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Original Article

What is the best pre-operative risk stratification tool for major

adverse cardiac events following elective vascular surgery? A

prospective observational cohort study evaluating pre-operative

myocardial ischaemia monitoring and biomarker analysis

B. M. Biccard,1 P. Naidoo2 and K. de Vasconcellos1

1 Consultant, Perioperative Research Unit, Department of Anaesthesia, 2 Specialist, National Health Laboratory Services,Department of Chemical Pathology, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban,KwaZulu-Natal, South Africa

SummaryAlthough brain natriuretic peptide has been shown to be superior to the revised cardiac risk index for risk stratification

of vascular surgical patients, it remains unknown whether it is superior to alternative dynamic risk predictors, such as

other pre-operative biomarkers (C-reactive protein and troponins) or myocardial ischaemia monitoring. The aim of this

prospective observational study was to determine the relative clinical utility of these risk predictors for the prediction of

postoperative cardiac events in elective vascular surgical patients. Only pre-operative troponin elevation (OR 56.8, 95%

CI 6.5–496.0, p < 0.001) and brain natriuretic peptide above the optimal discriminatory point (OR 6.0, 95% CI 2.7–12.9,

p < 0.001) were independently associated with cardiac events. Both brain natriuretic peptide and troponin risk

stratification significantly improved overall net reclassification (74.6% (95% CI 51.6%–97.5%) and 38.5% (95% CI 22.4–

54.6%, respectively)); however, troponin stratification decreased the correct classification of patients with cardiac

complications ()59%, p < 0.001). Pre-operative brain natriuretic peptide evaluation was the only clinically useful

predictor of postoperative cardiac complications.................................................................................................................................................................

Correspondence to: Dr B. Biccard

Email: [email protected]

Accepted: 19 November 2011

It is estimated that nearly a million patients each year

worldwide sustain major cardiac complications such as

cardiac death, myocardial infarction and cardiac arrest

following non-cardiac surgery [1]. A number of pre-

operative strategies have been used to identify at risk

patients, including clinical risk scores [2], the detection

of myocardial ischaemia by ambulatory Holter

monitoring [3], and biomarker analysis [4]. Although

individually, all of these approaches have been used to

risk-stratify patients in a clinically useful manner, only

the revised cardiac risk index (RCRI) [2] has been

incorporated into both American and European guide-

lines for pre-operative non-cardiac risk assessment [5, 6].

However, with regards to risk stratification for

vascular surgical patients, there has been significant

progress in determining appropriate stratification be-

yond the RCRI, by incorporating the pre-operative

brain natriuretic peptide (BNP) concentration. First, it

Anaesthesia 2012, 67, 389–395 doi:10.1111/j.1365-2044.2011.07020.x

Anaesthesia ª 2012 The Association of Anaesthetists of Great Britain and Ireland 389

has been shown, using appropriate reclassification

statistics [7, 8], that BNP has clinical utility for pre-

operative risk stratification of vascular patients [9], even

in the presence of RCRI risk stratification. Subse-

quently, a collaborative group conducted an individual

patient data meta-analysis, and based on the sample size

was able to determine that neither the RCRI score, nor

any of its components, was able to improve on a

BNP-based pre-operative risk stratification for elective

vascular surgical patients [10]. These studies have

established the superiority of BNP over the RCRI and

its individual component risk factors in pre-operative

risk stratification.

However, it has not yet been shown whether BNP

will still retain its powerful predictive ability when

compared with other dynamic risk predictors such as

pre-operative troponin levels and myocardial ischaemia

monitoring. To address this question, we conducted a

prospective observational study to determine the relative

importance and clinical utility of the RCRI, pre-operative

myocardial ischaemia and pre-operative elevation of the

biomarkers C-reactive protein (CRP), BNP and tropo-

nins, in the prediction of postoperative major adverse

cardiac events within 30 days of elective vascular surgery.

MethodsThis study was conducted at Inkosi Albert Luthuli

Central Hospital, in KwaZulu-Natal, South Africa, with

institutional ethics approval, and was registered with the

national administrative body (South African National

Clinical Trials Register). We recruited elective vascular

surgical patients between February 2008 and March

2011 who gave informed consent. Patients consented

for: collection of clinical risk factors alone; collection of

clinical risk factors with pre-operative biomarkers; or

collection of clinical risk factors and pre-operative

biomarkers, with ambulatory Holter monitoring.

