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R E S E A R C H A R T I C L E Open Access
Pre-operative clinical predictors for
cardiology referral prior to total joint
arthroplasty: the ‘asymptomatic’patient
Yassin Elsiwy
1,2
, Tristan Symonds
2
, Kenji Doma
2,3
, Kaushik Hazratwala
1,2
, Matthew Wilkinson
2,4
and
Hayley Letson
1*
Abstract
Background: No validated pre-operative cardiac risk stratification tool exists that is specific for total hip and total
knee arthroplasty (THA and TKA, respectively). To reduce the risk of post-operative cardiac complication, surgeons
need clear guidance on which patients are likely to benefit from pre-operative cardiac optimisation. This is
particularly important for asymptomatic patients, where the need is harder to determine.
Methods: Primary THA and TKA performed between January 1, 2010, and December 31, 2017, were identified from
a single orthopaedic practice. Over 25 risk factors were evaluated as predictors for patients requiring additional
cardiac investigation beyond an ECG and echocardiogram, and for cardiac abnormality detected upon additional
investigation. A multivariate logistic regression was conducted using significant predictor variables identified from
inferential statistics. A series of predictive scores were constructed and weighted to identify the influence of each
variable on the ability to predict the detection of cardiac abnormality pre-operatively.
Results: Three hundred seventy-four patients were eligible for inclusion. Increasing age (p< 0.001), history of
cerebrovascular accident (p= 0.018), family history of cardiovascular disease (FHx of CVD) (p< 0.001) and decreased
ejection fraction (EF) (p< 0.001) were significant predictors of additional cardiac investigation being required.
Increasing age (p= 0.003), male gender (p= 0.042), FHx of CVD (p= 0.001) and a reduced EF (p< 0.001) were
significantly predictive for the detection of cardiac abnormality upon additional cardiac investigation.
(Continued on next page)
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* Correspondence: Hayley.letson@jcu.edu.au
1
College of Medicine and Dentistry, James Cook University, 1 James Cook
Drive, Townsville, QLD 4811, Australia
Full list of author information is available at the end of the article
Elsiwy et al. Journal of Orthopaedic Surgery and Research (2020) 15:513
https://doi.org/10.1186/s13018-020-02042-5
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(Continued from previous page)
Conclusions: Increasing age, male gender, FHx of CVD and decreased ejection fraction are important risk factors to
consider for pre-operative cardiac optimisation in THA and TKA patients. These findings can be applied towards future
predictive models, to determine which asymptomatic patients are likely to benefit from pre-operative cardiac referral.
Keywords: Cardiac, Arthroplasty, TKA, THA, Risk factor, Complication
Background
Total joint arthroplasty (TJA) is the mainstay of treatment
for end-stage osteoarthritis (OA) of the hip and knee,
improving pain, function and quality of life [1]. Cardiac
complication represents a major cause of morbidity and
mortality after TJA and is associated with increased hos-
pital mortality, length of stay and health expenditure [2–6].
Though rates of major cardiac complication are reported
as low as 0.2–0.8%, this risk becomes increasingly signifi-
cant given the projected 174 and 673% increase in total hip
(THA) and total knee (TKA) arthroplasty, respectively, by
2030 in the USA [2,7–13].
Previous studies have identified risk factors for post-
operative cardiac complication in TKA and THA cohorts;
however, a recent systematic literature review identified
several limitations within the current literature [14].
Firstly, there is inconsistency regarding which risk factors
are significant predictors of cardiac complication.
Secondly, few studies extended their findings by way of a
predictive model of multiple significant risk factors that
can be adopted for cardiac risk stratification prior to TJA.
Although Waterman and colleagues proposed one such
model using age ≥80 years, history of cardiac disease, and
hypertension, this tool has not been validated and further
work is required to determine the value of other import-
ant risk factors in pre-operative cardiac risk stratification
[3]. Therefore, no validated, orthopaedic-specific model(s)
currently exist to facilitate pre-operative cardiac risk strati-
fication. Although pre-operative referral for cardiac opti-
misation is clear-cut for the symptomatic patient already
exhibiting signs of cardiovascular disease [15], the indica-
tion is less clear for asymptomatic patients not yet exhibit-
ing clinical manifestations of a potential underlying and
established cardiac disease.
The purpose of this study was to determine which risk
factors should prompt orthopaedic surgeons to refer
‘asymptomatic’patients for cardiac optimisation prior to
TKA or THA. The primary objective being to identify
risk factors which can be indicative of underlying and
undiagnosed cardiac disease in an asymptomatic patient,
which may otherwise place the patient at risk of cardiac
complication peri- or post-TJA. Based on prior analysis,
we hypothesised that age and history of cardiac disease
will be significant predictors. However, we also sought
to further determine the predictive value of additional
potentially important risk factors [14]. Significant risk
factors identified in this study may contribute to the
development of a more comprehensive cardiac risk
stratification tool that may better guide pre-operative
cardiac referral for the ‘asymptomatic’TJA patient.
