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Copyright © 2015 International Anesthesia Research SocietyDOI: 10.1213/ANE.0000000000000555

Patients 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.

METHODSDetailed 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 TrialSusan 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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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.

RESULTSBetween October 2010 and April 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

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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). A final 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).

DISCUSSIONThis 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-UpAll 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) 0.59Type 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%) 0.55Current 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%) 0.49Smoking 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.aP 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.bHeart disease defined as coronary artery disease, congestive heart failure, or arrhythmia.

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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 YearVariable 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 smoke5/84 (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.aP values calculated using the Fisher exact test.b95% CI for NNT calculated using method described by Bender.17

cSmoking 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.

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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

DISCLOSURESName: Susan M. Lee, MD, FRCPC.Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.Attestation: Susan M. Lee has seen the original study data, 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 yearCharacteristic Univariable OR (95% CI) P value Adjusted OR (95% CI) P valueRandomization status Intervention group 3.7 (1.2–13.8) 0.019 3.5 (1.02–13.9) 0.046Physical 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) 0.14Smoking 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.aCut points for age, pack-years, and Fagerström score are at median values. See text for sensitivity of models to varying cut points.bHeart 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 RegressionCharacteristic Adjusted OR (95% CI) P value Adjusted RR (95% CI) P valueRandomization status Intervention group 3.8 (1.2–11.9) 0.020 2.7 (1.1–6.7) 0.028Smoking 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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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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