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Increased Glycemic Variability in Patients with Elevated
Preoperative HbA1C Predicts Adverse Outcomes
Following Coronary Artery Bypass Grafting Surgery
Balachundhar Subramaniam, MD, MPH,* Adam Lerner, MD,* Victor Novack, MD, PhD,†‡
Kamal Khabbaz, MD,§ Maya Paryente-Wiesmann, MD,† Philip Hess, MD, PhD,*
and Daniel Talmor, MD, MPH*
BACKGROUND: In the setting of protocolized glycemic control, the relationship between postoperative glycemic variability on major adverse events (MAEs) after cardiac surgery is unknown for
patients with increased preoperative hemoglobin A1C (HbA1C >6.5%). In this study, we sought
to establish (a) whether postoperative glycemic variability is associated with MAEs after CABG
surgery and (b) whether preoperative HbA1C could identify patients at increased risk of postoperative glycemic variability.
METHODS: Patients undergoing coronary artery bypass grafting with or without valvular surgery
from January 2008 to May 2011 were enrolled in this prospective, single-center, observational
cohort study. Demographic, intraoperative, and postoperative outcome data were obtained from
institutional data collected for the Society of Thoracic Surgery (STS) database. The primary
outcome, MAE was a composite of in-hospital death, myocardial infarction (MI), reoperations,
sternal infection, cardiac tamponade, pneumonia, stroke, or renal failure. Glycemic variability
in the postoperative period was assessed by the coefficient of variation (CV). CV was used as
quartiles for the multivariate logistic regression. Variable selection in multivariable modeling
was based on clinical and statistical significance and was performed in a hierarchical fashion.
RESULTS: Of the 1461 patients enrolled, 9.8% had an MAE. Based on the established target of
HbA1C <6.5% for the diagnosis of diabetes mellitus, we considered HbA1C as a binary variable
(<6.5% and ≥6.5%) in our primary analysis. Multivariate logistic regression analyses for the preoperative variables only revealed that preoperative HbA1C (odds ratio [OR], 1.6; 95% confidence
interval [CI], 1.1–2.3; P = 0.02), history of MI (OR, 1.9; 95% CI, 1.3–2.8; P = 0.001), and STS
risk score per quartile (OR, 1.7; 95% CI, 1.4–2.1; P < 0.001) were associated with MAEs. When
postoperative variables were included in the analyses, postoperative glycemic variability (CV per
quartile) in the intensive care unit (OR, 1.3; 95% CI, 1.1–1.5; P = 0.03), mean glucose levels
averaged over the first 4 postoperative hours (OR, 1.2; 95% CI, 1.0–1.4; P = 0.03), history of MI
(OR, 1.8; 95% CI, 1.2–2.6; P = 0.004), and STS risk score per quartile (OR, 1.6; 95% CI, 1.3–2.0;
P < 0.001) were associated with MAEs. Glycemic variability as assessed by CV was increased postoperatively in patients with preoperative HbA1C ≥6.5% (0.20 ± 0.09 vs 0.16 ± 0.07, P < 0.001).
CONCLUSIONS: Postoperative glycemic variability is associated with MAEs after cardiac surgery. Glycemic variability is only measured when the patient leaves the intensive care unit, and
there is no opportunity to intervene earlier. Preoperative HbA1C identifies risk for postoperative
glycemic variability and may provide a more rational guide for targeting measures to reduce
variability.  (Anesth Analg 2014;118:277–87)

From the *Department of Anesthesiology, Critical Care and Pain Medicine,
Beth Israel Deaconess Medical Center, Boston, Massachusetts; †Clinical Research Center, Soroka University Hospital and Faculty of Health Sciences,
Ben-Gurion University of the Negev, Beer-Sheva, Israel; and Departments of
‡Anesthesiology and §Surgery, Beth Israel Deaconess Medical Center, Boston, Massachusetts. Victor Novack, MD, PhD, is currently with Faculty of
Health Science, Soroka University Medical Center, Ben-Gurion University,
Beer-Sheva, Israel.
Accepted for publication October 29, 2013.
Funding: Departmental sources only.
The authors declare no conflicts of interest.
This report was previously presented, in part, at the Society of Cardiovascular Anesthesiology meeting, 2011.
Reprints will not be available from the authors.
Address correspondence to Balachundhar Subramaniam, MD, MPH, Department of Anesthesiology, Beth Israel Deaconess Medical Center, CC 470 A,
One Deaconess Rd., Boston, MA 02215. Address e-mail to bsubrama@bidmc.
harvard.edu.
Copyright © 2014 International Anesthesia Research Society
DOI: 10.1213/ANE.0000000000000100

February 2014 • Volume 118 • Number 2

P

erioperative hyperglycemia has been shown to be
associated with an increased risk for major adverse
events (MAEs) after cardiac surgery.1–3 At the same
time, preventing hyperglycemia after cardiac surgery
is associated with a decreased frequency of deep sternal wound infection,4 myocardial ischemia, and other
adverse events.5 There continues to be a debate about the
efficacy and safety of intensive glucose control therapy
in the general intensive care unit (ICU) population as
data from large clinical trials suggest that intensive glucose control is associated with more frequent episodes of
hypoglycemia and mortality.6–8 The Society of Thoracic
Surgery (STS) currently recommends maintaining perioperative glucose levels <180 mg/dL in cardiac surgical
patients.9
The relationship between perioperative glycemic variability and preoperative long-term glucose control has
not been systematically studied. Preoperative HbA1C
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Glycemic Variability and Cardiac Surgery

