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doi: 10.1097/ALN.0b013e318210f560
Perioperative Medicine

Smoking and Perioperative Outcomes

Turan, Alparslan M.D.*; Mascha, Edward J. Ph.D.†; Roberman, Dmitry M.S.‡; Turner, Patricia L. M.D.§; You, Jing M.S.∥; Kurz, Andrea M.D.#; Sessler, Daniel I. M.D.**; Saager, Leif M.D.††

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Background: Patients are often concerned about the effects of smoking on perioperative risk. However, effective advice may be limited by the paucity of information about smoking and perioperative risk. Thus, our goal was to determine the effect of smoking on 30-day postoperative outcomes in noncardiac surgical patients.
Methods: We evaluated 635,265 patients from the American College of Surgeons National Surgical Quality Improvement Program database; 520,242 patients met our inclusion criteria. Of these patients, 103,795 were current smokers; 82,304 of the current smokers were propensity matched with 82,304 never-smoker controls. Matched current smokers and never-smokers were compared on major and minor composite morbidity outcomes and respective individual outcomes.
Results: Current smokers were 1.38 (95% CI, 1.11–1.72) times more likely to die than never smokers. Current smokers also had significantly greater odds of pneumonia (odds ratio [OR], 2.09; 95% CI, 1.80–2.43), unplanned intubation (OR, 1.87; 95% CI, 1.58–2.21), and mechanical ventilation (OR, 1.53; 95% CI, 1.31–1.79). Current smokers were significantly more likely to experience a cardiac arrest (OR, 1.57; 95% CI, 1.10–2.25), myocardial infarction (OR, 1.80; 95% CI, 1.11–2.92), and stroke (OR, 1.73; 95% CI, 1.18–2.53). Current smokers also had significantly higher odds of having superficial (OR, 1.30; 95% CI, 1.20–1.42) and deep (OR, 1.42; 95% CI, 1.21–1.68) incisional infections, sepsis (OR, 1.30; 95% CI, 1.15–1.46), organ space infections (OR, 1.38; 95% CI, 1.20–1.60), and septic shock (OR, 1.55; 95% CI, 1.29–1.87).
Conclusion: Our analysis indicates that smoking is associated with a higher likelihood of 30-day mortality and serious postoperative complications. Quantification of increased likelihood of 30-day mortality and a broad range of serious smoking-related complications may enhance the clinician's ability to motivate smoking cessation in surgical patients.
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What We Already Know about This Topic

❖ Although smoking worsens perioperative outcomes, the size of this effect is not well described.
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What This Article Tells Us That Is New

❖ In noncardiac surgical patients, smoking was associated with a 40% increase odds of 30-day mortality and a 30–100% increase odds of major morbidity, including surgical site infection, pneumonia, unplanned intubation, and septic shock.
IN the 20th century, smoking killed 100 million people worldwide; currently, 5.4 million deaths each year are related to smoking.‡‡ Smoking is associated with chronic diseases, economic losses to society, and a substantial burden on the healthcare system. Despite decreased smoking in recent decades, tobacco use remains the leading preventable cause of disease and death in the United States, causing approximately 443,000 deaths each year and costing approximately $157 billion in annual health-related economic losses.1
Approximately 20% of adult Americans smoke cigarettes, and at least that fraction of patients undergoing surgery are current smokers.2 The fact that long-term smoking is harmful is beyond question. Smoking is also thought to augment the risk of poor postoperative outcomes, especially impaired wound and tissue healing3–5 and cardiopulmonary complications.6,7 The extent to which smoking increases surgical risk remains poorly characterized. The results of previous studies6,8–10 have been inconclusive and limited by inadequate statistical power, restricting analysis to a single institution, with incomplete patient follow-up.
Thus, the current study tested the primary hypothesis that smoking worsens a composite of major 30-day postoperative morbidities in noncardiac surgical patients treated in 200 centers throughout the United States between January 1, 2005 and December 31, 2008. We also tested the secondary hypotheses that there is a dose-response relationship between amount of tobacco consumption and both surgical complications and duration of hospitalization.
