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Original Research: Sleep Disorders |

Sleep-Disordered Breathing and Postoperative Outcomes After Elective SurgerySleep-Disordered Breathing and Operative Outcomes: Analysis of the Nationwide Inpatient Sample FREE TO VIEW

Babak Mokhlesi, MD, FCCP; Margaret D. Hovda, MD; Benjamin Vekhter, PhD; Vineet M. Arora, MD; Frances Chung, MD; David O. Meltzer, MD, PhD
Author and Funding Information

From the Sleep Disorders Center (Drs Mokhlesi and Hovda), Section of Pulmonary and Critical Care; Center for Health and Social Sciences (Drs Vekhter, Arora, and Meltzer); Section of General Internal Medicine (Dr Arora); and Section of Hospital Medicine (Dr Meltzer), Department of Medicine, The University of Chicago, Chicago, IL; and the Department of Anesthesia (Dr Chung), University Health Network, University of Toronto, Toronto, ON, Canada.

Correspondence to: Babak Mokhlesi, MD, FCCP, Sleep Disorders Center, Section of Pulmonary and Critical Care, Department of Medicine, The University of Chicago, 5841 S Maryland Ave, MC6076, Chicago, IL 60637; e-mail: bmokhles@medicine.bsd.uchicago.edu


Funding/Support: This study was supported by The University of Chicago Institute for Translational Medicine and the Clinical and Translational Science Awards program [UL1 RR024999]. Dr Arora is supported by National Institute on Aging [K23 AG033763]. Dr Meltzer is supported by a Midcareer Career Development Award from the National Institutes of Health [1 K24 AG031326-01].

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians. See online for more details.


Chest. 2013;144(3):903-914. doi:10.1378/chest.12-2905
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Background:  Systematic screening and treatment of sleep-disordered breathing (SDB) or obstructive sleep apnea (OSA) in presurgical patients would impose a significant cost burden; therefore, it is important to understand whether SDB is associated with worse postoperative outcomes. We sought to determine the impact of SDB on postoperative outcomes in patients undergoing four specific categories of elective surgery (orthopedic, prostate, abdominal, and cardiovascular). The primary outcomes were in-hospital death, total charges, and length of stay (LOS). Two secondary outcomes of interest were respiratory and cardiac complications.

Methods:  Data were obtained from the Nationwide Inpatient Sample database. Regression models were fitted to assess the independent association between SDB and the outcomes of interest.

Results:  The cohort included 1,058,710 hospitalized adult patients undergoing elective surgeries between 2004 and 2008. SDB was independently associated with decreased mortality in the orthopedic (OR, 0.65; 95% CI, 0.45-0.95; P = .03), abdominal (OR, 0.38; 95% CI, 0.22-0.65; P = .001), and cardiovascular surgery groups (OR, 0.54; 95% CI, 0.40-0.73; P < .001) but had no impact on mortality in the prostate surgery group. SDB was independently associated with a small, but statistically significant increase in estimated mean LOS by 0.14 days (P < .001) and estimated mean total charges by $860 (P < .001) in the orthopedic surgery group but was not associated with increased LOS or total charges in the prostate surgery group. In the abdominal and cardiovascular surgery groups, SDB was associated with a significant decrease in adjusted mean LOS of 1.1 days and 0.35 days, respectively (P < .001 for both groups), and adjusted mean total charges of $3,814 and $4,592, respectively (P < .001 for both groups). SDB was independently associated with a significantly increased OR for emergent intubation and mechanical ventilation, noninvasive ventilation, and atrial fibrillation in all four surgical categories. Emergent intubation occurred significantly earlier in the postoperative course in patients with SDB. In the subgroup of patients requiring emergent intubation, LOS, total charges, pneumonias, and in-hospital death were significantly higher in those without SDB.

Conclusions:  In this large national study, despite the increased independent association of SDB with postoperative cardiopulmonary complications, the diagnosis of SDB was not independently associated with an increased rate of in-hospital death. SDB had a mixed impact on LOS and total charges by surgical category.

Figures in this Article

Sleep-disordered breathing (SDB) is increasingly recognized as a possible risk factor for adverse perioperative outcomes.16 Given the important implications of untreated SDB, the American Society of Anesthesiologists recommends screening patients prior to surgery for SDB and implementing treatment if SDB is present.7 However, despite this growing awareness, there is a paucity of large-scale studies that examined the impact of SDB on postoperative outcomes. Moreover, given that systematic screening would impose a significant cost burden, it is important to understand whether screening and perioperative treatment of SDB improves outcomes.

To date, most studies have focused on developing effective screening tools for SDB in the preoperative population1,810 or have outlined postoperative outcomes of patients with SDB in a relatively small population at single centers.1,2,4,5 These studies have shown worse postoperative outcomes in patients with SDB, such as increased rates of hypoxemia, respiratory failure, ICU transfers, increased hospital lengths of stay (LOSs), encephalopathy, and postoperative infections.1,2,46,11,12 The generalizability of these studies is limited by the small sample sizes and single-institution experiences. Memtsoudis et al13 used a large, national database to evaluate the effect of SDB on pulmonary outcomes in patients undergoing both elective and nonelective orthopedic and general surgical procedures. They reported significantly worse pulmonary outcomes, including aspiration pneumonia, ARDS, and emergent endotracheal intubation, in patients with SDB compared with those without SDB.

The association of SDB with postoperative outcomes, such as in-hospital death, total charges, and LOS, in patients undergoing elective surgery remains largely unexamined. Moreover, the effect of SDB on postoperative outcomes in specific elective procedures, such as orthopedic, urologic, abdominal, and cardiovascular surgeries, remains poorly characterized. Given that serious postoperative events are relatively uncommon occurrences, large nationally representative databases are unique and necessary tools to examine the association of SDB with important postoperative complications.

To that end, we analyzed the Nationwide Inpatient Sample (NIS) database to determine the impact of SDB on postoperative outcomes in patients undergoing four specific types of elective surgery: orthopedic, prostate, abdominal, and cardiovascular. We quantified the impact of SDB diagnosis on in-hospital death, total charges, LOS, respiratory outcomes, and cardiac outcomes. We hypothesized that SDB would be independently associated with worse postoperative outcomes after controlling for comorbid conditions and demographic characteristics. A secondary aim of the study was to explore the impact of the American Society of Anesthesiologists 2006 practice guidelines on postoperative outcomes in patients with SDB.

Data Source

Data were obtained from the NIS database, which is one of several databases that form the Healthcare Utilization Project, is sponsored by the Agency for Healthcare and Research Quality, and is the largest all-payer database in the United States. The NIS database has been used in a variety of research studies1416 because it contains information on ∼8 million hospitalizations per year from 1,050 hospitals in 44 states. The data approximate a 20% stratified sample of hospitals in the United States and have been collected annually since 1988.17 Each hospitalization record includes common demographic variables, hospital characteristics, and clinical data coded according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). This study was approved by The University of Chicago Institutional Review Board (BSD/UCH IRB approval # 10-567-E).