All patients’ characteristics and cardiac clinical risk

predictors were collected as per the definition of the

RCRI [2]. The clinical risk factor dataset was complete

for all recruited patients and was reviewed by BB for

accuracy; part of this dataset has been used for a

previous publication [9]. Troponin and CRP levels were

measured at some point in the 24 h before surgery. In

April 2008, the hospital changed the troponin assay

from troponin T to troponin I, and in August 2008, BNP

was added to the protocol, also measured in the 24 h

before surgery [11].

Starting in June 2008, a sub-cohort of patients

underwent pre-operative Holter monitoring for myo-

cardial ischaemia using a Schiller MT-200 ECG Holter

monitor (Shiller AG, Baar, Switzerland). Monitoring

started the day before surgery and was continued right

up to patients’ arrival in the operating theatre for

surgery. The number of patients recruited for this sub-

cohort was limited by the availability of the monitors.

The Holter data were only analysed (by KV) at the end

of the study, and the analysis was blinded as to outcome.

An a priori decision was taken to analyse the number of

episodes of ST depression lasting > 10 min. We defined

ST depression as a deviation > 1 mV from baseline

measured 60 ms after the J point. The end of the episode

of ST depression was defined as the return of the ST

deviation to < 1 mV from baseline for 60 s. The ST

segments were inspected visually and ST deviations due

to artefact were not considered. Modified V5 and V2

leads were analysed. The mean heart rate, maximum

heart rate and longest duration of time above

100 beats.min)1 were also analysed.

The peri-operative anaesthetic technique was at the

discretion of the attending anaesthetist. There was no

study protocol for the management of an elevated

postoperative troponin, and management was deter-

mined by the anaesthetic and surgical team on an

individual patient basis. Attending clinicians were not

blinded to the pre-operative biomarker results.

The samples for BNP and Troponin I were collected

in EDTA and serum separator tubes (Greiner Bio-One,

Frickenhausen, Germany), respectively. All samples

were centrifuged and analysed on receipt, using the

Advia Centaur� Xp (Siemens Healthcare, Malvern, PA,

USA), which involves chemiluminescent technology.

The CRP analysis was performed on serum samples,

using the latex enhanced immunoturbidimetric method.

The primary outcome was major adverse cardiac

events, defined as a composite of death within 30 days of

surgery, or a serum troponin result above the upper

reference limit within the first three postoperative days.

Categorical data were analysed using the Fisher’s exact

test or Pearson’s chi-squared test where appropriate; all

continuous data were compared using independent

samples t-test or Mann–Whitney U-test. The statistical

Anaesthesia 2012, 67, 389–395 Biccard et al. | Biomarkers and Holter monitoring for risk stratification

390 Anaesthesia ª 2012 The Association of Anaesthetists of Great Britain and Ireland

analysis was conducted in two stages; in the first stage, a

univariate analysis was conducted for all the patients who

had both pre-operative biomarker and Holter monitor-

ing data. All risk factors with a univariate association of

p < 0.1 with the study outcome were entered into

multivariate analysis, using binary logistic regression. If

none of the Holter risk factors were found to be

independent predictors of the study outcome, then the

second stage of the analysis consisted of a multivariate

analysis using the larger biomarker cohort. By only

including univariate predictors with p < 0.1 into a

subsequent multivariate regression, the events per var-

iable ratio were kept above ten, thus minimising the bias

associated with the estimate of risk [12]. A backward

stepwise modelling technique was also performed, based

on likelihood ratios with entry and removal probabilities

set at 0.05 and 0.1, respectively. For biomarker analysis,

categorical data were used. Positive pre-operative tropo-

nin levels were defined as above the upper reference level,

and positive pre-operative BNP and CRP levels were

defined as above the optimal discriminatory point

determined using a receiver-operating characteristic

curve for the study outcome.

Finally, to determine whether any of the indepen-

dent predictors identified in the logistic regression

significantly improved pre-operative risk prediction for

postoperative major adverse cardiac events, a category-

free net reclassification was used. This reclassification

method is deemed the most objective statistical tool for

evaluating the prognostic performance of a risk predic-

tor. The results from a category-free net reclassification

are independent of the clinical risk stratification tool

used during the study, and so can be used for objective

comparisons with potential future risk predictors [13].