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Materials and Methods

This retrospective cohort study was based on data used from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) that were acquired between 2005 and 2008. Institutional review board approval was obtained from the Cleveland Clinic, Cleveland, Ohio. Data were prospectively collected in a standardized manner, according to strict definitions of preoperative characteristics, intraoperative information, and postoperative outcomes. A dedicated surgical clinical nurse reviewer collected data from computerized and paper patient medical records, physicians' office records, and telephone interviews with patients. The accuracy and reproducibility of these data are well established.11,12
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Study Population
We excluded patients with preoperative pneumonia, requiring ventilator-assisted respiration at any time during the 48 h preceding surgery; preoperative systemic sepsis (defined as systemic inflammatory response syndrome, sepsis, or septic shock); coma lasting longer than 24 h; central nervous system tumor; disseminated cancer; open wound infection before surgery; or bleeding disorders.
Table 1
Table 1
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Current smokers were defined in the ACS-NSQIP database as “patients who reported smoking cigarettes in the year before admission for surgery.” Patients who smoked cigars or pipes or used chewing tobacco were not included. In addition, patients reported the amount of smoking in terms of pack-years. Never smokers were defined as those who reported not smoking in the previous year and also reported 0 lifetime pack-years (i.e., excluding those with a missing value for lifetime pack-years). Outcomes were defined according to the ACS-NSQIP report of any major morbidity and report of any minor morbidity (table 1).
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Statistical Analysis
Primary Outcome.
We first assessed the crude (unadjusted) association between smoking status and a collapsed composite (any versus none) of major morbidities, a collapsed composite of minor morbidities, and individual major and minor morbidities, using logistic regression.
For our primary analyses, we assessed the relationship between smoking and postoperative morbidity after propensity matching current smokers to never smokers on the available baseline confounding variables. We distinguished confounders (i.e., variables potentially affecting both smoking status and outcome) from mediator variables (i.e., variables such as history of chronic obstructive pulmonary disease, which might be caused by smoking and mediate the effect of smoking on outcome). Therefore, we matched on available baseline variables but not on variables deemed to potentially lie on the causal pathway between smoking and outcome. The following 14 conditions were deemed a priori to potentially mediate part of the effect of smoking on outcome: history of transient ischemic attacks, history of myocardial infarction, history of angina, history of revascularization/amputation, previous cardiac surgery, previous percutaneous coronary intervention, history of severe obstructive pulmonary disease, cerebrovascular accident/stroke with neurologic deficit, cerebrovascular accident/stroke with no neurologic deficit, hypertension requiring medication, hemiplegia, dyspnea, chemotherapy, and radiotherapy.
Reported current smokers (within 1 yr before admission for surgery) were matched to reported never smokers using propensity matching.13 We first estimated the propensity score (probability of being a smoker) for each patient using logistic regression based on the values of all covariables deemed to be potential confounders (except race and current procedural terminology code, which were exactly matched in the next step). We grouped each current procedural terminology code into 1 of 244 mutually exclusive clinically appropriate categories using the Agency for Healthcare Research and Quality's clinical classification software for services and procedures. Next, a 1-to-1 greedy matching algorithm (SAS macro: gmatch)§§ was used for the matching. Successful current to never smoker matches were then restricted to patients with the same race and Agency for Healthcare Research and Quality clinical classification software for services and procedures category, with estimated propensity scores within 0.001 U of one another. A single imputation based on all baseline variables were used for body mass index, which was missing for 2.2% of all patients.
The balance between current and never smokers on matched variables before and after matching was assessed using the standardized difference (i.e., the difference in means or proportions/pooled SD). The standardized difference enables direct comparison of the balance between groups independent of the sample size, in contrast to a P value resulting from an appropriate statistical test. Cohen14 proposed guidelines of 0.2, 0.5, and 0.8 to represent small, medium, and large standardized differences, respectively, in absolute values. To account for even minimal potential confounding, we prespecified a conservative criterion of greater than 0.1 absolute standardized difference as an indication of imbalance. Such variables were entered into the multivariable model comparing matched current with never smokers on outcomes to adjust for any residual imbalance.