Patient Cohort

The cohort was derived from all hospital admission records in the NIS database for adults aged ≥18 years who underwent elective orthopedic, prostate, abdominal, and cardiovascular surgery between 2004 and 2008. We selected records from the most recent 5 years in the NIS database to avoid significant changes in practice patterns. At the time of this study, 2008 was the last year that had become available for data extraction in the database. Patients were stratified by whether they received a diagnosis of SDB. ICD-9-CM codes were used to characterize SDB and surgical procedures (e-Appendix 1). The percentage of each surgical procedure within each surgical category is outlined in e-Appendix 1. Patients undergoing urgent or emergency surgery were excluded.

Patient Data

Patient demographics included age, sex, self-reported race/ethnicity, Charlson Comorbidity Index (CCI) score, income by quartile, health insurance source (ie, Medicare, Medicaid, private), teaching or nonteaching hospital status, and US region (northeast, south, west, midwest/central). The information about race was missing in ∼27% of cases because some participating states restrict data collection on race. The CCI is used to assign severity to a patient’s comorbid conditions. Common comorbid conditions are assigned varying weights, and the sum of the patient’s score indicates his or her cumulative comorbid condition, with higher scores indicating increased comorbidity.18 Income was divided into quartiles, with 1 being the poorest and 4 being the wealthiest. Income data were obtained from zip codes and demographic data from Nielson online demographic services.19

The primary outcomes compared between patients with and patients without SDB were cost in total hospital charges, LOS, and in-hospital death. Secondary respiratory outcomes were emergent endotracheal intubation and mechanical ventilation, CPAP/noninvasive ventilation (NIV) during hospitalization, tracheostomy, pneumonia, and respiratory failure. The NIS database does not provide detailed information about the timing of extubation after surgery; therefore, we were unable to identify patients who were not immediately extubated in the operating room or the postanesthesia care unit. Secondary cardiac outcomes included atrial fibrillation, cardiac rhythm conversion, and percutaneous coronary procedures.

Data Extraction

Total charges, LOS, and in-hospital death are variables available in the NIS database. Secondary outcomes were derived from ICD-9-CM and Clinical Classifications Software (CCS) codes (e-Appendix 2). CCS codes provide a classification scheme that facilitates analyzing data on procedures and diagnoses. CCS codes condense ICD-9-CM codes into fewer clinically meaningful groups that are easier to analyze.20

Statistical Analysis

For unadjusted comparisons between patients with and patients without SDB, continuous variables (age, LOS, and total charges) were presented as the mean ± SD and compared with Student t test. All other categorical variables were summarized as percentages and compared with χ2 test. Generalized linear models with log-link and γ-distributed errors were fitted to assess the independent association between SDB and log-transformed LOS and log-transformed total charges. We used the iteratively reweighted least squares method for maximum likelihood estimation of the model parameters. There is substantial statistical literature that recommends using generalized linear model techniques when modeling health-care costs and LOS because of unique features of such data, namely that they tend to be differentially dispersed around the mean (heteroskedastic) and prone to large outliers (right skewed).21,22 Mean adjusted LOS and total charges for patients with and without SDB were then estimated from the generalized linear models.

We constructed logistic regression models for outcomes that were dichotomous to determine the independent association of SDB and the outcomes of interest. Independent variables introduced in the generalized linear models and logistic regression models were age by quartiles, sex, type of health insurance, income by quartiles, CCI score, type of surgical procedure, year of the surgical procedure, hospital teaching status, US region where the hospital is located, and weekend admission. Race was not included in the models because it was missing for about 27% of admissions. We did not introduce obesity into the models because only 5% of patients in the cohort had a diagnosis of obesity, which likely is the result of significant underreporting. Moreover, BMI is not available in the NIS database.

We also examined for collinearity among variables introduced in the generalized linear models as well as in the logistic regression models. The percentage of patients who were dropped from the regression models because of a missing variable was negligible (between 1.5% and 2.5%). To measure model discrimination and the goodness of fit, several statistics were examined, including deviance, Pearson χ2, Akaike information criterion, Bayesian information criterion, and pseudo R2 statistics. Specifically, the deviance statistic measures how close the predicted values from the fitted model match the actual values from the raw data. If the proposed model is a good approximation of the truth, then the deviance should be relatively small (ie, < 1). Lower values of the Akaike and the Bayesian information criterion statistics also indicate better-fitted models. To assess the impact of the American Society of Anesthesiologists 2006 practice guidelines,7 we compared outcomes in patients undergoing surgery during the 2 years prior to the publication of the guidelines (2004-2005) to the 2 years after its publication (2007-2008). Stata 11 (StataCorp LP) statistical software was used for all analyses.

Demographics

The cohort included 1,058,710 patients undergoing elective surgeries between 2004 and 2008. The surgical categories included 783,723 orthopedic, 67,848 prostate, 79,101 abdominal, and 128,038 cardiovascular. SDB was reported in 43,502 (5.6%) of orthopedic, 2,779 (4.1%) of prostate, 2,633 (3.3%) of abdominal, and 6,006 (4.7%) of cardiovascular cases. The prevalence of SDB diagnosis increased progressively in each surgical group from 2004 to 2008 (Fig 1). Patient demographics for each surgical category are summarized in Table 1. The prevalence of CCI scores 3 to 4, indicating an increased burden of comorbid disease, were significantly elevated among patients with SDB, except in the abdominal surgical group. In general, patients with SDB were younger, predominantly men, had increased CCI, and had a lower percentage of Medicare coverage. Additionally, patients with SDB were more likely to undergo elective surgery at a teaching hospital.

Figure Jump LinkFigure 1. Prevalence of SDB in all surgical categories from 2004 to 2008. For each category, there was an increase in reporting SDB over the 5-year period. The year-to-year increase in SDB reporting was also significant in each surgical category (all P < .01). SDB = sleep-disordered breathing.Grahic Jump Location
Table Graphic Jump Location
Table 1 —Demographic Data for Surgical Categories Based on SDB Status

Data are presented as mean ± SD or %. SDB = sleep-disordered breathing.

Unadjusted Comparisons

In the unadjusted comparisons, patients with SDB incurred significantly more total charges in the orthopedic and prostate surgery groups (Table 2). Conversely, they incurred significantly fewer charges in the abdominal and cardiovascular surgery groups. Patients with SDB had a slight but significantly increased LOS in the orthopedic surgery group, whereas in the abdominal and cardiovascular surgery groups, they had a significantly shortened LOS. There was a significantly lower rate of in-hospital death in patients with SDB than in those without SDB in the orthopedic (0.07% vs 0.13%, P < .01), abdominal (0.5% vs 1.5%, P < .01), and cardiovascular (0.8% vs 1.9%, P < .01) surgery groups.

Table Graphic Jump Location
Table 2 —Unadjusted Outcomes by Surgical Category and SDB Status

Data are presented as mean ± SD or %. NIV = noninvasive ventilation. See Table 1 legend for expansion of other abbreviation.

a 

P < .01 comparing no SDB to SDB groups.

b 

P = .03 comparing no SDB to SDB groups.

c 

P = .02 comparing no SDB to SDB groups.

