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Association between preoperative ambulatory heart rate and postoperative myocardial injury: a retrospective cohort study
British Journal of Anaesthesia, 121 (4): 722e729 (2018) doi: 10.1016/j.bja.2018.06.016 Advance Access Publication Date: 6 August 2018 Cardiovascular
Association between preoperative ambulatory heart rate and postoperative myocardial injury: a retrospective cohort study K. S. Ladha1,2,*, W. S. Beattie1,2, G. Tait1,2 and D. N. Wijeysundera1,2,3,4 1
Department of Anaesthesia and Pain Management, Toronto General Hospital, Toronto, ON, Canada, 2Department of Anaesthesia, University of Toronto, Toronto, ON, Canada, 3Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, Canada and 4Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, ON, Canada
*Corresponding author. E-mail: [email protected]
Abstract Background: Resting heart rate is well established as a predictor of morbidity and mortality in the general population. However, the relationship between preoperative heart rate and perioperative outcomes, specifically myocardial injury, is unclear. Methods: This retrospective cohort study included patients undergoing elective major non-cardiac surgery from 2008 to 2014 at a multisite healthcare system. The exposure was ambulatory heart rate measured during the outpatient preoperative clinic visit, whereas the outcome of interest was myocardial injury (peak postoperative troponin I concentration >30 ng L1). Covariates included patient characteristics, comorbidities, and preoperative medications. We constructed several multivariable regression models that each modelled heart rate in a different manner, including as a simple continuous variable, categories, and fractional polynomials. Results: The cohort included 41 140 patients, of whom 4857 (11.8%) experienced myocardial injury. Based on pre-specified heart categories thresholds, a heart rate 90 beats min1 was associated with an elevated odds of myocardial injury compared with a heart rate <60 beats min1 (adjusted odds ratio, 1.22; 95% confidence interval, 1.06e1.39; P¼0.005). This result was consistent regardless of the method used for categorisation. When fractional polynomials were used to model heart rate, a ‘J-shaped’ relationship between heart rate and odds of myocardial injury was observed. Conclusions: This cohort study found that both very high preoperative heart rates, and possibly also very low heart rates, are associated with increased risk of myocardial injury. Whether heart rate is a modifiable risk factor, or rather simply a marker of underlying cardiac pathology, needs to be determined in further research. Keywords: troponin I; risk factors; heart rate; retrospective studies; morbidity; outcomes; perioperative complications
Editorial decision: 17 June 2018; Accepted: 17 June 2018 © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved. For Permissions, please email: [email protected]
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Exposure and outcome definitions Editor’s key points A higher-than-normal resting heart rate is an indicator of reduced cardiac reserve. Cardiorespiratory fitness is a key determinant of perioperative outcome. This study found higher heart rates were associated with perioperative myocardial injury and mortality. Whether or not heart rate control could reduce severe perioperative complications is unclear.
Myocardial injury is a prognostically important complication of non-cardiac surgery that affects 8%e22% of patients after elective procedures.1e3 Even when asymptomatic, myocardial injury is highly associated with increased morbidity and mortality.4 Thus, finding ways to mitigate myocardial injury has important implications for the millions of patients undergoing surgery worldwide every year. In current conceptual frameworks of cardiovascular physiology, heart rate is considered to be a key determinant of the balance between myocardial oxygen supply and demand. Although in theory lowering heart rate before operation using negative chronotropic agents such as beta blockers should reduce perioperative complications, the evidence regarding the efficacy of this approach is equivocal.5 One possible explanation is that previous studies used preset doses of medications that were not titrated to a target heart rate threshold. A challenge with implementing such an approach is that the optimal preoperative heart rate to minimise the risk of myocardial injury is not currently known. Several previous studies have elucidated the relationship between intraoperative heart rate and outcomes6e8; however, there are very limited data examining the connection between preoperative heart rate and postoperative myocardial injury. The single previous study that investigated this association used the heart rate recorded immediately before the induction of anaesthesia, which is likely not indicative of true preoperative heart rate.9 We therefore conducted a retrospective cohort study in a multihospital healthcare system to evaluate the link between preoperative ambulatory heart rate and postoperative myocardial injury.
The preoperative heart rate was defined as the ambulatory value documented during the preoperative evaluation clinic visit and measured in beats per minute. Heart rate values less than 40 beats min1 or greater than 140 beats min1 were excluded from the primary analysis as these values were likely to fall outside the expected physiologic range for ambulatory heart rates, and therefore likely related to data entry error. Records with missing heart rate information were also excluded from the study cohort. Myocardial injury was defined as a peak postoperative troponin I concentration exceeding 30 ng L1 or an International Classification of Disease 10th Edition diagnostic code for
Fig 1. Flowchart of cohort formation.
Methods Study population This retrospective cohort study was conducted at the University Health Network, a quaternary-care multisite healthcare system located in Toronto, Ontario, Canada. Data were obtained from linked institutional databases including the preoperative evaluation clinic database, surgical procedure database, and an electronic data warehouse containing administrative and laboratory data. Consecutive patients undergoing elective non-cardiac surgery from 2008 to 2014 with a prior preoperative anaesthesiology consultation at the institutional preoperative evaluation clinic were included in the cohort. Patients with missing data and those with an ASA Physical Status (ASA-PS) Classification 5 were excluded from the analysis. Institutional Research Ethics Board approval was obtained before the initiation of the data set assembly and analysis.
Fig 2. Box plot displaying the distribution of preoperative heart rate among patients without and with myocardial injury.
