Biblio AA Mars 2015

1. 582 www.anesthesia-analgesia.org March 2015 • Volume 120 • Number 3
Copyright © 2015 International Anesthesia Research Society
DOI: 10.1213/ANE.0000000000000555
P
atients who smoke experience increased periopera-
tive complications, particularly wound and pulmo-
nary complications.1,2
Large cohort studies have even
shown smoking to increase mortality after elective surgery.3–5
Undergoing surgery can serve as a “teachable moment” that
may motivate patients to engage in permanent smoking ces-
sation.6,7
A few studies have found that in addition to the
short-term benefits of smoking reduction on postoperative
complications,8,9
smoking cessation interventions initiated
in the perioperative period may increase the likelihood of
long-term cessation.10–12
However, a meta-analysis showed
that only intensive interventions, compared with brief
interventions, resulted in long-term cessation.13
The aim of this study was to determine whether a periop-
erative smoking cessation intervention designed to minimize
nursing and physician time in a busy preadmission clinic
would be successful in reducing smoking rates, including
long-term cessation. Another aim of this study was to explore
preoperative factors that might be associated with success-
ful long-term abstinence. Short-term results were previously
reported.14
We now report our 1-year follow-up outcomes.
METHODS
Detailed methods are previously described.14
This ran-
domized controlled trial was conducted at St. Joseph’s
Hospital, an ambulatory and short-stay hospital (with
anticipated surgical inpatient stays <3 days) affiliated with
the University of Western Ontario in London, Canada. The
research protocol was approved by the local research ethics
board, and written informed consent was obtained from all
study participants. This trial was registered at ClinicalTrials.
gov (NCT01260233).
Adult daily smokers of 2 or more cigarettes per day
were identified in the preadmission clinic at least 3 weeks
BACKGROUND: While surgery and perioperative smoking cessation interventions may motivate
patients to quit smoking in the short term, it is unknown how often this translates into permanent
cessation. In this study, we sought to determine the rates of long-term smoking cessation after
a perioperative smoking cessation intervention and predictors of successful cessation at 1 year.
METHODS: We previously reported short-term results from a perioperative randomized controlled
trial comparing usual care with an intervention involving (1) brief counseling by the preadmission
nurse, (2) smoking cessation brochures, (3) referral to a telephone quitline, and (4) a free 6-week
supply of transdermal nicotine replacement. We now report our 1-year follow-up outcomes.
RESULTS: Between October 2010 and April 2012, 168 patients were randomized. At 1 year, 127
patients (76%) were available for follow-up telephone interview. Smoking cessation occurred
in 8% of control patients compared with 25% of patients in the intervention group (relative
risk, 3.0; 95% confidence interval [CI], 1.2–7.8; P = 0.018). The number needed-to-treat to
achieve smoking cessation for 1 patient at 1 year postoperatively was 5.9 (95% CI, 3.4–25.9).
Multivariable logistic regression modeling found that the intervention (P = 0.020) and lower nic-
otine dependency at baseline (P < 0.001) were predictive of success at smoking cessation at
1 year. Poisson regression showed that adjusted for nicotine dependency, those randomized to
the intervention group were 2.7 times (95% CI, 1.1–6.7; P = 0.028) more likely to achieve long-
term cessation than those in the control group. Adjusted for randomization group, a low level of
nicotine dependency resulted in a relative risk of quitting of 5.1 (95% CI, 2.0–12.8; P = 0.001).
CONCLUSIONS: This study demonstrates that an intervention designed for a busy preadmis-
sion clinic results in decreased smoking rates not only around the time of surgery but also
continued benefit in smoking cessation at 1 year. Perioperative care providers have a unique
opportunity to assist patients in smoking cessation and achieve long-lasting results. (Anesth
Analg 2015;120:582–7)
Long-Term Quit Rates After a Perioperative Smoking
Cessation Randomized Controlled Trial
Susan M. Lee, MD, FRCPC,* Jennifer Landry, MD, FRCPC,*
Philip M. Jones, MD, FRCPC, MSc (Clinical Trials),*† Ozzie Buhrmann, BScPhm, RPh,‡
and Patricia Morley-Forster, MD, FRCPC*
From the *Department of Anesthesia & Perioperative Medicine, †Department of
Epidemiology & Biostatistics, University of Western Ontario, London, Ontario,
Canada; and ‡Pharmacy, St. Joseph’s Health Care, London, Ontario, Canada.
Accepted for publication October 6, 2014.