Across all surgical groups, there were significantly increased rates of emergent intubation and mechanical ventilation, use of CPAP/NIV, and respiratory failure in patients with SDB compared with those without SDB (Fig 2, Table 2). There were significantly lower rates of pneumonia in patients with SDB in the abdominal and cardiovascular surgery groups. Rates of tracheostomy did not differ between patients with and patients without SDB in the orthopedic, prostate, and abdominal surgery groups. There were significantly fewer tracheostomies in patients with SDB in the cardiovascular surgery group. The proportion of patients with atrial fibrillation and SDB was significantly increased in the orthopedic and prostate surgery groups. Rates of cardioversion and percutaneous coronary procedures were not significantly different between patients with and patients without SDB in all four surgical categories.

Figure Jump LinkFigure 2. Unadjusted outcomes for intubation, respiratory failure, CPAP/noninvasive ventilation use, and atrial fibrillation in patients with and without SDB. *P < .001; †P = .03. See Figure 1 legend for expansion of abbreviation.Grahic Jump Location
Adjusted Comparisons

In generalized linear models, after adjusting for the covariates, SDB was independently associated with a small, but statistically significant increase in estimated mean LOS by 0.14 days (P < .001) and estimated mean total charges by $860 (P < .001) in the orthopedic surgery group. SDB was not associated with increased LOS or total charges in the prostate surgery group. In the abdominal surgery group, SDB was associated with a decrease in an adjusted mean LOS of 1.1 days (P < .001) and an adjusted mean total charge of $3,814 (P < .001). Similarly, in the cardiovascular group, SDB was associated with a decrease in adjusted mean LOS of 0.35 days (P < .001) and an adjusted mean total charge of $4,592 (P < .001) (Fig 3, Table 3). On the basis of the various statistics measured, the model discrimination and model fit were excellent in all four surgical categories mostly because of the very large sample sizes.

Figure Jump LinkFigure 3. Mean adjusted length of stay and total charges in various surgical categories in patients with and without SDB. *P < .001. See Figure 1 legend for expansion of abbreviation.Grahic Jump Location
Table Graphic Jump Location
Table 3 —Regression Modeling to Estimate Adjusted Risk of Outcomes Associated With SDB

Generalized linear modeling for continuous variables (length of stay and total charges) and multivariate logistic regression for dichotomous variables were used to estimate regression coefficients or ORs for the presence of SDB after adjusting for age, sex, Charlson Comorbidity Index, insurance status, geographic region, teaching institution, income, weekend admission, year of surgery, and surgical procedure code. See Table 1 and 2 legends for expansion of abbreviations.

SDB was independently associated with decreased mortality in the orthopedic (OR, 0.65; 95% CI, 0.45-0.95; P = .03), abdominal (OR, 0.38; 95% CI, 0.22-0.65; P = .001), and cardiovascular (OR, 0.54; 95% CI, 0.40-0.73; P < .001) surgery groups. SDB had no impact on mortality in the prostate surgery group (Table 3). SDB was independently associated with a significantly increased OR for emergent intubation and mechanical ventilation, CPAP/NIV, and atrial fibrillation in all four surgical categories. SDB was independently associated with a significantly increased OR for respiratory failure in all surgical groups except abdominal (Table 3). There was no evidence that the American Society of Anesthesiologists 2006 practice guidelines had any impact on postoperative outcomes according to the regression models comparing the 2 years before and the 2 years after the publication of these guidelines (data not shown).

Outcomes of Patients Requiring Emergent Intubation and Mechanical Ventilation

In unadjusted analyses, we examined the impact of emergent intubation and mechanical ventilation on outcomes between patients with and patients without SDB (Table 4). Across all four surgical categories, the LOS, total charges, pneumonias, and in-hospital death increased significantly more in patients without SDB requiring emergent intubation and mechanical ventilation than those with SDB requiring the same intervention. Among the four surgical categories, unadjusted analysis demonstrated that emergent intubation in patients with SDB was associated with an increase in the LOS by about 1 to 2.5 days, an increase in total charges by about $8,000 to $26,000, an increase in pneumonias by about 2% to 5.4%, and an increase in in-hospital death by 0.9% to 4.4%. In contrast, emergent intubation in patients without SDB was associated with significantly worse outcomes, such as an increase in the LOS by about 6.5 to 11.7 days, an increase in total charges by about $60,000 to $100,000, an increase in pneumonias by about 11% to 24%, and an increase in in-hospital death by 6.6% to 24% (Table 4).

Table Graphic Jump Location
Table 4 —Unadjusted Outcomes Based on the Diagnosis of SDB and Need for Endotracheal Intubation in All Four Surgical Categories

Data are presented as mean ± SD or % unless otherwise indicated. day 0 = day of surgery; day 1 = postoperative day 1; NA = not applicable. See Table 1 and 2 legends for expansion of other abbreviations.

a 

P < .001 comparing no SDB/no intubation with no SDB/intubation categories.

b 

P < .001 comparing SDB/no intubation with SDB/intubation categories.

c 

P < .001 comparing SDB/intubation with no SDB/intubation categories.

We constructed regression models to assess whether emergent intubation in patients without SDB was independently associated with worse outcomes (Fig 4, Table 5). After adjusting for age, sex, CCI, hospital teaching status, and year of surgery, patients without SDB who required emergent intubation had significantly worse outcomes than those with SDB who required emergent intubation.

Figure Jump LinkFigure 4. Mean adjusted length of stay and total charges in patients with and without SBD who required emergent endotracheal intubation. *P < .001. See Figure 1 legend for expansion of abbreviations.Grahic Jump Location
Table Graphic Jump Location
Table 5 —Regression Modeling to Estimate Adjusted Risk of Outcomes Associated With No SDB Status in Patients Requiring Emergent Endotracheal Intubation

Generalized linear modeling for continuous variables (length of stay and total charges) and multivariate logistic regression for dichotomous variables were used to estimate regression coefficients or ORs in patients without SDB after adjusting for age, sex, Charlson Comorbidity Index, insurance status, teaching institution, and year of surgery. No SDB status in patients requiring intubation was independently associated with worse outcomes in all four surgical categories. See Table 1 legend for expansion of abbreviation.

The use of CPAP/NIV during the hospital course was significantly lower in patients without SDB requiring emergent intubation and mechanical ventilation. Of note, CPAP/NIV use during the hospitalization was not recorded in any patient who did not require emergent intubation and mechanical ventilation, regardless of the SDB status (Table 4). Across all four surgical categories, a significantly higher proportion of patients with SDB required emergent intubation and mechanical ventilation on the day of surgery or the first postoperative day compared with patients without SDB (Table 4).

We show for the first time, to our knowledge, that the diagnosis of SDB is not independently associated with a clinically significant increase in in-hospital death, LOS, or total charges in a large, nationally representative cohort of patients undergoing various types of elective surgery. In patients undergoing elective orthopedic, abdominal, and cardiovascular surgeries, SDB was associated with a significant decrease in in-hospital mortality. Like previous studies,35,13 we found that the diagnosis of SDB was independently associated with increased rates of emergent endotracheal intubation and mechanical ventilation, CPAP/NIV use, and respiratory failure across all four surgical categories studied. The present study also extends previous findings by showing that the diagnosis of SDB is independently associated with atrial fibrillation. One novel finding of the present study is that a significantly larger proportion of patients with SDB required emergent endotracheal intubation early in their postoperative course compared with those without SDB. The LOS, total charges, pneumonias, and in-hospital deaths increased dramatically more in patients without SDB requiring emergent intubation than in those with SDB requiring the same intervention.

