Advertisement

Self-Reported Smoking Compared to Serum Cotinine in Bariatric Surgery Patients: Smoking Is Underreported Before the Operation

  • Paula J. D. WolversEmail author
  • Sjoerd C. Bruin
  • Willem M. Mairuhu
  • Monique de Leeuw-Terwijn
  • Barbara A. Hutten
  • Dees P. M. Brandjes
  • Victor E. A. Gerdes
Open Access
Original Contributions
  • 83 Downloads

Abstract

Background

Smoking has been associated with postoperative complications and mortality in bariatric surgery. The evidence for smoking is based on self-report and medical charts, which can lead to misclassification and miscalculation of the associations. Determination of cotinine can objectively define nicotine exposure. We determined the accuracy of self-reported smoking compared to cotinine measurement in three phases of the bariatric surgery trajectory.

Methods

Patients in the phase of screening (screening), on the day of surgery (surgery), and more than 18 months after surgery (follow-up) were consecutively selected. Self-reported smoking was registered and serum cotinine was measured. We evaluated the accuracy of self-reported smoking compared to cotinine, and the level of agreement between self-report and cotinine for each phase.

Results

In total, 715 patients were included. In the screening, surgery, and follow-up group, 25.6%, 18.0%, and 15.5%, respectively, was smoking based on cotinine. The sensitivity of self-reported smoking was 72.5%, 31.0%, and 93.5% in the screening, surgery, and follow-up group, respectively (p < 0.001). The specificity of self-report was > 95% in all groups (p < 0.02). The level of agreement between self-report and cotinine was 0.778, 0.414, and 0.855 for the screening, surgery, and follow-up group, respectively.

Conclusions

Underreporting of smoking occurs before bariatric surgery, mainly on the day of surgery. Future studies on effects of smoking and smoking cessation in bariatric surgery should include methods taking into account the issue of underreporting.

Keywords

Bariatric surgery Smoking Cotinine Self-report Underreporting Complication 

Introduction

Bariatric surgery is the most effective weight loss therapy for treating morbid obesity. Besides weight loss, it contributes to improvements in comorbidity and reduces mortality [1, 2]. Smoking has been associated with postoperative complications and mortality in bariatric surgery [3, 4, 5]. Short-term effects of smoking cessation have shown to significantly improve pulmonary function and immune function [6, 7], and smoking cessation is thereby likely to decrease postoperative complications [8, 9, 10]. Therefore, patients are strictly urged to quit smoking before undergoing bariatric surgery.

However, the evidence on the associations between smoking and postoperative complications or influence on weight loss is based on self-report and medical charts [3, 4, 5, 11, 12, 13, 14, 15, 16, 17]. It may lead to misclassification if patients conceal their smoking habits. This is a common problem in studies [18, 19]. Studies on the relationship between self-reported smoking status and objectively measured nicotine exposure have shown that misclassification is greater in clinical situations where quitting expectations on part of the health care team influence self-report [18, 19, 20, 21]. Cotinine is the biomarker of choice to objectively define nicotine exposure. It can be measured in serum, urine, and saliva [18, 19, 22].

Therefore, we evaluated the accuracy of self-reported smoking in three groups of patients based on cotinine measurement: patients screened for bariatric surgery, patients on the day of bariatric surgery, and patients more than 18 months after surgery. Additionally, we evaluated whether smoking based on cotinine measurement and self-reported were associated with the occurrence of postoperative complications. We hypothesized that self-reported smoking status may be less accurate in the phase before surgery when patients may be afraid that smoking will contribute to rejection for bariatric surgery compared to smoking status during follow-up after the operation.

Materials and Methods

Setting and Study Population

Between the 4th of January 2017 and the 24th of April 2017, all bariatric surgery patients of a high-volume bariatric center of excellence visiting the laboratory were consecutively screened. As a part of the bariatric care protocol, blood samples are taken from patients at specific time points before and after surgery. Patients in this cohort were urged to quit smoking at least 2 weeks prior to the surgery. They were counseled and were offered support by the general practitioner or smoking cessation department. Three groups were selected: patients in the phase of screening for bariatric surgery (screening), on the day of bariatric surgery (surgery), and patients more than 18 months after bariatric surgery (follow-up). We asked patients whether they were willing to answer questions on smoking behavior and allow us to extract serum from the blood already drawn for clinical management. Exclusion criteria included age under 18 years old, bariatric surgery less than 18 months ago, or missing blood sample.

The local Medical Ethics Committee at former MC Slotervaart approved the study protocol. All patients provided written informed consent before enrolment.

Data Collection

After enrolment all patients were asked to report their current and past nicotine exposure. Afterwards, blood was drawn. Variables that were extracted from medical records included sex, age at time of cotinine measurement, preoperative weight and body mass index, type of bariatric surgery, primary or revisional bariatric surgery, history of abdominal surgery, hypertension, and diabetes mellitus. In the follow-up group, we also collected information on time after surgery, percentage total weight loss, proton pump inhibitor use, and postoperative presence of hypertension and diabetes.

Self-Reported Smoking and Smoking According to Cotinine

Current smoking was defined as smoking at least once during the past 2 days, because of the half-life of cotinine [19, 22, 23, 24, 25].

Self-Report

Patients filled out a written questionnaire questioning whether they had smoked (ever), had used nicotine replacing products, or were exposed to secondhand smoking during the last 48 h. Additionally, (former) smokers answered when they had smoked the last one.

Cotinine

Serum was extracted and samples were stored at − 20 °C until analysis. Samples were obtained prospectively and analysis was performed collectively. The standard samples (calibrators and controls) were handled similarly.

Cotinine was extracted with solid-phase extraction (SPE), and quantitation was performed by reversed-phase high-performance liquid chromatography (HPLC) with UV detection [26, 27, 28, 29, 30, 31]. Cotinine was detected with UV absorbance wavelength set at 259 nm and identified by retention time index (0.38). Internal standard used was 2-phenylimidazole. The quantitation limit of cotinine was 10 ng/ml. The intra-assay and inter-assay coefficients of variation were 8% and 13%, respectively. Cotinine test was defined as positive when cotinine concentration was ≥ 10 ng/ml.

The HPLC-system consisted of Varian ternair pump, Varian ProStar auto sampler, and Varian Prostar Diode Array Detector. The columns used were Bond Elut C2-solid phase extraction columns (3 ml/200 mg) and HPLC analytical column: Inertsil C8; 3.0 × 150 mm, 5 μ. Software used for controlling the HPLC-system and data processing was Galaxie chromatography software.

The technician and the blood samplers were non-smokers. Naturally, there was an interdiction to smoke in the analytical laboratory. The results of the cotinine test were never visible for the attending doctors.

Complications

Complications during the first 30 days after surgery were retrieved from the medical records. We used the Clavien Dindo classification for severity of the complications [32].

Statistical Analysis

Primary, we calculated the sensitivity and specificity of self-reported smoking, also for each group separately (screening, surgery, follow-up). Chi2 test was used to compare sensitivity between the three groups. The degree of agreement between self-report and cotinine measurement was expressed using Cohen’s kappa coefficient, also for each group.

Secondary, patients were grouped in four classification groups combining the self-report and the cotinine test (patients who reported smoking accurately, patients who concealed smoking, patients who correctly reported non-smoking, or those who reported smoking inaccurately). Then, baseline characteristics were compared using chi2 test, one-way ANOVA, or Kruskal Wallis test, in case of categorical variables, normal distributed variables, or non-normal distributed data respectively. Finally, in case of p value < 0.200, group differences were tested separately using chi2 test, unpaired T test, or Mann-Whitney U test if applicable. We calculated cotinine concentrations per classification group and described exposure to secondhand smoking and nicotine replacement products.

Finally, the associations between smoking, self-reported or defined by cotinine measurement, and complications during the first 30 days after bariatric surgery were explored using univariable logistic regression. We adjusted for possible confounding, by adding variables with p value < 0.400 after univariable logistic regression to the model.

Data analysis was performed using IBM SPSS Statistics software package for Windows version 22 (Chicago, Illinois).