measures the control of blood glucose levels over the previous to 4 months whereas the admission blood glucose
level could reflect an acute stress response. Even though
poor long-term glucose control is expected to increase
both perioperative10 and long-term11 adverse outcomes, its
influence on postoperative glycemic indices such as variability is not clear. In 1 study of 5728 critically ill patients
in medical and surgical ICUs, glycemic variability in
(mean and SD of blood glucose) was found to be associated with increased ICU and in-hospital mortality.12 Other
studies have confirmed that glycemic variability is associated with increased mortality in critically ill patients.13,14
Egi and Bellomo15 in a review proposed reducing glycemic
variability in the critically ill patients as a therapeutic target. Unfortunately, glycemic variability is a retrospective
diagnosis limiting interventions to prospectively reduce
its frequency. Furthermore, whether there is a relationship between glycemic variability and adverse events in
patients undergoing cardiac surgery is not known. Thus,
identifying patients at risk for glycemic variability might
be useful to target therapies to reduce its occurrence.
Preoperative HbA1C measurement is recommended as a
diagnostic tool for detecting patients at risk for increased
glycemic variability.16 An increased preoperative HbA1C
level also increases intraoperative insulin resistance.17
In this study, we tested the following hypotheses (a)
whether preoperative HbA1C can identify patients at risk
for postoperative glycemic variability and (b) whether postoperative glycemic variability is associated with risk for
postoperative MAEs after coronary artery bypass grafting
(CABG) surgery.

METHODS

Patients undergoing cardiac surgery from January 2008 to
May 2011 at the Beth Israel Deaconess Hospital, Boston, MA,
were enrolled in a prospective, observational cohort study
with IRB approval. Informed patient consent was waived
by the IRB. Patients undergoing CABG with or without valvular surgery were included. Patients having isolated valve
surgery, aortic surgery, or other procedures such as atrial
fibrillation ablation and pericardial window were excluded
from the study. All preoperative medications such as aspirin, statins, and β-blockade were continued until the time of
surgery and were restarted in the postoperative period day
1 unless contraindicated by the patient’s condition.
All patients were anesthetized with IV fentanyl and propofol (or etomidate in 10% of patients), and rocuronium was
given for skeletal muscle relaxation. Anesthesia was maintained with isoflurane 0.5% to 1.0% in 100% oxygen with
supplemental IV fentanyl given as intraoperative analgesic. A standard protocol for perioperative glycemic control
has been operational for almost a decade (Appendix 1).
Blood glucose levels are measured every hour in the postoperative period. The initial influence of such a protocol
on postoperative deep sternal wound infections has been
published.18
Standard cardioplegic solution was used throughout the
study period (K-60 mEq, Mg 8 mEq, dextrose 2.5 g, Tham
10 mEq, and normal saline 500 mL). Patients were placed
on a nonpulsatile cardiopulmonary bypass pump using the
arterial and venous cannulae. Alpha stat pH management

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was used to manage blood gases obtained during the cardiopulmonary bypass run. Mild hypothermia (34°C) was
used for CABG, and a temperature of 30°C to 32°C was used
for valve surgery.

Blood Glucose and HbA1C Laboratory Methods

Preoperative HbA1C was obtained either during their
preoperative anesthesia visit or on the day of surgery per
institutional standard of care. HbA1C was determined
from venous blood samples using Roche Integra TinaQuant, version 2.0 (Roche Diagnostics, Indianapolis, IN).
The normal range for HbA1C is 4.8% to 5.9%. HbA1C
measurements were made within 30 days before surgery. Serum glucose was measured with an enzymatic
assay (Roche/Hitachi P-Modules, Roche Diagnostics).
The normal range for serum glucose values is 91 to 123
mg/dL. ICU blood glucose was measured by fingerstick point-of-care testing with Lifescan surestep Flexx
(Milpitas, CA).

Outcome Assessment

Patient demographic, intraoperative, and postoperative
MAE data were obtained from our institutional STS database. The primary outcome of the study was MAE defined
as a composite of in-hospital mortality, myocardial infarction (MI), pneumonia, stroke, renal failure, superficial or
deep sternal infection requiring operative intervention and
mediastinal reexploration for reintervention for bleeding/
tamponade, valvular dysfunction, graft dysfunction, or
other complications. The STS version 2.61 definitions for
MAEs are shown in Appendix 2. If a patient was discharged
and sent home, the patient was given a 30-day appointment.
Those who missed the 30-day appointment were given a
call by the STS database coordinator to note the morbidity and mortality. State STS coordinators also run the Social
Security Death Index to capture those who died within 30
days after cardiac surgery, and this information was sent to
the individual hospital.

Statistical Analysis

Based on the established target of HbA1C <6.5% for the
diagnosis of diabetes mellitus,19 we considered HbA1C
as a binary variable (<6.5% and ≥6.5%) in our primary
analysis.
Preoperative HbA1C was also evaluated as a continuous variable in the secondary analysis. Postoperative glycemic variability was defined as the coefficient of variation
(CV) for glucose values obtained in the cardiac surgery unit
in the first 24 hours after CABG surgery. CV is defined as
SD divided by mean. It is the reverse of signal to noise ratio
and is a dimensionless number. In our patient population,
to calculate the CV, all glucose measurements were averaged for each 4-hour period (4, 8, 12, 16, and 24 hours),
and then the variability was calculated. This approach
minimized the potential bias wherein patients with complications would have a higher frequency of glucose measurements together with a more intensive treatment often
based on ­glucose-containing solution, leading to a higher
variance in glucose levels. STS risk scores are expressed as