Patients were reclassified into a high-(positive indepen-

dent risk predictors) or low-(negative independent risk

predictors) risk category. The success of this reclassifi-

cation is described by the change in net reclassification,

where a positive change reflects an improvement in risk

stratification. Net reclassification is the difference

between the proportion of patients correctly and incor-

rectly reclassified [8] according to the study outcome.

SPSS 15.0 for Windows (IBM, NY, USA), EpiCalc 2000

(Version 1.2, Brixton Health, UK) and SAS Software 9.1

(SAS Institute Inc., Cary, NC, USA) were used for data

analysis.

ResultsNine hundred and seventy-eight patients were eligible

for the study in the 3-year-period, of whom 788 patients

consented. The compositions of the cohorts are shown

in Fig. 1. The study outcome occurred in 136 out of the

788 recruited patients (17%, 95% CI 15–20%) and was

similar between the four cohorts: pre-operative troponin

cohort (98 ⁄ 534, 18%, 95% CI 15–22%); pre-operative

BNP cohort (65 ⁄ 403, 16%, 95% CI 13–20%), pre-

operative CRP cohort (87 ⁄ 508, 17%, 95% CI 14–20%);

and pre-operative Holter cohort (58 ⁄ 318, 18%, 95% CI

14–23).

The baseline patient characteristics are shown in

Table 1. Patients who sustained major adverse cardiac

events were older, had significantly more ischaemic

heart disease, diabetes and pre-operative troponin level

above the upper reference limit, and they also had

significantly higher pre-operative BNP and CRP levels.

They were also taking significantly more beta-blocker

and aspirin therapy pre-operatively.

Univariate analysis of the Holter cohort is shown in

Table 2; the only Holter variable associated with

major adverse cardiac events with a p < 0.1 was the

Eligible patients (n = 978)

Holter cohort (n = 318)

Preoperative biomarker cohort

Troponins (n = 560)

BNP (n = 403)

CRP (n = 508)

Recruited patients (n = 788) Clinical risk factors (n = 788)

Figure 1 Flow diagram of recruited patients. BNP, brainnatriuretic peptide; CRP, C-reactive protein.

Biccard et al. | Biomarkers and Holter monitoring for risk stratification Anaesthesia 2012, 67, 389–395


pre-operative overnight mean heart rate. Pre-operative

BNP and troponin elevation also had an association of

p < 0.1 for postoperative major adverse cardiac events.

However, on multivariate analysis, the mean pre-oper-

ative heart rate was removed from the model, leaving

only pre-operative troponin and BNP elevation inde-

pendently associated with the outcome. As a result, the

larger biomarker cohort was used for the subsequent

logistic regression of independent predictors associated

with major adverse cardiac events.

The univariate associations of the biomarker cohorts

with major adverse cardiac events are shown in Table 3.

The RCRI, pre-operative BNP, CRP and troponin

elevation were entered into the multivariate analysis,

with pre-operative troponin (OR 57, 95% CI 6–496,

p < 0.001) and BNP (OR 6.0, 95% CI 2.7–12.9,

p < 0.001) elevation being the only independent risk

factors associated with major adverse cardiac events.

The pre-operative BNP optimal cut-off had an area

under the curve of 0.69 (95% CI 0.62–0.75, p = 0.035).

Table 1 Baseline patient characteristics. Values are mean (SD) number (proportion), or median [IQR (range)].

Total cohort(n = 788)

Patients withMACE (n = 136)

Patients withoutMACE (n = 652) p value

Clinical risk factorsAge 58.2 (14.2) 62.4 (13.4) 57.4 (14.2) < 0.001Men 512 (65%) 80 (59%) 432 (66%) 0.11Ischaemic heart disease 275 (35%) 74 (54%) 201 (31%) < 0.001Diabetes 338 (43%) 78 (57%) 260 (40%) < 0.001Cardiac failure 37 (5%) 11 (8%) 26 (4%) 0.46Cerebrovascular accident 159 (20%) 23 (17%) 136 (21%) 0.35Creatinine > 177 lmol.l)1 18 ⁄ 730 (3%) 4 ⁄ 130 (3%) 14 ⁄ 600 (2%) 0.62