Results

During the screening period, a total of 742 patients was eligible for the study. Twenty-seven patients were excluded; 13 in the screening, nine in the surgery, and five patients in the follow-up group. Patient characteristics are described in Table 1. Median time after surgery of follow-up group was 2.9 years (interquartile range 2.0–3.8). Mean total weight loss was 30.5% (standard deviation 9.0). Postoperative, 43 (21.5%) patients had hypertension, 11 (5.5%) diabetes mellitus 2, and 62 (31.0%) patients used proton pump inhibitors.
Table 1

Demographic and clinical characteristics before surgery, per group (screening, surgery, follow-up)

 

Screening

Surgery

Follow-up

N = 199

N = 316

N = 200

Female gender, N (%)

157 (78.9)

268 (84.8)

171 (85.5)

Age (years), mean (SD)

44.6 (11.4)

46.3 (10.3)

48.2 (11.2)

Weight (kg), mean (SD)

124.2 (20.7)

122.0 (18.5)

124.9 (17.8)

Body mass index (kg/m2), median (IQR)

42.0 (39.7–45.8)

41.8 (39.6–44.6)

42.3 (39.8–46.3)

Hypertension, N (%)

78 (39.2)

133 (42.1)

72 (36.0)

Diabetes mellitus 2, N (%)

43 (21.6)

81 (25.6)

40 (20.0)

Previous abdominal surgery, N (%)c

102 (51.3)

153 (48.4)

118 (59.0)

Interval between cotinine and surgery (weeks) median (IQR)

19.0 (16.0–24.0)*

  

Primary bariatric surgery, N (%)

139 (95.2)a

288 (91.1)b

174 (87.0)

N number, IQR interquartile range, SD standard deviation

aOperated patients only. 53 patients were not operated.

bOne patient was not operated

cIncluding bariatric surgery

Smoking: Self-Reported and Based on Cotinine Measurement

In Table 2, self-reported smoking, positive cotinine (≥ 10 ng/ml), cotinine concentration, sensitivity, specificity and Cohen’s kappa coefficient per group are summarized. Smoking based on cotinine measurement was 25.6%, 18.4%, and 15.5% in the screening, surgery, and follow-up group, respectively. A history of smoking was reported by 41.2%, 49.4%, and 39.0% of the patients, in the screening, surgery, and follow-up group, respectively. The sensitivity of self-reported smoking was 72.5%, 31.0%, and 93.5% in the screening, surgery, and follow-up group, respectively (p < 0.001). The specificity of self-report was 99.6%, 99.3%, and 96.4% in the screening, surgery, and follow-up group, respectively (p < 0.02). The kappa between self-report and cotinine was 0.784 for the screening group, 0.414 for the surgery group, and 0.855 for the follow-up group.
Table 2

Self-reported smoking versus positive cotinine; sensitivity, specificity, and Cohen’s kappa coefficient

 

All patients

Screening

Surgery

Follow-up

 

N = 715

N = 199

N = 316

N = 200

Self-reported smoking, N (%)

92 (12.9)

38 (19.1)

19 (6.0)

35 (17.5)

Self-reported history of smoking, N (%)

316 (44.2)

82 (41.2)

156 (49.4)

78 (39.0)

Cotinine detected, N (%)

140 (19.6)

51 (25.6)

58 (18.4)

31 (15.5)

Cotinine concentration (ng/ml), median (IQR)ǂa

115.0 (50.3–213.5)

101.0 (26.0–159.0)ǂ

117.0 (28.0–236.8)ǂ

180 (81.0–249.0)ǂ

Sensitivity of self-report (%)

60.0

72.5#

31.0#

93.5#

Specificity of self-report (%)

98.6

99.6*

99.3*

96.4*

Cohen’s kappa coefficient

0.673

0.784

0.414

0.855

aOnly values of positive cotinine (concentration ≥ 10 ng/ml) are described

#Significant difference between groups: p < 0.001; *Significant differences between groups: p < 0.02; ǂSignificant differences between groups: p < 0.01

Fifty-three of 199 patients in the screening group were not operated for several reasons. In 22 (41.5%) of them, cotinine was detected, and 16 (30.2%) reported smoking. Once, an unsuccessful cessation of smoking was mentioned as reason for postponement of the operation.

Accuracy of Self-Report and Exposure to Other Types of Nicotine

We found no clinically relevant differences in baseline characteristics between patients who reported smoking accurately, patients who concealed smoking, patients who reported correctly to be non- smoking, or those who inaccurately reported to be smoking (data not shown).

In Table 3, cotinine values and characteristics of secondhand smoking exposure are shown, in all patients and grouped on accuracy of self-report. Overall, seven patients (< 1%) had used e-cigarettes, and no other type of nicotine replacement product was reported. Two of the seven patients reported also to be a current smoker (one in screening and one in follow-up group). Five patients (all in the surgery group) reported to have already stopped with cigarette smoking; the shortest cessation period was 2.3 months, the longest 4 years. All cotinine levels were above 180 ng/ml (mean 284.8 ng/ml (SD 67.4)).
Table 3

Description of cotinine values and secondhand smoking exposure, in all patients and grouped on accuracy of self-report

 

All patients

Correctly smoking

Concealed smoking

Correctly non-smoking

Incorrectly smoking

 

N = 715

N = 84

N = 56

N = 567

N = 8

Cotinine (ng/ml), median (IQR)

115.0 (50.3–213.5)

143.0 (79.5–230.3)

71.0 (19.0–163.8)

Exposure to SHS, N (%)

190 (26.6)a

52 (61.9)

29 (51.8)

101 (17.8)

8 (100.0)

Time of SHS (hours), median (IQR)

1.0 (0.2–18.0)

3.0 (0.4–48.0)

8.0 (0.8–30.0)

0.5 (0.2–4.5)

3.5 (0.3–7.0)

Cotinine in SHS group (ng/ml), median (IQR)

145.0 (78.0–232.5)

162.5 (84.8–230.3)

105.0 (64.5–253.5)

N number, IQR Interquartile range, SHS secondhand smoking

aIn 81 (42.6%) patients, cotinine was detected

30-Day Complications

For the relationship between self-reported smoking, cotinine, and complications within 30 days after surgery, we analyzed the screening group and the surgery group (Total n = 461). Overall, 73 (15.8%) of these patients had a surgical complication (bleedings (n = 22), nausea or dysphagia (n = 11), abdominal pain (n = 7), infection (n = 7), leakage of anastomosis (n = 6), stenosis (n = 5), allergic reaction (n = 3), other complications (n = 11), and one patient died. Forty-seven complications (10.2%) were classified as Clavien Dindo I, 15 (3.3%) as Clavien Dindo II; 10 complications (2.2%) were categorized as Clavien Dindo III, and one complication (0.2%) as Clavien Dindo V.

In the screening group, cotinine was detected in eight (33%) of the patients with a complication and in 21 (17%) without complication. Smoking was reported by seven patients (29%) with a complication and by 14 (12%) without a complication. The adjusted odds ratios for having a postoperative complication were 3.8 (1.3–11.3) for positive cotinine and 5.1 (1.6–16.4) for self-reported smoking. We adjusted for presence of type 2 diabetes, preoperative BMI, time before surgery, and primary bariatric surgery (Table 4).
Table 4

Associations of self-reported and cotinine measured smoking with 30-day complications in the screening group

 

Univariable OR (95 CI)

Adjusted OR (95 CI)a

Adjusted OR (95 CI)b

Adjusted OR (95 CI)c

Self-reported smoking

3.18 (1.12–9.00)

5.09 (1.58–16.42)

4.72 (1.49–14.98)

4.90 (1.56–15.43)

Positive cotinine

2.41 (0.91–6.35)

3.79 (1.27–11.29)

3.43 (1.18–10.01)

3.37 (1.16–9.77)

OR odds ratio, CI confidence interval

aAdjusted for BMI, DM2, preoperative time, and primary surgery (p < 0.4 at univariable analysis)

bAdjusted for BMI, DM2, and preoperative time (p < 0.3 at univariable analysis)

cAdjusted for DM2, preoperative time (p < 0.2 at univariable analysis)

In the surgery group, cotinine was detected in eight patients (15%) with a complication and in 49 (19%) without a complication. Smoking was reported by three patients (6%) with a complication and by 16 (6%) without a complication. In this group, the adjusted odds ratios for having a postoperative complication were 0.9 (0.4–2.2) for positive cotinine and 1.1 (0.3–3.9) for self-reported smoking. We adjusted for gender, preoperative BMI, age, and presence of hypertension (Table 5).
Table 5

Associations of self-reported and cotinine measured smoking with 30-day complications in the surgery group

 

Univariable OR (95 CI)

Adjusted OR (95 CI)1

Adjusted OR (95 CI)2

Adjusted OR (95 CI)3

Self-reported smoking

1.02 (0.29–3.64)

1.06 (0.28–3.92)

1.01 (0.28–3.71)

0.96 (0.26–3.51)

Positive cotinine

0.86 (0.38–1.96)

0.94 (0.41–2.20)

0.91 (0.39–2.09)

0.88 (0.38–2.02)

OR odds ratio, CI confidence interval

aAdjusted for sex, BMI, age, and hypertension (p < 0.4 at univariable analysis)

bAdjusted for sex, BMI, and hypertension (p < 0.3 at univariable analysis)

cAdjusted for sex and BMI (p < 0.2 at univariable analysis)

Discussion

This is the first study in bariatric surgery examining the accuracy of self-reported smoking status. When smoking was based on cotinine concentration, 15.5–25.6% of all patients was currently smoking on the different time points before and after surgery. As hypothesized, underreporting of smoking is present during the screening period before surgery (sensitivity 72.5%) and especially on the day of surgery (sensitivity 31.0%). This indicates that preoperative self-reported smoking is a poor indicator of actual smoking status compared to cotinine values. The self-reported smoking status is most reliable when patients are already operated (sensitivity 93.5%).