Matched current and never smokers were compared on the major and minor collapsed composite morbidity outcomes and individual outcome components using logistic regression. Models were fit both with and without adjustment for the a priori-specified mediator covariables. Analysis without adjustment for the mediator variables was intended to estimate the overall effect of smoking to the extent possible in a nonrandomized study. Analysis adjusting for the mediator variables was intended to isolate the individual contributions of the specified mediator variables on outcome and any residual effect of smoking (see table, Supplemental Digital Content 1, http://links.lww.com/ALN/A674). Supplemental Digital Content 2 (figure, http://links.lww.com/ALN/A675) distinguishes a confounding variable (a cause of both the exposure and outcome) from a mediator variable (a variable in the causal pathway between an exposure and outcome). The fit of each model was assessed by the Hosmer and Lemeshow goodness-of-fit test.15
Finally, although all components of our major morbidity composite are serious events (table 1), they would not likely be considered by researchers or patients to have exactly the same severity in the typical manifestation. Therefore, we conducted a sensitivity analysis to assess the relationship between smoking and our major morbidity composite, in which we weighed each component by a clinical severity weight. Weights were determined as the average score for that component (range, 1–100, with 100 being the most severe) among three otherwise noninvolved anesthesiologists. A multivariate (i.e., multiple outcomes per subject) generalized estimating equations model was used to estimate a common effect odds ratio (OR) across the components while applying the severity weights.16
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Secondary Outcomes.
For current smokers, we assessed the relationship between amount of tobacco consumed, measured in pack-years, and the collapsed composite any major morbidity outcome using multivariable logistic regression, adjusting for all potential confounders used for propensity matching. The linearity of the relationship was visually assessed by plotting the estimated logit (i.e., log[probability]/[1−probability]) of the outcome as a function of tobacco consumption, using a generalized additive model (univariable logistic regression incorporating a smooth [thin-plate regression spline] term for amount of tobacco consumed, with the smoothing parameter obtained via cross-validation).
The relationship between amount of tobacco consumed and outcome was also assessed by comparing tobacco consumption amount categories of current with never smokers, again adjusting for all confounders used for propensity matching.
SAS software version 9.2 for UNIX (SAS Institute Inc., Cary, NC) and R software version 2.8.1 for Windows (The R Foundation for Statistical Computing, Vienna, Austria) were used for all statistical analyses. We adjusted for multiple comparisons using Bonferroni correction.
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Fig. 1
Fig. 1
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There were 635,265 surgical cases available within the ACS-NSQIP database. We excluded patients who did not meet our inclusion and exclusion criteria (remaining patients, 520,242) or were current nonsmokers with missing or nonzero value of pack-years. Among the remaining 391,006 patients, 103,795 (26.5%) were current smokers and 287,211 (73.5%) were never smokers. Figure 1 shows details of the types and numbers of exclusions. Definitions of major morbidities, as defined in NSQIP, are also shown in Supplemental Digital Content 3 (table, http://links.lww.com/ALN/A676).
Table 2
Table 2
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Table 3
Table 3
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In an unadjusted analysis, smoking was associated with increased odds of any major morbidity: OR, 1.72 (95% CI, 1.67–1.78; P < 0.0001; table 2 and Supplemental Digital Content 4, table, http://links.lww.com/ALN/A677). Smoking was similarly associated with any minor morbidity: OR, 1.35 (95% CI, 1.31–1.40; P < 0.0001; table 2 and Supplemental Digital Content 4, table, http://links.lww.com/ALN/A677). However, an imbalance on many important baseline variables confirmed that adjustment for potential confounding covariables was necessary (table 3, left). For example, current smokers were more likely to be male, to be alcohol users, and to have more severe American Society of Anesthesiologist physical status (standardized difference greater than 0.20).
Among 520,242 patients, 505,632 (97.2%) had complete data for the primary covariable-adjusted analysis comparing reported current and never smokers. Among 14,610 patients (2.8%) who had at least one missing covariable value, 11,227 (77%) were only missing body mass index, which was imputed using a single imputation from all available data before performing the propensity score matching. Thus, only 0.65% of patients (3,383 of 520,242) were excluded from consideration in the propensity score matching because of missing covariable values.