Susan M. Lee is currently affiliated with the Department of Anesthesia &
Perioperative Care, University of California, San Francisco, San Francisco,
California.
Funding: Department of Anesthesia and Perioperative Medicine, University
of Western Ontario—internal research funds.
Supplemental digital content is available for this article. Direct URL citations
appear in the printed text and are provided in the HTML and PDF versions of
this article on the journal’s website (www.anesthesia-analgesia.org).
The authors declare no conflicts of interest.
This report was previously presented, in part, at the Canadian
Anesthesiologists’ Society meeting June 2014.
Reprints will not be available from the authors.
Address correspondence to Susan M. Lee, MD, FRCPC, Department of Anes-
thesia and Perioperative Care, University of California, San Francisco, 521 Par-
nassus Ave., San Francisco, CA 94143. Address e-mail to suze.lee@utoronto.ca.
Section Editor: Tong J. Gan
Society for Ambulatory Anesthesiology

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March 2015 • Volume 120 • Number 3 www.anesthesia-analgesia.org 583
preoperatively. Patients were ineligible if they were pregnant,
breastfeeding, poorly proficient in the English language, or
unable to consent. Randomization was computer generated
in a 1:1 ratio in randomly permuted blocks of sizes 2, 4, and 6.
Allocation was concealed by consecutively numbered sealed
opaque envelopes. The control group received usual care. The
intervention group received (1) brief counseling by the pread-
mission nurse, (2) smoking cessation brochures, (3) referral to
the Canadian Cancer Society’s free Smokers’ Helpline, which
proactively telephoned patients to provide ongoing counsel-
ing as agreed on by the patient, and (4) a free 6-week supply of
transdermal nicotine replacement therapy.All health care pro-
viders on the operative day were blinded. Blinded observers
collected self-reported smoking status of 7-day point preva-
lence abstinence by telephone interview 12 months postop-
eratively. For patients who had their original surgical date
postponed or cancelled, follow-up calls were made 12 months
after the original preadmission encounter.
The study was powered for the primary outcome of
smoking cessation on the day of surgery, anticipating a
baseline quit rate of 20% and an intervention group quit rate
of 40% based on previous studies.15,16
Accepting a 2-tailed α
error of 5% and a β error of 20%, 158 patients (79 per group)
were needed, and an additional 5 patients per group were
recruited to account for losses to follow-up.
This trial was analyzed by the intention to treat. Baseline
characteristics of patients remaining at 1-year follow-up were
analyzed by the Fisher exact test for categorical variables
(gender, surgery type, current diseases). Histograms were
generated to assess for normality of continuous variables
and if normally distributed (age, height, weight, body mass
index, number of years smoking, Fagerström score, exhaled
carbon monoxide) analyzed by t test. Nonparametric continu-
ous variables (cigarettes per day) were analyzed by Wilcoxon
rank-sum test. The 1-year outcome of smoking cessation was
analyzed with the Fisher exact test. The comparison was
repeated assuming all patients with missing data continued
to smoke (i.e., worst-case scenario analysis). Confidence inter-
vals (CI) for numbers needed-to-treat (NNT) were calculated
using the method described by Bender.17
Multivariable logistic regression modeling was used to
study baseline patient characteristics that could affect the
likelihood of abstinence at 1 year. Because the overall rate
of smoking cessation was low, an exact logistic regression
model was used.18
Prespecified predictors were selected
on the basis of the likely relationship between each poten-
tial explanatory variable and the primary outcome. The
predictor variables were as follows: randomization group,
age ≥55 years, gender, ASA physical status (class ≤2), obe-
sity, comorbid diabetes, hypertension, heart disease, chronic
obstructive pulmonary disease (COPD) or asthma, number
of pack-years of smoking ≥30, and the Fagerström score for
nicotine dependency <4. Univariable analyses were per-
formed on each predictor variable and then included in the
multivariable model if the P value of the univariable analy-
sis resulted in P < 0.1. A P value of 0.1 rather than 0.05 was
chosen as the marker to include in the multivariable analy-
sis to avoid exclusion of potentially important predictors
that were negatively confounded before adjusted analysis.
