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Pain Intensity and Opioid Utilization in Response to CPAP Therapy in Veterans with Obstructive Sleep Apnea on Chronic Opioid Treatment

Published Online:https://doi.org/10.5664/jcsm.6046Cited by:13

ABSTRACT

Study Objectives:

Sleep fragmentation has been linked to poor pain tolerance and lowered pain threshold. Little evidence exists on whether continuous positive airway pressure (CPAP) adherence in veterans with obstructive sleep apnea (OSA) who are taking opioids for non-malignant pain would ameliorate pain and reduce consumption of opioids.

Methods:

A retrospective case-control study was performed at a VA sleep center. Pain intensity was assessed using the Numerical Categorical Scale prior to CPAP treatment and 12-mo follow-up. Opioids intake was assessed using the morphine equivalent daily dose (MEDD). Adherence to CPAP was evaluated with the built-in meter.

Results:

We reviewed 113 patients with OSA (apnea-hypopnea index [AHI] 35.9 ± 29.5) using a MEDD of 61.6 mg (range 5–980 mg) and a control group of 113 veterans with OSA (AHI 33.4 ± 27.3) on no opioids treatment. CPAP adherence was significantly lower at 12 mo in opioid-treated patients compared to controls (37% versus 55%; p = 0.01). Greater pain intensity was the only independent variable associated with CPAP non-adherence at 12-mo follow-up (p = 0.03). Compared to baseline, no significant difference was observed in pain intensity or consumption of opioids in CPAP adherent patients.

Conclusions:

CPAP treatment did not reduce pain intensity or consumption of opioids in veterans with chronic pain who have coexisting OSA. CPAP adherence was lower in opioid-treated veterans with OSA compared to opioid-free veterans with OSA. Pain intensity was the only determinant of CPAP adherence. Future studies are needed to evaluate pain management program on adherence to CPAP.

Citation:

Jaoude P, Lal A, Vermont L, Porhomayon J, El-Solh AA. Pain intensity and opioid utilization in response to cpap therapy in veterans with obstructive sleep apnea on chronic opioid treatment. J Clin Sleep Med 2016;12(8):1105–1111.

INTRODUCTION

Chronic pain is a serious and highly prevalent condition among service members. Although the prevalence of chronic pain and opioid use associated with deployment is not well known, a recent report indicated that nearly half of a group of infantry soldiers who had seen combat in Afghanistan have reported experiencing chronic pain.1 The better body armor combined with advanced medical care on the battlefield had led to improved survival rates following serious residual injuries (such as limb amputations and severe nerve and musculoskeletal damage) caused by blasts or projectiles. The multiplicity of these wounds in soldiers, coupled with cognitive impairments associated with traumatic brain injury and mental health morbidity has caused an increase in dependency on opioids to suppress these ailments.

BRIEF SUMMARY

Current Knowledge/Study Rationale: There is a bidirectional relationship between sleep apnea and chronic pain. The aim of the study is to determine the effect of CPAP on pain intensity and opioids use in opioid-treated veterans with OSA.

Study Impact: Pain intensity was the only determinant of CPAP adherence. The results underscore pain management as a part of multidisciplinary approach to improve CPAP adherence.

The use of opioids has been associated with development of sleep-disordered breathing, including central apneas, ataxic breathing, and nocturnal hypoxemia.24 However, obstructive sleep apnea (OSA) is considered the predominant form of sleep-disordered breathing among veterans receiving chronic opioid therapy.5 However, this reduced pain sensitivity has been reported in healthy volunteers with induced rather than preexisting pain. It is speculated that the sleep disruption resulting from repeated arousals alters the nociceptive system, rendering it more susceptible to maladaptive plasticity. Preliminary investigations in healthy volunteers have suggested that although pain sensitivity is increased following sleep restriction, restoring sleep architecture ameliorates pain thresholds.6,7 Further, treatment of underlying sleep apnea with continuous positive airway pressure (CPAP) has been associated with reduced hyperalgesia in patients with sleep-disordered breathing.8 Given the bidirectional nature of sleep and pain, only scant data have been published about the interaction of sleep apnea, pain intensity and opioids use in opioid-treated veterans with chronic pain. The objectives of this study are to: (1) examine the association between pain intensity and OSA severity; (2) assess CPAP adherence in this population compared to a control group of veterans with OSA and not taking narcotics; (3) delineate factors associated with CPAP non-adherence in opioid-treated veterans; and (4) determine the extent CPAP treatment would result in alleviating the burden of pain and opioids consumption. We have amended the manuscript accordingly.