Methods
This study was approved by the Mater Health Services
North Queensland Human Research Ethics Committee
(Approval: MHS20180424-01). The study was a retro-
spective consecutive short series of data collected between
January 1, 2010, and December 30, 2017, at a single ortho-
paedic practice in Australia. Patients eligible for inclusion
were those who underwent a TKA or THA and received
pre-operative cardiology assessment including an electro-
cardiogram (ECG) and echocardiogram (ECHO) as a
baseline investigation. Pre-operative cardiology assessment
was standard practice for all patients, regardless of sus-
pected risk, for this institution. Surgery was performed by
a single surgeon and pre-operative cardiology review was
completed by one of seven cardiologists. Patients who
underwent bilateral TKA or THA, revision arthroplasty,
or had a surgical indication other than osteoarthritis were
excluded. As this study aims to risk-stratify within an
asymptomatic TJA population, patients identified to have
symptomatic cardiac disease including angina, evidence of
heart failure (New York Heart Association class > 1),
orthopnoea, paroxysmal nocturnal dyspnoea, and/or per-
ipheral oedema, at the initial surgical consultation, were
excluded. A flowchart of the inclusion and exclusion cri-
teria is provided as Supplementary Figure 1.
Data was collected by two investigators from electronic
records. Variables were selected based on a prior system-
atic review examining risk factors for cardiac complication
after TKA and THA [14]. The predictor variables included
patient demographics; medical history (cardiac and non-
cardiac); family history of cardiac disease (FHx of CVD),
defined as history of myocardial infarction, cardiac arrest,
sudden cardiac death, ischaemic heart disease, coronary
artery disease or congestive heart failure in a first-degree
relative; and results of baseline cardiac investigation (ECG
and ECHO). Data extracted were age, gender, body mass
index (BMI), diabetes, hypertension (HTN), hypercholes-
terolaemia, smoking history, alcohol history, chronic
kidney disease, chronic obstructive pulmonary disease,
asthma, obstructive sleep apnoea, chronic heart failure,
myocardial infarction, coronary artery disease (CAD),
Elsiwy et al. Journal of Orthopaedic Surgery and Research (2020) 15:513 Page 2 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
valvular disease, arrhythmia, peripheral vascular disease,
cerebrovascular accident (CVA) and venous thrombo-
embolism [14]. The outcome variables for this analysis
were (1) additional cardiac investigation (ACI) ordered by
cardiologist (e.g. angiogram, CT-coronary angiography
(CTCA), stress test, or myocardial perfusion scan (MIBI))
and (2) abnormality detected upon additional cardiac in-
vestigation (ADACI). Post-operative cardiac complications
(defined as cardiac event within 30 days), as well as surgical
delays (defined as a change in the date of surgery for car-
diac optimisation), and surgical cancellation by cardiolo-
gist, were also recorded.
Statistical methodology
Data analysis was performed using SPSS 24.0 (IBM) with
the alpha level set at 0.05. The measure of central ten-
dency and dispersion was reported as mean ± standard
deviation, and prevalence as frequencies. Based on
Shapiro-Wilk test, continuous parameters were log-
transformed prior to inferential statistical analyses. The
discriminant and predictive capacities for ACI and
ADACI were assessed using a three-tiered approach
(Supplementary Figure 2). Firstly, an independent ttest
was used to determine differences in age, BMI and ejec-
tion fraction (EF) between patients with, and without,
the need for ACI, and between patients with, and with-
out, ADACI. Similar inter-individual comparisons were
conducted for all other nominal variables using a chi-
squared test of independence. Crude odds ratio and asso-
ciated 95% confidence interval (CI) of all continuous and
nominal variables were calculated using MedCalc (Version
19.0.7, Belgium). Secondly, a stepwise, multivariate logistic
regression was conducted to determine whether the vari-
ables that were incorporated in the inferential statistics
(i.e. independent variables) predicted ACI and ADACI (i.e.
dependent variables). Adjusted odds ratio and associated
95% CI derived from the multivariate logistic regression
were also reported. According to the Hosmer and Leme-
show test, the dependent variables were not significant (p
> 0.05), demonstrating appropriate goodness of fit for the
multivariable logistic regression, and that the sample size
provided sufficient predictive capacity for the model.
Finally, the variance inflation factor of both multivariate
logistic regression models were < 5 for the independent
variables, indicating that the predictors demonstrated
minimal multi-collinearity [16].
Internal validation of significant predictor variables for ADACI
Based on the performance of predictor variables in the
multivariate logistic regression model, a series of predict-
ive scores were constructed that weighted the influence of
each predictor on the odds of a cardiac abnormality being
detected upon additional cardiac investigation. Numerical
variables were normalised against the mean of the cohort,
while categorical variables were allocated an arbitrary
value of 1 for presence of the risk factor and 0 for its
absence. To determine the discriminant capacity for
ADACI, receiver operator characteristics (ROC) curves
were generated to compute the area under the curve or c-
statistic (c-statistic; 95% CI). The model weighted predic-
tors based on the odds of an abnormality detected upon
additional cardiac investigation to closely reflect each risk
factors’weight in the predictive model. Risk factors with
an odds ratio 1 ≤OR < 2 were multiplied by 1 point, 2 ≤
OR < 3 multiplied by 2 points, and ≥3 multiplied by 3
points to produce a final score. The discriminative
capability of the model was evaluated using the c-statistic.
The stepwise, multivariate logistic regression allowed
selection of pertinent predictors for the ROC curves.
Appropriate model fitting of these explanatory variables
were assessed via Hosmer-Lemeshow goodness of fit test,
in conjunction with the likelihood-ratio chi-squared test.
