The presence of obstructive sleep apnea (OSA) in ambulatory surgical patients causes significant perioperative concern; however, few data exist to guide clinicians' management decisions. The objective of this study was to measure changes in perioperative sleep parameters among an ambulatory surgery population.
This study is a prospective, observational study of ambulatory patients undergoing orthopedic surgery on an extremity. Study subjects completed three unattended home sleep apnea tests: baseline before surgery, the first night after surgery (N1), and third night after surgery (N3). Anesthesia and surgical teams were blinded to study participation and patients received routine perioperative care.
Two hundred three subjects were enrolled and 166 completed the baseline home sleep test. Sixty-six (40.0%) had OSA at baseline, 35 patients received a new diagnosis, and 31 patients had a previous diagnosis of OSA. Of those with a previous diagnosis, 20 (64.5%) were compliant with continuous positive airway pressure therapy. Respiratory event index and SpO2 nadir did not significantly change postoperatively from baseline. Cumulative percentage of time oxygen saturation < 90% significantly increased N1 as compared to baseline for all patients except for those with moderate to severe OSA.
Ambulatory surgery had minimal effect on sleep parameters and there was no increase in adverse events among patients with either treated or untreated OSA.
Hudson AJ, Walter RJ, Flynn J, Szpisjak DF, Olsen C, Rodgers M, Capaldi VF 2nd, McDuffie B, Lettieri CJ. Ambulatory surgery has minimal impact on sleep parameters: a prospective observational trial. J Clin Sleep Med. 2018;14(4):593–602.
Current Knowledge/Study Rationale: Although the presence of obstructive sleep apnea (OSA) has been shown to be an independent risk factor for adverse outcomes among inpatients, several small studies among ambulatory surgical patients have not demonstrated an increase in perioperative risk. We aimed to study serial perioperative sleep parameter data among ambulatory surgical patients with and without OSA to evaluate the effect surgery and anesthesia may impose on this large surgical population.
Study Impact: Surgery and anesthesia had minimal effect on sleep parameters and there was no increase in adverse outcomes observed among patients with either treated or untreated OSA. Given the high prevalence of OSA observed in the ambulatory surgical population, further study exploring the effect of OSA on sleep parameters and outcomes is warranted.
The presence of obstructive sleep apnea (OSA) in ambulatory surgical patients causes significant perioperative concern; however, few data exist to guide clinicians in their management.1–3 Fluid shifts, decreased upper airway patency with increased collapsibility, residual anesthetics and opioids, and sleep fragmentation have all been postulated to acutely worsen existing OSA or potentially induce sleep-disordered breathing in susceptible individuals in the immediate period following surgery.4–6 Several studies have shown OSA to be an independent risk factor for perioperative complications including hypoxemia, respiratory failure, prolonged duration of mechanical ventilation, acute respiratory distress syndrome, unplanned intensive care unit days, and increased length of hospital stay for patients having surgery requiring an overnight stay.6–9
This concern has led to costly preoperative diagnostic studies prior to elective ambulatory procedures and subjected patients to unpredictable additional monitoring and hospitalizations. Over the past decade, several practice guidelines have been published but all rest on extrapolated data obtained from inpatient surgical populations, expert opinion, and professional consensus.10–12 Although based on plausible assumptions, the guidelines are not based on ambulatory outcome evidence, yet potentially affect millions of ambulatory surgical patients.
Current estimates of the prevalence of OSA range widely. According to the most recent epidemiologic studies, the prevalence of OSA in the general population is 24% to 49.7% among men and 9% to 23.4% among women.13–15 The prevalence among surgical patients may be even higher, especially within specific surgical populations such as bariatric surgery, in which prevalence has been reported to be as high as 71% to 88%.16–18 Furthermore, the prevalence of this already common sleep disorder appears to be rapidly increasing, with most remaining undiagnosed.19,20 Coincident with the observed rise in the prevalence of OSA is the rise in the number of ambulatory surgical procedures. The most recent published statistics for ambulatory surgery estimated that more than 53 million outpatient surgical procedures are performed annually in the United States, representing a 300% increase in outpatient procedure compared to the previous decade.21 Indeed, millions of patients with OSA present for ambulatory surgery each year. Therefore, any practice guideline to be widely adopted must be feasible in busy clinical practice and anchored to high-quality evidence-based data.
