(9101), 478–484. https://doi.org/10.1016/s0140-6736(97)11144-8.

29.

Saladini

Palatini

(2018). Arterial Distensibility, Physical Activity, and the Metabolic Syndrome. Current Hypertension Reports, 20(5), 39. https://doi.org/10.1007/s11906-018-0837-3.

30.

Seronde

M. F.

Geha

Puymirat

Chaib

Simon

Berard

Drouet

Bataille

Danchin

Schiele

(2014). Discharge Heart Rate and Mortality after Acute Myocardial Infarction. The American Journal of Medicine, 127(10), 954–962. https://doi.org/10.1016/j.amjmed.2014.06.034.

31.

Shigetoh

Adachi

Yamagishi

Enomoto

Fukami

Otsuka

Kumagae

Furuki

Nanjo

Imaizumi

(2009). Higher heart rate may predispose to obesity and diabetes mellitus: 20-year prospective study in a general population. American Journal of Hypertension, 22(2), 151–155. https://doi.org/10.1038/ajh.2008.331.

32.

Thomas

R. J.

Beatty

A. L.

Beckie

T. M.

Brewer

L. C.

Brown

T. M.

Forman

D. E.

Franklin

B. A.

Keteyian

S. J.

Kitzman

D. W.

Regensteiner

J. G.

Sanderson

B. K.

Whooley

M. A.

(2019). Home-Based cardiac rehabilitation: A scientific statement from the American association of cardiovascular and pulmonary rehabilitation, the american heart association, and the American college of cardiology. Journal of the American College of Cardiology, 74(1), 133–153. https://doi.org/10.1016/j.jacc.2019.03.008.

33.

Vijayan

(2021). Tackling AKI: prevention, timing of dialysis and follow-up. Nature Reviews. Nephrology, 17(2), 87–88. https://doi.org/10.1038/s41581-020-00390-3.

34.

Wang

Bellomo

(2017). Cardiac surgery-associated acute kidney injury: risk factors, pathophysiology and treatment. Nature Reviews Nephrology, 13(11), 697–711. https://doi.org/10.1038/nrneph.2017.119.

35.

Wells

G. A. B. S.

O'Connell

Peterson

Welch

Losos

Tugwell

(2015). The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Retrieved August. 17, 2015, from http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp

36.

Woodward

Webster

Murakami

Barzi

Lam

T. H.

Fang

Suh

Batty

G. D.

Huxley

Rodgers

(2014). The association between resting heart rate, cardiovascular disease and mortality: evidence from 112,680 men and women in 12 cohorts. European Journal of Preventive Cardiology, 21(6), 719–726. https://doi.org/10.1177/2047487312452501.

37.

Zhan

Xiong

Tan

(2016). The relationship between heart rate and mortality of patients with acute coronary syndromes in the coronary intervention era: Meta-analysis. Medicine (Baltimore), 95(46), Article e5371. https://doi.org/10.1097/MD.0000000000005371.

38.

Yang

L. T.

Pellikka

P. A.

Enriquez-Sarano

Scott

C. G.

Padang

Mankad

S. V.

Schaff

H. V.

Michelena

H. I.

(2020). Diastolic Blood Pressure and Heart Rate Are Independently Associated With Mortality in Chronic Aortic Regurgitation. Journal of the American College of Cardiology, 75(1), 29–39. https://doi.org/10.1016/j.jacc.2019.10.047.

39.

Zhang

Shen

(2016). Resting heart rate and all-cause and cardiovascular mortality in the general population: a meta-analysis. Cmaj, 188(3), Article e53–e63. https://doi.org/10.1503/cmaj.150535.

40.

Zhang

G. Q.

Zhang

(2009). Heart rate, lifespan, and mortality risk. Ageing Research Reviews, 8(1), 52–60. https://doi.org/10.1016/j.arr.2008.10.001

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