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mean and SD. In the final analysis, patients were divided
into HbA1C <6.5% and ≥6.5% groups, and postoperative
glucose control indices mean glucose, SD, and CV were
compared between these 2 groups.
A pre hoc power analysis was not performed since the
assumptions for the effect and distribution of the variables
in multivariate model (primary analysis) were not well
known. Therefore, it was determined that having almost
1500 subjects in the study with the frequency of the outcome
being approximately 10%, at least 8 to 10 variables could be
introduced into the logistic regression model.
Preoperative HbA1C distribution was tested for normality with Kolmogorov–Smirnov test. Categorical variables were analyzed by Pearson χ2 analysis or Fisher
exact test, and continuous variables were compared
using Student t test or the Mann-Whitney U test. The primary outcome of MAE was compared using the χ2 test.
Similarly, all the components of the primary outcome were
compared with the χ2 test as well. Length of stay being
nonnormally distributed was compared by the MannWhitney U test. Since full follow-up was available on all
participants and the primary interest was event occurrence rather than its timing, multivariate logistic regression analysis was used. Variable selection in multivariable
modeling was based on clinical importance for MAEs and
statistical significance (P < 0.05 in univariate analysis) and
performed in a hierarchical fashion set based on the presumed sequence of the clinical assessments. Demographic
characteristics such as gender and age were introduced
first followed by patient clinical characteristics such as
type of procedure, comorbidities (hypertension, chronic
obstructive pulmonary disease, congestive heart failure,
history of MI, or cerebrovascular accident) and STS score
quartiles, laboratory tests, and finally HbA1C level (either
<6.5% or ≥6.5%). A parsimonious model was reported.
Hosmer–Lemeshow testing was used to test the goodness
of fit for the logistic regression models. Logistic regression
modeling was done with (a) preoperative variables only
and (b) all variables including those from the postoperative period. Quartiles of CV and postoperative blood glucose values were used for the logistic regression model.
Analysis for correlation and interaction for the model
variables were performed. A sensitivity analysis was performed with tertiles and quintiles for CV and postoperative blood glucose values. The odds of developing MAEs
for the individual quartiles of CV and postoperative blood
glucose values were obtained from the final logistic regression model. A 2-tailed P value of <0.05 was considered
significant. SPSS 18.0 (SPSS Inc., Chicago, IL) was used for
statistical analysis.

RESULTS

There were 1461 patients included in this analysis.
Preoperative HbA1C distribution is shown in Figure  1.
Patient demographics, comorbid conditions, medications,
and STS risk scores for all the patients based on whether
HbA1C was ≥6.5% (458 patients, 31.3%) or <6.5% are listed
in Table 1. Patients with HbA1C ≥6.5% were younger, were
more likely to be female, and had a higher incidence of
hypertension, cardiovascular disease, and dyslipidemia.

February 2014 • Volume 118 • Number 2

Figure 1. Hemoglobin A1C (HbA1C) distribution in our patient population. HbA1C levels (%) are seen in the x-axis.

Among the 458 patients with HbA1C ≥6.5%, 402 had a clinical diagnosis of diabetes (rate of the diagnosis, 88.5%). In
those with HbA1C ≤6.5%, 160 had a diagnosis of diabetes
(rate of the diagnosis, 11%).
We observed MAEs in 143 patients (9.8%). MAEs were
higher in patients with HbA1C ≥6.5% (12.2% vs 8.7%,
P = 0.034). However, median levels of HbA1C did not differ between the patients with and without MAEs: 6.2%
(interquartile range [IQR], 5.7%–6.9%) vs 6.0% (interquartile range, 5.7%–6.7%), P = 0.125. The individual complications and their comparison are listed in Table  2. ICU
length of stay was longer in the HbA1C ≥6.5% group
(median [IQR], 44 [26–72] vs 48 [27–76] hours, P = 0.002).
Notably, the rate of deep sternal wound infections was
significantly higher in the high HbA1C group (2.2% vs
0.5%, P = 0.008).
The STS risk scores (mean ± SD) in the low versus high
HbA1C groups were 0.16 ± 0.13 and 0.18 ± 0.13 (P = 0.03)
for mortality and morbidity, 0.003 ± 0.002 and 0.005 ± 0.004
(P < 0.001) for deep sternal wound infection, and 0.015 ±
0.012 and 0.016 ± 0.015 (P = 0.04) for permanent stroke,
respectively.
Patients with MAEs had a higher rate of concomitant
CABG and valve surgery (44% vs 24%, P < 0.001), higher
STS predicted mortality risk (7% vs 2%, P < 0.001), older
age (70 vs 68 years, P = 0.004), and a longer cross-clamp
time (90 vs 77 minutes, P < 0.001) compared with those
with no MAEs based on univariate analysis. The CV was
higher in those with MAEs (0.24 ± 0.07) compared with
those without MAEs (0.21 ± 0.08, P = 0.001) by univariate
analysis. Prior diagnosis of diabetes was seen in 46% of
patients with MAEs and in 37% of patients with no MAEs
(P = 0.08).
The results of the multivariate logistic regression analysis are shown in Table 3. The final model included adjustment for preoperative factors such as the clinical diagnosis
of diabetes, STS risk score, age, gender, concomitant valve
surgery, and congestive heart failure. These results showed
that HbA1C ≥6.5% was associated with an increased
incidence of MAE (odds ratio [OR], 1.6; 95% confidence
interval [CI], 1.1–2.3; P = 0.02). Hosmer–Lemeshow lack

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Glycemic Variability and Cardiac Surgery

Table 1.  Comparison Between Baseline Variables of Patients with HbA1C >6.5% and <6.5%
Variables
Gender
  Male
  Female
Age (y), mean (±SD)
Procedure
  CABG
  CABG + valvular
Background diseases
  Diabetes
  Dyslipidemia
  Hypertension
  Smoking
  Congestive heart failure
  Cardiovascular disease
Previous myocardial infarction
  Chronic lung disease
  Dialysis
Preoperative medications

β-blockers
  ACE-I or ARBs
  Inotropes
  Steroids
  Aspirin
  Lipid lowering
STS risk algorithm (±SD) morbidity
or mortality