Accordingly, the results of the tests were not statistically
significant, indicating that the statistical model was well
calibrated, and our model improved from the null model.
Furthermore, the Akaike information criterion was exam-
ined across several iterations as part of the stepwise
process, with the model exhibiting the smallest value
selected to ensure a better-fit model. The assumption of
linearity of the multivariate logistic regression between the
log odds and predictor variables was assessed using the
Box-Tidwell test in Stata (version 16, Texas). With regard
to the ROC curves, the cut-off demonstrating the greatest
sensitivity and specificity was chosen.
Results
Cohort composition and patient characteristics
A total of 374 patients were eligible for inclusion. Four
hundred forty-three patients were directly identified
based on the inclusion criteria, though THA and TKA
are not the only procedures performed at this centre.
From a total of 443 patients, 7 patients were excluded
due to cancellation of surgery for non-medical reasons
and 62 patients were excluded due to missing crucial
data, namely ECG and ECHO reports, which were un-
able to be retrieved from medical records (Supplemen-
tary Figure 1). Of the 374 patients eligible for inclusion,
272 (72.7%) underwent TKA, and 102 (27.3%) under-
went THA (Additional Table 1). The mean age of the
cohort was 69.9 ± 9.0 years, and the majority of patients
were male (54.0%). Hypertension was the most common
risk factor (65.2%), followed by hypercholesterolaemia
(57.8%) and diabetes (15.0%) (Additional Table 1). More
than one third (35.3%) of patients reported a previous
cardiac history, and 19.3% reported a FHx of CVD.
There were 22 (5.9%) current smokers and 91 (24.3%)
ex-smokers in this population. The mean EF was 63.82
± 8.28%, and an ECG abnormality was found in 115
Elsiwy et al. Journal of Orthopaedic Surgery and Research (2020) 15:513 Page 3 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
patients (30.7%) during pre-operative cardiology review
(Additional Table 1).
In the total population, six patients did not proceed
with surgery following cardiology review: one had severe
valvular disease identified on history, one had ECG evi-
dence of a prior infarction not previously known, two
had evidence of severe stenosis on CTCA, and three
were cancelled due to non-cardiac reasons. Post-
operative arrhythmia (atrial fibrillation) was reported in
seven patients, with no other cardiac complications
post-operatively.
Additional cardiac investigation (ACI)
One hundred thirty-eight patients underwent ACI follow-
ing pre-operative cardiology review of ECG/ECHO. The
mean age was 72.3 ± 8.7 years, and age was found to be
significantly associated with ACI (p<0.001)(Table1).
Hypertension, previous cardiac history and FHx of cardiac
Table 1 Risk factors associated with additional cardiac investigation
Variable ACI Statistics
Yes
(n= 138)
No
(n= 236)
χ
2
pvalue zscore Crude OR 95% CI pvalue
Demographics
Age (years) 72.3 ± 8.7 68.5 ± 8.9 –< 0.001 –1.05 1.03–1.08 < 0.001
Male 81 (58.7%) 121 (51.3%) 1.93 0.164 1.4 1.35 0.88–2.06 0.170
Female 57 (41.3%) 115 (48.7%) 1.93 0.164 -1.4 0.74 0.48–1.13 0.170
BMI 31.4 ± 5.5 31.1 ± 5.3 –0.592 –1.01 0.97–1.05 0.590
Past medical Hx
Diabetes 27 (19.6%) 29 (12.3%) 3.62 0.057 1.9 1.74 0.98–3.08 0.060
Hypertension 105 (76.1%) 139 (58.9%) 11.35 0.001 3.4 2.22 1.39–3.55 <0.001
Hypercholesterolaemia 88 (63.8%) 128 (54.2%) 3.24 0.072 1.8 1.49 0.96–2.29 0.070
Smoking 11 (8.0%) 11 (4.7%) 5.97 0.050 1.3 2.03 0.85–4.88 0.110
Ex-smoker 41 (29.7%) 50 (21.2%) 5.97 0.050 1.9 1.67 1.03–2.7 0.040
Alcohol 13 (9.4%) 29 (12.3%) 0.72 0.397 - 0.8 0.74 0.37–1.48 0.400
CKD 12 (8.7%) 11 (4.7%) 2.47 0.292 1.6 1.95 0.83–4.54 0.120
Asthma 16 (11.6%) 28 (11.9%) 0.006 0.938 -0.1 0.97 0.51–1.87 0.940
COPD 7 (5.1%) 7 (3.0%) 1.07 0.300 1.0 1.75 0.60–5.09 1.000
OSA 13 (9.4%) 3 (12.7%) 0.93 0.336 -1.0 0.71 0.36–1.42 0.340
Past CV Hx
Cardiac Hx total 63 (45.7%) 69 (29.2%) 10.27 0.001 3.2 2.03 1.31–3.15 0.002
CCF 3 (2.2%) 5 (2.1%) 0.001 0.972 0.0 1.03 0.24–4.36 0.970
MI 14 (10.1%) 14 (5.9%) 2.23 0.135 1.5 1.79 0.83–3.88 0.140
CAD 38 (27.5%) 36 (15.3%) 8.28 0.004 2.9 2.11 1.26–3.53 0.005
Valvular disease 10 (7.2%) 15 (6.4%) 0.11 0.739 0.3 1.15 0.50–2.64 0.740
Arrhythmia 25 (18.1%) 30 (12.7%) 2.04 0.154 1.4 1.52 0.85–2.71 0.160
PVD 4 (2.9%) 3 (1.3%) 1.26 0.262 1.1 2.32 0.51 –10.52 0.280
CVA 19 (13.8%) 8 (3.4%) 14.00 <0.001 3.7 4.55 1.93 –10.70 <0.001