Many unanswered perioperative management questions remain for ambulatory surgical patients with OSA, including the suitability for ambulatory surgery and outpatient procedures requiring sedation, selection of anesthetic technique, length and location of postoperative monitoring, discharge criteria, and postoperative pain management. Several retrospective and prospective studies of the ambulatory surgical population have found no evidence of adverse outcomes.19,22–26 However, no study has provided data on the changes in perioperative sleep parameters that occur among ambulatory surgical patients. Data on the perioperative changes that occur among patients with OSA are critical to inform evidence-based practice guidelines.
The objective of this study was to further the understanding of potential risks imposed by OSA in the orthopedic ambulatory surgery population and measure changes in perioperative sleep parameters. Our study had three specific aims: establish the prevalence of OSA in patients undergoing ambulatory orthopedic surgery, measure the emergence or worsening of OSA in the early postoperative period, and determine if the presence of OSA increases adverse events. We hypothesized that patients with OSA would experience more unplanned admissions, adverse events, and/or deleterious changes in sleep parameters compared to patients without OSA.
This was a prospective, observational study approved by the Walter Reed National Military Medical Center and the Uniformed Services University of the Health Sciences Institutional Review Boards in Bethesda, Maryland (ClinicalTrials. gov Identifier: NCT01851798). This manuscript adheres to the applicable Strobe guidelines for reporting observational studies. The primary outcomes were postoperative unplanned admissions, death or readmission within 3 days, and other adverse perioperative events (difficult airway management, reintubation or airway obstruction requiring assisted ventilation, need for supplemental oxygen, and/or arrhythmias). The secondary outcomes were changes in polysomnography parameters measuring sleep-disordered breathing that occurred following surgery compared to the patient's baseline parameters. Patients underwent scheduled elective ambulatory orthopedic extremity surgery (ie, shoulder, arm, elbow, wrist, hand, hip, knee, ankle, or foot). Because all the orthopedic surgeries involved an extremity, regional anesthesia including individual nerve blocks or nerve plexus blocks (ie, brachial plexus or lumbar plexus) was an anesthetic option. Anesthesia and surgical teams were blinded to study participation. As such, all patients received routine perioperative care as planned by the surgical and anesthesia teams. Decisions pertaining to prolonged monitoring, admission, or discharge were at the clinical discretion of perioperative physicians. There were no interventions.
Consecutive English-speaking adult men and women, between 18 and 85 years old, who were American Society of Anesthesiologists (ASA) physical status I-II and scheduled for elective ambulatory orthopedic surgery were recruited. Patients who were pregnant or breastfeeding, chronic sedative or alcohol users, required home oxygen, or had a tracheostomy were excluded. Written informed consent was obtained for each enrolled study patient. Patients were recruited from May 2013 until January 2015.
All enrolled patients underwent three home sleep apnea tests (HSATs) with a five-channel portable HSAT device (T3, Nox Medical, Reykjavik, Iceland; FDA 510(k) k041724, November 7, 2008).27 The T3 device is a validated type 3 diagnostic tool for OSA. The five channels recorded for this study included two-lead electrocardiogram, pulse oximetry, nasal cannula (airflow), respiratory effort (single chest band), and body position sensor. The study investigators interpreted HSAT results according to guidelines published by the American Academy of Sleep Medicine in 2012.28,29 The interpreting sleep medicine physician was blinded to the surgery, anesthetic, postoperative pain management, OSA status, and continuous positive airway pressure (CPAP) usage.