All patients (n = 1461)

HbA1C <6.5% (n = 1003)

HbA1C ≥6.5% (n = 458)

1093 (74.8%)
368 (25.2%)
68 (±11)

764 (76.2%)
239 (23.8%)
68 (±11)

329 (71.8%)
129 (28.2%)
66 (±10)

1083 (74.1%)
378 (25.9%)

706 (70.4%)
297 (29.6%)

377 (82.3%)
81 (17.7%)

562 (38.6%)
n = 1455
1247 (85.6%)
n = 1456
1281 (89%)
n = 1455
279 (19.2%)
n = 1456
272 (18.7%)
n = 1454
251 (17.2%)
n = 1456
615 (42.3%)
n = 1455
184 (12.6%)
n = 1456
38 (2.6%)
n = 1456

160 (16%)
n = 1001
839 (83.8%)
n = 1001
862 (86.2%)
n = 1000
186 (18.6%)
n = 1001
169 (16.9%)
n = 999
159 (15.9%)
n = 1001
398 (39.8%)
n = 1000
133 (13.3%)
n = 1001
24 (2.4%)
n = 1001

402 (88.5%)
n = 454
408 (89.7%)
n = 455
419 (92.1%)
n = 455
93 (20.4%)
n = 455
103 (22.6%)
n = 455
92 (20.2%)
n = 455
217 (47.7%)
n = 455
51 (11.2%)
n = 455
14 (3.1%)
n = 455

<0.001

833 (84.8%)
n = 726
396 (46.3%)
n = 856
12 (1.4%)
n = 858
28 (3.3%)
n = 858
799 (93.1%)
n = 858
746 (87.1%)
n = 856
0.168 (±0.127)

504 (84.6%)
n = 596
248 (41.7%)
n = 595
10 (1.7%)
n = 597
20 (3.4%)
n = 597
550 (92.1%)
n = 597
512 (85.9%)
n = 596
0.162 (±0.125)

222 (85.4%)
n = 260
148 (56.7%)
n = 261
2 (0.8%)
n = 261
8 (3.1%)
n = 261
249 (95.4%)
n = 261
234 (90%)
n = 260
0.179 (±0.131)

0.758

P
0.076

<0.001
<0.001

0.003
0.001
0.404
0.009
0.042
0.005
0.269
0.451

<0.001
0.364
0.829
0.081
0.100
0.030

HbA1C = hemoglobin A1C; CABG = coronary artery bypass grafting; ACE-I = angiotensin-converting enzyme inhibitor; ARBs = angiotensin receptor blockers;
STS = Society of Thoracic Surgery.
P < 0.05 is considered significant.

Table 2.  Comparison Between Outcome Variables of Patients with HbA1C >6.5% and <6.5%
Variables
Perioperative/postoperative complications
  MI
  Reoperation (bleeding)
  Deep sternal infection
  Stroke
  Pneumonia
  Renal failure
  Tamponade
  Death
Atrial fibrillation

All patients (n = 1461)
143 (9.8%)
5 (0.3%)
32 (2.2%)
15 (1%)
19 (1.3%)
36 (2.5%)
44 (3%)
1 (0.1%)
42 (2.9%)
122 (26.4%)

HbA1C <6.5% (n = 1003)
87 (8.7%)
5 (0.5%)
22 (2.2%)
5 (0.5%)
12 (1.2%)
21 (2.1%)
27 (2.7%)
0
30 (3%)
264 (26.3%)

HbA1C ≥6.5% (n = 458)
56 (12.2%)
0
10 (2.2%)
10 (2.2%)
7 (1.5%)
15 (3.3%)
17 (3.7%)
1 (0.2%)
12 (2.6%)
122 (26.6%)

P
0.034
0.333
0.990
0.008
0.603
0.177
0.290
0.313
0.704
0.90

χ2 test for all variables.
HbA1C = hemoglobin A1C; MI = myocardial Infarction
P < 0.05 is considered significant.

of ­goodness-of-fit test for this model was nonsignificant
(χ2 = 5.80, P = 0.67).
Glycemic variability in the postoperative period as
assessed by the CV was higher in the HbA1C ≥6.5% group

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compared with the HbA1C ≤6.5% group (0.26 ± 0.09 vs 0.20
± 0.07, P < 0.001). CV was used as quartiles (≤0.16, 0.17–0.20,
0.21–0.25, ≥0.26) for the multivariable logistic regression
model for assessing risk for MAEs. Glycemic variability

anesthesia & analgesia

Table 3.  Logistic Regression for Postoperative Complications
95% CI
Preoperative risk factors
  STS score per quartilea
  Valvular surgery
  History of MI
  HbA1C ≥6.5%
Preoperative and postoperative risk factors
  STS score per quartilea
  Valvular surgery
  History of MI
  Glucose levels averaged over first 4 h following the procedure, per quartileb
  Coefficient of variation for glucose within 24 h after the procedure, per quartilec