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Table 1 Descriptive statistical data of study cohort. All values presented as n (%) unless noted otherwise. ASA, American Society of Anesthesiologists; ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blockers; ENT, ear, nose, throat; eGFR, estimated glomerular filtration rate; TURP, transurethral resection of the prostate
N Heart rate, mean (SD) Male sex Age (yr) 18e40 41e60 61e80 >80 ASA-physical status 1 2 3 4 Coronary artery disease Heart failure Hypertension Atrial fibrillation Pacemaker Cerebrovascular diseases Peripheral vascular disease Smoking history Never smoker Current or recent smoker Remote smoker Diabetes mellitus Revised Cardiac Risk Index 0 1 2 3 4 5 Liver disease Asthma Chronic obstructive pulmonary disease Preoperative anaemia Non-anaemic Mild Moderate Severe Unknown History of cancer No Non-metastatic Metastatic Renal Disease eGFR 60 ml min1 1.73 m2 eGFR 30e59 ml min1 1.73 m2 eGFR <30 ml min1 1.73 m2 or dialysis No preoperative creatinine Surgery type Abdominal Endovascular aorta Vascular Urology Hysterectomy/prostatectomy Major ENT Plastics Orthopaedicsdjoint Spine Thoracic Neurosurgery TURP Minor ENT Other minor/ambulatory Preoperative medication use Beta blocker Non-dihydropyridine calcium channel blocker Aspirin ACE inhibitor ARB Statin
No myocardial injury
Myocardial injury
36283 73.9 (12.8) 17431 (48.0)
4857 72.9 (13.8) 2764 (56.9)
4590 (12.7) 14074 (38.8) 15642 (43.1) 1977 (5.4)
260 (5.4) 1187 (24.4) 2869 (59.1) 541 (11.1)
1701 (4.7) 12889 (35.5) 19625 (54.1) 2068 (5.7) 4227 (11.7) 601 (1.7) 15151 (41.8) 1428 (3.9) 359 (1.0) 1759 (4.8) 565 (1.6)
63 (1.3) 655 (13.5) 2873 (59.2) 1266 (26.1) 1277 (26.3) 220 (4.5) 2965 (61.0) 446 (9.2) 121 (2.5) 471 (9.7) 689 (14.2)
17769 (49.0) 5431 (15.0) 13083 (36.1) 5247 (14.5)
1976 (40.7) 786 (16.2) 2095 (43.1) 1149 (23.7)
27451 (75.7) 7372 (20.3) 1220 (3.4) 206 (0.6) 31 (0.1) 3 (<0.01) 809 (2.2) 3721 (10.3) 1990 (5.5)
2310 (47.6) 1540 (31.7) 699 (14.4) 248 (5.1) 51 (1.1) 9 (0.2) 170 (3.5) 456 (9.4) 513 (10.6)
23894 (65.9) 2121 (5.8) 763 (2.1) 34 (0.1) 9471 (26.1)
3393 (69.9) 624 (12.8) 253 (5.2) 7 (0.1) 580 (11.9)
23151 (63.8) 8534 (23.5) 4598 (12.7)
2901 (59.7) 1125 (23.2) 831 (17.1)
30466 (84.0) 3109 (8.6) 576 (1.6) 2132 (5.9)
3794 (78.1) 782 (16.1) 162 (3.3) 119 (2.5)
5182 (14.3) 177 (0.5) 144 (0.4) 1486 (4.1) 3777 (10.4) 3574 (9.9) 1146 (3.2) 7547 (20.8) 2178 (6.0) 2723 (7.5) 2209 (6.1) 1933 (5.3) 1345 (3.7) 2862 (7.9)
1011 (20.8) 368 (7.6) 223 (4.6) 268 (5.5) 385 (7.9) 424 (8.7) 41 (0.8) 624 (12.8) 318 (6.5) 354 (7.3) 223 (4.6) 60 (1.2) 56 (1.2) 502 (10.3)
3906 (10.8) 666 (1.8) 5827 (16.1) 4898 (13.5) 3246 (8.9) 7801 (21.5)
1147 (23.6) 161 (3.3) 1515 (31.2) 1066 (21.9) 640 (13.2) 1813 (37.3)
P-value
Standardised difference
<0.001 <0.001 <0.001
0.076 e0.18 0.47
<0.001
0.76
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
e0.38 e0.17 e0.39 e0.21 e0.12 e0.19 e0.48 0.17
<0.001 <0.001
e0.24 0.67
<0.001 0.060 <0.001 <0.001
e0.076 0.029 e0.19 0.44
<0.001
0.13
<0.001
0.31
<0.001
0.65
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001
e0.35 e0.094 e0.36 e0.22 e0.14 e0.35
Preoperative heart rate and myocardial injury
Table 2 Results from multivariable regression models predicting myocardial injury using different techniques to adjust for heart rate. CI, confidence interval Method used to model heart rate (beats min¡1) Single continuous term Change per 10 beats min1 Quartiles <65 65e73 74e82 >83 Deciles <59 59e63 64e66 67e70 71e73 74e76 77e80 81e84 85e91 >91 Pre-specified cut-off values <60 60e69 70e79 80e89 >89
Adjusted odds ratio (95% CI)
P-value
1.04 (1.01e1.07)
0.003
1 (ref) 0.99 (0.9e1.09) 1.04 (0.95e1.15) 1.16 (1.05e1.28)
e 0.82 0.41 0.003
1 (ref) 0.94 (0.82e1.08) 1.02 (0.88e1.18) 0.99 (0.86e1.13) 0.91 (0.78e1.06) 1.07 (0.92e1.23) 0.95 (0.82e1.1) 1.02 (0.88e1.19) 1.11 (0.96e1.29) 1.22 (1.06e1.41)
e 0.38 0.83 0.89 0.23 0.38 0.52 0.79 0.15 0.006
1 (ref) 0.98 (0.88e1.10) 1.00 (0.89e1.12) 1.06 (0.94e1.20) 1.22 (1.06e1.39)
e 0.75 0.97 0.32 0.005
a postoperative myocardial infarction. Troponin was measured with the Abbott ARCHITECT i2000 analyser (Abbott Diagnostics, Abbott Park, IL, USA). In the primary analysis, patients with no postoperative troponin measurements were
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assumed to not have myocardial injury. Sensitivity analyses were also conducted to assess the validity of this assumption (see Statistical Analysis section).
Covariates Preoperative information was obtained from the history and physical examination conducted during the preoperative clinic visit and stored in the electronic medical record. These data were obtained first by a nurse practitioner and pharmacist, after which the data were verified by a consultant anaesthesiologist. Clinically sensible covariates of interest were selected a priori based on being plausible potential confounders of the association between heart rate and myocardial injury. These covariates included patient characteristics (i.e. age, sex), comorbidities (i.e. hypertension, atrial fibrillation, coronary artery disease, heart failure, permanent pacemaker, diabetes mellitus, chronic obstructive pulmonary disease, asthma, liver disease, non-metastatic cancer, metastatic cancer, renal disease, anaemia), long-term cardiac medications, and surgical procedure type. Anaemia was determined by preoperative haemoglobin concentration and categorised as non-anaemic, mild, moderate, or severe according to the World Health Organization’s published definition.10 Renal impairment was identified using estimated glomerular filtration rate (eGFR) estimated by the CKD-EPI equation and the preoperative creatinine concentration.11 We did not have information regarding race/ethnicity and thus this adjustment was not used in the calculation of eGFR. Patients were categorised according to their eGFR, based on previously defined cut-offs.12 Age was categorised as 18e40, 41e60, 61e80, and >80 yr. Smoking status was classified as current smoker, recent quitter (i.e. quit in the past 30 days), remote smoker, or no smoking history. Preoperative cardiac medications of
Fig 3. Results from multivariable regression model predicting myocardial injury with heart rate modelled using fractional polynomials.