At the time of study enrollment, a trained study investigator counseled the patient on operation of the HSAT device. If an enrolled patient had a previous diagnosis of OSA, the patient was asked to continue usual usage of CPAP, connecting the HSAT device to the CPAP air circuit. A study investigator called the patient at home each night the patient was scheduled to complete a home test to encourage completion, answer any questions, and be available for device troubleshooting. The patient manually started the device recording prior to sleep onset. The device was programmed to end recording after 10 hours.
All patients completed 3 separate nights of portable sleep monitoring. The baseline HSAT was performed between 1 to 30 days before the surgical procedure. The second HSAT was administered at home on the night of surgery, which was the first sleep period immediately following surgery (N1). The third and final HSAT was administered on the third major sleep period after surgery (N3). Patients were discharged from the hospital using standard discharge criteria. In the event of an unplanned admission, a T3 device was provided to the patient and the patient administered his/her own HSAT as would otherwise be conducted at home as long as the patient was capable of conducting the test (ie, extubated with stable mental status and vital signs.) Postoperatively, patients completed a questionnaire regarding pain medication use and pain scores for 3 days. When applicable, postoperative CPAP use was also recorded. After the third and final HSAT, the device and questionnaire were mailed to the research team in a preaddressed, postage-paid box.
The results of the three home tests were uploaded into the CareFusion 3.2.09695 platform (CareFusion, San Diego, California, United States). Test results were interpreted by a board-certified/board-eligible sleep medicine physician with extensive experience in the use of HSAT at an American Academy of Sleep Medicine accredited Home Sleep Apnea Testing Center. For each patient the respiratory event index (REI), oxygen desaturation index (ODI), oxygen saturation nadir (SpO2 nadir), and percentage of cumulative time SpO2 was below 90% (CT90) were recorded. All sleep studies were scored and reviewed postoperatively. In accordance with the standards published by the American Academy of Sleep Medicine, an REI ≥ 5 events/h was defined as mild OSA, REI 15 to < 30 events/h was defined as moderate, and > 30 events/h was defined as severe.28 The surgical and anesthesia teams were blinded to these results.
All patients were notified of their baseline results after completion of the study. Patients without a prior established diagnosis of OSA with an REI ≥ 5 events/h on the baseline preoperative HSAT were defined as patients with OSA and offered a full evaluation and treatment as necessary by a sleep medicine physician.
The STOP-BANG questionnaire was administered to all patients.30 For those with a previous diagnosis of OSA who were treated with CPAP, objective measures of CPAP use were recorded. Intraoperative data included type of anesthetic, surgical duration, airway management, medications including type and amount of opioid use converted to parenteral morphine equivalents (PME), and notation of the following adverse events: direct laryngoscopy attempts, intubating grade, and airway management changes due to adverse conditions.31 Postoperative data included postanesthesia care unit (PACU) time, type and amount of opioid use converted to PME, adverse events occurring in the PACU (reintubation or airway obstruction requiring assisted ventilation, need for supplemental oxygen, and/or arrhythmias), and the occurrence of unplanned hospital admission, death, or readmission within 3 days. Postoperative data also included pain scores for the first 3 days and type and amount of opioid use converted to PME.31 Additional data collected included demographic data, surgical procedure, comorbid disease, ASA physical status, and airway examination.
Perioperative and HSAT data were entered into Stata 14 for Windows (StataCorp LP, College Station, Texas, United States) for statistical analysis. Categorical data were compared using chi-square or two-tailed Fisher exact tests, as appropriate. The data were reported as frequency count and percentage. Continuous numerical variables were compared with the Student t test, Wilcoxon rank-sum test, or Kruskal-Wallis test, as appropriate. Pairwise comparisons of nonparametric data were adjusted with the Bonferroni correction. Data are reported as mean (± standard deviation) or median (interquartile range). A value of P < .05 was accepted as statistically significant. Mixed models with polysomnography parameters as outcome and night of study as predictor were used to analyze the differences between perioperative nights. Night of study was treated as a fixed within-subjects effect and subject was treated as a random effect. Mixed models use all available data, because patients who did not complete all three home sleep tests can contribute to parameter estimates for all time points for which they provided data. Pairwise comparisons of the postoperative value of each polysomnography parameter for each night to preoperative baseline were adjusted with the Šidák correction. The achieved sample size was sufficient to detect small to medium effect sizes in outcomes. Specifically, a sample of 66 patients with OSA and 100 patients without OSA has 80% power to detect a difference between groups of .45 standard deviations with a 5%, two-sided significance level. For comparisons over time, 134 paired samples have 80% power to detect a mean difference from baseline of .24 standard deviations.