The findings suggest that upper airway complications may be the main cause of respiratory failure and need for emergent intubation in patients with SDB. This speculation is supported by the fact that in patients with SDB, emergent intubation occurred earlier in their postoperative course. Moreover, we speculate that in patients with SDB, the primary reason for emergent intubation may have been related to a rapidly reversible upper airway complication in the setting of sedative and opioid administration in the immediate postoperative period. The fact that emergent intubation in patients with SDB led to a significantly less increase in LOS, total charges, pneumonias, and in-hospital deaths than in patients without SDB who also required emergent intubation supports this speculation. It is well known that patients with SDB have elevated pharyngeal closing pressures as well as decreased velopharyngeal and oropharyngeal cross-sectional areas, leading to increased upper airway instability in the postoperative setting compared with age- and BMI-matched control patients.23,24 Another possibility is that clinicians may have had a lower threshold for intubating patients with known SDB in the setting of postoperative respiratory complications. In the present analysis, patients without SDB who required emergent intubation had a significant increase in rates of in-hospital death, LOS, total charges, respiratory failure, and pneumonia. On average, they were intubated later in their postoperative course and had higher rates of pneumonia, and we suspect that their intubation was indicative of a global decline or complication that took much longer to recover from, resulting in worse outcomes.

Although SDB was independently associated with cardiopulmonary complications across all four surgical categories, it remains unclear why SDB was not independently associated with increased LOS, total charges, and in-hospital death. It is well-known from prior postoperative cohorts that patients with SDB are more obese than their counterparts without SDB.1,4,5,25 A growing body of literature has been reporting an obesity paradox such that in patients with a variety of illnesses, including postoperative patients, obesity is associated with reduced mortality.16,2629 Although the phenomenon of the obesity paradox is not entirely understood, possible explanations in favor of causality have included earlier presentation to medical care as well as more optimal medical therapy,3032 cardioprotective metabolic effects of increased body fat,33 and increased metabolic reserves.34 Another invoked putative mechanism by which SDB could decrease mortality is ischemia preconditioning. Studies have reported that in patients with acute myocardial infarction, those with SDB and intermittent hypoxemia during sleep have less severe cardiac injury and better coronary collateral circulation.3540 Therefore, it is plausible that SDB does not increase in-hospital mortality in the immediate postoperative setting despite the increased risk of upper airway complications. Although untreated severe SDB is strongly associated with increased cardiovascular mortality,4145 it remains unclear whether in the acute postoperative setting untreated SDB can also lead to increased cardiovascular mortality.

Another reason for lower postoperative mortality in patients with SDB could be that the diagnosis leads to early effective intervention, which decreases this adverse outcome, whereas the population without SDB includes patients with SDB who experience adverse outcomes because of failure to identify the condition. However, it is unlikely that the lower mortality in patients with SDB was related to better postoperative care because respiratory complications were more common in patients with SDB. Moreover, CPAP/NIV use during the hospitalization was not recorded in any patient with or without SDB who did not require intubation (Table 4). In patients with SDB, rates of CPAP/NIV use ranged from 2.9% to 4.7% across the four surgical categories, and CPAP/NIV use was only reported in patients requiring emergent intubation during their hospital course. Such a low use of CPAP/NIV in the postoperative setting in patients with SDB has been previously reported.5,46,47 In a recent randomized controlled trial of patients undergoing elective hip and knee arthroplasty, patients suspected of having moderate or severe SDB were randomized to auto-CPAP therapy during the postoperative period vs standard care. Empirical auto-CPAP therapy led to a 1-day increase in median LOS.48 This study raises new questions about the role for empirical postoperative auto-CPAP therapy.

In general, tracheostomies were rare in all four surgical categories. The only significant difference was between patients with and patients without SDB in the cardiovascular surgery group. We speculate that tracheostomies were less common in patients with SDB in this group because they had significantly fewer pneumonias. It is also possible that a higher rate of CPAP/NIV use in the SDB group decreased the need for prolonged mechanical ventilation and, ultimately, the need for tracheostomy. In this analysis, SDB was not independently associated with an increased risk of pneumonia in patients undergoing elective surgery. Although Memtsoudis et al13 found an increased risk of pneumonia in patients with SDB, the cohort also included emergent and urgent surgeries. In fact, 47% of the orthopedic and 65% of the abdominal surgery cohorts underwent urgent or emergent surgeries. Patients undergoing nonelective surgeries are probably at higher risk of pneumonia than those undergoing elective surgeries.

This study has several limitations that are mainly inherent to the analysis of large administrative databases. Some of these limitations include the inability to ascertain the presence or absence of SDB and its severity, lack of information about home CPAP therapy or CPAP/NIV adherence during the postoperative period, and lack of information regarding complications after hospital discharge. The NIS database also lacks information about advanced services, such as critical care, step down, and telemetry. Moreover, the database lacks information about readmissions and long-term outcomes. It was not possible to ascertain whether atrial fibrillation was present before surgery or developed after surgery.

Additional limitations include a possibility of coding error or reporting bias; however, there is no reason that these errors or biases should differ among patients categorized as having or not having SDB. We also could not ascertain whether the diagnostic codes for SDB were part of the admission or discharge diagnoses; therefore, it is conceivable that respiratory complications could have led to the new discharge diagnosis of SDB. Although prevalence estimates of SDB from community-based studies range from 5% to 28% in women and 17% to 26% in men,4952 the prevalence of clinically significant and symptomatic SDB in the community is significantly lower at 2% to 9%. It is precisely this group of patients that is more likely to be clinically recognized and to receive treatment for SDB. Moreover, studies that reported a high prevalence of SDB in presurgical patients were subject to significant selection bias because not all patients underwent polysomnography.1,53,54 In the largest single academic center registry study of presurgical patients undergoing anesthesia, Ramachandran et al25 reported an SDB prevalence of 7% in 43,576 adults. Therefore, we believe that the NIS database does not represent a significant underreporting of clinically significant cases of SDB. Additionally, we demonstrate increasing prevalence of SDB with each successive year, as it has been shown in the past with nationwide surveys.13,55 Finally, we do not have clinical information on all potential confounders, such as BMI.

In summary, with the use of a large and nationally representative database, we report that SDB is independently associated with significant postoperative cardiopulmonary complications but not with in-hospital mortality, LOS, and total charges after elective surgery. Given the overall low rates of complications, prospective, large-scale, multicenter randomized controlled trials are needed to assess the impact of SDB treatment on patient outcomes during the postoperative period. Further research is needed to better elucidate the impact of SDB on postoperative mortality, particularly in certain high risk subgroups of SDB, such as obesity hypoventilation syndrome, the overlap syndrome, or SDB accompanied by pulmonary hypertension.