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Fig 4. Results from the sensitivity analysis. (A) Multivariable regression model with heart modelled as a categorical variable using prespecified values. Odds ratios represent odds of having myocardial injury for patients with a heart rate greater than 90 beats min1 compared with patients with a heart rate less than 60 beats min1. (B) Multivariable regression model with heart modelled using fractional polynomials. IPW, inverse probability weighting.
interest included negative chronotropic agents (i.e. beta blockers, non-dihydropyridine calcium channel blockers), aspirin, angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs), and statins. We also
included two summary measures of comorbidity: the Revised Cardiac Risk Index (RCRI)13 and the ASA-PS class. Patients with an ‘unknown’ covariate value were assumed not to have that condition. This assumption was made for less
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than 1% of the sample for each covariate (Supplementary Table S1). The exception to this was for preoperative anaemia and renal disease where unknown values were analysed as a separate category given that they represented a larger proportion of the cohort.
Statistical analysis We initially conducted bivariate analyses to characterise unadjusted differences between patients who did and those who did not suffer myocardial injury (i.e. t-tests for continuous variables and c2 tests for categorical variables). Additionally, we examined the characteristics of patients across different categories specified based on preoperative heart rate values. The adjusted association between preoperative heart rate and postoperative myocardial injury was then examined using multivariable logistic regression models. All clinically sensible covariates were included in the model. Given that the exposure of interest, heart rate, was a continuous variable, we constructed several regression models that each modelled heart rate in a different mannerdto ensure that the results were not sensitive to the underlying assumption of the nature of relationship between heart rate and the outcome of interest. Specifically, we included heart rate as a single continuous term, categories based on quartiles, categories based on deciles, categories based on pre-specified cut-offs (<60, 60e69, 70e79, 80e89, 90 beats min1), and fractional polynomials (to account for any non-linear relationship between the exposure and outcome).14 We undertook several sensitivity analyses to further assess the robustness of our results. These included repeating the analyses after excluding patients with permanent pacemakers or atrial fibrillation, repeating the analyses after excluding patients on negative chronotropic agents, repeating the analyses while including patients with ‘extreme’ heart rate values (i.e. <40 and >140 beats min1), repeating the analyses after excluding patients with ‘unknown’ covariate values, and repeating the analyses while using inverse probability weighting to account for missing heart rate values.15 To explore the assumption of no myocardial injury for patients with missing troponin values, we repeated the analysis using 30 day mortality as the outcome of interest as we had this information for all patients in the cohort. These sensitivity analyses were conducted using both the pre-specified categorisation of heart rate (i.e. <60, 60e69, 70e79, 80e89, 90 beats min1) and the fractional polynomial analysis. All statistical analyses were performed in Stata Version 14.2 (StataCorp, College Station, TX, USA) with significance defined as a two-tailed P<0.05. Sample size was determined using the available data, and no a priori calculation was performed.
Results A total of 41 140 patients met all inclusion criteria and formed the study cohort (Fig. 1). Of these patients, 4857 (11.8%) individuals suffered myocardial injury after surgery. There was no clinically significant difference between the mean preoperative heart rates of patients who did and those who did not suffer myocardial injury [mean (standard deviation, SD); 73 (14) vs 74 (13), P<0.001]. Figure 2 is a boxplot showing the distributions of heart rate between the two groups. The characteristics of patients who did vs those who did not suffer
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myocardial injury are presented in Table 1. In general, patients with myocardial injury were more likely to be older males with a greater burden of comorbidities. When examining baseline characteristics across strata defined by preoperative heart rate, patients with slower heart rates had a higher prevalence of coronary artery disease, heart failure, and preoperative beta-blocker usage (Supplementary Table S2). After excluding patients with pacemakers and preoperative negative chronotropic agent use, similar trends in baseline characteristics were found although less pronounced (Supplementary Table S3). In multivariable logistic regression analyses, elevated heart rates were generally associated with increased odds of myocardial injury. When hear rate was included as a continuous term in the regression model, a 10 beats min1 increase in preoperative heart rate was associated with an increased odds of myocardial injury [adjusted odds ratio (OR), 1.04; 95% confidence interval (CI), 1.01e1.07; P¼0.003]. When heart rate was instead categorised, the highest heart rate category was consistently associated with a significantly higher risk of myocardial injury compared with the lowest heart rate category. For example, when using categories based on prespecified thresholds, a heart rate 90 beats min1 was associated with an adjusted OR of 1.22 (95% CI, 1.06e1.39; P¼0.005) of myocardial injury compared with a heart rate of <60 beats min1. This result was consistent regardless of the method used for categorisation (Table 2). When fractional polynomials were used to model heart rate, we found a ‘J-type’ relationship between heart rate and the probability of myocardial injury (Fig. 3). The point effect estimates of the association between preoperative heart rate and myocardial injury were consistent across the sensitivity analyses (Fig. 4). When 30 day mortality was considered as the outcome, patients with a heart rate greater than 90 beats min1 had a 2.86-higher odds (95% CI, 1.56e5.23; P<0.001) of dying within 30 days compared with patients with a preoperative heart rate less than 60 beats min1. In fractional polynomial analysis, there was a similar ‘J-curve’ relationship although the slope at lower heart rates was less pronounced (Supplementary Fig. S1).