Two hundred three subjects were enrolled. Twenty-two patients withdrew, nine failed to complete the baseline sleep study, and six had a technical failure with the HSAT device. Thus, 166 completed the baseline HSAT, 134 completed both the baseline and N1 tests, and 134 completed the baseline and N3 tests. One hundred nineteen patients completed all three sleep tests.
The demographic data summarized in Table 1 included patients who completed the baseline HSAT. The study population represented a young, predominantly male cohort—mean age 40 ± 11 years, 69% male. Among the cohort, 66 patients (40.0%) had OSA. Baseline testing revealed OSA in 35 patients in whom OSA was previously undiagnosed. Thirty-one patients had a previous diagnosis of OSA, 20 (64.5%) of whom were adherent with CPAP therapy. Among the patients with a baseline REI > 5 events/h, 11 had an REI > 15 events/h. The mean REI for those with OSA was 9.1 events/h, OSA not on CPAP was 12.2 events/h, and OSA on CPAP was 2.2 events/h (Table 2). The REI among those without OSA was 2.0 events/h. Statistically significant differences existed between patients with and without OSA in sex, age, body mass index, neck circumference, STOP-BANG score, baseline REI, and prevalence of hypertension and hyperlipidemia (Table 1).
Perioperative home sleep apnea testing parameters.
Perioperative home sleep apnea testing parameters.
Sleep testing parameters stratified by OSA status are summarized in Table 2. Baseline REI for each group was compared to N1 and N3 using a mixed model as described in the Methods section. The only significant change in REI was a decrease in REI on N3 compared to baseline for patients with moderate to severe OSA (REI: baseline 27.6 events/h, N3 16.2 events/h). For all other groups, REI did not significantly change postoperatively from baseline.
Overall, more hypoxemia was noted postoperatively among the cohort, regardless of the presence of OSA. CT90 significantly increased at N1 as compared to baseline in the overall cohort and in patients with OSA, patients without OSA, patients with preoperatively diagnosed OSA, patients with OSA who are adherent to CPAP, and patients with OSA who are not adherent to CPAP. The only subgroup for which the CT90 did not significantly worsen was among patients with moderate to severe OSA. CT90 for the overall cohort as well as patients without OSA remained significantly worse on N3 compared to baseline, and among patients with OSA there was no difference between N3 and baseline. Stratifying by anesthetic technique revealed CT90 to be significantly increased on N1 following general anesthesia but not regional (Table 3). This was true for both patients with OSA and those without OSA but persisted on N3 only for those without OSA.
Perioperative home sleep apnea testing parameters stratified by OSA and anesthetic technique.
Perioperative home sleep apnea testing parameters stratified by OSA and anesthetic technique.
SpO2 nadir at baseline ranged from 78% in patients with moderate to severe OSA to 87% in patients without OSA. SpO2 nadir significantly increased (improved) on N1 compared to baseline among patients with moderate to severe OSA. For all others, no significant change was observed. ODI did not change significantly among the cohort or any subgroups on N1 or N3 compared to baseline.