In most bariatric surgery studies, smoking is based on self-report and medical chart review [3, 11, 12, 13, 14, 15, 16, 17, 33, 34, 35, 36, 37, 38, 39, 40]. Our study shows that it really matters at which moment smoking status is reported and that is likely that underreporting may influence results and conclusions of studies. We evaluated how and at which moment smoking status was evaluated in all studies since 2010 in which smoking status was an outcome or was associated with bariatric surgery outcome (Table 6) [3, 11, 12, 13, 14, 15, 16, 17, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59]. This evaluation showed that all studies were based on self-report or medical chart, and in only a few studies, a specific questionnaire was used. It also indicated that in many studies, the definitions of smoking or former smoking were not well described, and the percentages of lost to follow-up were high. This underlines that results of these studies are based on an imprecise method to evaluate smoking status and patient groups with many missing data which will definitely influence results.
Table 6

Evaluation of definitions of smoking in all studies since 2010 in which smoking was an outcome or was associated with a bariatric outcome

Author, year of publication

Description study (number of patients, type of surgery, follow-up)

Definition of smoking

Timing and method of registration

Outcome measures

Conclusions

Comments

Birkmeyer, 2010 [54]

Retrospective

15,275

35.2% LAGB, 5.6% LSG, 59.2% RYGB

1 month

Current or past smoking

Preop: medical record

30-day complications

Preop smoking prevalence 39%

LAGB 36%

LSG 41%

RYGB 40%

No univariable analysis for smoking and complications

Revisional surgery was excluded

Data from MBSC dataset

Odom, 2010 [55]

Retrospective

203 RYGB

28.1 months (SD 18.9)

Not available

Preop: medical record

Predictors for postoperative weight gain (> 15% from nadir weight)

Preop smoking prevalence 4.4%

Preop smoking was not a predictor for weight gain after RYGB

18% RespR (survey not concerned smoking)

Unclear whether it concerned open and/or laparoscopic surgery and whether revisional surgery was included

Finks, 2011 [43]

Retrospective  25,469

54% LGBP, 4.3% OGBP, 31.5% LAGB, 8.9% SG, 1.3% DS

1 month

Any smoking history

Preop: medical record

30-day mortality and complication rate

Preop smoking prevalence 39%

LGBP 40.9%

OGBP 39.5%

LAGB 36%

SG 37.0%

DS 43.4%

Smoking was associated with serious complications in overall population

OR 1.2 (1.02–1.40)

Revisional surgery was excluded

Previous venous thromboembolism, mobility limitations, coronary artery disease, age over 50 years, pulmonary disease, male gender, and smoking history were associated with serious complications

Data from MBSC dataset, overlapping cohort Birkmeyer [54]

Turner, 2011 [44]

Retrospective

32,426 LGBP

1 month

Smoking within 1 year before surgery

Preop: medical record

30-day morbidity and mortality risk

Preop smoking prevalence: 12.3%

Smoking was not a ‘strong contributor’ to predicted probability of 30-day morbidity and mortality

Unclear whether revisional surgery was included

Nomogram was not externally validated (AUC 0.629 (0.614–0.645))

Data from ACS NSQIP dataset

Adams, 2012 [36]

Retrospective

36 LAGB, 25 (L + O)RYGB

Veterans

2 years

Never user, former user (quit at least 1 year prior to surgery), or recent user (quit within the year before surgery)

Medical record; Preoperative (~ 2 months before), 6, 12, and 24 months after surgery

Prevalence smoking and substance use disorder; Association EWL

Preop smoking prevalence:

Recent 15.5% (all quit within 5 months prior to surgery)

Former 37.9%

Never 46.6%

Postop prevalence smoking 15.5% (all recent quitters resumed)

Former smokers 37.9%

Never 46.6%

EWL was related to smoking status at 6 and 12 months after surgery, but not anymore after 24 months. Recent smokers lost more weight than both never and former smokers.

History of substance use disorder was not related to EWL

The authors state that there was a marginally significant relationship between history of substance use disorder and weight loss at 12 months and 24 months after surgery, despite p values of 0.08 and 0.09 respectively

Veterans are older, more likely to be male, and have high rates of tobacco and substance use disorders.

Unclear whether revisional surgery was included

Gupta, 2012 [45]

Retrospective

11,023

55.2% LRYGB, 11.1% ORYGB, 30.3% LAGB, 2.5% other GBP, 0.4% VBG, 0.5% BPD

1 month

Smoking within 1 year before surgery

Preop: medical record

30-day postop comorbidity risk

Preop smoking prevalence: 12.5%

LRYGB 12.4%; ORYGB 14.7%; LAGB 12.3%; BPD 11.9%; VBG 17.1%; Other GB 6.8%, p 0.01

Smoking was not associated with increased 30-day morbidity risk

Sleeve not included

Unclear whether revisional surgery was included

Model was validated; moderate discriminative ability (training set AUC 0.69, validation set AUC 0.66)

Data from ACS NSQIP dataset

King, 2012 [46]

Prospective

1945

69.9% RYGB, 25.2% LAGB, 2.6% LSG, 2.3% BGB/BPD

2 years

Not available

Preop: self-report, max 30 days prior

Postop: questionnaire after 12 and 24 months

Prevalence of alcohol use disorder; pre- and postoperative associations with alcohol use disorder

Prevalence smoking:

Preop 2.2%

Postop 1-year 7.9%

Postop 2-year 9.3%

Prevalence alcohol use disorder:

Preop 7.6%; Postop 2-year 9.6%

Smoking was associated with alcohol use disorder (OR 1.83 (1.22–2.76))

Revisional surgery was excluded

Unclear whether it concerned open/laparoscopic surgery

Only cigarette smoking

44% RespR

19% LTFU after 2 years

LABS-2 study [47]

Ramanan, 2012 [49]

Retrospective

32,889

51.1% LRYGB, 33.6% LAGB, 8.7% ORYGB, 3.5% other

1 month

Smoking within 1 year before surgery

Preop: medical record

30-day mortality risk

Preop smoking prevalence: 12.3%

LRYGB 12.4%; ORYGB 14.8%; LAGB 11.8%; BPD 12.6%; VBG 8.4%; Other GB 8.9%

Smoking was not associated with increased 30-day mortality risk

Sleeve not included

Unclear whether revisional surgery was included

Model was validated; high discriminative ability (AUCs ≥ 0.8)

Data from ACS NSQIP dataset

Wood, 2012 [38]

Retrospective

2028 (L + O)RYGB

4 years

History of smoking

Preop: medical record

Constructing database

Preop smoking prevalence 11%

Never 51%

Quit 38%

Unknown 8%

Preop program 6–12 months, goal 10% TWL

< 26% LTFU (no weight < 2 years in database) after 4 years

Unclear whether revisional surgery was included

Arterburn, 2013 [50]

Retrospective

124 LRYGB, 392 ORYGB

Veterans

12 months

Smoking within past year

Preop: medical record

TWL

Preop smoking prevalence: 13.4%

Smoking was not related to TWL at 12 months

Veterans are older, more likely to be male, with lower incomes and greater comorbidity burden than the general bariatric population.

37% LTFU after 12 months

Conason, 2013 [11]

Prospective

100 LRYGB, 55 LAGB

2 years

Frequency of smoking cigarettes during last month on 10-point Likert scale (0 = never, 5 = occasionally, 10 = all of the time)

Preop: written questionnaire, 3 weeks prior to surgery

Postop: written questionnaire 1, 3, 6, 12, 24 months

Prevalence smoking, alcohol and drug

No difference prevalence in smoking;

preop 10.4%, 24-months postop 8.1%

Increase in alcohol use after LRYGB compared to baseline (preop: 1.86, 1 year: 1.91 p 0.048, 2 years: 3.08 p = 0.011. No increase in alcohol use after LAGB

76% LTFU after 2 years

RespR unclear

No changes in complaints about reported substance use

Only cigarettes included

Unclear whether revisional surgery was included

Lent, 2013 [40]

Prospective

899 RYGB

34.9 months (SD 12.8)

Current: yes/no

Amount of cig/day

Amount of PY

Previous: 100 cig lifetime

Preop: survey during preop preparation program

Postop: survey per mail

Prevalence smoking, alcohol

Relation with weight loss (EWL)

Preop smoking prevalence 19.4% .