VTE 8 (5.8%) 6 (2.5%) 2.56 0.110 1.6 2.36 0.80–6.95 0.120
Family Hx of cardiac disease 42 (30.4%) 30 (12.7%) 17.60 <0.001 4.2 3.00 1.77–5.09 <0.0001
Baseline cardiac Ix
ECG abnormality 53 (38.4%) 62 (26.3%) 6.02 0.014 2.5 1.75 1.12–2.74 0.001
Ejection fraction (%) 62.0 ± 10.2 64.9 ± 6.7 –0.005 –0.96 0.93–0.99 0.002
Numerical predictor variables (age, BMI, ejection fraction) are presented as mean ± standard deviation and were assessed using an independent ttest. Categorical
predictor variables are presented as frequencies and were assessed using chi-squared test of independence. The crude ratio and associated 95% CI are also
reported. Significant predictor variables are indicated by p< 0.05
ACI additional cardiac investigation, OR odds ratio, BMI body mass index, CV cardiovascular, Hx history, CCF chronic heart failure, MI myocardial infarction, CAD
coronary artery disease, PVD peripheral vascular disease, CVA cerebrovascular accident, VTE venous thromboembolism, CKD chronic kidney disease, COPD chronic
obstructive pulmonary disease, OSA obstructive sleep apnoea, ECG electrocardiogram, Ix investigation
Elsiwy et al. Journal of Orthopaedic Surgery and Research (2020) 15:513 Page 4 of 10
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disease were more prevalent in this cohort compared to
the total patient population, and these variables were all
significant for additional cardiac investigation (p=0.001,
p=0.001andp< 0.001, respectively) (Table 1). More spe-
cifically, a history of CAD or CVA was significantly associ-
ated with ACI (p= 0.004 and p<0.001,respectively).An
ECG abnormality, such as heart block, atrial fibrillation,
evidence of prior ischaemic events, or ectopic beats, was
detected in 38.4% patients that went on to have additional
cardiac investigation (p= 0.010). The mean EF was similar
to the mean for the total patient population (62.0% vs.
63.8%), and EF was significantly associated with ACI (p=
0.005) (Table 1).
According to the logistic regression, age (OR 1.06, 95%
CI 1.03–1.09, p< 0.001), CVA (OR 3.83, 95% CI 1.26–
11.61, p= 0.018) and FHx of CVD (OR 4.20, 95% CI
2.27–7.80, p<0.001) significantly predicted the need for
ACI, while increased EF was predictive of cardiologist
not referring for further investigation (OR 0.94, 95% CI
0.91–0.97, p< 0.001) (Table 2). Hypertension and coron-
ary artery disease increased the likelihood of requiring
ACI 1.63-fold and 1.67-fold, respectively; however, these
risk factors were not significant predictors (p= 0.080
and p= 0.106, respectively).
Abnormality detected upon additional cardiac
investigation (ADACI)
Fifty patients were found to have an abnormality upon
additional cardiac investigation. The mean age was 73.3
± 8.4 years, and age was found to be significantly associ-
ated with the incidence of an abnormal cardiac finding
(p=0 .004) (Table 3). Male gender was more prevalent
compared with the total cohort (70% vs. 54%) and was
significantly associated with ADACI (p= 0.015), as were
current and ex-smokers (both p= 0.009) (Table 3). In
contrast to the ACI group, cardiac history was not sig-
nificantly associated with detection of an abnormality on
additional cardiac investigation (p= 0.601). However,
CVA and FHx of CVD were significant (p= 0.010 and p
= 0.014, respectively). ECG abnormality (p= 0.005) and
EF (p< 0.001) from baseline cardiac assessment were
also both significantly associated with ADACI (Table 3).
A total of 17 patients in the ADACI group had a surgi-
cal delay by the cardiologist to undergo pre-operative
cardiac optimisation, whereas the remaining patients
were able to be optimised without surgical delay. In the
delayed group, 12 had an ECG abnormality, either atrial
fibrillation or heart block, and five had an EF < 50% at
baseline. Consequently, 15 had an abnormal angiogram
and two had an abnormal MIBI.
Logistic regression revealed increased age, male gender,
FHx of CVD, and reduced EF significantly predicted de-
tection of a cardiac abnormality pre-operatively (Table 4).
For every increased year in age, there was a significant 7%
increase in ADACI (OR 1.07; 1.02–1.12 95% Cl, p=
0.003), while every percentage increase in EF reduced the
likelihood of finding an abnormality by 7% (OR 0.93;
0.89–0.97 95% Cl, p< 0.001). Male gender was also a sig-
nificant predictor for ADACI (OR 2.22; 1.03–4.79 95% Cl,
p= 0.042). FHx of CVD was a strong predictor and signifi-
cantly increased the likelihood of finding a cardiac abnor-
mality 3.6-fold (OR 3.61; 1.65–7.89 95% CI, p=0.001).