Continuous predictor variables were dichotomized at their
median values, rounded to the nearest clinically meaningful
value. Analyses were repeated with cut points 1 standard
deviation above and below (25th and 75th percentiles for
the nonparametric predictor pack-years) to assess the sen-
sitivity of the resulting models to changes in cut points. The
Hosmer-Lemeshow goodness-of-fit test (using 10 groups)
was used to test model fit, and the c-statistic (the area under
the receiver operating characteristic curve) was used to test
model discrimination. Poisson regression using robust stan-
dard errors was performed to produce more interpretable
relative risks in the final model.19
A 2-tailed P value of <0.05
was considered significant for all analyses. Stata version 13.0
(StataCorp LP, College Station, TX) was used for all analyses.
RESULTS
Between October 2010 andApril 2012, 168 patients were ran-
domized. Results for smoking status on the day of surgery
and at 30 days postoperatively are previously reported.14
At 1 year, 127 patients (76%) were available for follow-up
telephone interview. The telephone interview occurred a
median of 369 (interquartile range [IQR], 366–378) days after
surgery. As shown in Table 1, baseline characteristics were
similarly balanced at baseline and for those that remained
at 1-year follow-up. There were more patients with baseline
diabetes (P = 0.040) and hypertension (P = 0.052) in the inter-
vention group remaining at 1 year. However, these were the
2 characteristics that appeared unbalanced at baseline due
to chance, suggesting that losses to follow-up were nonin-
formative. Details of losses to follow-up are shown in the
Consolidated Standards of Reporting Trials (CONSORT)
flow chart in Figure 1. As shown in Table 2, smoking cessa-
tion occurred in 5 of 60 (8%) control patients compared with
17 of 67 (25%) patients in the intervention group (relative
risk, 3.0; 95% CI, 1.2–7.8; P = 0.018). The NNT to achieve
smoking cessation for 1 patient at 1 year postoperatively
was 5.9 (95% CI, 3.4–25.9). Among those who did not quit,
the number of cigarettes smoked per day did not differ sig-
nificantly between groups (P = 0.23), with the control group
smoking an average of 14.5 (IQR, 7.5–20) cigarettes per day
compared with the intervention group that smoked an aver-
age of 12.2 (IQR, 5–20) cigarettes per day.
Continuous variables were dichotomized for logistic
regression analyses. Age was dichotomized at 50 years and
was not predictive of smoking cessation by univariable
analysis (P = 1.0), which was consistent with cut points of
40 (P = 1.0) and 60 (P = 0.30). There were few patients with
American Society of Anesthesiologists class 1 or 4, so ASA
class was dichotomized to ASA 1 and 2 versus ASA 3 and 4.
Pack-years of smoking were dichotomized at 20 pack-years
and were not predictive of smoking cessation by univariable
analysis at this cut point (P = 0.20), although this was some-
what sensitive to varying cut points (P = 0.53 for 10 pack-
years, P = 0.086 for 30 pack-years). By univariable analysis, the
Fagerström score was predictive of long-term cessation at cut
points of 4 (P < 0.001) and 6 (P = 0.033) but not at 2 (P = 0.42).
The association between baseline risk factors and suc-
cessful abstinence at 1 year postoperatively using exact
logistic regression is shown in Table 3. On the basis of uni-
variable analysis, the following predictors were used for
the multivariable model: randomization group, history of
COPD, and Fagerström score. Because of the sensitivity of
univariable models to varying cut points for pack-years of

3. 584 www.anesthesia-analgesia.org anesthesia analgesia
Long-Term Quitting After Perioperative Smoking Cessation
smoking, the multivariable model was repeated including
varying cut points. Pack-years was not a significant predic-
tor at any cut point in the adjusted models (P = 0.95, 0.97,
and 0.69 for cut points of 10, 20, and 30 pack-years). Pack-
years were therefore not included in the final model.
As shown in Table 3, in addition to the intervention
(adjusted odds ratio [OR], 3.5; 95% CI, 1.02–13.9; P = 0.046),
a lower level of nicotine dependency at baseline (as deter-
mined by Fagerström20
score 4) was predictive of success
at smoking cessation at 1 year (adjusted OR, 6.3; 95% CI,
1.9–24.8; P = 0.001). Although none of the 22 patients with
a history of COPD achieved long-term cessation, it was not
a statistically significant predictor in the multivariable exact
logistic regression model (adjusted OR, 0.22; 95% CI, 0–1.51;
P = 0.14). Afinal model using the intervention group and the
Fagerström score as predictors in an ordinary logistic regres-
sion model is shown in Table 4. The model performed well,
with a high c-statistic of 0.79 indicating good discrimination
and a Hosmer-Lemeshow test indicating good fit (P = 0.99).