Anesthetic technique and opioid use is summarized in Table 4. General anesthesia was administered alone or in combination with regional anesthesia for 89% of patients with no difference between patients with or without OSA. There was no difference in anesthetic technique between patients with and without a previous diagnosis of OSA. Eighty-seven percent of patients managed their postoperative pain with opioids. No statistically significant difference emerged between patients with and without OSA in total, intraoperative, PACU, or home opioid requirements. The only statistically significant finding was an increase in opioid use among patients with OSA who were admitted for non–pain-related reasons compared to patients with OSA who were discharged to home (total opioid use: 24.2 [20.0, 30.0] mg versus 10.0 [5.1, 18.1] mg, respectively, P = .030).
Anesthetic technique and opioid use.
Anesthetic technique and opioid use.
Outcome data are summarized in Table 5. Overall there were 24 admissions with 1 attributed to oxygen desaturation in the operating room. There was no mortality, readmission within 3 days, or acute respiratory failure. There was no difference in adverse events between patients with and without OSA. Pain was the most common reason for admission. The second most common reason was prolonged monitoring due to concern for OSA either based on previous diagnosis or presumed diagnosis by the surgeon or anesthesiologist due to clinical findings during the perioperative course. The perioperative physician made the decision for prolonged monitoring empirically on the day of surgery as all clinicians were blinded to the study data. Overall there were 61 PACU complications, defined by the need for supplemental oxygen, airway management, or observed arrhythmias (bradycardia, tachycardia, premature ventricular contractions, or new onset of atrial fibrillation). There were no differences in PACU complications between patients with and without OSA.
No difficult or failed intubations were observed (Table 5). One surrogate measure of difficult airway management, intubating grade, revealed a trend toward worse grades among patients with a preoperative diagnosis of OSA who were adherent to CPAP compared to both patients without OSA and patients who had a previous diagnosis of OSA but were not adherent to CPAP—this did not reach statistical significance (P = .057).
The results of this prospective study examining ambulatory surgery outcomes in conjunction with preoperative and postoperative home sleep testing revealed a high prevalence of OSA. Nearly half of the cohort had OSA, most previously undiagnosed. A preoperative diagnosis of OSA or concern for OSA risk was the second most common reason for overnight monitoring whereas no trend emerged for OSA identified from baseline testing. Interestingly, most recorded sleep parameters did not change in the postoperative period. Overall, REI and ODI did not worsen on the night of surgery (N1) or postoperative night 3 (N3) in patients with and without OSA.
Our N1 findings are consistent with those reported in two similar studies examining serial polysomnography among an inpatient surgical cohort.4,5 In a study reporting serial polysomnography through postoperative night 7 of an inpatient cohort, the apnea-hypopnea index (AHI) and ODI did not change significantly on N1 following inpatient surgery, with the exception of AHI for patients without OSA.4 Chung et al.'s study of serial polysomnography through postoperative night 3 reported mixed results.5 AHI statistically increased on both N1 and N3 in the patients without OSA, with mild OSA, and with moderate OSA. However, the AHI decreased on N1 in patients with severe OSA.5 The median increase in AHI on N1, while statistically significant for patients with mild and moderate OSA, was small: mild OSA (preoperative AHI 9.6 events/h to N1 10.6 events/h), moderate OSA (preoperative AHI 21.9 events/h to N1 22.2 events/h). The unexpected finding of AHI peaking on N3 in Chung et al.'s study has important implications for the appropriate number of days of postoperative monitoring for at risk inpatients.
The SpO2 nadir did not change postoperatively from baseline. The overnight SpO2 nadir was, as expected, lower at baseline and postoperatively in those with OSA compared to those without OSA, but neither group statistically changed from their respective baselines. Prior reports have observed patients with OSA experience desaturation postoperatively.6,19,24,25 However, these reports did not necessarily compare postoperative data to preoperative measures of the SpO2. As such, these observations may merely reflect the effect of OSA on oxygenation and not a true representation of the effects of surgery. In our study, we did not find that patients experienced further reductions in their oxygenation during the postoperative observation period.