Discussion In a large cohort of patients undergoing elective non-cardiac surgery at a multihospital healthcare network, we found a consistent association between higher preoperative heart rates and an increased likelihood of suffering postoperative myocardial injury. In fractional polynomial analyses, lower heart rates were also found to be detrimental. These findings were consistent across a range of sensitivity analyses. A prior study has demonstrated that elevated preoperative heart rates are associated with myocardial injury.9 However, this previous work relied on heart rates recorded immediately before the induction of anaesthesia, which were susceptible to premedication, anxiety, and preoperative fasting. Additionally, our analysis showed an association between low preoperative heart rates and myocardial injury which has not been previously described. Although the nature of the association was robust across our analyses, the underlying mechanism for this relationship remains to be fully delineated. It is possible that patients with higher preoperative higher heart rates are more likely to experience an imbalance between oxygen supply and demand in the
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perioperative period. If so, preoperative heart reduction could potentially be beneficial, although our data suggest that overly aggressive reduction may be harmful. Another possible explanation is that elevated preoperative heart rates may be a marker of undiagnosed cardiopulmonary and autonomic impairment resulting in subclinical heart failure.16 If this is the main reason for the increased morbidity associated with higher heart rates, a seemingly simple strategy of reducing the heart rate with negative chronotropic agents may actually be harmful. The association between low heart rate and myocardial injury has not been previously described in the perioperative literature. Importantly, the association persisted even when we excluded patients on negative chronotropic agents or with pacemakers. Although there are no data available from the perioperative period, our findings are surprising given that it is well established within the general population that lower resting heart rates are protective against cardiac events.17 However, bradycardia measured in the preoperative clinic might represent chronotropic incompetence, which has been associated with increased morbidity and mortality in the general population.18 Bradycardia may also be related to subclinical heart failure as it has been previously shown that increasing heart rate in patients with bradycardic sick sinus syndrome reduces presentation of overt heart failure.19,20 How exactly these concepts apply to the perioperative period requires further research. Therefore, our data cannot discern whether heart rate is a modifiable risk factor that can be mitigated using chronotropic agents/interventions, or rather that extreme heart rate values are simply markers of a ‘sick heart’. Potential methods to elucidate the mechanism behind the association involve further physiological tests of patients with extreme heart rates and linking ambulatory heart rates values to heart rate values during surgery. Regardless of the mechanism, given these findings, it seems reasonable that future trials examining the perioperative use of chronotropic medications should include protocols to titrate medications to heart rate targets rather than pre-specified doses. Our data suggest that heart rates between 60 and 85 beats min1 would be an appropriate target range, although exact thresholds need to be determined. The study has several limitations that should be considered. First, routine postoperative troponin surveillance was not implemented for all patients in the cohort. Nonetheless, it is unlikely that the preoperative heart rate would have influenced the decision to measure a postoperative troponin level; hence, differential surveillance is unlikely to have biased the estimated association between preoperative heart rate and myocardial injury. Additionally, the rate of myocardial injury observed in our study cohort was generally consistent with previous studies that implemented routine postoperative troponin testing, suggesting that we captured a significant proportion of patients who truly suffered postoperative myocardial injury.1e3 An additional sensitivity analysis using 30 day mortality as the outcome also demonstrated a qualitatively similar relationship. Second, our study was observational in nature, and unmeasured confounding is a concern. However, our database had comprehensive information regarding a multitude of comorbid conditions, allowing for extensive confounder control. Our results also remained robust through several sensitivity analyses to ensure that the assumptions made in our initial analysis did not bias the findings. Third, although our study had the benefit of measuring vital signs in an ambulatory setting, these measurements may
still have been impacted by anxiety and thus not representative of true resting heart rate. It is also important to note that the heart rate value used represents a single static measure of a patient’s dynamic physiology and may not be indicative of the change in heart rate in response to stress. Other factors such as blood pressure, pulse pressure, and heart rate variability may also determine a patient’s risk for future adverse events.21e24 Therefore, future studies should incorporate more sophisticated models that incorporate multiple correlated physiological parameters, all of which could be continuously recorded in the preoperative period. Despite these limitations, this study provides additional strong evidence of an association between preoperative heart rate and postoperative myocardial injury. Specifically, patients presenting with very high heart rates, and possibly also very low heart rates, are at increased risk of suffering myocardial injury. Whether heart rate can be the target of an intervention or whether it simply acts a marker of underlying cardiac pathology remains to be seen, and future research should focus on identifying the underlying mechanisms between this association.
Authors’ contributions Study conception and design: K.S.L., W.S.B., D.N.W. Data analysis: K.S.L. Data acquistion: G.T. Statistical support: D.N.W. Drafting of manuscript: K.S.L. Revision of the manuscript: W.S.B., G.T., D.N.W.
Declaration of interest The authors declare that they have no conflicts of interest.
Funding KL, WSB, and DNW are supported in part by Merit Awards from the Department of Anaesthesia at the University of Toronto. WSB is the Fraser Elliott Chair of Cardiac Anaesthesia at the Toronto General Hospital. DNW is supported in part by a New Investigator Award from the Canadian Institutes of Health Research.
Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.bja.2018.06.016.
References 1. Botto F, Alonso-Coello P, Villar JC, et al. Myocardial injury after noncardiac surgery: a large, international, prospective cohort study establishing diagnostic criteria, characteristics, predictors, and 30-day outcomes. Anesthesiology 2014; 120: 564e78 2. van Waes JAR, Nathoe HM, de Graaff JC, et al. Myocardial injury after noncardiac surgery and its association with short-term mortality. Circulation 2013; 127: 2264e71 3. van Waes JAR, Grobben RB, Nathoe HM, et al. One-year mortality, causes of death, and cardiac interventions in patients with postoperative myocardial injury. Anesth Analg 2016; 123: 29e37