Finally, a Poisson regression, also shown in Table 4, was per-
formed to produce more easily interpreted relative risks and
showed that adjusted for the Fagerström score, those ran-
domized to the intervention group were 2.7 times (95% CI,
1.1–6.7, P = 0.028) more likely to achieve long-term cessation
than those in the control group. Adjusted for randomiza-
tion group, a low level of nicotine dependency resulted in a
relative risk of quitting of 5.1 (95% CI, 2.0–12.8, P = 0.001).
Anonymized raw data and all statistical analyses are avail-
able as online supplemental content (Supplemental Digital
Contents 1–3, http://links.lww.com/AA/B58; http://links.
lww.com/AA/B59; http://links.lww.com/AA/B60).
DISCUSSION
This study demonstrates that a smoking cessation interven-
tion started preoperatively is successful at achieving smok-
ing cessation at least as long as 12 months after surgery. The
strengths of this study include the ease of implementation
of the intervention and the long duration of follow-up. This
trial design intentionally minimized the time spent in clinic
and did not involve any additional visits beyond the regu-
larly scheduled preadmission appointment, which should
simplify clinical implementation of similar programs.
Furthermore, the finding of successful self-reported smok-
ing cessation 1 year after surgery suggests a public health
benefit beyond the immediate perioperative period.
A Cochrane review suggested that long-term cessation
occurs after intensive perioperative interventions, requiring
weekly counseling sessions for 4 to 8 weeks but not after
brief single-encounter interventions.13
Thus, the design
used in this study might offer a compromise that is brief
in terms of minimizing nursing or physician time, yet still
effective at long-term cessation. As found in previous stud-
ies, in addition to the smoking cessation intervention, the
level of nicotine dependency at baseline was predictive of
smoking status at 1-year follow-up.10,12
However, this study
may have been limited by small sample size in determin-
ing other predictors of long-term cessation. Further inves-
tigation into a wider array of predictors will be useful in
Table 1. Baseline Characteristics of All Study Participants and Those Remaining at 1-Year Follow-Up
All study participants Remaining at 1 year
Control
(n = 84)
Intervention
(n = 84)
Control
(n = 60)
Intervention
(n = 67) P valuea
Physical characteristics
Female 49 (58%) 43 (51%) 37 (62%) 37 (55%) 0.48
Age (years) 47 (12.3) 48 (13.2) 49 (10.6) 48 (13.1) 0.72
Height (cm) 168 (9.6) 169 (9.2) 168 (9.9) 167 (8.6) 0.63
Weight (kg) 77 (18.1) 79 (16.9) 76 (18.1) 78 (16.1) 0.71
BMI (kg/m2
) 27 (6.2) 28 (4.6) 27 (6.3) 28 (4.6) 0.59
Type of surgery
Dental 1 (1%) 3 (4%) 0 2 (3%) 0.50
Head and neck 12 (14%) 7 (8%) 8 (13%) 6 (9%) 0.57
General surgery 13 (15%) 18 (21%) 7 (12%) 16 (24%) 0.11
Gynecologic 12 (14%) 11 (13%) 9 (15%) 8 (12%) 0.79
Ophthalmologic 5 (6%) 6 (7%) 4 (7%) 4 (6%) 1.00
Plastic 5 (6%) 4 (5%) 5 (8%) 4 (6%) 0.73
Urologic 16 (19%) 11 (13%) 13 (22%) 8 (12%) 0.16
Orthopedic, including hand and upper limb 20 (24%) 24 (29%) 14 (23%) 19 (28%) 0.55
Current disease
Diabetes 7 (8%) 15 (18%) 4 (7%) 13 (19%) 0.040
Hypertension 16 (19%) 30 (36%) 12 (20%) 24 (36%) 0.052
Heart diseaseb
0 5 (6%) 0 4 (6%) 0.12
COPD or asthma 18 (21%) 14 (17%) 12 (20%) 10 (15%) 0.49
Smoking habits
Cigarettes per day before trial enrollment 16 (9.7) 15 (7.5) 15 (9.6) 15 (7.3) 0.63
Number of years smoking before trial enrollment 27 (13.1) 27 (13.6) 30 (12.4) 28 (13.9) 0.48
Fagerström score (out of 10) 4.3 (2.3) 3.9 (2.1) 4.3 (2.3) 3.9 (2.1) 0.36
Exhaled CO level (ppm) before randomization 21.9 (12.5) 23.1 (11.6) 21.2 (10.9) 22.6 (11.1) 0.47
Values are mean (SD) or n (percentage).