We successfully propensity matched 82,304 current smokers (79% of the total) with 82,304 controls for a total of 164,608 patients. Our propensity-matched subset retained 93 (78%) of 120 categories before matching the Agency for Healthcare Research and Quality's clinical classification software for services and procedures categories (Supplemental Digital Content 5, table, http://links.lww.com/ALN/A678). The 27 (22%) unmatched Agency for Healthcare Research and Quality's clinical classification software for services and procedures categories represented only 0.1% of current smokers (n = 118). As seen in the standardized differences in table 3, right, covariables were much better balanced as a result of propensity matching. However, age was slightly unbalanced between current and never smokers, with a standardized difference of −0.11; thus, we adjusted for age when comparing current with never smokers on outcomes.
In the propensity-matched subset, smoking was independently associated with increased odds of any major morbidity after adjusting for both age only and age and the mediator covariables (P < 0.0001 for both, table 2); the corresponding estimated ORs (95% CIs) (smoker vs. never smoker) were 1.40 (1.33–1.47) and 1.27 (1.21–1.34), respectively. In the model without the mediator covariable adjustment, the smoking OR assesses the overall association between smoking and outcome, including the effects through the expected mediator variables and any unmeasured variables (table 2); in the model adjusting for age and mediator covariables estimates the “residual” effect of smoking after removing the effects of the mediator variables on the outcome. The difference between the two ORs (1.40 vs. 1.27) is our best estimation of how much smoking-related disease mediates the effect of smoking on major morbidity. On the log odds scale, approximately 29% of the observed overall smoking effect was explained by the prespecified mediator variables.
Our severity-weighted common effect OR (95% CI) for smokers versus never smokers in the sensitivity analysis was 1.45 (1.37–1.54), similar to the primary analysis estimate of 1.40 (1.33–1.47).
Results were similar for minor complications. Smoking was independently associated with increased odds of any minor morbidity when adjusting for both age only and age and the mediator covariables (P < 0.0001 for both, table 2).
Fig. 2
Fig. 2
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For individual major morbidities, smoking status was independently associated with higher risk of 30-day mortality, organ space surgical site infection, pneumonia, unplanned intubation, ventilation for longer than 48 h, cerebrovascular accident/stroke, cardiac arrest, myocardial infarction, sepsis, and septic shock, after adjusting for age (fig. 2 and Supplemental Digital Content 1, table, left, http://links.lww.com/ALN/A674). Furthermore, after removing the purported effects of smoking via the mediator variables (i.e., by adjusting for age and the mediator covariables), smoking remained associated with higher risk of organ space surgical site infection, pneumonia, unplanned intubation, ventilation for longer than 48 h, stroke/cerebrovascular accident, sepsis, and septic shock (Supplemental Digital Content 1, table, right, http://links.lww.com/ALN/A674).
Fig. 3
Fig. 3
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For the 56,903 matched current smokers with a non-missing value of pack-years (69% of the matched 82,304 current smokers), the logit of the estimated probability of any major morbidity (without covariable adjustment) increases rather linearly with increased amounts of tobacco consumed but begins to taper as pack-years reach approximately 30 (fig. 3). In a multivariable dose-response analysis for current smokers, the odds of having any major morbidity increased linearly and quadratically with amount of smoking after adjusting for covariables. Therefore, we do not report an overall dose-response OR for pack-years of smoking.
Fig. 4
Fig. 4
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We also grouped the 56,903 current smokers with a nonmissing value of pack-years into four categories based on the quartiles of reported pack-years and compared them with the 82,304 never smokers on any major morbidity in a multivariable analysis. Current smokers in the four pack-year quartiles reported smoking a median (quartile 1-quartile 3) of 6 (3–10), 17 (15–20), 30 (28–37), and 60 (50–75) pack-years, respectively. The odds of major morbidity did not differ significantly between light smokers (1–10 pack-years) and never smokers (P = 0.12), whereas the odds versus never smokers were significantly greater in all patients who smoked for longer than 10 pack-years (fig. 4) (P < 0.001 for all). Finally, within current smokers, there were no significant differences between the second, third, and fourth pack-year quartiles, but the odds of morbidity were significantly higher than for the first quartile. The Bonferroni-corrected significance criterion for this hypothesis was 0.005 because of the 10 pairwise comparisons (i.e., 0.05/10).