METHODS

Subjects

We conducted a retrospective analysis of all veterans seen at Western New York Healthcare System sleep clinic who were receiving around-the-clock opioid medications for non-malignant pain and in whom OSA had been newly diagnosed by polysomnogram between January 2012 and September 2014. Patients were considered to be on chronic opioids if they regularly used short- or long-acting opioids around the clock for at least 6 mo with their daily dose stable for at least 4 w. Exclusion criteria were concomitant central apnea (central apnea index ≥ 5), use of nocturnal oxygen or non-invasive positive airway pressure prior to OSA diagnosis, presence of elevated arterial carbon dioxide levels (when available) while awake (PaCO2 ≥ 50 mmHg) due to intrinsic lung disease, or neuro-muscular or musculoskeletal disorders. A comparable control group of veterans with OSA who were not using opioids was selected using the same exclusion criteria. The study protocol was approved by the institutional review board of the Western New York Veteran Affairs Healthcare System, Buffalo, NY.

Data Collection

Baseline biometric parameters of age, body mass index (BMI), and sex were extracted from the electronic medical records. Collected data included also demographics, clinical diagnoses, Charlson Comorbidity Index, and polysomonographic indices. Epworth Sleepiness Scale (ESS), pain intensity, and medication use were evaluated at the time of initial sleep evaluation and at 12-mo follow-up after CPAP initiation. For opioid agents, the morphine equivalent daily dose (MEDD) was calculated based on established recommendations.9 We categorized non-opioid medications that were prescribed into the following groups: (1) benzodiazepines, (2) antidepressants, (3) anticonvulsants, and (4) non-steroidal anti-inflammatory agents. The pain intensity was assessed using the Numerical Categorical Scale (0 to 10 points), with 0 indicating “no pain”, 10 “maximum pain”, whereas the other scores, from 2 to 9, indicated intermediary perceived pain levels.10 Case records were reviewed at the 12-mo follow-up visit for change in pain intensity, opioid medications, and CPAP adherence.

Polysomnogram

Standard polysomnographic techniques were used according to international recommendations11 on all participants. Data from the electrooculogram, electroencephalogram, electrocardiogram, electromyogram, airflow measurement, and pulse oximeter were recorded with Sandman digital recording equipment (Tyco, Ottawa, Canada). The apnea-hypopnea index (AHI) was calculated from the polysomnographic data, using widely accepted criteria.11 Apneas were defined as cessation of airflow for at least 10 s, and hypopneas were defined by a decrease in airflow for at least 10 s accompanied by an oxyhemoglobin desaturation of 4% or greater. The diagnosis of OSA was considered present when AHI was ≥ 5/h. Severity of OSA was graded as mild (5 ≤ AHI < 15/h), moderate (15 ≤ AHI < 30/h), or severe (AHI ≥ 30/h).

Follow-up

All patients were managed according to the prevailing international recommendations for these diseases. Patients received individual counseling about CPAP during a scheduled clinic appointment after their initial sleep clinic consultation and polysomnographic studies were completed. During this appointment they received one-on-one counseling by a respiratory therapist on sleep hygiene, adjunctive/conservative methods to improve sleep, and the importance of treatment adherence. Patients were also provided specific counseling on the proper use and maintenance of CPAP and underwent personalized, formal mask-fitting sessions to anticipate potential complications and tips to avoid them. CPAP use was objectively measured in all patients during follow-up evaluation using a downloadable monitoring smart-card (Respironics Encore Anywhere) and annually thereafter. Adherence with CPAP was determined at a subsequent clinical follow-up visit using CPAP memory cards. Adequate adherence was defined as CPAP usage for 4 h or more per 24 h for a minimum of 70% of days during a consecutive 30-day period.12,13