BMI = body mass index = (weight [kg]/height [m2
]). COPD = Chronic obstructive pulmonary disease. CO = carbon monoxide. Percentages may not add to 100
due to rounding.
a
P value by Fisher exact test for categorical variables (gender, types of surgeries, and current diseases), Wilcoxon rank-sum test for cigarettes per day, and t test for
all other continuous variables. P values are not calculated for baseline characteristics of all participants because any imbalances are due to randomization/chance.
b
Heart disease defined as coronary artery disease, congestive heart failure, or arrhythmia.

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March 2015 • Volume 120 • Number 3 www.anesthesia-analgesia.org 585
tailoring smoking cessation interventions perioperatively to
have the most long-term benefit.
It is unclear which specific component of the inter-
vention used in this study (brief counseling, brochures,
telephone quitline, or nicotine replacement) was most
responsible for the outcome because it is common to com-
bine strategies to maximize outcome.1
However, given that
a previous study of a telephone counseling and newsletter
program (without nicotine replacement), initiated at the
time of surgical or diagnostic outpatient procedure, did
not show a reduction in smoking at 1 year,21
we suspect
that nicotine replacement therapy is a vital component of a
successful perioperative smoking cessation program. The
findings of our study, with its NNT of only 6, may serve
as a call to action for governments and health insurers
to take advantage of the teachable moment6
and support
Figure 1. Consolidated Standards
of Reporting Trials (CONSORT)
flow chart. Details of excluded
patients: (a) Scheduling problems
included patients missing their
preadmission appointment, sur-
gical date or location moved, or
having no time to be assessed
during the appointment; and (b)
of the 36 ineligible patients, 15
smoked 2 cigarettes per day,
10 smoked something other than
cigarettes, 2 were under age
18 years, 5 were already in the
study or another smoking cessa-
tion study, and 1 had a previous
allergic reaction to transdermal
nicotine. *Abstinence confirmed
by preoperative exhaled carbon
monoxide ≤10 ppm.
Table 2. Smoking Cessation and Reduction at 1 Year
Variable Control Intervention RR (95% CI) P valuea
NNT (95% CIb
)
Smoking cessationc
5/60 (8%) 17/67 (25%) 3.0 (1.2–7.8) 0.018 5.9 (3.4–25.9)
Smoking cessation, assuming all lost to
follow-up continued to smoke
5/84 (6%) 17/84 (20%) 3.4 (1.3–8.8) 0.011 7.0 (4.1–24.5)
Smoking reduction by 50% or more
compared with baseline
11/84 (13%) 15/84 (18%) 1.4 (0.67–2.8) 0.52 —
Quit or reduced by 50% or more compared
with baseline
16/84 (19%) 32/84 (38%) 2 (1.2–3.4) 0.010 5.3 (3.1–18.6)
RR = relative risk; CI = confidence interval; NNT = number needed-to-treat.
a
P values calculated using the Fisher exact test.
b
95% CI for NNT calculated using method described by Bender.17
c
Smoking cessation defined as self-reported continuous abstinence for 7 days before phone call without biological confirmation.
—, NNT not reported for smoking reduction since 95% CI of RR crosses 1.

5. 586 www.anesthesia-analgesia.org anesthesia analgesia
Long-Term Quitting After Perioperative Smoking Cessation
more widespread funding of drugs for smoking cessation
therapy around the time of surgery.
The loss to follow-up may limit the validity of the results.
However, the results are preserved if one assumes that
all lost to follow-up continued to smoke. As with several
previous long-term follow-up studies after perioperative
smoking cessation interventions, smoking status determi-
nation was limited to self-report rather than biochemical
verification.10,12
Self-reported smoking cessation has vary-
ing accuracy when compared with biochemical validation22
and is dependent on the type of test and the population
under study. Encouragingly, another Canadian periopera-
tive smoking cessation study did use biochemical valida-
tion with urine cotinine at 12 months postoperatively and
found good correlation (0.91–0.95) to self-reported smoking
status.11
Furthermore, discrepancies between self-reported
abstinence and exhaled carbon monoxide on the day of sur-
gery in our original study were infrequent (6–7%) and did
not differ between groups (P = 1.0).14
Our study design used 3 weeks preoperatively as the
minimum time to be eligible for inclusion to the trial based
on prior literature that has shown that 2 weeks may not be
adequate to reduce postoperative complications,16
while
4 weeks is.23
The need to see patients 3 weeks preopera-
tively hindered patient recruitment because many of the
patients were referred too late to be included in the trial.