We did, however, observe that the overall burden of hypoxemia increased in all patients, as measured by the CT90. Our CT90 findings contrast with the inpatient findings.4 Among inpatient patients with OSA, CT90 changed from a baseline of 1.3% to 0.6% on N1. Although a trend toward postoperative worsening in these parameters was observed on N3, N1 data demonstrated either no change or a trend toward improvement (ie, lower CT90 and higher SpO2 nadir).4 This may be due to 53% of the inpatients with OSA receiving supplemental oxygen on the night of surgery confounding the analysis.4 Because our cohort was an ambulatory surgical population, only one patient received supplemental oxygen on N1 following an unplanned admission. Without postoperative supplemental oxygen, we were able to observe the increase on CT90 (ie, more time spent with an oxygen saturation below 90%). Furthermore, even though our cohort did not receive supplemental oxygen, the SpO2 nadir in our study did not decline as was similarly observed among the inpatient cohort.
Stratifying our data by anesthetic technique (Table 3), CT90 statistically increased N1 in patients who received a general anesthetic regardless of their OSA status. Interestingly, the statistical finding persisted to N3 for patients without OSA but resolved by N3 for those with OSA. Many potential explanations exist for an increase in CT90 postoperatively compared to preoperative measures without a lower SpO2 nadir, including decreased functional residual capacity resulting from general anesthesia, pain, supine positioning and immobility, opioids, and relative hypoventilation. Hypoxemia, as represented by increased CT90, may prove to be more important postoperatively than SpO2 nadir, respiratory indices (AHI/REI), or ODI.32,33
The night of surgery, fraught with the use of pain medication, residual anesthetics, and fluid shifts, has been long considered the period of greatest risk for patients with OSA. Although our data did not include sleep architecture, data available from perioperative studies including EEG provide a basis for understanding our results. Decreases in sleep efficiency, percentage of rapid eye movement (REM) sleep, and slow wave sleep with a nadir on the night of surgery have been consistently demonstrated. REM starts to return to preoperative baseline on N3 but does not fully recover to preoperative baseline levels until night 7.4 As events of hypopnea and apnea occur predominantly with REM sleep, the significant decline of REM on the night of surgery is consistent with findings of a decline or no change in REI and ODI on N1 in our study. This improvement in sleep parameters may simply be the result of poor and fragmented sleep. Less sleep results in fewer apneic events, which results in artificially improved REI, ODI, and SpO2. Thus, fragmented sleep may be counterintuitively protective for ambulatory surgical patients with OSA.
Overall, we did not find a relationship between unplanned admissions or other adverse events and OSA status. Stratifying by severity (no OSA, mild OSA, moderate OSA, severe OSA) did not reveal a relationship, nor did anesthetic technique (general anesthesia/combined general and regional versus regional anesthesia/monitored anesthesia care) (Table 5). In all analyses of unplanned admissions or other adverse events and OSA status, no statistically significant relationship emerged except for prolonged monitoring. We found that those with known OSA who were adherent to CPAP were admitted more frequently (Table 5) for overnight monitoring. Although counterintuitive, this may be due to an empiric concern for risk associated with OSA despite otherwise meeting all discharge criteria when compared both to those with OSA who are not adherent to CPAP therapy and also when compared to those without OSA. The uncertainty surrounding the suitability of ambulatory surgery, the appropriate duration of monitoring and concern for the safety of patients discharged to their home following anesthesia may have affected the disposition decision for patients with OSA who were adherent to CPAP. Indeed, CPAP use may have been falsely interpreted as a surrogate marker of OSA severity in the busy perioperative environment. The data from this study suggest these admissions were potentially unnecessary, increasing costs and resource utilization with limited additional benefits while potentially increasing risks to the patient.