Statistical Analyses

Means and standard deviations were calculated for all demographic and polysomnographic variables and standard sleep parameters. The D'Agostino-Pearson test for normality was conducted for each variable within conditions. If the assumptions of normality were met, then a t test was used for comparisons. Otherwise, the Wilcoxon test was used. A chi-square analysis with the Fisher exact test (when appropriate) was used to compare categorical data. One-way analysis of variance or analysis of variance on ranks with the Dunn test of multiple comparisons was used to compare the baseline among treatment groups. The correlation among pain intensity and CPAP adherence was calculated using Spearman correlation coefficient. All p values ≤ 0.05 (two-tailed) were considered statistically significant. We performed a total of 12 multiple imputations by using the Markov Monte Carlo method to impute missing data for variables used in the analyses because a general rule of thumb is to select the number of imputations using the average missing data rate.14 Multiple imputations predict values for incomplete variables by using regression equations from complete variables. This minimizes assumptions that the subset of cases with complete data is representative of the entire sample population. Multiple imputations are used so that biases resulting from missing data are reduced; it provides better, nearly unbiased estimates than other methods of handling missing data.

We performed stratified analyses to identify determinants of CPAP adherence in veterans who were prescribed opioids versus those who were opioid-free using Mantel-Haenszel tests of homogeneity. A stepwise logistic regression was performed to identify independent factors associated with CPAP adherence (dependent variable) in opioid-treated patients. Variables selected to predict CPAP adherence were chosen based on previous research including age, BMI, AHI, CPAP pressure, ESS, insomnia, comorbid diseases, and pain intensity.15,16 Only those variables that produced a value of p < 0.15 in univariate analysis were entered into the stepwise regression model. Collinearity was evaluated by the variance inflation factor. All analyses were performed with statistical software (SAS release 8.1, SAS Institute, Cary, NC).

RESULTS

A total of 113 opioid-treated patients and 113 controls were identified during the study period. Sociodemographic and clinical characteristics of the study population are presented in Table 1. Patients with OSA who are chronic opioid users were on average middle-aged but slightly younger than the comparator group. The anthropomorphic characteristics of opioid-treated patients and sleep clinic controls were similar. Morbid obesity was observed in 26 of the opioid-treated patients (23%) and 18 of the controls (16%; p = 0.24). Although the prevalence of coronary artery disease and diabetes was not different between the two groups, opioid-treated patients suffered more frequently from depression (p < 0.001) and insomnia (p = 0.01) and were more likely to have received a diagnosis of PTSD (p < 0.001). In addition, the pain intensity of opioid-treated veterans was significantly higher than the controls (2.5 ± 1.4 versus 1.6 ± 1.1; p < 0.001)

Table 1 Characteristics of study population with obstructive sleep apnea.

Table 1

The median daily dosage of all opioids was 61.6 mg of morphine equivalents (range 5–980 mg) in the opioid-treated group. The most commonly prescribed opioid medications were hydrocodone (n = 71) and oxycodone (n = 13). Eleven percent had a combination of two or more opioid analgesics. Musculoskeletal disorders accounted for the majority of the opioid prescriptions (83%), including back pain and peripheral joint diseases. Neuropathies were a distant second at 11%. In terms of non-opioid medications, a significantly higher proportion of those subjects who were chronically taking opioids, as compared with those not on opioids, were also taking benzodiazepines (21% versus 9%, p = 0.01) and zolpidem (18% versus 4%, p = 0.001). The median daily dosage in diazepam equivalents was 15 mg/day (range 2.25–40 mg/day). Seventy four percent of opioid-treated patients were prescribed at least one other agent with a central nervous system action: selective serotonin reuptake inhibitors (n = 28), gabapentin (n = 25), tricyclic antidepressants (n = 16), and baclofen (n = 9). Twenty opioid-dependent patients (18%) were on two or more of these agents in addition to their opioid therapy.

Opioid-treated patients had more subjective excessive daytime sleepiness and shorter sleep time in comparison with veterans on no opioid therapy. Despite the comparable severity of sleep apnea between the two groups, arousal frequency was significantly reduced in opioid-treated patients compared to controls (Table 1).