Smoking status and history of CVA did not demonstrate
any significant predictive capacity (Table 4).
To further validate these findings, significant predic-
tors for ADACI were evaluated in a predictive model
using ROC curve analysis. EF and Age were normalised
against the mean of the cohort, with a Mean EF of 63.8%
and a Mean Age of 69.9 years. As outlined in the
“Methods”section, the model weighted the influence of
the predictor variables based on the odds ratio produced
by the logistic regression, as follows: (2* Gender) + (3*
FHx of CVD) + (Mean EF/Patient EF) + (Age/Mean
Age). This model produced a cut-off score of 3.99 and
maintained reasonable predictive capacity (c-statistic =
0.71), with 70% sensitivity and 65% specificity, and good
calibration (p> 0.05).
Discussion
Cardiovascular complication continues to represent up to
20% of all major post-operative complication following TJA
[12,14] and will become increasingly important with an
ageing, increasingly obese, and more medically complex
arthroplasty population [12,17,18]. Pre-operative cardi-
ology optimisation is essential to reduce post-operative car-
diac events. However, no validated, orthopaedic-specific
cardiac risk stratification tools are available to guide
Table 2 Multivariate logistic regression for additional cardiac
investigation
95% Cl
Predictors βS.E pOR Low High
Demographics
Age 0.06 0.02 <0.001 1.06 1.03 1.09
HTN 0.49 0.28 0.080 1.63 0.94 2.83
Cardiac history
CAD 0.51 0.32 0.106 1.67 0.90 3.12
CVA 1.34 0.57 0.018 3.83 1.26 11.61
FHx of CVD 1.44 0.32 < 0.001 4.20 2.27 7.80
Baseline cardiac Ix
ECG abnormality 0.29 0.28 0.309 1.34 0.77 2.33
Ejection fraction −0.06 0.02 < 0.001 0.94 0.91 0.97
Multivariate logistic regression results with adjusted odds ratios (OR) and
associated 95% confidence intervals (Cl). Significant predictor variables are
indicated by p< 0.05
HTN hypertension, CAD coronary artery disease, CVA cerebrovascular accident,
FHx family history, CVD cardiovascular disease, Ix
investigation, ECG electrocardiogram
Elsiwy et al. Journal of Orthopaedic Surgery and Research (2020) 15:513 Page 5 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
orthopaedic referral of patients to cardiology [3,19–21].
There has been no previous research on cardiac referral for
the asymptomatic preoperative TJA population in which
cardiac disease not yet exhibiting clinical manifestation but
capable of contributing to risk of postoperative cardiac
complication may exist. Over-referral is unsustainable
[4,22–24] and may unnecessarily delay surgery [25,26];
however under-referral may lead to missed pre-operative
optimisation for asymptomatic patients who otherwise
would have benefited, and may contribute to post-operative
cardiac complication.
The need for a pre-operative risk stratification tool for
lower limb arthroplasty was recognised by Waterman
et al., who proposed the TJA Cardiac Risk Index Score
[3]. Though the results of this study are promising, their
use of the National Surgical Quality Improvement Pro-
gram (NSQIP) database limited their analysis as only risk
factors recorded in the database could be evaluated, and
Table 3 Risk factors associated with abnormality detected upon additional cardiac investigation
ADACI Statistics
Yes (n= 50) No (n= 324) χ
2
pvalue zscore Crude OR 95% CI pvalue
Demographics
Age (years) 73.3 ± 8.4 69.4 ± 9.0 - 0.004 - 1.05 1.02–1.09 0.005
Male 35 (70%) 167 (51.5%) 5.94 0.015 2.4 2.19 1.15–4.17 0.020
Female 15 (30%) 157 (48.5%) 5.94 0.015 -2.4 0.46 0.24–0.87 0.020
BMI 31.4 ± 5.9 31.2 ± 5.3 - 0.880 - 1.01 0.95–1.06 0.870
Past medical Hx
Diabetes 9 (18%) 47 (14.5%) 0.42 0.519 0.6 1.30 0.59–2.84 0.520
Hypertension 37 (74%) 207 (63.9%) 1.95 0.162 1.4 1.61 0.82–3.15 0.170
Hypercholesterolaemia 30 (60.0%) 186 (57.4%) 0.12 0.730 0.3 1.11 0.61–2.04 0.730
Smoking 6 (12.0%) 16 (4.9%) 9.52 0.009 2.0 3.39 1.22–9.42 0.020
Ex-smoker 18 (36.0%) 73 (22.5%) 9.52 0.009 2.1 2.23 1.16–4.29 0.020
Alcohol 6 (12.0%) 36 (11.1%) 0.03 0.853 0.2 1.09 0.43–2.74 0.850
Asthma 7 (14.0%) 37 (11.4%) 0.28 0.598 0.5 1.26 0.53–3.01 0.600
COPD 2 (4.0%) 12 (3.7%) 0.01 0.918 0.1 1.08 0.24–4.99 0.920
OSA 4 (8.0%) 39 (12.0%) 0.69 0.405 -0.8 0.64 0.22–1.86 0.410
Past CV Hx
Cardiac Hx total 16 (32%) 116 (35.8%) 0.27 0.601 -0.5 0.84 0.45–1.59 0.600
CCF 0 (0.0%) 8 (2.5%) 1.26 0.261 -1.1 0.37 0.02–6.49 0.500
MI 6 (12.0%) 22 (6.8%) 1.70 0.193 1.3 1.87 0.72–4.87 0.200
CAD 11 (22%) 63 (19.4%) 0.18 0.673 0.4 1.14 0.56–2.34 0.710
Valvular disease 5 (10.0%) 20 (6.2%) 1.02 0.313 1.0 1.69 0.60–4.73 0.320
Arrhythmia 6 (12.0%) 49 (15.1%) 0.34 0.562 -0.6 0.79 0.32–1.94 0.610
PVD 2 (4.0%) 5 (1.5%) 1.42 0.233 1.2 2.66 0.50–14.09 0.250
CVA 8 (16.0%) 19 (5.9%) 6.64 0.010 2.6 2.45 1.04–5.74 0.040
VTE 4 (8.0%) 10 (3.1%) 2.90 0.088 1.7 2.73 0.82–9.07 0.100