Although our study is small, our prospective findings using baseline sleep study results are consistent with several retrospective and prospective studies examining ambulatory surgery patients with OSA employing a variety of methods to identify patients with OSA including ICD-9 codes, historical sleep studies, patient report, and propensity scoring.19,22–26 A retrospective analysis of 234 non-otorhinolaryngologic ambulatory surgical patients with diagnosis via polysomnography of OSA using 1:1 matched controls did not find OSA to be an independent risk factor for unplanned hospital admissions or other adverse events.26 A prospective study using propensity scoring to assign OSA status did not find a relationship between unplanned hospital admission and OSA among ambulatory surgical patients.19 A retrospective study of an otorhinolaryngologic surgical population using Medicaid managed care claims data for patients with reported OSA observed no differences between patients managed on an inpatient or out-patient basis, suggesting the safety of managing patients with OSA for ambulatory surgery.22 Another retrospective study of ambulatory orthopedic surgical patients with OSA identified by ICD-9 codes found no association with OSA to adverse outcomes or unplanned admissions.25 A fourth retrospective study of ambulatory laparoscopic gastric banding patients identified as high risk for OSA if they had received CPAP or met at least three STOP-BANG criteria found no association with adverse outcomes including unplanned admissions.24 And, last, a large historical cohort study of patients with sleep studies who underwent ambulatory surgical did not find an increased rate of unplanned admissions associated with OSA.23 Thus to date, and including our study, no association between OSA and an increased risk of adverse perioperative outcomes among ambulatory surgical patients has been found.
Several specific areas of perioperative management of ambulatory surgical patients warrant further discussion. The first is the issue of residual anesthetics and their effect on patients with OSA. Based on expert opinion, the ASA practice guidelines as well the Society of Ambulatory Anesthesia guidelines recommend against the use of general anesthesia for patients with OSA.11,12 In this cohort, CT90 increased statistically postoperatively as discussed but did so among both patients with and without OSA. And, in fact, among patients without OSA the increase in CT90 persisted to N3 but resolved by N3 among patients with OSA. No significant change occurred in any other sleep testing parameter among patients receiving general anesthesia. Further investigation of the clinical effect of general anesthesia on patients with OSA who are undergoing ambulatory surgery is warranted.
Another concern affecting the suitability of OSA patients for ambulatory surgery involves the postoperative pain management plan. According to published guidelines, patients with OSA who are otherwise optimized may be considered for most types of ambulatory surgery, “if postoperative pain relief can be provided predominantly with non-opioid analgesic techniques.”12 In addition to opioid-mediated central respiratory depression via mu and kappa receptors and tonic inhibition of upper airway dilator muscles that occurs in all patients, patients with OSA are considered more sensitive to the respiratory depressant effects of opioids.34–37 In our study, 87% of patients managed their postoperative pain with opioids, but no statistical difference in intraoperative or postoperative opioid use was identified between patients with and without OSA. Likewise, in a similar study of inpatients, no such relationship between OSA and opioid requirements was found in the first 24 hours.4 Further investigation of the clinical relationship between OSA and opioid sensitivity is warranted.
Regarding postoperative opioid use after ambulatory procedures, we found routine use of low-potency oral opioid medication. When managed at home, patients tolerated mild to moderate pain and were restrained in their self-treatment of pain. In contrast, since the publication of The Joint Commission Statement on Pain Management in 2001 and The Institute of Medicine's publication Relieving Pain in America in 2011 as well as the widespread interpretation that “pain is a vital sign,” inpatient nurses and physicians may be less tolerant of patients' reported mild to moderate postoperative pain and treat it with potent intravenous opioids.38,39 In our study, a trend toward greater opioid use was observed in patients who were admitted to the hospital (for a reason unrelated to pain) compared to patients discharged to their home. This relationship was statistically significant in patients with OSA, indeed the very cohort thought to be most at risk from opioids. In a prescient statement a surgical colleague wrote, “The goal of perioperative pain control should be the control of pain, not its elimination. Postoperative moderate and manageable pain, although rarely touted as such, may be protective against critical respiratory depression in all patients.”22 He continued, “In our zeal to eliminate all pain, we may contribute to the unintended consequence of over medication and risk of respiratory compromise.”22 Although multimodal postoperative analgesia is relevant to all ambulatory surgical patients, OSA status may not require special consideration.