Postop smoking prevalence 14.8%

Smoking preop and smoking postop were not related to EWL (median EWL 74.6%)

83% LTFU after 3 years

RespR unclear

Surveys were not anonymous

Preop program 6–12 months, goal 10% TWL

Unclear whether it concerned laparoscopic/open surgery

Revisional surgery was excluded

Cohort overlaps with previous study in Geisinger MC (Wood 2012) [38]

Benotti, 2014 [51]

Retrospective

185,315

51.9% (L + O)RYGB, 40.4% LAGB, 4.6% SG, 3.1% other

1 month

No: none, rare

Yes: occasional, frequent

Preop: medical record

30-day mortality risk

Preop smoking prevalence:

RYGB 5%

Smoking was not related to 30-day mortality in RYGB patients

Higher BMI, higher age, male, pulmonary hypertension, congestive heart failure and liver disease were associated with higher mortality rates

Timing of registration was not available

15% LTFU after 1 month

Revisional surgery was excluded

Cohort overlaps with previous studies in Geisinger MC (Wood 2012, Lent 2013) [38, 40]

Gordon, 2014 [48]

Retrospective

333 RYGB

44.4 months (19.7)

Daily tobacco consumption of any amount

Preop: medical record, assessment included self-report questionnaire

Association preop personality and psychosocial assessment with EWL

Preop smoking prevalence 15.2%

Univariable association between smoking and higher EWL after 6 and 24 months, but not after 12 and ‘last’ observation. No multivariable association between smoking and EWL.

Psychosocial variables and personality traits were associated to EWL

Revisional surgery was excluded. Unclear whether it concerned open/laparoscopic surgery

74.3% RespR

54.7% LTFU after 2 years

Haskins, 2014 [3]

Retrospective

5749 open bariatric surgery, 35,696 laparoscopic bariatric surgery

1 month

One or more cigarette within 1 year prior to surgery

Preop: medical record

30-day complications

Preop smoking prevalence: not available

Significant effects of smoking on morbidity (OR(95CI))

Organ space infection 1.45 (1.08–1.94)

Prolonged intubation 1.82 (1.26–2.63)

Pneumonia: 1.90 (1.42–2.54)

Reintubation 1.62 (1.12–2.34)

Sepsis 1.49 (1.11–2.00)

Shock: 1.78 (1.16–2.74)

Length of stay > 7 days 1.37 (1.12–1.67)

Cigar, pipe, tobacco chewing excluded

Unclear whether revisional surgery was included

Data from ACS NSQIP dataset

Also results available for open and laparoscopic surgery separately

Still, 2014 [41]

Retrospective

2444 (L + O)RYGB

3 years

Smoker: current or history of smoking (≥ 100 cig)

Preop: medical record

Relations with EWL

Preop smoking prevalence: not available

No history of smoking was related to 4.8% less EWL at 36 or more months after surgery (mean EWL 61.3%, SD 26.9)

40% LTFU after 3 years

Preop program 6–12 months, goal 10% TWL

Patients must be tobacco free for at least 6 months prior to surgery, documented by serum nicotine level.

Cohort overlaps with previous studies in Geisinger MC (Wood 2012, Lent 2013, Benotti 2014) [38, 40, 51]

Coblijn, 2015 [34]

Retrospective

350 LRYGB

41 months (range 24–71)

Not available

Preop: medical record

Marginal ulcers

Preop smoking prevalence 20.6%

Preop smoking related to marginal ulcer development; OR 2.85 (1.03–7.84)

39% of patients with marginal ulcer were smoker vs 19.3% without marginal ulcer, p 0.019

Revisional surgery was included

0% LTFU

Maniscalco, 2015 [42]

Prospective

28 IB, 30 LAGB,

5 LRYGB, 15 LSG

12 months

Smoking at least 10 cigarettes per day

Written questionnaire, < 1 month before intervention, 3, 6, 12 months after intervention

Smoking habit after intervention for morbid obesity in smokers

weight loss (BMI reported)

Quitting rates 12 months after intervention:

IB 14%, LAGB 3%, RYGB/LSG: 5%.

No difference in weight loss between quitters and persistent smokers.

No differences in quitting rate between intervention groups after 12 months.

No correlation between weight loss and amount of cigarettes.

Exclusion of non-smokers and smokers smoking less than 10 cig per day.

Suggestion to stop smoking, but no specific smoking cessation program

Questions on smoking: initiation age, duration, number of cigarettes, cessation attempts, Fagerstrom test.

During follow-up: number of cigarettes and reasons for quitting

Weight loss was not further described. BMI pre- and post-intervention was reported.

Unclear whether revisional surgery was included.

RespR and LTFU not reported.

Mitchell, 2015 [56]

Prospective

201 RYGB

3 years

Current/recent

Preop: self-report

Prevalence of addictive behaviors

Preop smoking prevalence: 8.0%

8–18.4% develops alcohol use disorder after 3 years depending on criteria used

Preop smoking prevalence in patients not included 19.1%

74.6% RespR

17% LTFU after 3 years

Unclear whether it concerned open/laparoscopic surgery, banded RYGB included, revisional surgery was excluded

Subgroup of LABS-2 study [47]

Moser, 2015 [17]

Retrospective

184 LSG

22 months (SD 7)

Medical chart yes/no/former

HSI (only postoperative)

Preop: medical record

Post: telephone

Prevalence smoking; Association EWL

Preop smoking prevalence 33.7%.

Former smoking: 31.0%

Never smoking: 35.3%

Postop prevalence smoking: 43.3%

Former smoking preop: no one relapsed after surgery

Never smoking preop: no one started postop.

Stopped smoking after surgery 20.6%

Smoking pre/post nor heaviness of smoking is related to EWL (24 months EWL 74%, SD 22)

47% LTFU 24 months

Unclear whether revisional surgery was included

Mitchell, 2016 [16]

Prospective 1670 (L + O)RYGB, 548 LAGB 36 months

Never, always, stopped, started, sometimes, initially yes/no

Pre and postop: self-report

Prevalence smoking; predictors TWL

Preop smoking prevalence:

RYGB:

Smoking 1.2%

Never 89.1%

Other 9.7%

LAGB:

Smoking 1.4%

Never 91.9%

Other 6.7%

Postop prevalence smoking

RYGB

Never 89.1%

Always 0.8%

Other 10.1%

LAGB:

Never 91.9%

Always 0.4%

Other 7.7%

TWL RYGB:

Never smoker: − 31.0% (0.3)

Always smoker − 34.8% (1.7) p 0.02

TWL LAGB:

Smoking behavior resulted not in significant difference TWL.

49.7% LTFU at 36 months (information on smoking and/or weight missing)

For RYGB, the behavioral changes that resulted in a significant difference in percent weight change are eating or drinking meal replacements, keep eating when feeling full, eating continuously during the day, binge eating, binge eating disorder, loss of control eating, alcohol use disorder, and smoking.

Revisional surgery was excluded.

In manuscript, more specific subgroups reported in category other

Cohort LABS-2 study [47]

Wood, 2016 [37]

Retrospective

1145 RYGB

9.3 years

History of smoking

Preop: medical record

Preop factors associated with long-term TWL

History of smoking was related to 2.8% more TWL (mean TWL 22.5% (SD 13.1))

29.7% LTFU after 9 years

6-month preop program, goal 10% TWL

Unclear whether it concerned open and/or laparoscopic surgery. Revisional surgery was excluded

Cohort overlaps with previous studies in Geisinger MC (Wood 2012, Lent 2013, Benotti 2014, Still 2014) [38, 40, 51, 41]

Coblijn, 2017 [33]

Retrospective

1709

75.1% LRYGB, 6.4% LSG, 17.4% revisional LRYGB, 1.2% other

> 12 months

Not available

Preop: medical record

Risk on complications

Preop smoking prevalence: 25.1%

Smoking was not associated with increased risk of complications

Not validated

Table 8 contains two different numbers on smoking; total 1457; sum yes plus no is 1657

Overlapping data and cohort with Coblijn 2015 [34]

Haskins, 2017 [12]

Retrospective

33,714 LSG

1 month

Smoker: last year prior to surgery at least one cigarette

Preop: medical record

Prevalence smoking; effect smoking on 30-day postoperative morbidity and mortality

Preop smoking prevalence 9.8%

Smoking was associated with a composite morbidity event (4.3 versus 3.7%, OR 1.23 (1.01–1.48), serious morbidity event (0.9 versus 0.6%, OR 1.9 (1.25–2.89), and 30-day mortality (0.2 versus 0.1%, OR 4.51 (1.95–10.42)). Smokers were more likely to have unplanned reintubation, OR 1.88 (1.01–3.50)