OR

Lower

Upper

P

1.70
1.25
1.89
1.56

1.38
1.00
1.29
1.07

2.10
1.56
2.75
2.27

<0.001
0.05
0.001
0.02

1.64
1.25
1.75
1.20
1.27

1.33
1.00
1.20
1.02
1.06

2.00
1.57
2.56
1.42
1.45

<0.001
0.045
0.004
0.026
0.024

Correlations (all correlations weak except STS score and valvular surgery, ρ = 0.56) and interactions (nonsignificant) among these variables are provided in
Appendix 3.
HbA1C = hemoglobin A1C; OR = odds ratio; CI = confidence interval; STS = Society of Thoracic Surgery; MI = myocardial infarction; CV = coefficient of variation.
a
Study population divided into 4 equal groups for STS mortality score: ≤1%, 2%–3%, 4%–5%, and ≥5%.
b
Study population divided into 4 equal groups (quartiles of glucose levels averaged over first 4 hours): ≤111.5, 112.6–125.7, 125.8–140.8, and ≥140.9 mg/dL.
ORs for individual glucose quartiles are provided in Appendix 3.
c
Study population divided into 4 equal groups (quartiles of coefficient of variation for glucose within 24 hours): ≤0.16, 0.17–0.20, 0.21–0.25, and ≥0.26. ORs
for individual CV quartiles are provided in Appendix 3.
Probability of postoperative complication based on the logistic model for preoperative risk factors only can be calculated as: P = 1/(1 + e–(–4.56 + STS_quartile × 0.53 +
valve_surgery × 0.22 + history_MI × 0.63 + HbA1C_above_6.5 × 0.44)
).
Probability of postoperative complication based on the logistic model for preoperative and postoperative risk factors can be calculated as: P = 1/(1 + e–(–5.35 +
STS_quartile × 0.48 + valve_surgery × 0.23+history_MI × 0.54 + glucose_4hours_quartile × 0.21 + CV_24_hours_quartile × 0.24)
).

Table 4.  Comparison Between Glucose Baseline Variables of Patients with HbA1C >6.5% and <6.5%
Variables
Glucose level averaged over first
4 h following the surgery
Hypoglycemia events (Glucose <60)
Hypoglycemia events % (glucose <60)
Hyperglycemia events (glucose >200)
Hyperglycemia events % (glucose >200)
Coefficient of variation for glucose
within 24 h following the procedure

All patients (n = 1461)
129 (±26)
n = 1400
1.30 (±0.80)
n = 80
0.05 (±0.02)
n = 80
2.40 (±2.15)
n = 339
0.08 (±0.06)
n = 339
0.17 (±0.08)
n = 1400

HbA1C <6.5% (n = 1003)
125 (±25)
n = 956
1.21 (±0.41)
n = 42
0.06 (±0.03)
n = 42
1.76 (±1.68)
n = 128
0.07 (±0.04)
n = 128
0.16 (±0.07)
n = 956

HbA1C ≥6.5% (n = 458)
135 (±27)
n = 444
1.39 (±1.07)
n = 38
0.04 (±0.02)
n = 38
2.79 (±2.31)
n = 211
0.09 (±0.07)
n = 211
0.20 (±0.09)
n = 444

P
<0.001
0.318
0.001
<0.001
<0.001
<0.001

Values are expressed as mean (±SD).
Hypoglycemia/hyperglycemia events = number of events per patient per day; hypoglycemia/hyperglycemia events (%) = number of events/total number of
measurements and total number of patients in the group.
Correlation between hemoglobin A1C (HbA1C) and glucose level averaged over first 4 hours following the surgery, r = 0.21, P < 0.001.
Correlation between HbA1C and coefficient of variance for glucose within 24 hours following the procedure, r = 0.27, P < 0.001.
Hyperglycemia and hypoglycemia events (%) were done to minimize the effect of measurements on the incidence of hyperglycemia and hypoglycemia.

was associated with risk for MAEs (OR, 1.3; 95% CI, 1.1–1.5;
P = 0.01). Other factors in this model were STS score per
quartile (OR, 1.6; 95% CI, 1.3–2.0; P < 0.001), CABG and
valvular surgery (OR, 1.3; 95% CI, 1.0–1.6; P = 0.045), mean
glucose levels averaged over the first 4 hours (OR, 1.2; 95%
CI, 1.0–1.4; P = 0.026), and history of MI (OR, 1.8; 95% CI,
1.2–2.6; P = 0.004; Table 3). There were no significant interactions or correlations between the variables (except STS risk
score and valvular surgery correlation, ρ = 0.56). Hosmer–
Lemeshow lack of goodness-of-fit test for this model was
nonsignificant (χ2 = 6.20, P = 0.62).
When glycemic variability was studied using tertiles or quintiles, the multivariate risks associated with
risk for MAEs were as follows: OR, 1.4 (95% CI, 1.1–1.8;
P = 0.005) and OR, 1.2 (95% CI, 1.0–1.4; P = 0.018), respectively. When postoperative blood glucose was studied using

February 2014 • Volume 118 • Number 2

tertiles or quintiles, the multivariate risks associated with
risk for MAEs were as follows: OR, 1.2 (95% CI, 0.98–1.5;
P = 0.08) and OR, 1.2 (95% CI, 1.0–1.3; P = 0.012), respectively. Logistic regression equations, ORs for the individual
quartiles of CV and postoperative blood glucose, correlation, and interactions for the final model variables have
been provided in Appendix 3.

HbA1C and Postoperative Glucose Variables

The mean blood glucose values at 4, 24, and 48 hours
after surgery are shown in Table 4. Average blood glucose
levels in both groups were <180 mg/dL. Glucose levels
averaged over the first 4 hours after surgery were higher
in the high HbA1C group (135 ± 27 vs 125 ± 25 mg/dL,
P < 0.001). Hypoglycemic events (defined as a blood glucose <60 mg/dL per patient per day) were similar between

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Glycemic Variability and Cardiac Surgery

the high and low HbA1C groups (1.20 ± 0.4 vs 1.39 ± 1.1,
P = 0.318). Hypoglycemic events when defined as a percentage (number of events/total number of measurements) was higher in the low HbA1C group (6% ± 3% vs
4% ± 2%, P = 0.001). Hyperglycemic events (defined as a
blood glucose level >200 mg/dL level per patient per day,
2.79 ± 2.31 vs 1.76 ± 1.68, P < 0.001) or defined as a percentage (number of events/total number of measurements,
9 ±7 vs 7 ± 4, P < 0.001) was higher in the HbA1C ≥6.5%
group (Table 4).
We performed a sensitivity analysis in which all the glucose values during the first 24 postoperative hours were
used for the calculation of the CV. Similar to the primary
analysis, CV adjusted for STS score, type of surgery, history
of MI, and glucose levels averaged over the first 4 postoperative hours was associated with the MAE.