CKD 3 (6.0%) 20 (6.2%) 0.47 0.790 0.0 0.97 0.28–3.37 0.970
Family Hx of cardiac disease 16 (32.0%) 56 (17.3%) 6.03 0.014 2.5 2.25 1.16–4.36 0.020
Baseline cardiac Ix
ECG abnormality 24 (48.0%) 91 (28.1%) 8.07 0.005 2.8 2.36 1.29–4.33 0.005
Ejection fraction (%) 58.9 ± 11.6 64.6 ± 7.3 - 0.002 - 0.93 0.89–0.96 <0.001
Numerical predictor variables (age, BMI, ejection fraction) are presented as mean ± standard deviation and were assessed using an independent ttest. Categorical
predictor variables are presented as frequencies and were assessed using chi-squared test of independence. The crude ratio and associated 95% CI are also
reported. Significant predictor variables are indicated by p< 0.05
ADACI abnormality detected upon additional cardiac investigation, OR odds ratio, BMI body mass index, CV cardiovascular, Hx history, CCF chronic heart failure, MI
myocardial infarction, CAD coronary artery disease, PVD peripheral vascular disease, CVA cerebrovascular accident, VTE venous thromboembolism, CKD chronic
kidney disease, COPD chronic obstructive pulmonary disease, OSA obstructive sleep apnoea, Ix investigation, ECG electrocardiogram
Elsiwy et al. Journal of Orthopaedic Surgery and Research (2020) 15:513 Page 6 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
patient follow-up was limited to only 30 days post-
operative [3,27]. Improved cohort design and broader
evaluation of risk factors is required to determine all risk
factors that should guide pre-operative cardiology refer-
ral. Risk factors identified by the recent review by Elsiwy
et al. were evaluated in this series and tested for their
capacity to predict the need for additional cardiac inves-
tigation pre-operatively and abnormality detected upon
additional cardiac investigation. Uniquely in this study,
all patients, including asymptomatic patients without
known cardiac disease, were referred to cardiology pre-
operatively for ECG/ECHO, thereby avoiding the bias of
selective referral of only higher-risk patients. Further-
more, in contrast to all previous literature, risk factors in
this study were not measured against their capacity to
predict post-operative cardiac events, but rather the
ability to predict cardiologist referral for additional in-
vestigation pre-operatively, and cardiac abnormalities
detected during additional investigation, which may
prompt cardiac optimisation and/or surgical delay. The
additional advantage of examining these outcome vari-
ables in a population that has all been referred to cardi-
ology is that the overall cardiovascular profile of an
asymptomatic population can be determined, patients
with silent disease can be identified and risk factors
which can better guide pre-operative cardiac referral can
be determined.
Importantly, our cohort had a demographic profile
comparable to previous arthroplasty populations
described in the literature. The mean age and the cardiac
complication rate (1.87%) was similar to the Waterman
study [3,14], while the cardiovascular risk profile of our
cohort was similar to that described by Łęgosz et al. in
their analysis of cardiovascular risk of arthroplasty popu-
lations [4]. No patients in our series suffered a major
cardiac event or post-operative mortality, reflecting the
experience and expertise of the orthopaedic surgeon.
Given the small cohort size of 374 patients, this is also
consistent with the low rates of mortality (0.18%) and inci-
dence of major cardiac complication (myocardial infarction
or cardiac arrest) previously reported (0.2%) [2,3], but may
also be due to the fact that all patients were referred for
cardiology review and optimisation pre-operatively. The
importance of this is further demonstrated by the finding of
ischaemic lesions during ACI in 17 ‘asymptomatic’patients
that went on to have surgical delays.
By assessing predictors of additional cardiac investigation,
this study highlights for the first time the risk factors that
cardiologists are more likely to utilise for pre-operative risk
stratification of TKA and THA patients. In this series,
cardiologists were more likely to conduct additional
cardiac investigation based on increasing age, FHx of
CVD, previous CVA, and reduced EF. Of interest, al-
though CVA was considered important by the cardiolo-
gists, it did not show a significant predictive capacity
for detecting cardiac abnormality. The link between
CVA and post-operative cardiac complication is con-
troversial [14]; however, these results support previous
literature in which CVA was not significantly associated
with post-operative cardiac complication [2,7]. In con-
trast, reduced EF was a consistent predictor for both
ACI and ADACI, though current literature does not
support the use of routine echocardiogram in pre-
operative cardiovascular screening [28,29].