The length of hospital stay, unplanned readmission, and readmission rates were comparable between the 2 groups

Revisional surgery was excluded

Data from ACS NSQIP dataset

Kedrin, 2017 [59]

Retrospective

348

63% LRYGB, 5% LSG, 4% LAGB, 28% ORYGB

60 months (37.2)

Not available

Before index colonoscopy: medical record

Association of bariatric surgery with proportion of colorectal adenomas

Prevalence of smoking:

Bariatric surgery after colonoscopy: 16.75%

Bariatric surgery ≥ 1 year before colonoscopy: 18.4%

Bariatric surgery before index screening colonoscopy was associated with decreased proportions adenomas (OR 0.37 (0.19–0.69))

Index screening colonoscopy in patients without family history of colorectal cancer, before or after bariatric surgery

King, 2017 [57]

Prospective

2218

70.6% (L + O)RYGB, 24.9% LAGB, 4.3% other

7 years

Not available

Preop: self-report, max 30 days prior to surgery

Association with initiation/continuation of prescribed opioid use

Preop smoking prevalence: 12.4%

RYGB: 13.7%, LAGB: 8.5%, Other: 13.3%

Preop smoking was not associated with postoperative initiation or continuation of prescribed opioid use

27% LTFU after 7 years

Only cigarette smoking

Revisional surgery was excluded

Cohort LABS-2 study [47]

King 2012 reported different preop smoking prevalence (2.2%)

Pierik, 2017 [35]

Retrospective

1670 LRYGB; 118 LSG

33.5 months (range 6–95)

Not available

Preop: medical record

Explanation of abdominal complaints

Preop smoking prevalence: 41.5%

No difference between smoking for explained vs unexplained abdominal complaints, nor for no abdominal pain vs pain

Revisional surgery was included

Overlapping cohort with Coblijn 2015 and Coblijn 2017 [33, 34]

Cayci, 2018 [39]

Prospective

40 LSG

12 months

Not available

Preop: medical record

Lower urinary tract functions and urination volume

Preop smoking prevalence: 25%

Preop non-smokers: postop improved lower urinary system functions (OAB-Q and urination volume)

Preop smokers: postop improved OAB-Q score, no difference in urination volume

Unclear whether revisional surgery was included.

Inadomi, 2018 [13]

Retrospective

49,772

49.8% (L + O)RYGB, 50.2% (L + O)SG

3 years

Never smoker: no history of smoking Former smoker: quit at least 1 year before operation Recent smoker: quit between 3 months–1 year before operation

Preop: Medical record

30-day complications

EWL

Prevalence preop smoking:

Recent 7.7%

Former 33.4%

Never 58.8%

Serious complications:

RYGB: Risk-adjusted rate 5.4% (recent smoker) vs 2.9% (never), p 0.04

Any complication:

RYGB: Risk-adjusted rate: 11.5% never, 10.5% former, (p < 0.05) 14.3% recent (ns)

Complication rates were not affected by smoking status in LSG

EWL differed significantly between recent smokers (73.4%) and never smokers (69.7%) 24 months after surgery, but not any more after 3 years

3 months lower bound in definition “recent smoking” was dependent on policies regarding minimum length of smoke-free period preoperatively of 39 sites

82% LTFU after 3 years

Data from MBSC, overlapping data Birkmeyer 2010 and Finks 2011 [43, 54]

Lent, 2018 [15]

Retrospective

2918 RYGB

7 years

History of smoking

Preop: medical record

Predictors for below/average/above average postoperative weight loss trajectories (TWL)

Preop smoking prevalence: 51.6%

WL trajectories after 7 years:

Above average WL 38.5% (7.37), average WL 24.06% (7.48) and below average WL 12.67% (8.41)

Smokers were more likely to be in above average WL group compared to average and below average WL group

Preop program 6–12 months, goal 10% TWL

42.5% LTFU after 7 yrs.

Unclear whether it concerned open or laparoscopic surgery. Revisional surgery was excluded.

Cohort overlaps with previous studies in Geisinger MC (Wood 2012, Lent 2013, Benotti 2014, Still 2014, Wood 2016) [37, 38, 40, 41, 51]

Kowalewski, 2018 [14]

Retrospective

47 LAGB, 84 LSG

LAGB 11.2 years (SD 1.2); LSG 8 years (7.1–10.7)

Not available

Preop: medical record

Postop: online survey

Prevalence smoking;

EWL

Preop smoking prevalence

LAGB 51%, LSG 62%

Postop smoking prevalence:

LAGB 43%, LSG 33%

In both surgery groups: no difference in EWL

Qualification for surgery: encourage to cease smoking 6 months prior to the surgery

Revisional surgery was excluded

39.9% LTFU

Signorini, 2018 [53]

Retrospective

184 LSG

80.7 months (SD 7.3)

Medical record yes/no/former

HSI (only postoperative)

Preop: medical record

Postop: Telephone

Prevalence smoking;

EWL

Postop prevalence smoking: 27.5%

20.7% preop smokers stopped postop

14.7% preop ex-smokers relapsed

Smoking pre/post nor heaviness of smoking was related to EWL

45% LTFU after 81 months

Unclear whether revisional surgery was included

Same cohort as Moser 2015 with longer follow-up [17]

Spaniolas, 2018 [52]

Retrospective

35,074 (L + O)RYGB

10 years

Not available

Preop: medical record

Complication/marginal ulcers

Preop smoking prevalence: 12.7%

17.8% with preop history smoking developed marginal ulcer within 8 years

Tobacco use was associated with complications HR 1.56 (1.41–1.73)

Prevalence of preoperative tobacco use (14.6%) in table is different from other numbers on smoking in manuscript.

Exclusion for analysis of in-hospital deaths

Unclear whether revisional surgery was included

95.7% LTFU after 9 years

Similar data and cohort as Altieri 2017, not reported in table [58]

Cig cigarette(s), EWL excess body weight loss, LTFU loss to follow-up, RespR response rate, TWL total weight loss, WL weight loss, Type of surgery/procedure: BGB banded gastric bypass, BPD biliopancreatic diversion with switch, DS duodenal switch, IB intra-gastric balloon, LAGB laparoscopic adjustable gastric band, LGBP laparoscopic gastric bypass procedure, (L/O)RYGB (laparoscopic/open) Roux-en-Y gastric bypass, (L/O)SG (laparoscopic/open) sleeve gastrectomy, VBG vertical banded gastroplasty, Questionnaires: HIS Heavy smoking index. Questionnaire categorizing smoking in “heavy smoker, moderate smoker and light smoker” depending on the amount of cigarettes per day and the time after waking taking first cigarette; OAB-Q OverActive Bladder-Questionnaire (higher score means more complaints), Datasets: MBSC Michigan Bariatric Surgery Collaborative, a regional consortium of 25 hospitals and 62 surgeons performing bariatric surgery in Michigan. ACS NSQIP American College of Surgeons National Surgical Quality Improvement Program data set. Nationally validated, risk-adjusted, outcomes-based clinical registry program to measure and improve the quality of surgical care, that includes more than 617 hospitals in the USA, 62 hospitals in Canada and 8 hospitals in Middle East region

Possible explanations for inaccurate self-report are high quitting expectations from the health care team, embarrassment for failing to quit, fear for gaining weight as a result of smoking cessation (which interferes with the preoperative weight goal), the stigma associated with smoking, and fear for rejection [18, 19, 20, 60]. However, fear for rejection could have been an extra motivation to stop (at least temporarily) and accept the offered support, which would abate the necessity of concealing. And, misclassification is a common problem in settings where there is no surgery involved [18, 19, 20, 61, 62, 63, 64]. Our patients were informed that the self-report would be solely related to cotinine and would never be visible for their attending doctor. Hence, the problem of misclassification could be larger in normal practice, when patients report smoking to their attending doctor, without verification. Consequently, patients who disclose their smoking are not encouraged to attempt cessation, are poorly informed about possible positive effects of quitting, and receive no individual support.

Smoking is known to contribute to short-term and long-term postoperative complications [3, 4, 5, 12, 13, 15, 16, 34, 36, 46, 65]. In our study, current smoking based on cotinine and self-report at the phase of screening was associated with complications after surgery, but smoking at the day of surgery not. Studies on the timing of preoperative smoking cessation to effect short-term postoperative complications in the field of bariatric surgery are scarce. Mean time until surgery was approximately 5 months. Exact timing of the smoking cessation and the success rate in the screening group is unclear but was ineffective to improve complication rates. We suggest further research on this topic should use cotinine to assess preoperative smoking.