DISCUSSION

The results of this study suggest that postoperative
glycemic variability is a predictor of MAEs, especially deep sternal wound infection. Postoperative
glycemic variability is increased in patients with poor
preoperative glycemic control (HbA1C ≥6.5%). This
suggests that improving glycemic variability after surgery, particularly in patients with preoperative HbA1C
≥6.5%, may provide a strategy to reduce MAEs after
cardiac surgery.
This work expands on a previous finding that demonstrates the relationship between preoperative blood glucose
control and MAEs after cardiac surgery by suggesting the
importance of postoperative glycemic variability.10 Halkos
et al.10 have previously shown that a preoperative HbA1C
>7%, the established standard for long-term glucose control
at that time, was associated with MAE. They also derived
HbA1C cutoff values for mortality (threshold, 8.6%; OR,
4), renal failure (threshold, 6.7; OR, 2.1), cerebrovascular
accident (threshold, 7.6; OR, 2.24), and deep sternal wound
infection (threshold, 7.8; OR, 5.3). Our patient population
differs from that study in that the Gaussian distribution for
HbA1C values was lower with lesser spread as shown in
Figure 1. Despite this, there continued to be an association
between MAEs and higher preoperative HbA1C values.
Furthermore, Halkos et  al.11 did not attempt to show the
relationship between preoperative HbA1C and postoperative glycemic variability. In our study, glycemic variability
was associated with risk for MAEs. Glycemic variability can
be due to many factors such as effectiveness of preoperative
glycemic control, perioperative stress, and method of blood
glucose control used.
Tight blood glucose control in the range of 80 to 110 mg/
dL has been questioned.7,8 With moderate control goal of
<180 mg/dL, previous work has shown differences in the
postoperative outcome in patients undergoing noncardiac
surgery between IV insulin infusions and those receiving
intermittent insulin therapy.20 The differences in postoperative outcome, even in the moderately controlled patients,
with established insulin infusion therapy can perhaps
be attributed to either or both their baseline, long-term
control and their blood glucose variability as suggested
by our study.

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Blood glucose variability has been shown by Egi et al.13
to influence MAEs in critically ill patients (both medical
and surgical). In this study, the authors concluded that
even though both the groups had similar postoperative
blood glucose values, blood glucose variability as measured by CV may have played a role in contributing to
the adverse postoperative outcomes. This issue was highlighted by Duncan et al.21 in a large retrospective series of
cardiac surgery patients. Glycemic variability when measured by a mean absolute glucose change per hour22 or an
SD14 was associated with increased mortality in critically
ill patients. None of these studies has shown a way to
predict those patients who are at increased risk of glycemic variability. Neither have they shown any relationship
between l­ ong-term glucose control and glycemic variability in the ICU.
There are several plausible explanations of why glycemic variability might lead to MAEs after cardiac surgery.
Glycemic variability has been shown to increase cell apoptosis in human umbilical vein endothelial cells,23 cause
endothelial injury in diabetics,24 and induce neuropathy.25
Although the exact mechanism is yet to be elucidated, it
has been proposed that oscillation of glucose levels leads
to increased free radical formation. It is possible that the
cells are not able to sufficiently increase their own intracellular antioxidant defenses.24 This results in endothelial
dysfunction and oxidative stress during oscillation of glucose levels.
Blood glucose variability as assessed by CV was similar between the high and low HbA1C groups when analyzed in those with a history of diabetes. It is possible
that blood glucose variability might play an important
role in nondiabetics and their postoperative outcome.
Unfortunately, the association between HbA1C and postoperative blood glucose variability may not be modifiable. The possible connection between preoperative
HbA1C, postoperative blood glucose control, and postoperative outcomes has not yet been shown. Whether
or not blood glucose variability is a modifiable factor is
unknown and requires further study. Perhaps patients
with higher HbA1C levels might need a different protocol to reduce variability.
In our study, the final model with preoperative risk factors alone showed that STS morbidity and mortality risk
index was a significant predictor of MAEs. Future studies should evaluate whether adding preoperative HbA1C
instead of a history of diabetes to the existing STS risk
prediction model26 or to the European system for cardiac
operative risk evaluation27 improves the accuracy and discrimination28–30 of these indices.
Our study has certain limitations. Even though the
composite outcome was the primary outcome, deep
sternal infection was the major factor influencing the
results of the relationship between postoperative glycemic variability and MAEs. Given the small number of
patients in our study, we cannot make conclusions on
the role of postoperative glycemic variability and other
component outcomes of MAEs such as stroke and MI.
Postoperative blood glucose measurements were done
only during ICU admission or for the first 24 hours,

anesthesia & analgesia

whichever was shorter. The number of measurements
and the timing of measurements were not uniform
among all patients after the first 24 hours. Unmeasured
blood glucose levels during and beyond this period
could have influenced the postoperative outcomes.
Even though a repeated measures analysis for postoperative blood glucose levels was not performed, graphical representation of mean and SD of blood glucose
gives a clear picture of the significant overlap between
the 2 groups. Furthermore, the incidence of hypoglycemia can be well captured by a continuous glucose
monitoring device. Our hourly measurements could
have missed some hypoglycemic episodes. Continuous
glucose monitoring is not a standard of care for managing critically ill patients worldwide. We did not analyze
the intraoperative blood glucose levels in our study.
However, previous work by Gandhi et al. 31 and Duncan
et  al. 21 have not shown that intraoperative blood glucose management influences postoperative outcomes
when the control and intervention groups have blood