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4. Mauermann E, Puelacher C, Lurati Buse G. Myocardial injury after noncardiac surgery: an underappreciated problem and current challenges. Curr Opin Anaesthesiol 2016; 29: 403e12 5. Wijeysundera DN, Duncan D, Nkonde-Price C, et al. Perioperative beta blockade in noncardiac surgery: a systematic review for the 2014 ACC/AHA guideline on perioperative cardiovascular evaluation and management of patients undergoing noncardiac surgery: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014; 130: 2246e64 € hrig R, et al. Intra-operative 6. Hartmann B, Junger A, Ro tachycardia and peri-operative outcome. Langenbecks Arch Surg 2003; 388: 255e60 7. Leung JM, Dzankic S. Relative importance of preoperative health status versus intraoperative factors in predicting postoperative adverse outcomes in geriatric surgical patients. J Am Geriatr Soc 2001; 49: 1080e5 8. Reich DL, Bennett-Guerrero E, Bodian CA, Hossain S, Winfree W, Krol M. Intraoperative tachycardia and hypertension are independently associated with adverse outcome in noncardiac surgery of long duration. Anesth Analg 2002; 95: 273e7 9. Abbott TEF, Ackland GL, Archbold RA, et al. Preoperative heart rate and myocardial injury after non-cardiac surgery: results of a predefined secondary analysis of the VISION study. Br J Anaesth 2016; 117: 172e81 10. WHO. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. vitamin and mineral nutrition information system. Geneva: World Health Organization; 2011 (WHO/NMH/NHD/MNM/11.1), http://www.who.int/vmnis/ indicators/haemoglobin. pdf. [Accessed 14 February 2018] 11. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009; 150: 604e12 12. Johnson CA, Levey AS, Coresh J, Levin A, Lau J, Eknoyan G. Clinical practice guidelines for chronic kidney disease in adults: Part I. Definition, disease stages, evaluation, treatment, and risk factors. Am Fam Phys 2004; 70: 869e76 13. Lee TH, Marcantonio ER, Mangione CM, et al. Derivation and prospective validation of a simple index for prediction
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of cardiac risk of major noncardiac surgery. Circulation 1999; 100: 1043e9 Royston P, Ambler G, Sauerbrei W. The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol 1999; 28: 964e74 Seaman SR, White IR. Review of inverse probability weighting for dealing with missing data. Stat Methods Med Res 2013; 22: 278e95 Abbott TEF, Minto G, Lee AM, Pearse RM, Ackland GL. POM-HR, POMO-O and OPTIMISE study groups. Elevated preoperative heart rate is associated with cardiopulmonary and autonomic impairment in high-risk surgical patients. Br J Anaesth 2017; 119: 87e94 Zhang D, Shen X, Qi X. Resting heart rate and all-cause and cardiovascular mortality in the general population: a meta-analysis. CMAJ 2016; 188: E53e63 Brubaker PH, Kitzman DW. Chronotropic incompetence: causes, consequences, and management. Circulation 2011; 123: 1010e20 Alboni P, Menozzi C, Brignole M, et al. Effects of permanent pacemaker and oral theophylline in sick sinus syndrome the THEOPACE study: a randomized controlled trial. Circulation 1997; 96: 260e6 S, Fuca G. Development of heart failure in Alboni P, Scarfo bradycardic sick sinus syndrome. Ital Heart J 2001; 2: 9e12 Abbott TEF, Pearse RM, Archbold RA, et al. Association between preoperative pulse pressure and perioperative myocardial injury: an international observational cohort study of patients undergoing non-cardiac surgery. Br J Anaesth 2017; 119: 78e86 Anderson TA. Heart rate variability: implications for perioperative anesthesia care. Curr Opin Anaesthesiol 2017; 30: 691e7 Venkatesan S, Myles PR, Manning HJ, et al. Cohort study of preoperative blood pressure and risk of 30-day mortality after elective non-cardiac surgery. Br J Anaesth 2017; 119: 65e77 Ackland GL, Abbott TEF, Pearse RM, et al. Arterial pulse pressure and postoperative morbidity in high-risk surgical patients. Br J Anaesth 2018; 120: 94e100
Handling editor: P.S. Myles
Association between preoperative ambulatory heart rate and postoperative myocardial injury: a retrospective cohort study K. S. Ladha1,2,*, W. S. Beattie1,2, G. Tait1,2 and D. N. Wijeysundera1,2,3,4 1
Department of Anaesthesia and Pain Management, Toronto General Hospital, Toronto, ON, Canada, 2Department of Anaesthesia, University of Toronto, Toronto, ON, Canada, 3Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, Canada and 4Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, ON, Canada
*Corresponding author. E-mail: [email protected]
Abstract Background: Resting heart rate is well established as a predictor of morbidity and mortality in the general population. However, the relationship between preoperative heart rate and perioperative outcomes, specifically myocardial injury, is unclear. Methods: This retrospective cohort study included patients undergoing elective major non-cardiac surgery from 2008 to 2014 at a multisite healthcare system. The exposure was ambulatory heart rate measured during the outpatient preoperative clinic visit, whereas the outcome of interest was myocardial injury (peak postoperative troponin I concentration >30 ng L1). Covariates included patient characteristics, comorbidities, and preoperative medications. We constructed several multivariable regression models that each modelled heart rate in a different manner, including as a simple continuous variable, categories, and fractional polynomials. Results: The cohort included 41 140 patients, of whom 4857 (11.8%) experienced myocardial injury. Based on pre-specified heart categories thresholds, a heart rate 90 beats min1 was associated with an elevated odds of myocardial injury compared with a heart rate <60 beats min1 (adjusted odds ratio, 1.22; 95% confidence interval, 1.06e1.39; P¼0.005). This result was consistent regardless of the method used for categorisation. When fractional polynomials were used to model heart rate, a ‘J-shaped’ relationship between heart rate and odds of myocardial injury was observed. Conclusions: This cohort study found that both very high preoperative heart rates, and possibly also very low heart rates, are associated with increased risk of myocardial injury. Whether heart rate is a modifiable risk factor, or rather simply a marker of underlying cardiac pathology, needs to be determined in further research. Keywords: troponin I; risk factors; heart rate; retrospective studies; morbidity; outcomes; perioperative complications
Editorial decision: 17 June 2018; Accepted: 17 June 2018 © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved. For Permissions, please email: [email protected]
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Exposure and outcome definitions Editor’s key points A higher-than-normal resting heart rate is an indicator of reduced cardiac reserve. Cardiorespiratory fitness is a key determinant of perioperative outcome. This study found higher heart rates were associated with perioperative myocardial injury and mortality. Whether or not heart rate control could reduce severe perioperative complications is unclear.
Myocardial injury is a prognostically important complication of non-cardiac surgery that affects 8%e22% of patients after elective procedures.1e3 Even when asymptomatic, myocardial injury is highly associated with increased morbidity and mortality.4 Thus, finding ways to mitigate myocardial injury has important implications for the millions of patients undergoing surgery worldwide every year. In current conceptual frameworks of cardiovascular physiology, heart rate is considered to be a key determinant of the balance between myocardial oxygen supply and demand. Although in theory lowering heart rate before operation using negative chronotropic agents such as beta blockers should reduce perioperative complications, the evidence regarding the efficacy of this approach is equivocal.5 One possible explanation is that previous studies used preset doses of medications that were not titrated to a target heart rate threshold. A challenge with implementing such an approach is that the optimal preoperative heart rate to minimise the risk of myocardial injury is not currently known. Several previous studies have elucidated the relationship between intraoperative heart rate and outcomes6e8; however, there are very limited data examining the connection between preoperative heart rate and postoperative myocardial injury. The single previous study that investigated this association used the heart rate recorded immediately before the induction of anaesthesia, which is likely not indicative of true preoperative heart rate.9 We therefore conducted a retrospective cohort study in a multihospital healthcare system to evaluate the link between preoperative ambulatory heart rate and postoperative myocardial injury.