Cotinine detected at the day of surgery was not a predictor for complications, possibly due to occasional smoking out of fear for the operation and due to a short period of smoking cessation on the other hand. Clearly, the level of cotinine on the day of surgery does not represent the impairment of organs by rarely occasional smoking or the improvement after short-term smoking cessation. The exact duration of cessation is unknown, because cotinine is generally only detectable during the first 48–96 h after inhalation of a cigarette [19, 22, 23, 24, 25, 66, 67].

Noteworthy is the preoperative group that is not (yet) operated; 41.5% had positive cotinine test at screening. Reasons for forgoing, postponement, or rejections are diverse; only once an unsuccessful cessation of smoking was mentioned. Other studies have shown that a history of smoking is associated with longer wait times [68, 69, 70].

In spite of the preoperative urgent advice to quit smoking for a lifetime, 15% of all the patients after surgery appeared to be a current smoker. This emphasizes the ongoing need for routinely monitoring the smoking status, better counseling, and the necessity for more effective long-term smoking cessation strategies, also after surgery.

We used solid-phase extraction in combination with high-performance liquid chromatography (HPLC) to measure cotinine levels [19, 26, 27, 28, 29, 30, 31]. The intra- and inter-assay variabilities indicate safe and valid use of HPLC in this study. To rule out possible interference in the determination of cotinine, we checked for peak purity for each positive cotinine test. We did not focus on the correlation between cotinine concentration and reported number of cigarettes, which may be relevant when investigating a dose response reaction.

It still may be difficult to correctly identify a smoker using cotinine taking into account the short half-life, the time between smoking and sample collection, variation in metabolism of nicotine (race, ethnicity, gender, medications, diet, age, genetic variation in CYP2A6 enzyme, pregnancy, liver or kidney disease), intermittent smokers, patients heavily exposed to secondhand smoking (SHS), and interference by species of the nightshades family. [19, 22, 25, 67, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80]

Nevertheless, our cut-off value was relatively high; patients (heavily) exposed to secondhand smoking often do not reach serum levels above 3 ng/ml, and in non-smoking subjects, cotinine concentrations are below 1 ng/ml [22, 67, 78, 81, 82, 83, 84]. In active smokers, much higher concentrations have been found (often above 100 ng/ml) [73, 81, 83, 85]. In agreement with this, no positive cotinines were detected in the “correctly non-smoking and incorrectly smoking” group. Thus, the high levels of cotinine (in total and SHS subgroup) in the patients that concealed smoking and the fact that the SHS part of this group had not lower but higher values supports active smoking as explanation of increased cotinine instead of very heavy secondhand smoking. Due to variation in metabolism of cotinine and divergent steady-state levels before cessation, we cannot exclude that patients truly had stopped more than 48 h before, although this is unlikely the case in all patients [19, 22, 25, 67, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80].

Only five (< 1%) patients reported to have solely used electronic cigarettes during the last 48 h. We collected no further information about brand, generation, composition of vapor, level of nicotine, or experience of vaping. All these factors can influence actual nicotine delivery. Although, by vaporizing, patients often do not reach the blood levels that can be achieved by cigarette smoking [86, 87, 88]. Therefore, it is unlikely that cotinine levels of above 180 ng/ml can be explained by the use of e-cigarettes. No use of other types of nicotine replacement products was reported.

The effect of selection bias seems limited. In this large sample, we included all patients consecutively and only 27 (3.6%) patients were excluded. This high inclusion rate was established by the inclusion setting at the laboratory where the patients had to draw blood anyway. Potentially, all 27 exclusions could have been smoking; nonetheless, the percentage is low (3.6%) and suggests that reliable conclusions can be made.

In conclusion, smoking is underreported especially in patients at the day of bariatric surgery, but also in the trajectory months before surgery. Self-reported smoking is most reliable when patients are already operated. Cotinine-based and self-reported smoking was associated with the occurrence of postoperative complications at the phase of screening, but smoking at the day of screening was not. In most previous studies on smoking and bariatric surgery, outcome smoking was assessed by different types of self-report and often without precise definition and timing of smoking status. Future studies on risks of smoking should include cotinine measurement or other methods to address the issue of underreporting. In addition, reporting and evaluation of current policies on smoking cessation and intervention studies on the effects of smoking cessation before and after bariatric surgery are warranted.

Notes

Acknowledgments

We thank all colleagues of the former MC Slotervaart who have contributed to the care for the patients in this study, especially the colleagues at the laboratory and the medium care department. We thank Fidessa Straat for helping with the recruitment of patients. The bariatric surgery team of the MC Slotervaart will continue at the Spaarne Gasthuis after the bankruptcy of the hospital in 2018.

Funding Information

This study was funded by the SKWOSZ (Foundation for Clinical Scientific Research at Medical Center Slotervaart).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Statement

All procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments. This study was approved by the Institutional review board.