Pre-operative medicationsBeta-blockers 267 (34%) 82 (60%) 185 (28%) < 0.001Statins 685 (87%) 125 (92%) 560 (86%) 0.07Aspirin 714 (91%) 131 (96%) 583 (89%) 0.009

Pre-operative biomarkersTroponin I elevation 25 ⁄ 509 (5%) 20 ⁄ 98 (20%) 5 ⁄ 436 (1%) < 0.001BNP; pg.ml)1 33.6 [12.5–93.8

(2.1–3893.0)]76.8 [39.4–337.7(4.5–3893.0)]

28.7 [11.1–74.7(2.1–3138.0)]

< 0.001

CRP; g.dl)1 19 [5.4–67(0–263.0)]

28 [8–108(0.1–263.0)]

17.3 [5.0–62.2(0–210.0)]

0.008

BNP, brain natriuretic peptide; CRP, C-reactive protein; MACE major adverse cardiac events.

Table 2 Univariate predictors of major adverse cardiacevents in the Holter cohort.

OR (95% CI) p value

Clinical risk factorsRCRI score 1.3 (1.0–1.7) 0.11

Holter risk predictorsEpisodes of ST depression 1.0 (0.9–1.2) 0.66Mean heart rate 1.02 (0.99–1.04) 0.09Maximum heart rate 1.00 (0.99–1.02) 0.58Longest durationabove 100 beats.min)1

1.00 (1.00–1.00) 0.12

Pre-operative biomarkersBNP; pg.ml)1 1.00 (1.00–1.00) < 0.001BNP above optimaldiscriminatory pointof 48.1 pg.ml)1

4.6 (2.1–10.0) < 0.001

CRP; g.dl)1 1.01 (0.99–1.01) 0.12CRP above optimaldiscriminatory pointof 22 g.dl)1

1.7 (0.9–3.4) 0.13

Troponin I > 0.1 ng.ml)1 38.3 (4.6–320.0) 0.001

BNP, brain natriuretic peptide; bpm, beats.min)1; CRP, C-reactive protein; RCRI, revised cardiac risk index.

Table 3 Univariate predictors of major adverse cardiacevents in the biomarker cohort.

OR (95% CI) p value

Clinical risk factorsRCRI score 1.5 (1.2–1.8) < 0.001

Pre-operative biomarkersBNP above optimaldiscriminatory pointof 39.4 pg.ml)1

5.0 (2.7–9.4) < 0.001

CRP above the optimaldiscriminatory pointof 22 g.dl)1

1.8 (1.1–2.9) 0.012

Troponin I > 0.1 ng.ml)1 22.1 (8.1–60.0) < 0.001

BNP, brain natriuretic peptide; CRP, C-reactive protein; RCRI,revised cardiac risk index.



The reclassification improvement for postoperative

major adverse cardiac events associated with pre-oper-

ative troponin and BNP elevation is shown in Table 4.

Both pre-operative troponin and BNP analysis signifi-

cantly improved overall risk stratification. However,

pre-operative troponin risk stratification significantly

decreased correct classification of patients who devel-

oped major adverse cardiac events.

DiscussionThis study has shown that, in elective vascular surgical

patients, only pre-operative BNP and troponins have the

capacity to change risk prediction significantly. Impor-

tantly, this ability to risk-stratify is independent of the

pre-operative clinical risk factors found in the RCRI, a

finding consistent with earlier work on pre-operative

BNP in vascular patients [14]. As a result of the RCRI’s

poor ability to risk-stratify vascular surgical patients [15,

16], it is important to identify whether alternative risk

stratification tools (i.e. pre-operative ECG and pre-

operative biomarkers) are appropriate for this popula-

tion.

We statistically defined a risk factor with clinical

utility as one that was both independently associated

with the outcome and also significantly improved pre-

operative risk category classification for subsequent

major adverse cardiac events [7, 17]. Adopting the risk

predictors identified in this study should result in a

significant change in pre-operative risk categorisation,

which could potentially alter pre-operative clinical

management.