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Comprehensive warnings about the dangers of tobacco can change attitudes toward smoking, especially among patients about to undergo surgery.17 Thus, the preoperative period is an excellent opportunity to address the health risks associated with smoking and studies show that patients frequently request information on the effects of smoking on the risks of anesthesia and surgery. Furthermore, smoking cessation therapy seems to be effective in increasing preoperative spontaneous abstinence, so the preoperative period might represent a high-impact opportunity for smoking cessation (i.e., a “teachable moment”).18 Consequently, the American Society of Anesthesiology has implemented a smoking cessation initiative with the goal of encouraging anesthesiologists to advise their patients to quit smoking and refer them to expert resources.∥∥ Our current results contribute to this strategy because they show, in a large data set, how smoking compromises surgical outcomes.
Among the current surgical patient population in the ACS-NSQIP database, approximately 26.5% were current smokers, which well exceeds the 20% national average,2,19 presumably because smoking causes complications that require surgery or reflect that the definition we used of “current smoking” differs from that used to calculate national smoking prevalence. We found that smokers undergoing noncardiac surgery had an estimated 40% increased odds of developing major morbidity and mortality within 30 days of surgery over never smokers, representing a modest difference in the actual percentage with the major morbidity. For example, the odds of pneumonia were doubled; the odds of experiencing an unplanned intubation were 1.9 times higher; and the odds of experiencing postoperative ventilation lasting longer than 48 h were approximately 50% increased. However, because the incidence of these complications was low, the increase in the absolute percentage of complications was modest.
It is not surprising that pulmonary problems predominate, given that smoking impairs mucus transport; provokes goblet cell hyperplasia, causing stimulation of mucus overproduction20; impairs pulmonary macrophage function21; and increases bronchial reactivity by stimulation of airway inflammation.22 The available data related to smoking are mixed but suggest a modest increase in risk for postoperative pulmonary complications among current smokers.23 Our findings are consistent with those of previous studies6,9,24 that report a significant association between smoking and postoperative respiratory complications in various types of surgical procedures.
Although it is reasonably well established that smoking promotes the development of cardiovascular disease, the relationship between smoking and perioperative cardiovascular complications remains controversial.25 Most studies6,10,26,27 have been unable to identify preoperative smoking status as an independent risk factor for cardiac events during or after either cardiac or noncardiac surgery. However, most studies were also fairly small and focused on specific types of surgery, making it difficult to generalize the results. In contrast to previous reports, we observed a statistically significant increase in cardiovascular complications: in our large data set, smokers had 57% increased odds of experiencing 30-day postoperative cardiac arrest, 80% increased odds of experiencing a myocardial infarction, and 73% increased odds of experiencing stroke. Our results are supported by the observation that preoperative smoking abstinence improves postoperative cardiovascular outcomes in patients undergoing general and orthopedic surgery.28
Surgeons have long recognized that smoking impairs healing of certain surgical wounds.29–31 This is not surprising because smoking provokes peripheral arterial disease, and some of the constituents of cigarette smoke, such as the vasoconstrictors nicotine and carbon monoxide, compromise immune function and reduce tissue oxygenation.32–34 Tissue oxygenation is the main predictor of wound healing. Low tissue oxygenation is associated with impaired wound healing and increased incidence of surgical wound infections.35 Neumayer et al.36 included the data from Veterans Affairs medical centers with data from the ACS and determined that smoking was independently associated with surgical site infections. As in previous studies, we observed that smokers undergoing noncardiac surgery had 30% increased odds of superficial and 42% increased odds of deep incisional infections; more seriously, we also observed 30% increased odds of sepsis, 38% increased odds of organ space infections, and 55% increased odds of septic shock. Finally, there were 61% increased odds of wound disruption in smokers. Thus, our results contribute to the evidence that smoking impairs healing and augments the risk of surgical site infection.
The risk of major complications in patients who smoked 1–10 pack-years did not differ from never smokers. Conversely, the complication risk was similar in patients who smoked 11–22, 23–40, and more than 40 pack-years. This is consistent with the results of previous studies,37–39 in which exposure rates above certain levels produced minimal further risk of complications.