Informed Consent Statement

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Arterburn DE, Courcoulas AP. Bariatric surgery for obesity and metabolic conditions in adults. BMJ. 2014;349(aug 27):g349–g3961.Google Scholar
  2. 2.
    Mingrone G, Panunzi S, De Gaetano A, et al. Bariatric-metabolic surgery versus conventional medical treatment in obese patients with type 2 diabetes: 5 year follow-up of an open-label, single-centre, randomised controlled trial. Lancet. 2015;386(9997):964–73.Google Scholar
  3. 3.
    Haskins IN, Amdur R, Vaziri K. The effect of smoking on bariatric surgical outcomes. Surg Endosc. 2014;28(11):3074–80.Google Scholar
  4. 4.
    Livingston EH, Arterburn D, Schifftner TL, et al. National surgical quality improvement program analysis of bariatric operations: modifiable risk factors contribute to bariatric surgical adverse outcomes. J Am Coll Surg. 2006;203(5):625–33.Google Scholar
  5. 5.
    Theadom A, Cropley M. Effects of preoperative smoking cessation on the incidence and risk of intraoperative and postoperative complications in adult smokers: a systematic review. Tob Control. 2006;15(5):352–8.Google Scholar
  6. 6.
    Tønnesen H, Nielsen PR, Lauritzen JB, et al. Smoking and alcohol intervention before surgery: evidence for best practice. Br J Anaesth. 2009;102(3):297–306.Google Scholar
  7. 7.
    Akrawi WBJL. A pathophysiological basis for informed preoperative smoking cessation counseling. J Cardiothorac Vasc Anesth. 1997;11(5):629–40.Google Scholar
  8. 8.
    Mills E, Eyawo O, Lockhart I, et al. Smoking cessation reduces postoperative complications: a systematic review and meta-analysis. Am J Med. 2011;124(2):144–54.Google Scholar
  9. 9.
    Moller A, Villebro N, Pedersen T. Interventions for preoperative smoking cessation (review). Cochrane Database Syst Rev. 2014 Mar 27;(3):CD002294.  https://doi.org/10.1002/14651858.CD002294.pub4
  10. 10.
    Gourgiotis S, Aloizos S, Aravosita P, et al. The effects of tobacco smoking on the incidence and risk of intraoperative and postoperative complications in adults. Surgeon. 2011;9(4):225–32.Google Scholar
  11. 11.
    Conason A, Teixeira J, Hsu CH, et al. Substance use following bariatric weight loss surgery. JAMA Surg. 2013;148(2):145–50.Google Scholar
  12. 12.
    Haskins IN, Nowacki AS, Khorgami Z, et al. Should recent smoking be a contraindication for sleeve gastrectomy? Surg Obes Relat Dis. 2017;13(7):1130–5.Google Scholar
  13. 13.
    Inadomi M, Iyengar R, Fischer I, et al. Effect of patient-reported smoking status on short-term bariatric surgery outcomes. Surg Endosc. 2018;32(2):720–6.Google Scholar
  14. 14.
    Kowalewski PK, Olszewski R, Waledziak MS, et al. Cigarette smoking and its impact on weight loss after bariatric surgery: a single center, retrospective study. Surg Obes Relat Dis. 2018;14(8):1163–6.Google Scholar
  15. 15.
    Lent MR, Hu Y, Benotti PN, et al. Demographic, clinical, and behavioral determinants of 7-year weight change trajectories in roux-en-Y gastric bypass patients. Surg Obes Relat Dis. 2018;14(11):1680–5.Google Scholar
  16. 16.
    Mitchell JE, Christian NJ, Flum DR, et al. Postoperative behavioral variables and weight change 3 years after bariatric surgery. JAMA Surg. 2016;151(8):752–7.Google Scholar
  17. 17.
    Moser F, Signorini FJ, Maldonado PS, et al. Relationship between tobacco use and weight loss after bariatric surgery. Obes Surg. 2016;26(8):1777–81.Google Scholar
  18. 18.
    Gorber SC, Schofield-Hurwitz S, Hardt J, et al. The accuracy of self-reported smoking: a systematic review of the relationship between self-reported and cotinine-assessed smoking status. Nicotine Tob Res. 2009;11(1):12–24.Google Scholar
  19. 19.
    Florescu A. Methods for quantification of exposure to cigarette smoking and environmental tobacco smoke: focus on developmental toxicology. Drug Monitor. 2009;31(1):14–30.Google Scholar
  20. 20.
    Patrick DL, Cheadle A, Thompson DC, et al. The validity of self-reported smoking: a review and meta-analysis. Am J Public Health. 1994;84(7):1086–93.Google Scholar
  21. 21.
    Ambwani S, Boeka AG, Brown JD, et al. Socially desirable responding by bariatric surgery candidates during psychological assessment. Surg Obes Relat Dis. 2013;9(2):300–5.Google Scholar
  22. 22.
    Benowitz NL. Cotinine as a biomarker of environmental tobacco smoke exposure. Epidemiol Rev. 1996;18(2):188–204.Google Scholar
  23. 23.
    Haley NJ, Sepkovic DW, Hoffmann D. Elimination of cotinine from body fluids: disposition in smokers and nonsmokers. Am J Public Health. 1989;79(8):1046–8.Google Scholar
  24. 24.
    Zevin S, Jacob 3rd P, Benowitz N. Cotinine effects on nicotine metabolism. Clin Pharmacol Ther. 1997;61(6):649–54.Google Scholar
  25. 25.
    Verification SfRoNaTSoB. Biochemical verification of tobacco use and cessation. Nicotine Tob Res. 2002;4(2):149–59.Google Scholar
  26. 26.
    Zuccaro P, Altieri I, Rosa M, et al. Determination of nicotine and 4 metabolites in the serum of smokers by high-performance liquid-chromatography with ultraviolet detection. J Chromatogr-Biomed. 1993;621(2):257–61.Google Scholar
  27. 27.
    Pacifici R, Pichini S, Altieri I, et al. Determination of nicotine and 2 major metabolites in serum by solid-phase extraction and high-performance liquid-chromatography, and high-performance liquid-chromatography particle beam mass-spectrometry. J Chromatogr-Biomed. 1993;612(2):209–13.Google Scholar
  28. 28.
    Zuccaro P, Altieri I, Rosa M, et al. Solid-phase extraction of nicotine and its metabolites for high-performance liquid-chromatographic determination in urine. J Chromatogr B. 1995;668(1):187–8.Google Scholar
  29. 29.
    Teeuwen HWA, Aalders RJW, Vanrossum JM. Simultaneous estimation of nicotine and cotinine levels in biological-fluids using high-resolution capillary-column gas-chromatography combined with solid-phase extraction work-up. Mol Biol Rep. 1989;13(3):165–75.Google Scholar
  30. 30.
    Pichini S, Altieri I, Pacifici R, et al. Simultaneous determination of cotinine and trans-3'-hydroxycotinine in human serum by high-performance liquid-chromatography. J Chromatogr-Biomed. 1992;577(2):358–61.Google Scholar
  31. 31.
    Pichini S, Altieri I, Passa AR, et al. Cotinine content in control sera. J Anal Toxicol. 1995;19(4):267–8.Google Scholar
  32. 32.
    Dindo D, Demartines N, Clavien PA. Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg. 2004;240(2):205–13.Google Scholar
  33. 33.
    Coblijn UK, Karres J, de Raaff CAL, et al. Predicting postoperative complications after bariatric surgery: the bariatric surgery index for complications. BASIC Surg Endosc. 2017;31(11):4438–45.Google Scholar
  34. 34.
    Coblijn UK, Lagarde SM, de Castro SM, et al. Symptomatic marginal ulcer disease after roux-en-Y gastric bypass: incidence, risk factors and management. Obes Surg. 2015;25(5):805–11.Google Scholar
  35. 35.
    Pierik AS, Coblijn UK, de Raaff CAL, et al. Unexplained abdominal pain in morbidly obese patients after bariatric surgery. Surg Obes Relat Dis. 2017;13(10):1743–51.Google Scholar
  36. 36.
    Adams CE, Gabriele JM, Baillie LE, et al. Tobacco use and substance use disorders as predictors of postoperative weight loss 2 years after bariatric surgery. J Behav Health Serv Res. 2012;39(4):462–71.Google Scholar
  37. 37.
    Wood GC, Benotti PN, Lee CJ, et al. Evaluation of the association between preoperative clinical factors and long-term weight loss after roux-en-Y gastric bypass. JAMA Surg. 2016;151(11):1056–62.Google Scholar
  38. 38.
    Wood GC, Chu X, Manney C, et al. An electronic health record-enabled obesity database. BMC Med Inform Decis Mak. 2012;28:12.Google Scholar
  39. 39.
    Cayci HM, Oner S, Erdogdu UE, et al. The factors affecting lower urinary tract functions in patients undergoing laparoscopic sleeve gastrectomy. Obes Surg. 2018;28(4):1025–30.Google Scholar
  40. 40.
    Lent MR, Hayes SM, Wood GC, et al. Smoking and alcohol use in gastric bypass patients. Eat Behav. 2013;14(4):460–3.Google Scholar
  41. 41.
    Still CD, Wood GC, Chu X, et al. Clinical factors associated with weight loss outcomes after Roux-en-Y gastric bypass surgery. Obesity (Silver Spring, Md). 2014;22(3):888–94.Google Scholar
  42. 42.
    Maniscalco M, Carratu P, Faraone S, et al. Smoking habit in severe obese after bariatric procedures. Tob Induc Dis. 2015;13(1):20.Google Scholar
  43. 43.
    Finks JF, Kole KL, Yenumula PR, et al. Predicting risk for serious complications with bariatric surgery: results from the Michigan bariatric surgery collaborative. Ann Surg. 2011;254(4):633–40.Google Scholar
  44. 44.
    Turner PL, Saager L, Dalton J, et al. A nomogram for predicting surgical complications in bariatric surgery patients. Obes Surg. 2011;21(5):655–62.Google Scholar
  45. 45.
    Gupta PK, Franck C, Miller WJ, et al. Development and validation of a bariatric surgery morbidity risk calculator using the prospective, multicenter NSQIP dataset. J Am Coll Surg. 2011;212(3):301–9.Google Scholar
  46. 46.
    King WC, Chen JY, Mitchell JE, et al. Prevalence of alcohol use disorders before and after bariatric surgery. JAMA. 2012;307(23):2516–25.Google Scholar
  47. 47.