For cardiac risk stratification, the detection of a car-
diac abnormality is most relevant, and these patients are
most likely to benefit from pre-operative cardiac opti-
misation since they have demonstrated cardiac disease.
Similar to multiple studies showing a strong association
between increasing age and post-THA and TKA cardiac
complication [2,3,7,10–12], our analysis demonstrated
a significant predictive capacity of every year of increase
in age for the detection of a cardiac abnormality pre-
operatively. Male gender was also a significant predictor
for ADACI and should therefore be considered in pre-
operative risk stratification. Interestingly, this was not
reported by Waterman et al., although the influence of
male gender on increasing the risk of cardiovascular
complication is well established in the literature [10,13].
A 3.6-fold increased risk of detecting a cardiac abnor-
mality with a FHx of CVD suggests that this risk factor
should also be considered in pre-operative cardiac risk
stratification. Notably, this risk factor has not been
Table 4 Multivariate logistic regression for abnormality
detected upon additional cardiac investigation
95% Cl
Predictors βS.E p OR Low High
Demographics
Age 0.07 0.02 0.003 1.07 1.02 1.12
Gender 0.80 0.39 0.042 2.22 1.03 4.79
Current smoker 0.80 0.67 0.231 2.22 0.60 8.17
Ex-smoker −0.46 0.40 0.250 0.63 0.29 1.38
Cardiac history
CVA 1.01 0.59 0.087 2.74 0.86 8.71
Family history
FHx of CVD 1.28 0.40 0.001 3.61 1.65 7.89
Baseline cardiac Ix
ECG abnormality 0.23 0.39 0.556 1.26 0.59 2.68
Ejection fraction −0.08 0.02 < 0.001 0.93 0.89 0.97
Multivariate logistic regression results for abnormality detected upon
additional cardiac investigation with adjusted odds ratios (OR) and associated
95% confidence intervals (Cl). Significant predictor variables are indicated by p
< 0.05
CVA cerebrovascular accident, FHx family history, CVD cardiovascular disease,
Ix investigation, ECG electrocardiogram
Elsiwy et al. Journal of Orthopaedic Surgery and Research (2020) 15:513 Page 7 of 10
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considered in this context in previous literature and was
not addressed in the review by Elsiwy et al. [14]. Al-
though family history is a long-established risk factor for
cardiovascular disease, perhaps it has not been consid-
ered in previous arthroplasty studies due to its non-
modifiable nature [30]. In contrast to having a family
history, patient cardiac history was not a significant
predictor for ADACI. While an association between
previous cardiac history and post-operative cardiac
complication has been reported [2,3,7,11,12,31], it
is possible that patients in the current study were pre-
viously optimised so the influence of their cardiac his-
tory had been mitigated or reversed. This is also
potentially true of patients with diabetes, hyperten-
sion, hypercholesterolaemia and other risk factors
linked to cardiac complicationpost-TJAinthelitera-
ture that did not show significance in our series.
These findings that increased age, male gender, FHx of
CVD and decreased ejection fraction predicted cardiac
abnormality were internally validated in a predictive
model that produced a statistically significant c-statistic
> 0.70 which was equivalent to the model proposed by
Waterman [3], and had reasonable discriminative cap-
acity and no evidence of statistically significant lack of fit
(p> 0.05) [32]. The proposed model demonstrates an
innovative method of producing an algorithm that can
be used to risk-stratify TJA patients and guide pre-
operative cardiac referral. In contrast to the methods
previously described by Waterman et al. [3] and several
other surgical risk calculators [19,21,33] where various
iterations are tested until the highest c-statistic is ob-
tained, risk factors included in the our model were
weighted based on their odds ratios, taking into consid-
eration the individual influence of each risk factor.
Limitations
The authors acknowledge several limitations that pre-
vent translation of the proposed risk stratification
model into clinical practice at this time. Due to the
small population, additional risk factors that occur in
low incidence within this population were unable to be
accurately investigated, and continuous variables age
and EF were not able to be stratified to evaluate specific
ranges. Small sample size may also explain why being a
previous or current smoker showed significant associ-
ation but did not achieve significant predictive capacity
in the regression model, in contrast to current litera-
ture [14]. Furthermore, some parameters such as the
American Society of Anesthesiologists (ASA) score,
which has previously shown significance in the litera-
ture,werenotavailableinthisdataset[3]. However,
this study avoids the inherent limitations of larger
database-driven studies already discussed and provides
auniquecohortofasymptomaticpatients,whowereall
referred to cardiology prior to TKA or THA. Another
limitation is the inclusion of EF as a predictive risk fac-
tor. Since routine echocardiography is not currently
evidence-based practice [29], not all patients will have
this measurement readily available for pre-operative
risk stratification. There is evidence, however, that
echocardiography has a role in pre-operative evaluation
of higher risk patients [29]. Given the results of the
current study which suggest that even asymptomatic
TJA patients may be high risk if they are over 70, male
and have a family history of cardiovascular disease, the
benefit of echocardiography in this subset of patients
needs further evaluation. While the bias of cardiac
referral has been removed in this study, there is still
potential bias in the provision of additional cardiac investi-
gation by the cardiologist. We attempted to minimise this
bias by including seven different cardiologists, and the
consistency between regression models of ACI and
ADACI suggest appropriate selection of patients for add-
itional cardiological intervention in this study. Finally, the
categorical representation of risk factors such as hyper-
tension, diabetes and hypercholesterolaemia does not
distinguish between patients in whom these comorbidi-
ties are well controlled versus poorly controlled. Future
studies should aim to collect objective parameters such
as systolic blood pressure, blood sugar level and total
cholesterol so as to improve assessment of the impact
of these comorbidities.