Author contributions: Dr Mokhlesi had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Dr Mokhlesi: contributed to the study concept and design, obtaining the funding, study supervision, data analysis and interpretation, statistical analysis, drafting of the manuscript, and critical revision of the manuscript for important intellectual content.

Dr Hovda: contributed to the data analysis and interpretation, drafting of the manuscript, and critical revision of the manuscript for important intellectual content.

Dr Vekhter: contributed to the data analysis and interpretation, statistical analysis, and critical revision of the manuscript for important intellectual content.

Dr Arora: contributed to the study concept and design, data analysis and interpretation, statistical analysis, drafting of the manuscript, and critical revision of the manuscript for important intellectual content.

Dr Chung: contributed to the study concept and design, data analysis and interpretation, drafting of the manuscript, and critical revision of the manuscript for important intellectual content.

Dr Meltzer: contributed to the study concept and design; administrative, technical, or material support; study supervision; data analysis and interpretation; statistical analysis; drafting of the manuscript; and critical revision of the manuscript for important intellectual content.

Financial/nonfinancial disclosures: The authors have reported to CHEST that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Role of sponsors: The sponsor had no role in the design and conduct of the study, in the collection, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript.

Additional information: The e-Appendixes can be found in the “Supplemental Materials” area of the online article.

CCI

Charlson Comorbidity Index

CCS

Clinical Classifications Software

ICD-9-CM

International Classification of Diseases, Ninth Revision, Clinical Modification

LOS

length of stay

NIS

Nationwide Inpatient Sample

NIV

noninvasive ventilation

SDB

sleep-disordered breathing

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Chung F, Yegneswaran B, Liao P, et al. Validation of the Berlin questionnaire and American Society of Anesthesiologists checklist as screening tools for obstructive sleep apnea in surgical patients. Anesthesiology. 2008;108(5):822-830. [CrossRef] [PubMed]
 
Ramachandran SK, Josephs LA. A meta-analysis of clinical screening tests for obstructive sleep apnea. Anesthesiology. 2009;110(4):928-939. [CrossRef] [PubMed]
 
Liao P, Yegneswaran B, Vairavanathan S, Zilberman P, Chung F. Postoperative complications in patients with obstructive sleep apnea: a retrospective matched cohort study. Can J Anaesth. 2009;56(11):819-828. [CrossRef] [PubMed]
 
Flink BJ, Rivelli SK, Cox EA, et al. Obstructive sleep apnea and incidence of postoperative delirium after elective knee replacement in the nondemented elderly. Anesthesiology. 2012;116(4):788-796. [CrossRef] [PubMed]
 
Memtsoudis S, Liu SS, Ma Y, et al. Perioperative pulmonary outcomes in patients with sleep apnea after noncardiac surgery. Anesth Analg. 2011;112(1):113-121. [CrossRef] [PubMed]
 
Nanchal R, Kumar G, Taneja A, et al; Milwaukee Initiative in Critical Care Outcomes Research (MICCOR) Group of Investigators. Pulmonary embolism: the weekend effect. Chest. 2012;142(3):690-696. [CrossRef] [PubMed]
 
Chandra D, Stamm JA, Taylor B, et al. Outcomes of noninvasive ventilation for acute exacerbations of chronic obstructive pulmonary disease in the United States, 1998-2008. Am J Respir Crit Care Med. 2012;185(2):152-159. [CrossRef] [PubMed]
 
Memtsoudis SG, Bombardieri AM, Ma Y, Walz JM, Chiu YL, Mazumdar M. Mortality of patients with respiratory insufficiency and adult respiratory distress syndrome after surgery: the obesity paradox. J Intensive Care Med. 2012;27(5):306-311. [CrossRef] [PubMed]
 
Healthcare Cost and Utilization Project; Agency for Healthcare Research and Quality. What is the NIS? Health Cost and Utilization Project website. http://www.hcup-us.ahrq.gov/nisoverview.jsp#Whatis. Accessed November 10, 2012.
 
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383. [CrossRef] [PubMed]
 
The Nielsen Company. Welcome to SiteReports. The Nielson Company website. http://www.claritas.com/sitereports/default.jsp. Accessed July 19, 2012.
 
Healthcare Cost and Utilization Project; Agency for Healthcare Research and Quality. Clinical Classifications Software (CCS) for ICD-9-CM. Healthcare Cost and Utilization Project website. http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed November 10, 2012.
 
Manning WG, Mullahy J. Estimating log models: to transform or not to transform? J Health Econ. 2001;20(4):461-494. [CrossRef] [PubMed]
 
Nelder J, Wedderburn R. Generalized linear models. J R Stat Soc [Ser A]. 1972;135(3):370-384. [CrossRef]
 
Isono S, Remmers JE, Tanaka A, Sho Y, Sato J, Nishino T. Anatomy of pharynx in patients with obstructive sleep apnea and in normal subjects. J Appl Physiol. 1997;82(4):1319-1326. [PubMed]
 
Isono S. Obstructive sleep apnea of obese adults: pathophysiology and perioperative airway management. Anesthesiology. 2009;110(4):908-921. [CrossRef] [PubMed]
 
Ramachandran SK, Kheterpal S, Consens F, et al. Derivation and validation of a simple perioperative sleep apnea prediction score. Anesth Analg. 2010;110(4):1007-1015. [CrossRef] [PubMed]
 
Bucholz EM, Rathore SS, Reid KJ, et al. Body mass index and mortality in acute myocardial infarction patients. Am J Med. 2012;125(8):796-803. [CrossRef] [PubMed]
 
Carnethon MR, De Chavez PJ, Biggs ML, et al. Association of weight status with mortality in adults with incident diabetes. JAMA. 2012;308(6):581-590. [CrossRef] [PubMed]
 
Jackson RS, Black JH III, Lum YW, et al. Class I obesity is paradoxically associated with decreased risk of postoperative stroke after carotid endarterectomy. J Vasc Surg. 2012;55(5):1306-1312. [CrossRef] [PubMed]
 
Martino JL, Stapleton RD, Wang M, et al. Extreme obesity and outcomes in critically ill patients. Chest. 2011;140(5):1198-1206. [CrossRef] [PubMed]
 
Oreopoulos A, McAlister FA, Kalantar-Zadeh K, et al. The relationship between body mass index, treatment, and mortality in patients with established coronary artery disease: a report from APPROACH. Eur Heart J. 2009;30(21):2584-2592. [CrossRef] [PubMed]
 
Chang VW, Asch DA, Werner RM. Quality of care among obese patients. JAMA. 2010;303(13):1274-1281. [CrossRef] [PubMed]
 
Schenkeveld L, Magro M, Oemrawsingh RM, et al. The influence of optimal medical treatment on the ‘obesity paradox’, body mass index and long-term mortality in patients treated with percutaneous coronary intervention: a prospective cohort study. BMJ Open. 2012;:2e000535.
 