The preoperative heart rate was defined as the ambulatory value documented during the preoperative evaluation clinic visit and measured in beats per minute. Heart rate values less than 40 beats min1 or greater than 140 beats min1 were excluded from the primary analysis as these values were likely to fall outside the expected physiologic range for ambulatory heart rates, and therefore likely related to data entry error. Records with missing heart rate information were also excluded from the study cohort. Myocardial injury was defined as a peak postoperative troponin I concentration exceeding 30 ng L1 or an International Classification of Disease 10th Edition diagnostic code for
Fig 1. Flowchart of cohort formation.
Methods Study population This retrospective cohort study was conducted at the University Health Network, a quaternary-care multisite healthcare system located in Toronto, Ontario, Canada. Data were obtained from linked institutional databases including the preoperative evaluation clinic database, surgical procedure database, and an electronic data warehouse containing administrative and laboratory data. Consecutive patients undergoing elective non-cardiac surgery from 2008 to 2014 with a prior preoperative anaesthesiology consultation at the institutional preoperative evaluation clinic were included in the cohort. Patients with missing data and those with an ASA Physical Status (ASA-PS) Classification 5 were excluded from the analysis. Institutional Research Ethics Board approval was obtained before the initiation of the data set assembly and analysis.
Fig 2. Box plot displaying the distribution of preoperative heart rate among patients without and with myocardial injury.
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Table 1 Descriptive statistical data of study cohort. All values presented as n (%) unless noted otherwise. ASA, American Society of Anesthesiologists; ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blockers; ENT, ear, nose, throat; eGFR, estimated glomerular filtration rate; TURP, transurethral resection of the prostate
N Heart rate, mean (SD) Male sex Age (yr) 18e40 41e60 61e80 >80 ASA-physical status 1 2 3 4 Coronary artery disease Heart failure Hypertension Atrial fibrillation Pacemaker Cerebrovascular diseases Peripheral vascular disease Smoking history Never smoker Current or recent smoker Remote smoker Diabetes mellitus Revised Cardiac Risk Index 0 1 2 3 4 5 Liver disease Asthma Chronic obstructive pulmonary disease Preoperative anaemia Non-anaemic Mild Moderate Severe Unknown History of cancer No Non-metastatic Metastatic Renal Disease eGFR 60 ml min1 1.73 m2 eGFR 30e59 ml min1 1.73 m2 eGFR <30 ml min1 1.73 m2 or dialysis No preoperative creatinine Surgery type Abdominal Endovascular aorta Vascular Urology Hysterectomy/prostatectomy Major ENT Plastics Orthopaedicsdjoint Spine Thoracic Neurosurgery TURP Minor ENT Other minor/ambulatory Preoperative medication use Beta blocker Non-dihydropyridine calcium channel blocker Aspirin ACE inhibitor ARB Statin
No myocardial injury
Myocardial injury
36283 73.9 (12.8) 17431 (48.0)
4857 72.9 (13.8) 2764 (56.9)
4590 (12.7) 14074 (38.8) 15642 (43.1) 1977 (5.4)
260 (5.4) 1187 (24.4) 2869 (59.1) 541 (11.1)
1701 (4.7) 12889 (35.5) 19625 (54.1) 2068 (5.7) 4227 (11.7) 601 (1.7) 15151 (41.8) 1428 (3.9) 359 (1.0) 1759 (4.8) 565 (1.6)
63 (1.3) 655 (13.5) 2873 (59.2) 1266 (26.1) 1277 (26.3) 220 (4.5) 2965 (61.0) 446 (9.2) 121 (2.5) 471 (9.7) 689 (14.2)
17769 (49.0) 5431 (15.0) 13083 (36.1) 5247 (14.5)
1976 (40.7) 786 (16.2) 2095 (43.1) 1149 (23.7)
27451 (75.7) 7372 (20.3) 1220 (3.4) 206 (0.6) 31 (0.1) 3 (<0.01) 809 (2.2) 3721 (10.3) 1990 (5.5)
2310 (47.6) 1540 (31.7) 699 (14.4) 248 (5.1) 51 (1.1) 9 (0.2) 170 (3.5) 456 (9.4) 513 (10.6)
23894 (65.9) 2121 (5.8) 763 (2.1) 34 (0.1) 9471 (26.1)
3393 (69.9) 624 (12.8) 253 (5.2) 7 (0.1) 580 (11.9)
23151 (63.8) 8534 (23.5) 4598 (12.7)
2901 (59.7) 1125 (23.2) 831 (17.1)
30466 (84.0) 3109 (8.6) 576 (1.6) 2132 (5.9)
3794 (78.1) 782 (16.1) 162 (3.3) 119 (2.5)
5182 (14.3) 177 (0.5) 144 (0.4) 1486 (4.1) 3777 (10.4) 3574 (9.9) 1146 (3.2) 7547 (20.8) 2178 (6.0) 2723 (7.5) 2209 (6.1) 1933 (5.3) 1345 (3.7) 2862 (7.9)
1011 (20.8) 368 (7.6) 223 (4.6) 268 (5.5) 385 (7.9) 424 (8.7) 41 (0.8) 624 (12.8) 318 (6.5) 354 (7.3) 223 (4.6) 60 (1.2) 56 (1.2) 502 (10.3)
3906 (10.8) 666 (1.8) 5827 (16.1) 4898 (13.5) 3246 (8.9) 7801 (21.5)
1147 (23.6) 161 (3.3) 1515 (31.2) 1066 (21.9) 640 (13.2) 1813 (37.3)
P-value
Standardised difference
<0.001 <0.001 <0.001
0.076 e0.18 0.47
<0.001
0.76
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
e0.38 e0.17 e0.39 e0.21 e0.12 e0.19 e0.48 0.17
<0.001 <0.001
e0.24 0.67
<0.001 0.060 <0.001 <0.001
e0.076 0.029 e0.19 0.44
<0.001
0.13
<0.001
0.31
<0.001
0.65
<0.001 <0.001 <0.001 <0.001 <0.001 <0.001
e0.35 e0.094 e0.36 e0.22 e0.14 e0.35
Preoperative heart rate and myocardial injury
Table 2 Results from multivariable regression models predicting myocardial injury using different techniques to adjust for heart rate. CI, confidence interval Method used to model heart rate (beats min¡1) Single continuous term Change per 10 beats min1 Quartiles <65 65e73 74e82 >83 Deciles <59 59e63 64e66 67e70 71e73 74e76 77e80 81e84 85e91 >91 Pre-specified cut-off values <60 60e69 70e79 80e89 >89
Adjusted odds ratio (95% CI)
P-value
1.04 (1.01e1.07)
0.003
1 (ref) 0.99 (0.9e1.09) 1.04 (0.95e1.15) 1.16 (1.05e1.28)
e 0.82 0.41 0.003
1 (ref) 0.94 (0.82e1.08) 1.02 (0.88e1.18) 0.99 (0.86e1.13) 0.91 (0.78e1.06) 1.07 (0.92e1.23) 0.95 (0.82e1.1) 1.02 (0.88e1.19) 1.11 (0.96e1.29) 1.22 (1.06e1.41)
e 0.38 0.83 0.89 0.23 0.38 0.52 0.79 0.15 0.006
1 (ref) 0.98 (0.88e1.10) 1.00 (0.89e1.12) 1.06 (0.94e1.20) 1.22 (1.06e1.39)
e 0.75 0.97 0.32 0.005
a postoperative myocardial infarction. Troponin was measured with the Abbott ARCHITECT i2000 analyser (Abbott Diagnostics, Abbott Park, IL, USA). In the primary analysis, patients with no postoperative troponin measurements were
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assumed to not have myocardial injury. Sensitivity analyses were also conducted to assess the validity of this assumption (see Statistical Analysis section).