In nonrandomized studies dealing with long-term exposures, it is crucial to distinguish between confounding variables (i.e., those thought to be causes of both the outcome and the exposure [smoking]) and mediator variables (i.e., those purported to lie on the causal pathway between the exposure and outcome, such as chronic obstructive pulmonary disease and other smoking-related comorbidities). Propensity matching smokers and never smokers on mediator variables, in addition to confounders, would “match away” the long-term effects of smoking that occur through the mediator variables. Therefore, we propensity matched smokers and never smokers only on a priori-defined confounding variables. We were then able to assess the relative contributions of the a priori-defined mediator variables and any residual smoking effect on outcome. We found that the odds of major morbidity were 40% higher in smokers than never smokers when the mediator variables were not included in the model, versus 27% higher when the mediator variables were included. Thus, approximately 29% of the observed overall smoking effect (on the log OR scale) was explained by the mediator variables and 71% was explained by either the smoking variable itself, independent of the measured morbidities caused by smoking; or, more likely, by a combination of unmeasured factors, measurement error, and potential reporting bias.
An advantage of using the ACS-NSQIP registry is that it includes pooled data from numerous academic and nonacademic institutions throughout the country. The ACS-NSQIP registry provides a large sample size and good generalizability. Furthermore, inclusion criteria are uniform and reliability is enhanced by uniform data collection and auditing. However, as with any database, there are limitations to this registry, which we will highlight.
It is impossible to randomize smoking status in a prospective controlled manner. Thus, we used epidemiologic tools to evaluate registry data. However, there are distinct limitations to retrospective analysis of quality improvement registries. For example, exposure to cigarette smoke in our study was self-reported. Therefore, recall bias is a potential concern, although previous work40 indicates that smokers accurately report the details of their habit. Another limitation of our study is that exact smoking history in the weeks and days before surgery is not recorded in the ACS-NSQIP database. For example, the current smokers may have quit nearly a year before their surgery. Similarly, we do not know the extent to which smoking habits changed before or after surgery. Thus, our study likely underestimates the adverse effects of perioperative smoking to the extent that patients restricted or eliminated smoking just before or after surgery. For these reasons, our ORs for the overall effects of smoking may well be underestimates of the true impact of smoking on perioperative outcomes.
The ACS-NSQIP database has missing values for nontrivial numbers of patients for smoking status and pack-years of smoking, either of which may have resulted in overestimation or underestimation of the true relationship between smoking and the major morbidity. Nevertheless, sensitivity analysis did not identify any substantial differences in baseline variables or outcomes between smokers who had a nonmissing versus a missing value of smoking pack-years. Another limitation of the current study is the obverse of a healthy user effect; smokers also engage in other activities that are hazardous to health, including alcohol abuse, lack of primary care, and inadequate access to screening. Although some of these factors were included in our propensity matching, there are additional uncorrected factors.
We were unable to adjust for the type of hospital (e.g., rural vs. urban or teaching vs. nonteaching), skill level of surgeons, or variations in surgical approaches used within a common procedure code. The inability to adjust for these variables might have confounded the relationship between smoking status and outcome in our study. Our study also includes a heterogeneous set of surgical procedures that carry a wide range of risk. Although we matched on procedure, we cannot conclude that the smoking effect is consistent across the procedures, because this was not assessed; in addition, it would be difficult to assess with so many different procedures.
Components of a composite outcome should ideally have identical clinical importance, frequency, and treatment effect, although not always attainable in practice.41 Our composite is heterogeneous in that it includes several organ systems that might directly or indirectly be affected by smoking. Frequencies across components were similar (i.e., each of the individual components occurred in less than 1.5% of the never smokers). In addition, all of the chosen components represent serious events, although with mortality included, they cannot be claimed to be of equal severity. Our sensitivity analysis, in which we incorporated clinical severity ratings of three staff anesthesiologists to the assessment of smoking, and our major morbidity composite gave results similar to the main analysis.
In summary, our analysis of a large well-validated registry indicates that smoking is associated with higher perioperative risk for cardiopulmonary, wound-related, and infectious complications. Previous studies indicate that surgery is a teachable moment for smoking cessation and is associated with an increased likelihood of success. Our results enhance motivation for smoking cessation in general by demonstrating that the risks of serious adverse outcomes are higher in the month after surgery, although the increased risk may motivate abstinence; these data do not permit us to evaluate the potential effects of abstinence on risk.
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