Hastie CE, Padmanabhan S, Slack R, et al. Obesity paradox in a cohort of 4880 consecutive patients undergoing percutaneous coronary intervention. Eur Heart J. 2010;31(2):222-226. [CrossRef] [PubMed]
 
Doehner W, Clark A, Anker SD. The obesity paradox: weighing the benefit. Eur Heart J. 2010;31(2):146-148. [CrossRef] [PubMed]
 
Shah N, Redline S, Yaggi HK, et al. Obstructive sleep apnea and acute myocardial infarction severity: ischemic preconditioning? [published online ahead of print October 23, 2012]. Sleep Breath. In press. doi:10.1007/s11325-012-0770-7.
 
Steiner S, Schueller PO, Schulze V, Strauer BE. Occurrence of coronary collateral vessels in patients with sleep apnea and total coronary occlusion. Chest. 2010;137(3):516-520. [CrossRef] [PubMed]
 
Lavie L, Lavie P. Coronary collateral circulation in sleep apnea: a cardioprotective mechanism? Chest. 2010;137(3):511-512. [CrossRef] [PubMed]
 
Ozeke O, Ozer C, Gungor M, Celenk MK, Dincer H, Ilicin G. Chronic intermittent hypoxia caused by obstructive sleep apnea may play an important role in explaining the morbidity-mortality paradox of obesity. Med Hypotheses. 2011;76(1):61-63. [CrossRef] [PubMed]
 
Lavie L, Lavie P. Ischemic preconditioning as a possible explanation for the age decline relative mortality in sleep apnea. Med Hypotheses. 2006;66(6):1069-1073. [CrossRef] [PubMed]
 
Berger S, Aronson D, Lavie P, Lavie L. Endothelial progenitor cells in acute myocardial infarction and sleep-disordered breathing. Am J Respir Crit Care Med. 2013;187(1):90-98. [CrossRef] [PubMed]
 
Campos-Rodriguez F, Martinez-Garcia MA, de la Cruz-Moron I, Almeida-Gonzalez C, Catalan-Serra P, Montserrat JM. Cardiovascular mortality in women with obstructive sleep apnea with or without continuous positive airway pressure treatment: a cohort study. Ann Intern Med. 2012;156(2):115-122. [CrossRef] [PubMed]
 
Marin JM, Carrizo SJ, Vicente E, Agusti AG. Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet. 2005;365(9464):1046-1053. [PubMed]
 
Punjabi NM, Caffo BS, Goodwin JL, et al. Sleep-disordered breathing and mortality: a prospective cohort study. PLoS Med. 2009;6(8):e1000132. [CrossRef] [PubMed]
 
Young T, Finn L, Peppard PE, et al. Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin sleep cohort. Sleep. 2008;31(8):1071-1078. [PubMed]
 
Gami AS, Howard DE, Olson EJ, Somers VK. Day-night pattern of sudden death in obstructive sleep apnea. N Engl J Med. 2005;352(12):1206-1214. [CrossRef] [PubMed]
 
Spurr KF, Graven MA, Gilbert RW. Prevalence of unspecified sleep apnea and the use of continuous positive airway pressure in hospitalized patients, 2004 National Hospital Discharge Survey. Sleep Breath. 2008;12(3):229-234. [CrossRef] [PubMed]
 
Guralnick AS, Pant M, Minhaj M, Sweitzer BJ, Mokhlesi B. CPAP adherence in patients with newly diagnosed obstructive sleep apnea prior to elective surgery. J Clin Sleep Med. 2012;8(5):501-506. [PubMed]
 
O’Gorman SM, Gay PC, Morgenthaler TI. Does auto-titrating positive airway pressure therapy improve postoperative outcome in patients at risk for obstructive sleep apnea syndrome? A randomized controlled clinical trial [published online ahead of print January 3, 2013]. Chest. In press. doi:10.1378/chest.12-0989.
 
Bixler EO, Vgontzas AN, Lin HM, et al. Prevalence of sleep-disordered breathing in women: effects of gender. Am J Respir Crit Care Med. 2001;163(3 pt 1):608-613. [CrossRef] [PubMed]
 
Bixler EO, Vgontzas AN, Ten Have T, Tyson K, Kales A. Effects of age on sleep apnea in men: I. Prevalence and severity. Am J Respir Crit Care Med. 1998;157(1):144-148. [CrossRef] [PubMed]
 
Durán J, Esnaola S, Rubio R, Iztueta A. Obstructive sleep apnea-hypopnea and related clinical features in a population-based sample of subjects aged 30 to 70 yr. Am J Respir Crit Care Med. 2001;163(3 pt 1):685-689. [CrossRef] [PubMed]
 
Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med. 1993;328(17):1230-1235. [CrossRef] [PubMed]
 
Fidan H, Fidan F, Unlu M, Ela Y, Ibis A, Tetik L. Prevalence of sleep apnoea in patients undergoing operation. Sleep Breath. 2006;10(3):161-165. [CrossRef] [PubMed]
 
Finkel KJ, Searleman AC, Tymkew H, et al. Prevalence of undiagnosed obstructive sleep apnea among adult surgical patients in an academic medical center. Sleep Med. 2009;10(7):753-758. [CrossRef] [PubMed]
 
Namen AM, Dunagan DP, Fleischer A, et al. Increased physician-reported sleep apnea: the National Ambulatory Medical Care Survey. Chest. 2002;121(6):1741-1747. [CrossRef] [PubMed]
 

Figures

Figure Jump LinkFigure 1. Prevalence of SDB in all surgical categories from 2004 to 2008. For each category, there was an increase in reporting SDB over the 5-year period. The year-to-year increase in SDB reporting was also significant in each surgical category (all P < .01). SDB = sleep-disordered breathing.Grahic Jump Location
Figure Jump LinkFigure 2. Unadjusted outcomes for intubation, respiratory failure, CPAP/noninvasive ventilation use, and atrial fibrillation in patients with and without SDB. *P < .001; †P = .03. See Figure 1 legend for expansion of abbreviation.Grahic Jump Location
Figure Jump LinkFigure 3. Mean adjusted length of stay and total charges in various surgical categories in patients with and without SDB. *P < .001. See Figure 1 legend for expansion of abbreviation.Grahic Jump Location
Figure Jump LinkFigure 4. Mean adjusted length of stay and total charges in patients with and without SBD who required emergent endotracheal intubation. *P < .001. See Figure 1 legend for expansion of abbreviations.Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1 —Demographic Data for Surgical Categories Based on SDB Status

Data are presented as mean ± SD or %. SDB = sleep-disordered breathing.

Table Graphic Jump Location
Table 2 —Unadjusted Outcomes by Surgical Category and SDB Status

Data are presented as mean ± SD or %. NIV = noninvasive ventilation. See Table 1 legend for expansion of other abbreviation.

a 

P < .01 comparing no SDB to SDB groups.

b 

P = .03 comparing no SDB to SDB groups.

c 

P = .02 comparing no SDB to SDB groups.

Table Graphic Jump Location
Table 3 —Regression Modeling to Estimate Adjusted Risk of Outcomes Associated With SDB

Generalized linear modeling for continuous variables (length of stay and total charges) and multivariate logistic regression for dichotomous variables were used to estimate regression coefficients or ORs for the presence of SDB after adjusting for age, sex, Charlson Comorbidity Index, insurance status, geographic region, teaching institution, income, weekend admission, year of surgery, and surgical procedure code. See Table 1 and 2 legends for expansion of abbreviations.