Clinical implications and future research
Despite these limitations, the predictive model pre-
sented here has broader implications for clinical prac-
tice which may be used to assist orthopaedic surgeons
with more evidence-based referral to cardiology prior
to TJA. The finding that a family history of cardiovas-
cular disease had the greatest power in our model sug-
gests the presence of this risk factor should prompt the
surgeon to refer for pre-operative cardiac assessment.
In the absence of FHx of CVD, male patients should
also be referred given that a male of mean age and with
a mean EF would reach the model cut-off score. This
study also highlights the value of age and ejection frac-
tion in determining which patients would benefit from
pre-operative cardiology referral prior to undergoing
TKA or THA. Additionally, the proposed model may
serve as an example for future studies attempting to
create a TJA-specific pre-operative cardiac referral tool,
particularly for asymptomatic patients. Future investi-
gations in this area should employ larger and more
tailored patient cohorts that are specifically designed to
develop a comprehensive tool that considers all risk
factors associated with cardiac complication post-TJA
that have been identified in the literature.
Elsiwy et al. Journal of Orthopaedic Surgery and Research (2020) 15:513 Page 8 of 10
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Conclusions
As arthroplasty procedures increase exponentially, a vali-
dated pre-operative cardiac risk stratification tool is cru-
cial to assist decision-making by orthopaedic surgeons
with regard to patient referral for pre-operative cardiac
optimisation. Increasing age, male gender, FHx of CVD,
and reduced EF were identified as significant predictors
for detection of cardiac abnormality in asymptomatic
patients on pre-operative work-up. Future predictive
models require larger cohorts which evaluate all poten-
tial risk factors of post-operative cardiac complication.
Targeted pre-operative cardiac optimisation will not only
reduce the risk of post-operative cardiac complication,
but also has the potential to reduce health expenditure
and surgical delays.
Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s13018-020-02042-5.
Additional file 1. Table 1. Demographic characteristics and risk factors
of patients in the total patient population. Supplementary Figure 1:
Inclusion and exclusion criteria. TKA = total knee arthroplasty; THA = total
hip arthroplasty; ECG = electrocardiogram; ECHO = echocardiogram.
Supplementary Figure 2: A simplified flowchart of the analytical process.
All data analysis used SPSS, V.24, P < 0.05 statistically significant. TKA =
Total Knee Arthroplasty; THA = Total Hip Arthroplasty.
Abbreviations
ACI: Additional cardiac investigation; ADACI: Abnormality detected on
additional cardiac investigation; ASA: American Society of Anesthesiologists;
BMI: Body mass index; CAD: Coronary artery disease; CI: Confidence interval;
CTCA: CT-coronary angiography; CVA: Cerebrovascular accident;
CVD: Cardiovascular disease; ECG: Electrocardiogram; ECHO: Echocardiogram;
EF: Ejection fraction; FHx: Family history; HTN: Hypertension; MIBI: Myocardial
perfusion scan; NSQIP: National Surgical Quality Improvement Program;
OR: Odds ratio; ROC: Receiver operator characteristics; THA: Total hip
arthroplasty; TJA: Total joint arthroplasty; TKA: Total knee arthroplasty
Acknowledgements
The authors would like to acknowledge the valuable contributions of Mrs.
Andrea Grant for administrative support, Mrs. Alicia Harris for assistance with
data collection, Dr. Jodie Morris for editorial assistance, and the Orthopaedic
Research Institute of Queensland (ORIQL) and College of Medicine and
Dentistry, James Cook University, for the ongoing research support.
Authors’contributions
YE, KH and HL developed the research question and study design. YE and TS
completed the data collection. YE, KD and HL completed the data analysis
and interpretation. YE drafted the manuscript, which was edited and
reviewed by KD, KH, MW and HL. The authors have read and approved the
final manuscript.
Funding
This research did not receive any specific grant from funding agencies in the
public, commercial or not-for-profit sectors.
Availability of data and materials
The datasets used and analysed during the current study are available from
the corresponding author on reasonable request.
Ethics approval and consent to participate
This study was approved by the Mater Health Services North Queensland
Human Research Ethics Committee (Approval: MHS20180424-01). A waiver of
consent was approved in accordance with the National Statement 2.3.10 that
this constitutes a negligible or low risk to participants who have already re-
ceived standard of care.
Consent for publication
Not applicable
Competing interests
The authors declare that they have no competing interests.
Author details
1
College of Medicine and Dentistry, James Cook University, 1 James Cook
Drive, Townsville, QLD 4811, Australia.
2
Orthopaedic Research Institute of
Queensland, 7 Turner Street, Townsville, QLD 4812, Australia.
3
College of
Healthcare Sciences, James Cook University, 1 James Cook Drive, Townsville,
QLD 4811, Australia.
4
School of Medicine, University of Tasmania, Medical
Science Precinct, 17 Liverpool Street, Hobart, TAS 7000, Australia.
Received: 3 September 2020 Accepted: 28 October 2020
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