Pre-operative troponins significantly improved pa-

tient risk reclassification overall, but worsened the

reclassification of the sub-cohort of patients who had a

major adverse cardiac event by classifying many of them

as low-risk. As this would have significant negative

clinical impact, we do not advocate using pre-operative

troponins as a screening test to exclude high-risk

patients. In contrast, pre-operative BNP was an inde-

pendent predictor of major adverse cardiac events and

significantly improved the risk stratification of patients

with and without major adverse cardiac events.

There are a number of potential reasons why pre-

operative BNP may be a better predictor of postoper-

ative cardiac complications than troponins. Brain

natriuretic peptide is rapidly secreted from ventricular

myocytes when exposed to even minor elevations in

ventricular pressure or volume loading [18], or from

myocardial ischaemia [18, 19]. This allows pre-operative

BNP elevation to identify a vulnerable ventricle at risk of

a major adverse event. In contrast, troponin elevations,

as detected by standard sensitivity troponin assays, most

commonly reflect myocyte necrosis as the final common

pathway of a damaged ventricle. Pre-operative troponin

elevation probably reflects a ventricle that is too far

down the pathway of cardiovascular injury to provide

further clinically useful pre-operative risk stratification

information. This is further emphasised by the pattern

of troponin increase commonly seen in the postopera-

tive period, where only 12% of patients who sustain a

peri-operative myocardial infarction have troponin

elevation on postoperative day 1, while 77% have

troponin elevation by day 3 [20]. Similarly, in our

study, only 4.7% of patients had pre-operative troponin

elevation, compared with 15.9% with postoperative

troponin elevation. These findings demonstrate that

pre-operative troponin elevation is poorly associated

with postoperative cardiac events.

Table 4 Net reclassification change for postoperative major adverse cardiac events using pre-operative BNP and tro-ponins.

Reclassification changeof patients withoutMACE

Reclassification changeof patients with MACE Overall net reclassification change

Cohort proportion p-value proportion p-value proportion p value

BNP above the optimaldiscriminatory point

+21% < 0.0001 +54% < 0.0001 +75% (95% CI 52–98%) < 0.0001

Troponin above theupper reference limit

+98% < 0.0001 )59% < 0.0001 +39% (95% CI 22–55%) 0.0006

BNP, brain natriuretic peptide; MACE, major adverse cardiac event.



With regards to the Holter monitoring used for this

study, only two channels (modified V2 and V5) were

analysed. This is certainly not equivalent to the 12-lead

ECG monitoring used in other studies of peri-operative

myocardial ischaemia, and may have decreased the

sensitivity of our Holter data. However, we do not

consider this to be a significant limitation, as the

combination of two precordial leads has a reported

sensitivity of over 90% for the detection of myocardial

ischaemia [21].

This is the first study to compare the performance of

three different pre-operative risk stratification tech-

niques (i.e., clinical risk predictors, biochemical risk

markers and ECG Holter monitoring) simultaneously.

These findings have important implications for the

development of an evidence-based clinical approach to

risk prediction in vascular surgical patients, and warrant

verification in an independent cohort.

We can now summarise our understanding of the

role of BNP in pre-operative risk stratification for

vascular surgical patients as follows: (1) BNP improves

RCRI risk stratification [9]; (2) BNP is superior to and

independent of the RCRI and its individual compo-

nents [10]; and (3) BNP remains independently

predictive even in the presence of pre-operative

troponin evaluation and myocardial ischaemia

monitoring.

Based on the recommended process for the critical

evaluation of novel biomarkers of cardiovascular risk

before integration in clinical practice, a progressive six-

phase evaluation has been recommended [22]. Brain

natriuretic peptide evaluation has now reached the

fourth stage (that of clinical utility). As it has been

shown significantly to outperform the RCRI as well as

other methods, we believe that we have now established

BNP as the modality of choice for estimating risk in

these patients.

We propose that future research into risk stratifi-

cation for vascular surgical patients should include the

verification of these results in an independent vascular

surgical cohort and determination of whether pre-

operative management of vascular surgical patients

based on pre-operative BNP levels improves clinical

outcome (stage 5 of the evaluation of novel biomarkers)

[22]. These findings should also be evaluated in non-

vascular, non-cardiac surgery.

AcknowledgementsWe thank Tecmed South Africa for providing us with

Holter hardware and software at cost price. The study

itself was funded through a grant from the Medical

Research Council of South Africa, awarded to BB.

Competing interestsNo competing interests declared.

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