Table Graphic Jump Location
Table 4 —Unadjusted Outcomes Based on the Diagnosis of SDB and Need for Endotracheal Intubation in All Four Surgical Categories

Data are presented as mean ± SD or % unless otherwise indicated. day 0 = day of surgery; day 1 = postoperative day 1; NA = not applicable. See Table 1 and 2 legends for expansion of other abbreviations.

a 

P < .001 comparing no SDB/no intubation with no SDB/intubation categories.

b 

P < .001 comparing SDB/no intubation with SDB/intubation categories.

c 

P < .001 comparing SDB/intubation with no SDB/intubation categories.

Table Graphic Jump Location
Table 5 —Regression Modeling to Estimate Adjusted Risk of Outcomes Associated With No SDB Status in Patients Requiring Emergent Endotracheal Intubation

Generalized linear modeling for continuous variables (length of stay and total charges) and multivariate logistic regression for dichotomous variables were used to estimate regression coefficients or ORs in patients without SDB after adjusting for age, sex, Charlson Comorbidity Index, insurance status, teaching institution, and year of surgery. No SDB status in patients requiring intubation was independently associated with worse outcomes in all four surgical categories. See Table 1 legend for expansion of abbreviation.

References

Chung F, Yegneswaran B, Liao P, et al. STOP questionnaire: a tool to screen patients for obstructive sleep apnea. Anesthesiology. 2008;108(5):812-821. [CrossRef] [PubMed]
 
Gupta RM, Parvizi J, Hanssen AD, Gay PC. Postoperative complications in patients with obstructive sleep apnea syndrome undergoing hip or knee replacement: a case-control study. Mayo Clin Proc. 2001;76(9):897-905. [PubMed]
 
Kaw R, Chung F, Pasupuleti V, Mehta J, Gay PC, Hernandez AV. Meta-analysis of the association between obstructive sleep apnoea and postoperative outcome. Br J Anaesth. 2012;109(6):897-906. [CrossRef] [PubMed]
 
Kaw R, Pasupuleti V, Walker E, Ramaswamy A, Foldvary-Schafer N. Postoperative complications in patients with obstructive sleep apnea. Chest. 2012;141(2):436-441. [CrossRef] [PubMed]
 
Mador MJ, Goplani S, Gottumukkala VA, El-Solh AA, Akashdeep K, Khadka G, Abo-Khamis M. Postoperative complications in obstructive sleep apnea. Sleep Breath. 2012;17(2):727-734. [CrossRef] [PubMed]
 
Hwang D, Shakir N, Limann B, et al. Association of sleep-disordered breathing with postoperative complications. Chest. 2008;133(5):1128-1134. [CrossRef] [PubMed]
 
Gross JB, Bachenberg KL, Benumof JL, et al; American Society of Anethesiologists Task Force on Perioperative Management. Practice guidelines for the perioperative management of patients with obstructive sleep apnea: a report by the American Society of Anesthesiologists Task Force on Perioperative Management of patients with obstructive sleep apnea. Anesthesiology. 2006;104(5):1081-1093. [CrossRef] [PubMed]
 
Abrishami A, Khajehdehi A, Chung F. A systematic review of screening questionnaires for obstructive sleep apnea. Can J Anaesth. 2010;57(5):423-438. [CrossRef] [PubMed]
 
Chung F, Yegneswaran B, Liao P, et al. Validation of the Berlin questionnaire and American Society of Anesthesiologists checklist as screening tools for obstructive sleep apnea in surgical patients. Anesthesiology. 2008;108(5):822-830. [CrossRef] [PubMed]
 
Ramachandran SK, Josephs LA. A meta-analysis of clinical screening tests for obstructive sleep apnea. Anesthesiology. 2009;110(4):928-939. [CrossRef] [PubMed]
 
Liao P, Yegneswaran B, Vairavanathan S, Zilberman P, Chung F. Postoperative complications in patients with obstructive sleep apnea: a retrospective matched cohort study. Can J Anaesth. 2009;56(11):819-828. [CrossRef] [PubMed]
 
Flink BJ, Rivelli SK, Cox EA, et al. Obstructive sleep apnea and incidence of postoperative delirium after elective knee replacement in the nondemented elderly. Anesthesiology. 2012;116(4):788-796. [CrossRef] [PubMed]
 
Memtsoudis S, Liu SS, Ma Y, et al. Perioperative pulmonary outcomes in patients with sleep apnea after noncardiac surgery. Anesth Analg. 2011;112(1):113-121. [CrossRef] [PubMed]
 
Nanchal R, Kumar G, Taneja A, et al; Milwaukee Initiative in Critical Care Outcomes Research (MICCOR) Group of Investigators. Pulmonary embolism: the weekend effect. Chest. 2012;142(3):690-696. [CrossRef] [PubMed]
 
Chandra D, Stamm JA, Taylor B, et al. Outcomes of noninvasive ventilation for acute exacerbations of chronic obstructive pulmonary disease in the United States, 1998-2008. Am J Respir Crit Care Med. 2012;185(2):152-159. [CrossRef] [PubMed]
 
Memtsoudis SG, Bombardieri AM, Ma Y, Walz JM, Chiu YL, Mazumdar M. Mortality of patients with respiratory insufficiency and adult respiratory distress syndrome after surgery: the obesity paradox. J Intensive Care Med. 2012;27(5):306-311. [CrossRef] [PubMed]
 
Healthcare Cost and Utilization Project; Agency for Healthcare Research and Quality. What is the NIS? Health Cost and Utilization Project website. http://www.hcup-us.ahrq.gov/nisoverview.jsp#Whatis. Accessed November 10, 2012.
 
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383. [CrossRef] [PubMed]
 
The Nielsen Company. Welcome to SiteReports. The Nielson Company website. http://www.claritas.com/sitereports/default.jsp. Accessed July 19, 2012.
 
Healthcare Cost and Utilization Project; Agency for Healthcare Research and Quality. Clinical Classifications Software (CCS) for ICD-9-CM. Healthcare Cost and Utilization Project website. http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccs.jsp. Accessed November 10, 2012.
 