Covariates Preoperative information was obtained from the history and physical examination conducted during the preoperative clinic visit and stored in the electronic medical record. These data were obtained first by a nurse practitioner and pharmacist, after which the data were verified by a consultant anaesthesiologist. Clinically sensible covariates of interest were selected a priori based on being plausible potential confounders of the association between heart rate and myocardial injury. These covariates included patient characteristics (i.e. age, sex), comorbidities (i.e. hypertension, atrial fibrillation, coronary artery disease, heart failure, permanent pacemaker, diabetes mellitus, chronic obstructive pulmonary disease, asthma, liver disease, non-metastatic cancer, metastatic cancer, renal disease, anaemia), long-term cardiac medications, and surgical procedure type. Anaemia was determined by preoperative haemoglobin concentration and categorised as non-anaemic, mild, moderate, or severe according to the World Health Organization’s published definition.10 Renal impairment was identified using estimated glomerular filtration rate (eGFR) estimated by the CKD-EPI equation and the preoperative creatinine concentration.11 We did not have information regarding race/ethnicity and thus this adjustment was not used in the calculation of eGFR. Patients were categorised according to their eGFR, based on previously defined cut-offs.12 Age was categorised as 18e40, 41e60, 61e80, and >80 yr. Smoking status was classified as current smoker, recent quitter (i.e. quit in the past 30 days), remote smoker, or no smoking history. Preoperative cardiac medications of
Fig 3. Results from multivariable regression model predicting myocardial injury with heart rate modelled using fractional polynomials.
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Fig 4. Results from the sensitivity analysis. (A) Multivariable regression model with heart modelled as a categorical variable using prespecified values. Odds ratios represent odds of having myocardial injury for patients with a heart rate greater than 90 beats min1 compared with patients with a heart rate less than 60 beats min1. (B) Multivariable regression model with heart modelled using fractional polynomials. IPW, inverse probability weighting.
interest included negative chronotropic agents (i.e. beta blockers, non-dihydropyridine calcium channel blockers), aspirin, angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs), and statins. We also
included two summary measures of comorbidity: the Revised Cardiac Risk Index (RCRI)13 and the ASA-PS class. Patients with an ‘unknown’ covariate value were assumed not to have that condition. This assumption was made for less
Preoperative heart rate and myocardial injury
than 1% of the sample for each covariate (Supplementary Table S1). The exception to this was for preoperative anaemia and renal disease where unknown values were analysed as a separate category given that they represented a larger proportion of the cohort.
Statistical analysis We initially conducted bivariate analyses to characterise unadjusted differences between patients who did and those who did not suffer myocardial injury (i.e. t-tests for continuous variables and c2 tests for categorical variables). Additionally, we examined the characteristics of patients across different categories specified based on preoperative heart rate values. The adjusted association between preoperative heart rate and postoperative myocardial injury was then examined using multivariable logistic regression models. All clinically sensible covariates were included in the model. Given that the exposure of interest, heart rate, was a continuous variable, we constructed several regression models that each modelled heart rate in a different mannerdto ensure that the results were not sensitive to the underlying assumption of the nature of relationship between heart rate and the outcome of interest. Specifically, we included heart rate as a single continuous term, categories based on quartiles, categories based on deciles, categories based on pre-specified cut-offs (<60, 60e69, 70e79, 80e89, 90 beats min1), and fractional polynomials (to account for any non-linear relationship between the exposure and outcome).14 We undertook several sensitivity analyses to further assess the robustness of our results. These included repeating the analyses after excluding patients with permanent pacemakers or atrial fibrillation, repeating the analyses after excluding patients on negative chronotropic agents, repeating the analyses while including patients with ‘extreme’ heart rate values (i.e. <40 and >140 beats min1), repeating the analyses after excluding patients with ‘unknown’ covariate values, and repeating the analyses while using inverse probability weighting to account for missing heart rate values.15 To explore the assumption of no myocardial injury for patients with missing troponin values, we repeated the analysis using 30 day mortality as the outcome of interest as we had this information for all patients in the cohort. These sensitivity analyses were conducted using both the pre-specified categorisation of heart rate (i.e. <60, 60e69, 70e79, 80e89, 90 beats min1) and the fractional polynomial analysis. All statistical analyses were performed in Stata Version 14.2 (StataCorp, College Station, TX, USA) with significance defined as a two-tailed P<0.05. Sample size was determined using the available data, and no a priori calculation was performed.