There are several limitations to this study. The small cohort size and few complications may have prevented us from observing a difference between groups. We also chose to study all patients presenting for ambulatory surgery regardless of CPAP adherence to replicate the conditions found in a routine ambulatory surgery environment. When possible, subgroup analyses were completed but we acknowledge the inclusion of patients adherent to CPAP therapy may have mitigated some of the changes in sleep parameters observed in the cohort. In addition, a type 3 home sleep testing device was used, and as such, was unable to measure electroencephalographic (EEG) changes, alterations in sleep architecture, and arousals which have been previously shown to occur in response to acute pain. To further advance understanding of ambulatory surgery risk in patients with OSA and before widespread implementation of specific practice guidelines is undertaken, larger studies expanding the cohort to include all ambulatory surgical patients are warranted.
In conclusion, ambulatory surgery had minimal effect on sleep parameters among patients with ASA physical classification I and II, and there was no increase in adverse events among patients with either treated or untreated OSA. Data from this study support the practice of standard perioperative care for ASA physical classification I and II ambulatory surgical patients without the need to alter care or decision making solely based on OSA status. Although a difference between those who did or did not use CPAP was not observed, there are many potentially confounding factors or complications that were unmeasured and continued CPAP use in the postoperative period is recommended to mitigate potential risks. The absence of significant adverse sleep changes among patients with and without OSA in the context of general anesthesia and postoperative opioid use has significant clinical implications for the perioperative management of ambulatory surgical patients. The data do not support the need for preoperative testing, diagnosis or treatment for those suspected of mild to moderate OSA, or change in standard management of ambulatory patients including anesthetic technique and postoperative pain management strategy due solely to OSA status. As surgical techniques, anesthesia practices, and postoperative pain management continue to evolve for ambulatory patients, OSA may not pose the physiologic risk previously thought for this large surgical population. As this study suggests, there may be many patients with OSA presenting for ambulatory surgery who may safely undergo routine perioperative management including discharge to their home following standard discharge criteria.
Work for this study was performed at Walter Reed National Military Medical Center and the Uniformed Services University of the Health Science, Bethesda, MD. All authors have seen and approved the manuscript. The authors report no conflicts of interest. The identification of specific products or scientific instrumentation is considered an integral part of the scientific endeavor and does not constitute endorsement or implied endorsement on the part of the author, Department of Defense, or any component agency. The views expressed in this article are those of the authors and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense, or United States Government.
American Society of Anesthesiologists
body mass index
continuous positive airway pressure
percentage of cumulative time SpO2 was below 90%
home sleep apnea test
first sleep period immediately following surgery
third major sleep period after surgery
oxygen desaturation index
obstructive sleep apnea
post anesthesia care unit
parenteral morphine equivalents
respiratory event index
rapid eye movement
oxygen saturation nadir
The authors acknowledge the help of Karen Sheikh, MS, Department of Sleep Medicine, Walter Reed National Military Medical Center, Bethesda, MD, for her help with data collection and preliminary analysis of the data. Author contributions: Arlene J. Hudson (concept, design, data collection, writing, statistical analysis, critical revisions, final approval, overall responsibility), Robert J. Walter, MD, DHCE (sleep study interpretation and analysis, critical revisions, final approval), John Flynn, MD (data collection, critical revisions, final approval), Dale F. Szpisjak, MD, MPH (statistical analysis, critical revisions, final approval), Cara Olsen, DrPH (design, statistical analysis, critical revisions, final approval), Matthew Rodgers, MD (sleep study interpretation and analysis, critical revisions, final approval), Vincent F. Capaldi II, ScM, MD (sleep study interpretation and analysis, critical revisions, final approval), Brent McDuffie, MSN (data collection, critical revisions, final approval), and Christopher J. Lettieri, MD (concept, design, sleep study interpretation and analysis, critical revisions, final approval).
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