glucose levels <200 mg/dL. Although we had established protocols to maintain intraoperative blood glucose levels between 100 and 150 mg/dL, these values
were not easily obtained in all patients. Patients were
divided to above and below HbA1C groups. There
could have been patients with HbA1c >6.5% with no
diagnosis of diabetes and <6.5% with diagnosis of diabetes. The influence of diabetes diagnosis could have
influenced the results. Finally, we did not have information to distinguish whether patients had type 1 or
type 2 diabetes, as STS does not collect these data.
In summary, in patients undergoing CABG surgery,
postoperative glycemic variability was found to be
associated with MAEs, especially deep sternal wound
infection. Postoperative glycemic variability was found
to be higher in patients with a preoperative HbA1C
≥6.5%. Currently it is unknown whether blood glucose
control variability as measured by CV is a modifiable
risk factor.

APPENDIX 1

(Continued)

February 2014 • Volume 118 • Number 2

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Glycemic Variability and Cardiac Surgery

APPENDIX 1. (Continued)

APPENDIX 2: STS VERSION 2.61 DEFINITIONS
FOR POSTOPERATIVE OUTCOMES
Diabetes
A history of diabetes, regardless of duration of disease or
need for anti-diabetic agents.
Complications
Indicate whether a postoperative event occurred during the
hospitalization for surgery. This includes the entire postoperative period up to discharge, even if over 30 days.
Myocardial Infarction (0–24 Hours Postop)
Indicate the presence of a peri-operative Myocardial
Infarction (MI) (0–24 hours postop) as documented by the
following criteria:

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• The CK-MB (or CK if MB not available) must be
greater than or equal to 5 times the upper limit of normal, with or without new Q waves present in two or
more contiguous ECG leads. No symptoms required.
• (>24 hours post-op) Indicate the presence of a perioperative MI (>24 hours post-op) as documented by at
least one of the following criteria:
1.  Evolutionary ST-segment elevations
2.  Development of new Q-waves in two or more contiguous ECG leads
3.  New or presumably new LBBB pattern on the ECG
4.  The CK-MB (or CK if MB not available) must be
greater than or equal to 3 times the upper limit of
normal.

anesthesia & analgesia

Reoperations (Cardiac)
Operative re-intervention was required for bleeding/ tamponade, valvular dysfunction, graft occlusion and or other
complications.
Sternum Infection
Indicate whether the patient, within 30 days postoperatively,
had a deep sternal infection involving muscle, bone, and/or
mediastinum REQUIRING OPERATIVE INTERVENTION.
Must have ALL of the following conditions:
1.  Wound opened with excision of tissue (I&D) or
­re-exploration of mediastinum
2.  Positive culture
3.  Treatment with antibiotics
Pneumonia
Indicate whether the patient had Pneumonia diagnosed by
any of the following: Positive cultures of sputum, transtracheal fluid, bronchial washings, and/or clinical findings
consistent with the diagnosis of pneumonia (which may
include chest x-ray diagnostic of pulmonary infiltrates).
Renal Failure
Indicate whether the patient had acute or worsening renal
failure resulting in one or more of the following:
1.  Increase of serum creatinine to >2.0, and 2x most
recent preoperative creatinine level.
2.  A new requirement for dialysis postoperatively.
Stroke
Indicate whether the patient has a postoperative stroke (i.e.,
any confirmed neurological deficit of abrupt onset caused
by a disturbance in cerebral blood supply) that did not
resolve within 24 hours.

2) Model with preoperative risk factors
We have applied the stepwise forward logistic regression
model for pre-operative analysis. Stay criteria p<0.10.
Step 1: Procedure type: Valvular surgery stays
Step 2: Comorbidities (HTN, CHF, history of MI, history
of CVA, COPD) and STS quartiles. Previous MI and STS
quartiles stay.
Step 3: HBA1C stays

 
Step 1
Step 2

Step 3

Step 2

Step 3

1) Quartiles of glucose levels and CV for postoperative MAE
(Major Adverse Events)

Variables
Glucose levels
averaged over first
4 hours following
the procedure, per
quartile*
Coefficient of
variation (CV) for
glucose within 24
hours following
the procedure, per
quartile**

Q1 (ref)
Q2
Q3
Q4
Q1 (ref)
Q2
Q3
Q4

OR
1
1.02
1.16
1.74
1
1.04
1.20
1.80

95% CI
Lower
Upper
0.57
0.67
1.05

1.82
2.02
2.88

P-value

Step 4

0.94
0.59
0.03
Step 5

0.54
0.64
1.00

February 2014 • Volume 118 • Number 2

1.98
2.19
3.26

0.92
0.60
0.05

OR
1.58
0.05
1.2
1.72
1.92
0.01
1.25
1.7
1.88
1.56
0.01

95%
Lower
1.33
 
0.97
1.4
1.31
 
1
1.38
1.29
1.07
 

C.I.
Upper
1.89
 
1.49
2.12
2.79
 
1.56
2.1
2.743
2.26
 

3) Model with perioperative risk factors (including
Coefficient of Variation, CV)
We have applied the stepwise forward logistic regression
model for postoperative analysis. The following variables
were included: age, history of MI, CVA, CHF and COPD;
HBA1C levels (above vs. below 6.5%), STS score, valvular
surgery, levels of glucose and CV. Final model was reapplied on the total population.