Results A total of 41 140 patients met all inclusion criteria and formed the study cohort (Fig. 1). Of these patients, 4857 (11.8%) individuals suffered myocardial injury after surgery. There was no clinically significant difference between the mean preoperative heart rates of patients who did and those who did not suffer myocardial injury [mean (standard deviation, SD); 73 (14) vs 74 (13), P<0.001]. Figure 2 is a boxplot showing the distributions of heart rate between the two groups. The characteristics of patients who did vs those who did not suffer
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myocardial injury are presented in Table 1. In general, patients with myocardial injury were more likely to be older males with a greater burden of comorbidities. When examining baseline characteristics across strata defined by preoperative heart rate, patients with slower heart rates had a higher prevalence of coronary artery disease, heart failure, and preoperative beta-blocker usage (Supplementary Table S2). After excluding patients with pacemakers and preoperative negative chronotropic agent use, similar trends in baseline characteristics were found although less pronounced (Supplementary Table S3). In multivariable logistic regression analyses, elevated heart rates were generally associated with increased odds of myocardial injury. When hear rate was included as a continuous term in the regression model, a 10 beats min1 increase in preoperative heart rate was associated with an increased odds of myocardial injury [adjusted odds ratio (OR), 1.04; 95% confidence interval (CI), 1.01e1.07; P¼0.003]. When heart rate was instead categorised, the highest heart rate category was consistently associated with a significantly higher risk of myocardial injury compared with the lowest heart rate category. For example, when using categories based on prespecified thresholds, a heart rate 90 beats min1 was associated with an adjusted OR of 1.22 (95% CI, 1.06e1.39; P¼0.005) of myocardial injury compared with a heart rate of <60 beats min1. This result was consistent regardless of the method used for categorisation (Table 2). When fractional polynomials were used to model heart rate, we found a ‘J-type’ relationship between heart rate and the probability of myocardial injury (Fig. 3). The point effect estimates of the association between preoperative heart rate and myocardial injury were consistent across the sensitivity analyses (Fig. 4). When 30 day mortality was considered as the outcome, patients with a heart rate greater than 90 beats min1 had a 2.86-higher odds (95% CI, 1.56e5.23; P<0.001) of dying within 30 days compared with patients with a preoperative heart rate less than 60 beats min1. In fractional polynomial analysis, there was a similar ‘J-curve’ relationship although the slope at lower heart rates was less pronounced (Supplementary Fig. S1).
Discussion In a large cohort of patients undergoing elective non-cardiac surgery at a multihospital healthcare network, we found a consistent association between higher preoperative heart rates and an increased likelihood of suffering postoperative myocardial injury. In fractional polynomial analyses, lower heart rates were also found to be detrimental. These findings were consistent across a range of sensitivity analyses. A prior study has demonstrated that elevated preoperative heart rates are associated with myocardial injury.9 However, this previous work relied on heart rates recorded immediately before the induction of anaesthesia, which were susceptible to premedication, anxiety, and preoperative fasting. Additionally, our analysis showed an association between low preoperative heart rates and myocardial injury which has not been previously described. Although the nature of the association was robust across our analyses, the underlying mechanism for this relationship remains to be fully delineated. It is possible that patients with higher preoperative higher heart rates are more likely to experience an imbalance between oxygen supply and demand in the
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perioperative period. If so, preoperative heart reduction could potentially be beneficial, although our data suggest that overly aggressive reduction may be harmful. Another possible explanation is that elevated preoperative heart rates may be a marker of undiagnosed cardiopulmonary and autonomic impairment resulting in subclinical heart failure.16 If this is the main reason for the increased morbidity associated with higher heart rates, a seemingly simple strategy of reducing the heart rate with negative chronotropic agents may actually be harmful. The association between low heart rate and myocardial injury has not been previously described in the perioperative literature. Importantly, the association persisted even when we excluded patients on negative chronotropic agents or with pacemakers. Although there are no data available from the perioperative period, our findings are surprising given that it is well established within the general population that lower resting heart rates are protective against cardiac events.17 However, bradycardia measured in the preoperative clinic might represent chronotropic incompetence, which has been associated with increased morbidity and mortality in the general population.18 Bradycardia may also be related to subclinical heart failure as it has been previously shown that increasing heart rate in patients with bradycardic sick sinus syndrome reduces presentation of overt heart failure.19,20 How exactly these concepts apply to the perioperative period requires further research. Therefore, our data cannot discern whether heart rate is a modifiable risk factor that can be mitigated using chronotropic agents/interventions, or rather that extreme heart rate values are simply markers of a ‘sick heart’. Potential methods to elucidate the mechanism behind the association involve further physiological tests of patients with extreme heart rates and linking ambulatory heart rates values to heart rate values during surgery. Regardless of the mechanism, given these findings, it seems reasonable that future trials examining the perioperative use of chronotropic medications should include protocols to titrate medications to heart rate targets rather than pre-specified doses. Our data suggest that heart rates between 60 and 85 beats min1 would be an appropriate target range, although exact thresholds need to be determined. The study has several limitations that should be considered. First, routine postoperative troponin surveillance was not implemented for all patients in the cohort. Nonetheless, it is unlikely that the preoperative heart rate would have influenced the decision to measure a postoperative troponin level; hence, differential surveillance is unlikely to have biased the estimated association between preoperative heart rate and myocardial injury. Additionally, the rate of myocardial injury observed in our study cohort was generally consistent with previous studies that implemented routine postoperative troponin testing, suggesting that we captured a significant proportion of patients who truly suffered postoperative myocardial injury.1e3 An additional sensitivity analysis using 30 day mortality as the outcome also demonstrated a qualitatively similar relationship. Second, our study was observational in nature, and unmeasured confounding is a concern. However, our database had comprehensive information regarding a multitude of comorbid conditions, allowing for extensive confounder control. Our results also remained robust through several sensitivity analyses to ensure that the assumptions made in our initial analysis did not bias the findings. Third, although our study had the benefit of measuring vital signs in an ambulatory setting, these measurements may
still have been impacted by anxiety and thus not representative of true resting heart rate. It is also important to note that the heart rate value used represents a single static measure of a patient’s dynamic physiology and may not be indicative of the change in heart rate in response to stress. Other factors such as blood pressure, pulse pressure, and heart rate variability may also determine a patient’s risk for future adverse events.21e24 Therefore, future studies should incorporate more sophisticated models that incorporate multiple correlated physiological parameters, all of which could be continuously recorded in the preoperative period. Despite these limitations, this study provides additional strong evidence of an association between preoperative heart rate and postoperative myocardial injury. Specifically, patients presenting with very high heart rates, and possibly also very low heart rates, are at increased risk of suffering myocardial injury. Whether heart rate can be the target of an intervention or whether it simply acts a marker of underlying cardiac pathology remains to be seen, and future research should focus on identifying the underlying mechanisms between this association.
Authors’ contributions Study conception and design: K.S.L., W.S.B., D.N.W. Data analysis: K.S.L. Data acquistion: G.T. Statistical support: D.N.W. Drafting of manuscript: K.S.L. Revision of the manuscript: W.S.B., G.T., D.N.W.
Declaration of interest The authors declare that they have no conflicts of interest.
Funding KL, WSB, and DNW are supported in part by Merit Awards from the Department of Anaesthesia at the University of Toronto. WSB is the Fraser Elliott Chair of Cardiac Anaesthesia at the Toronto General Hospital. DNW is supported in part by a New Investigator Award from the Canadian Institutes of Health Research.
Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.bja.2018.06.016.
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Handling editor: P.S. Myles