However, given that long-term cessation was achieved with
higher success in the intervention group in this study, future
research could focus on shorter preoperative cessation inter-
vals because there would likely be a long-term public health
impact even if a reduction of postoperative complications
could not be shown.
This study demonstrated that an intervention designed to
work within existing infrastructure in a preadmission clinic
results in decreased smoking rates not only around the time
of surgery but also at 1 year. Anesthesiologists and periop-
erative providers have a unique opportunity to help patients
achieve both short-term and long-term smoking cessation. E
DISCLOSURES
Name: Susan M. Lee, MD, FRCPC.
Contribution: This author helped design the study, conduct the
study, analyze the data, and write the manuscript.
Attestation:SusanM.Leehasseentheoriginalstudydata,reviewed
the analysis of the data, and approved the final manuscript.
Name: Jennifer Landry, MD, FRCPC.
Contribution: This author helped design the study, conduct the
study, analyze the data, and write the manuscript.
Attestation: Jennifer Landry has seen the original study data,
reviewed the analysis of the data, and approved the final
manuscript.
Name: Philip M. Jones, MD, FRCPC, MSc (Clinical Trials).
Contribution: This author helped design the study, conduct the
study, analyze the data, and write the manuscript.
Attestation: Philip M. Jones 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.
Table 3. Baseline Characteristics Associated with Abstinence at 1 year
Characteristic Univariable OR (95% CI) P value Adjusted OR (95% CI) P value
Randomization status
Intervention group 3.7 (1.2–13.8) 0.019 3.5 (1.02–13.9) 0.046
Physical characteristics
Female 1.3 (0.47–3.9) 0.75 —
Age (≥50 years)a
1.03 (0.36–2.9) 1.00 —
ASA class (1–2) 1.4 (0.49–3.9) 0.68 —
Obese (BMI ≥30 kg/m2
) 1.3 (0.42–4.0) 0.73 —
Comorbidities
Diabetes 2.3 (0.55–8.1) 0.29 —
Hypertension 2.0 (0.67–5.7) 0.24 —
Heart diseaseb
1.6 (0.03–21.2) 1.00 —
COPD or asthma 0.12 (0–0.76) 0.020 0.22 (0–1.5) 0.14
Smoking habits
Pack-years (≥20)a
0.47 (0.14–1.4) 0.20 —
Fagerström score (4)a 7.6 (2.4–28.8) 0.001 6.3 (1.9–24.8) 0.001
Univariable and adjusted odds ratios (OR) for the association between baseline characteristics and smoking cessation at 1 year postoperatively (n = 127) using
exact logistic regression.
ASA = American Society of Anesthesiologists; BMI = body mass index = (weight [kg]/height [m2
]); COPD = chronic obstructive pulmonary disease.
a
Cut points for age, pack-years, and Fagerström score are at median values. See text for sensitivity of models to varying cut points.
b
Heart disease defined as coronary artery disease, congestive heart failure, or arrhythmia.
—, variable excluded for multivariable analysis.
Table 4. Baseline Characteristics Associated with Abstinence at 1 Year by Ordinary Logistic Regression and
Poisson Regression
Characteristic Adjusted OR (95% CI) P value Adjusted RR (95% CI) P value
Randomization status
Intervention group 3.8 (1.2–11.9) 0.020 2.7 (1.1–6.7) 0.028
Smoking habits
Fagerström score (4) 7.9 (2.6–23.9) 0.001 5.1 (2.0–12.8) 0.001
Model including interaction between Fagerström score and randomization group showed no appreciable interaction (P = 0.90 for interaction term).
RR = relative risk; CI = confidence interval; OR = odds ratio.

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March 2015 • Volume 120 • Number 3 www.anesthesia-analgesia.org 587
Name: Ozzie Buhrmann, BScPhm, RPh.
Contribution: This author helped design the study and con-
duct the study.
Attestation: Ozzie Buhrmann has seen the original study
data, reviewed the analysis of the data, and approved the final
manuscript.
Name: Patricia Morley-Forster, MD, FRCPC.
Contribution: This author helped design the study, conduct the
study, analyze the data, and write the manuscript.
Attestation: Patricia Morley-Forster has seen the original study
data, reviewed the analysis of the data, and approved the final
manuscript.
This manuscript was handled by: Peter S.A. Glass, MB ChB, FFA.
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