Step 1a

APPENDIX 3
A.Logistic regression equations, correlation
matrix and Interactions

Valvular surgery
Constant
Valvular surgery
STS quartiles
Previous MI
Constant
Valvular surgery
STS quartiles
Previous MI
HBA1C >6.5%
Constant

p-value
<0.001
<0.001
0.1
<0.001
0.001
<0.001
0.05
<0.001
0.001
0.02
<0.001

STS quartiles
Constant
STS quartiles
CV for glucose,
quartiles
Constant
STS quartiles
Glucose, quartiles
CV for glucose,
quartiles
Constant
STS quartiles
Previous MI
Glucose, quartiles
CV for glucose,
quartiles
Constant
STS quartiles
Valvular surgery
Previous MI
Glucose, quartiles
CV for glucose,
quartiles
Constant

p-value
<0.001
<0.001
<0.001
0.003

OR
1.957
0.017
1.853
1.307

<0.001
<0.001
0.013
0.010

95% C.I.
Lower
Upper
1.636
2.341
1.545
1.097

2.221
1.558

0.009
1.839
1.229
1.266

1.532
1.045
1.059

2.207
1.445
1.515

<0.001
<0.001
0.022
0.020
0.013

0.006
1.808
1.529
1.213
1.255

1.504
1.063
1.031
1.049

2.172
2.201
1.427
1.501

<0.001
<0.001
0.046
0.006
.027
.010

0.005
1.613
1.254
1.715
1.202
1.269

1.301
1.004
1.171
1.022
1.059

2.000
1.566
2.513
1.414
1.520

.000

.005

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Glycemic Variability and Cardiac Surgery

B) Correlation matrix:

a
b

HBA1C
COV
Glucose Valvular History
>6.5% quartiles quartiles surgery of MI
0.156 0.164
<0.001 <0.001

Glucose
rho
quartiles Sig.
(2-tailed)
N
1400
1419
Valvular
rho
-0.126 0.081
surgery Sig.
<0.001 0.002
(2-tailed)
N
1461
1419
History
rho
0.074 0.063
of MI
Sig.
0.005 0.017
(2-tailed)
N
1455
1413
STS
rho
-0.005 0.219
quartiles Sig.
0.85
<0.001
(2-tailed)
N
1455
1413

0.057
0.032
1419
0.077
0.004

-0.132
<0.001

1413
1474
0.093
0.561 0.132
<0.001 <0.001 <0.001
1413

1474

1473

c) Interactions

We have introduced interactions between the variables
included in the final models. Each interaction was added
separately to the model. The following tables present p-values
for the interactions terms. We have shown the models for the
interaction terms with the borderline significance.
1) Pre-operative risk assessment model, p-values for
interaction term are presented (a*b)

a
b
STS score
per
quartile
Valvular
surgery
History of MI

STS score per
quartile

Valvular
surgery
0.91

History of MI
0.16

HbA1C ≥
6.5 gm%
0.65

0.30

0.08*
0.65

* The model with the interaction term:

Variables
STS score per quartile*
Valvular surgery
History of MI
HbA1C ≥ 6.5 gm%
Valvular surgery*
HbA1C ≥ 6.5 gm%

OR
1.70
1.41
1.90
2.91
0.69

95% CI
Lower
Upper
1.38
2.10
1.09
1.83
1.30
2.78
1.34
6.32
0.46
1.04

P-value
<.001
0.01
0.01
0.02
0.08

2) Perioperative risk (includes preoperative and
postoperative variables such as Coefficient of Variation)
assessment model, p-values for interaction term are
presented (a*b)

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STS score per
quartile
Valvular
surgery
History of MI
Glucose levels
averaged
over first
4 hours
following the
procedure,
per quartile

Coefficient
of Variation
Glucose
(CV) for
levels
glucose
averaged
within
over first
24 hours
4 hours
STS
following the following the
score
procedure,
Valvular History procedure,
per
quartile surgery of MI per quartile per quartile
0.76
0.18
0.41
0.61
0.40

0.53

0.73

0.85

0.90
0.28

STS-Society of Thoracic Surgery Risk Score
MI-Myocardial Infarction
HbA1C-Hemoglobin A1C
DISCLOSURES

Name: Balachundhar Subramaniam, MD, MPH.
Contribution: This author helped design the study, conduct the
study, analyze the data, and write the manuscript.
Attestation: Balachundhar Subramaniam has seen the original
study data, reviewed the analysis of the data, approved the
final manuscript, and is the author responsible for archiving
the study files.
Name: Adam Lerner, MD.
Contribution: This author helped conduct the study and write
the manuscript.
Attestation: Adam Lerner reviewed the analysis of the data and
approved the final manuscript
Name: Victor Novack, MD, PhD.
Contribution: This author helped design the study, analyze the
data, and write the manuscript.
Attestation: Victor Novack has seen the original study data,
reviewed the analysis of the data, and approved the final
manuscript.
Name: Kamal Khabbaz, MD.
Contribution: This author helped conduct the study and write
the manuscript.
Attestation: Kamal Khabbaz reviewed the analysis of the data
and approved the final manuscript.
Name: Maya Paryente-Wiesmann, MD.
Contribution: This author helped analyze the data and write
the manuscript.
Attestation: Maya Paryente-Wiesmann has seen the original
study data, reviewed the analysis of the data, and approved the
final manuscript.
Name: Philip Hess, MD, PhD.
Contribution: This author helped conduct the study and write
the manuscript.
Attestation: Philip Hess reviewed the analysis of the data and
approved the final manuscript.
Name: Daniel Talmor, MD, MPH.

anesthesia & analgesia

Contribution: This author helped design the study, conduct the
study, analyze the data, and write the manuscript.
Attestation: Daniel Talmor has seen the original study data,
reviewed the analysis of the data, and approved the final
manuscript.
This manuscript was handled by: Charles W. Hogue, Jr., MD.
ACKNOWLEDGMENTS

The authors thank Michelle A. Doherty, STS coordinator,
for database maintenance and Victoria Nielsen and Sapna
Govindan for perioperative glycemic control data collection
and database merger.
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