Manning WG, Mullahy J. Estimating log models: to transform or not to transform? J Health Econ. 2001;20(4):461-494. [CrossRef] [PubMed]
 
Nelder J, Wedderburn R. Generalized linear models. J R Stat Soc [Ser A]. 1972;135(3):370-384. [CrossRef]
 
Isono S, Remmers JE, Tanaka A, Sho Y, Sato J, Nishino T. Anatomy of pharynx in patients with obstructive sleep apnea and in normal subjects. J Appl Physiol. 1997;82(4):1319-1326. [PubMed]
 
Isono S. Obstructive sleep apnea of obese adults: pathophysiology and perioperative airway management. Anesthesiology. 2009;110(4):908-921. [CrossRef] [PubMed]
 
Ramachandran SK, Kheterpal S, Consens F, et al. Derivation and validation of a simple perioperative sleep apnea prediction score. Anesth Analg. 2010;110(4):1007-1015. [CrossRef] [PubMed]
 
Bucholz EM, Rathore SS, Reid KJ, et al. Body mass index and mortality in acute myocardial infarction patients. Am J Med. 2012;125(8):796-803. [CrossRef] [PubMed]
 
Carnethon MR, De Chavez PJ, Biggs ML, et al. Association of weight status with mortality in adults with incident diabetes. JAMA. 2012;308(6):581-590. [CrossRef] [PubMed]
 
Jackson RS, Black JH III, Lum YW, et al. Class I obesity is paradoxically associated with decreased risk of postoperative stroke after carotid endarterectomy. J Vasc Surg. 2012;55(5):1306-1312. [CrossRef] [PubMed]
 
Martino JL, Stapleton RD, Wang M, et al. Extreme obesity and outcomes in critically ill patients. Chest. 2011;140(5):1198-1206. [CrossRef] [PubMed]
 
Oreopoulos A, McAlister FA, Kalantar-Zadeh K, et al. The relationship between body mass index, treatment, and mortality in patients with established coronary artery disease: a report from APPROACH. Eur Heart J. 2009;30(21):2584-2592. [CrossRef] [PubMed]
 
Chang VW, Asch DA, Werner RM. Quality of care among obese patients. JAMA. 2010;303(13):1274-1281. [CrossRef] [PubMed]
 
Schenkeveld L, Magro M, Oemrawsingh RM, et al. The influence of optimal medical treatment on the ‘obesity paradox’, body mass index and long-term mortality in patients treated with percutaneous coronary intervention: a prospective cohort study. BMJ Open. 2012;:2e000535.
 
Hastie CE, Padmanabhan S, Slack R, et al. Obesity paradox in a cohort of 4880 consecutive patients undergoing percutaneous coronary intervention. Eur Heart J. 2010;31(2):222-226. [CrossRef] [PubMed]
 
Doehner W, Clark A, Anker SD. The obesity paradox: weighing the benefit. Eur Heart J. 2010;31(2):146-148. [CrossRef] [PubMed]
 
Shah N, Redline S, Yaggi HK, et al. Obstructive sleep apnea and acute myocardial infarction severity: ischemic preconditioning? [published online ahead of print October 23, 2012]. Sleep Breath. In press. doi:10.1007/s11325-012-0770-7.
 
Steiner S, Schueller PO, Schulze V, Strauer BE. Occurrence of coronary collateral vessels in patients with sleep apnea and total coronary occlusion. Chest. 2010;137(3):516-520. [CrossRef] [PubMed]
 
Lavie L, Lavie P. Coronary collateral circulation in sleep apnea: a cardioprotective mechanism? Chest. 2010;137(3):511-512. [CrossRef] [PubMed]
 
Ozeke O, Ozer C, Gungor M, Celenk MK, Dincer H, Ilicin G. Chronic intermittent hypoxia caused by obstructive sleep apnea may play an important role in explaining the morbidity-mortality paradox of obesity. Med Hypotheses. 2011;76(1):61-63. [CrossRef] [PubMed]
 
Lavie L, Lavie P. Ischemic preconditioning as a possible explanation for the age decline relative mortality in sleep apnea. Med Hypotheses. 2006;66(6):1069-1073. [CrossRef] [PubMed]
 
Berger S, Aronson D, Lavie P, Lavie L. Endothelial progenitor cells in acute myocardial infarction and sleep-disordered breathing. Am J Respir Crit Care Med. 2013;187(1):90-98. [CrossRef] [PubMed]
 
Campos-Rodriguez F, Martinez-Garcia MA, de la Cruz-Moron I, Almeida-Gonzalez C, Catalan-Serra P, Montserrat JM. Cardiovascular mortality in women with obstructive sleep apnea with or without continuous positive airway pressure treatment: a cohort study. Ann Intern Med. 2012;156(2):115-122. [CrossRef] [PubMed]
 
Marin JM, Carrizo SJ, Vicente E, Agusti AG. Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet. 2005;365(9464):1046-1053. [PubMed]
 
Punjabi NM, Caffo BS, Goodwin JL, et al. Sleep-disordered breathing and mortality: a prospective cohort study. PLoS Med. 2009;6(8):e1000132. [CrossRef] [PubMed]
 
Young T, Finn L, Peppard PE, et al. Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin sleep cohort. Sleep. 2008;31(8):1071-1078. [PubMed]
 
Gami AS, Howard DE, Olson EJ, Somers VK. Day-night pattern of sudden death in obstructive sleep apnea. N Engl J Med. 2005;352(12):1206-1214. [CrossRef] [PubMed]
 
Spurr KF, Graven MA, Gilbert RW. Prevalence of unspecified sleep apnea and the use of continuous positive airway pressure in hospitalized patients, 2004 National Hospital Discharge Survey. Sleep Breath. 2008;12(3):229-234. [CrossRef] [PubMed]
 
Guralnick AS, Pant M, Minhaj M, Sweitzer BJ, Mokhlesi B. CPAP adherence in patients with newly diagnosed obstructive sleep apnea prior to elective surgery. J Clin Sleep Med. 2012;8(5):501-506. [PubMed]
 
O’Gorman SM, Gay PC, Morgenthaler TI. Does auto-titrating positive airway pressure therapy improve postoperative outcome in patients at risk for obstructive sleep apnea syndrome? A randomized controlled clinical trial [published online ahead of print January 3, 2013]. Chest. In press. doi:10.1378/chest.12-0989.
 
Bixler EO, Vgontzas AN, Lin HM, et al. Prevalence of sleep-disordered breathing in women: effects of gender. Am J Respir Crit Care Med. 2001;163(3 pt 1):608-613. [CrossRef] [PubMed]
 
Bixler EO, Vgontzas AN, Ten Have T, Tyson K, Kales A. Effects of age on sleep apnea in men: I. Prevalence and severity. Am J Respir Crit Care Med. 1998;157(1):144-148. [CrossRef] [PubMed]
 
Durán J, Esnaola S, Rubio R, Iztueta A. Obstructive sleep apnea-hypopnea and related clinical features in a population-based sample of subjects aged 30 to 70 yr. Am J Respir Crit Care Med. 2001;163(3 pt 1):685-689. [CrossRef] [PubMed]
 
Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med. 1993;328(17):1230-1235. [CrossRef] [PubMed]
 
Fidan H, Fidan F, Unlu M, Ela Y, Ibis A, Tetik L. Prevalence of sleep apnoea in patients undergoing operation. Sleep Breath. 2006;10(3):161-165. [CrossRef] [PubMed]
 
Finkel KJ, Searleman AC, Tymkew H, et al. Prevalence of undiagnosed obstructive sleep apnea among adult surgical patients in an academic medical center. Sleep Med. 2009;10(7):753-758. [CrossRef] [PubMed]
 
Namen AM, Dunagan DP, Fleischer A, et al. Increased physician-reported sleep apnea: the National Ambulatory Medical Care Survey. Chest. 2002;121(6):1741-1747. [CrossRef] [PubMed]
 
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CHEST Journal Articles
Unrecognized Sleep Apnea in the Surgical Patient*: Implications for the Perioperative Setting
PubMed Articles
  • CHEST Journal
    Print ISSN: 0012-3692
    Online ISSN: 1931-3543