    Belle SH, Berk PD, Chapman WH, et al. Baseline characteristics of participants in the longitudinal assessment of bariatric surgery-2 (LABS-2) study. Surg Obes Relat Dis. 2013;9(6):926–35.Google Scholar
  48. 48.
    Gordon PC, Sallet JA, Sallet PC. The impact of temperament and character inventory personality traits on long-term outcome of roux-en-Y gastric bypass. Obes Surg. 2014;24(10):1647–55.Google Scholar
  49. 49.
    Ramanan B, Gupta PK, Gupta H, et al. Development and validation of a bariatric surgery mortality risk calculator. J Am Coll Surg. 2012;214(6):892–900.Google Scholar
  50. 50.
    Arterburn D, Livingston EH, Olsen MK, et al. Predictors of initial weight loss after gastric bypass surgery in twelve veterans affairs medical centers. Obes Res Clin Pract. 2013;7(5):e367–76.Google Scholar
  51. 51.
    Benotti P, Wood GC, Winegar DA, et al. Risk factors associated with mortality after roux-en-Y gastric bypass surgery. Ann Surg. 2014;259(1):123–30.Google Scholar
  52. 52.
    Spaniolas K, Yang J, Crowley S, et al. Association of long-term anastomotic ulceration after roux-en-Y gastric bypass with tobacco smoking. JAMA Surg. 2018;153(9):862–4.Google Scholar
  53. 53.
    Signorini FJ, Polero V, Viscido G, et al. Long-term relationship between tobacco use and weight loss after sleeve gastrectomy. Obes Surg. 2018;28(9):2644–9.Google Scholar
  54. 54.
    Birkmeyer NJ, Dimick JB, Share D, et al. Hospital complication rates with bariatric surgery in Michigan. JAMA. 2010;304(4):435–42.Google Scholar
  55. 55.
    Odom J, Zalesin KC, Washington TL, et al. Behavioral predictors of weight regain after bariatric surgery. Obes Surg. 2010;20(3):349–56.Google Scholar
  56. 56.
    Mitchell JE, Steffen K, Engel S, et al. Addictive disorders after roux-en-Y gastric bypass. Surg Obes Relat Dis. 2015 Jul-Aug;11(4):897–905.  https://doi.org/10.1016/j.soard.2014.10.026. Epub 2014 Nov 13.
  57. 57.
    King WC, Chen J-Y, Belle SH, et al. Surgery for use of prescribed opioids before and after bariatric surgery: prospective evidence from a U.S. multicenter cohort study. Surg Obes Relat Dis. 2017;13:1337–46.Google Scholar
  58. 58.
    Altieri MS, Aurora Pryor B, Jie Yang B, et al. The natural history of perforated marginal ulcers after gastric bypass surgery. Surg Endosc. 2018;32(3):1215-1222.  https://doi.org/10.1007/s00464-017-5794-4. Epub 2017 Aug 25.
  59. 59.
    Kedrin D, Gandhi SC, Wolf M, et al. Bariatric surgery prior to index screening colonoscopy is associated with a decreased rate of colorectal adenomas in obese individuals. Clin Transl Gastroenterol. 2017;8(2):e73.Google Scholar
  60. 60.
    Aubin HJ, Farley A, Lycett D, et al. Weight gain in smokers after quitting cigarettes: meta-analysis. BMJ (Online). 2012;345(7868):1–21.Google Scholar
  61. 61.
    Boyd NR, Windsor RA, Perkins LL, et al. Quality of measurement of smoking status by self-report and saliva cotinine among pregnant women. Matern Child Health J. 1998;2(2):77–83.Google Scholar
  62. 62.
    Coultas DB, Howard CA, Peake GT, et al. Discrepancies between self-reported and validated cigarette smoking in a community survey of New Mexico Hispanics. Am Rev Respir Dis. 1988;137(4):810–4.Google Scholar
  63. 63.
    Hald J, Overgaard J, Grau C. Evaluation of objective measures of smoking status--a prospective clinical study in a group of head and neck cancer patients treated with radiotherapy. Acta Oncol. 2003;42(2):154–9.Google Scholar
  64. 64.
    Lewis SJ, Cherry NM, Mc LNR, et al. Cotinine levels and self-reported smoking status in patients attending a bronchoscopy clinic. Biomarkers. 2003;8(3–4):218–28.Google Scholar
  65. 65.
    Wilson JA, Romagnuolo J, Karl Byrne T, et al. Predictors of endoscopic findings after roux-en-Y gastric bypass. Am J Gastroenterol. 2006;101(10):2194–9.Google Scholar
  66. 66.
    Benowitz NL, Jacob 3rd. P. Daily intake of nicotine during cigarette smoking. Clin Pharmacol Ther. 1984;35(4):499–504.Google Scholar
  67. 67.
    Benowitz NL, Dains KM, Dempsey D, et al. Urine nicotine metabolite concentrations in relation to plasma cotinine during low-level nicotine exposure. Nicotine Tob Res. 2009;11(8):954–60.Google Scholar
  68. 68.
    Alvarez R, Bonham AJ, Buda CM, Carlin AM, Ghaferi AA, Varban OA. Factors associated with long wait times for bariatric surgery. Ann Surg. 2018. https://doi.org/10.1097/SLA.0000000000002826. [Epub ahead of print].
  69. 69.
    Heinberg LJ, Ashton K, Windover A. Moving beyond dichotomous psychological evaluation: the Cleveland clinic behavioral rating system for weight loss surgery. Surg Obes Relat Dis. 2010;6(2):185–90.Google Scholar
  70. 70.
    Heinberg L, Marek R, Haskins IN, et al. 30-day readmission following weight loss surgery: can psychological factors predict nonspecific indications for readmission? Surg Obes Relat Dis. 2017;13(8):1376–81.Google Scholar
  71. 71.
    Benowitz NL, Jacob 3rd. P. Nicotine and cotinine elimination pharmacokinetics in smokers and nonsmokers. Clin Pharmacol Ther. 1993;53(3):316–23.Google Scholar
  72. 72.
    Benowitz NL, Pomerleau OF, Pomerleau CS, et al. Nicotine metabolite ratio as a predictor of cigarette consumption. Nicotine Tob Res. 2003;5(5):621–4.Google Scholar
  73. 73.
    Benowitz NL, Bernert JT, Caraballo RS, et al. Optimal serum cotinine levels for distinguishing cigarette smokers and nonsmokers within different racial/ethnic groups in the United States between 1999 and 2004. Am J Epidemiol. 2009;169(2):236–48.Google Scholar
  74. 74.
    Benowitz NL, Schultz KE, Haller CA, et al. Prevalence of smoking assessed biochemically in an urban public hospital: a rationale for routine cotinine screening. Am J Epidemiol. 2009;170(7):885–91.Google Scholar
  75. 75.
    Benowitz NL, Hukkanen J, Jacob 3rd. P. Nicotine chemistry, metabolism, kinetics and biomarkers. Handb Exp Pharmacol. 2009;192:29–60.Google Scholar
  76. 76.
    Benowitz NL. Nicotine addiction. N Engl J Med. 2010;362(24):2295–303.Google Scholar
  77. 77.
    Bernert JT, Jacob 3rd P, Holiday DB, et al. Interlaboratory comparability of serum cotinine measurements at smoker and nonsmoker concentration levels: a round-robin study. Nicotine Tob Res. 2009;11(12):1458–66.Google Scholar
  78. 78.
    Kim S. Overview of cotinine cutoff values for smoking status classification. Int J Environ Res Public Health. 2016;13(12)Google Scholar
  79. 79.
    Kyerematen GA, Damiano MD, Dvorchik BH, et al. Smoking-induced changes in nicotine disposition: application of a new HPLC assay for nicotine and its metabolites. Clin Pharmacol Ther. 1982;32(6):769–80.Google Scholar
  80. 80.
    Siegmund B, Leyden DE, Zikulnig E, et al. The contribution of dietary nicotine and dietary cotinine to salivary cotinine levels as a nicotine biomarker. Food Chem. 2001;74(3):259–65.Google Scholar
  81. 81.
    Jarvis M, Tunstall-Pedoe H, Feyerabend C, et al. Biochemical markers of smoke absorption and self reported exposure to passive smoking. J Epidemiol Community Health. 1984;38(4):335–9.Google Scholar
  82. 82.
    Ballbe M, Martinez-Sanchez JM, Sureda X, et al. Cigarettes vs. e-cigarettes: passive exposure at home measured by means of airborne marker and biomarkers. Environ Res. 2014;135:76–80.Google Scholar
  83. 83.
    Flouris AD, Chorti MS, Poulianiti KP, et al. Acute impact of active and passive electronic cigarette smoking on serum cotinine and lung function. Inhal Toxicol. 2013;25(2):91–101.Google Scholar
  84. 84.
    Metsios GS, Flouris AD, Jamurtas AZ, et al. A brief exposure to moderate passive smoke increases metabolism and thyroid hormone secretion. J Clin Endocrinol Metab. 2007;92(1):208–11.Google Scholar
  85. 85.
    Schnoll RA, Patterson F, Wileyto EP, et al. Nicotine metabolic rate predicts successful smoking cessation with transdermal nicotine: a validation study. Pharmacol Biochem Behav. 2009;92(1):6–11.Google Scholar
  86. 86.
    Hartmann-Boyce J, McRobbie H, Bullen C, et al. Electronic cigarettes for smoking cessation. Cochrane Database Syst Rev. 2016;9:CD010216.Google Scholar
  87. 87.
    Vansickel AR, Cobb CO, Weaver MF, et al. A clinical laboratory model for evaluating the acute effects of electronic “cigarettes”: nicotine delivery profile and cardiovascular and subjective effects. Cancer Epidemiol Biomark Prev. 2010;19(8):1945–53.Google Scholar
  88. 88.
    Eissenberg T. Electronic nicotine delivery devices: ineffective nicotine delivery and craving suppression after acute administration. Tob Control. 2010;19(1):87–8.Google Scholar

Copyright information

© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Paula J. D. Wolvers
    • 1
    • 2
    Email author
  • Sjoerd C. Bruin
    • 3
  • Willem M. Mairuhu
    • 2
  • Monique de Leeuw-Terwijn
    • 4
  • Barbara A. Hutten
    • 5
  • Dees P. M. Brandjes
    • 2
  • Victor E. A. Gerdes
    • 1
    • 2
  1. 1.Department of Internal Medicine, Spaarne GasthuisHoofddorpthe Netherlands
  2. 2.Department of Vascular Medicine, Amsterdam UMCUniversity of AmsterdamAmsterdamthe Netherlands
  3. 3.Department of Surgery, Spaarne GasthuisHoofddorpthe Netherlands
  4. 4.Department of Clinical ChemistryAmsterdamThe Netherlands
  5. 5.Department of Clinical Epidemiology and Biostatistics and Bioinformatics, Amsterdam UMCUniversity of AmsterdamAmsterdamthe Netherlands

Personalised recommendations