Mean and standard deviation of pain severity in the opioid-treated group based on AHI are shown in Figure 1. Analysis of variance showed no significant difference between mean pain intensities or MEDD among mild, moderate, and severe sleep apnea (p = 0.86 and p = 0.29; respectively). There was also no correlation between AHI and pain intensity at baseline (r = 0.02; p = 0.76) (Figure 2).

Figure 1: Distribution of pain severity according to severity of obstructive sleep apnea.

Severity of obstructive sleep apnea was graded as mild (5 ≤ AHI < 15/h), moderate (15 ≤ AHI < 30/h), and severe (AHI ≥ 30/h). AHI, apneahypopnea index.

Figure 2: Relationship between pain intensity and apneahypopnea index (AHI).

All patients underwent in-laboratory CPAP titration. The mean CPAP pressure following the titration study was 9.8 ± 3.2 cm H2O for the opioid-treated group and 9.1 ± 2.7 cm H2O for the control group (p = 0.09). Of the 113 opioid-dependent patients, 93 patients had returned for 12-mo follow-up visit. Of those 93 patients, CPAP usage data were missing from six records. Chronic opioid users had lower CPAP adherence at 12 mo compared to non-opioid users (37% versus 55%; p = 0.01). Specifically, CPAP was used 56.2% ± 21.7% of nights in patients on opioids compared to 73.3% ± 14.6% in controls (p = 0.002). The mean nightly use of CPAP during nights used was 3.7 ± 1.4 h/night among the opiate group, compared with 5.5 ± 2.1 h/night among the control group (p < 0.001).

Table 2 depicts the clinical characteristics of opioid-treated veterans adherent to CPAP therapy and those who were CPAP therapy non-adherent. No significant difference was found between the two groups in age, sex, and burden of comorbidities. The sleep indices and OSA severity between the CPAP-adherent and CPAP-non-adherent group were also comparable. The optimal CPAP for adherent and non-adherent patients was 9.7 ± 3.3 cm H2O and 9.4 ± 3.2 cm H2O, p = 0.6; respectively. Stratified analyses identified Caucasian males younger than 60 y to be associated with CPAP non-adherence in veterans who were prescribed opioids than those who were opioid-free (Table 3).

Table 2 Comparison of clinical characteristics of CPAP-adherent and CPAP-non-adherent Veterans with OSA on chronic opioids.

Table 2

Table 3 Stratified analyses of determinants of continuous positive airway pressure adherence in Veterans with sleep apnea who were receiving chronic opioids versus those who were opioid free.

Table 3

CPAP non-adherent patients reported higher pain intensity than those patients adherent to CPAP treatment at baseline and at 12 mo (2.7 ± 1.4 versus 2.1 ± 1.4; p = 0.03; 2.6 ± 1.5 versus 1.9 ± 1.5; p = 0.05; respectively) (Figure 3). At 12-mo follow-up, there was no significant decline in pain intensity from baseline in either group (difference in means 0.23 (95% confidence interval [CI] −0.38 to 0.84) in CPAP-adherent patients and 0.22 (95% CI −0.27 to 0.73) in CPAP-non-adherent patients (Figure 3). Of interest, the baseline pain intensity for veterans who showed up for follow-up at the 12-mo appointment was 2.53 ± 1.41 versus 2.55 ± 1.7 for those who failed to return for follow-up (p = 0.97). By multivariate analysis, pain intensity at baseline was the single predictor of CPAP non-adherence at 12 mo (OR 1.46; 95% CI 1.047–2.03; p = 0.03). The MEDD was not different between CPAP-adherent and CPAP-non-adherent patients at 12-mo follow up (p = 0.22). Compared to baseline, MEDD dropped from 65.2 ± 111.9 mg/d to 50.8 ± 109.0 mg/d at 12-mo follow-up (p < 0.001 by Wilcoxon test).

Figure 3: Pain intensity at baseline and at 12-mo follow-up in continuous positive airway pressure (CPAP)-adherent (A) and CPAP-non-adherent (NA) patients who are taking opioids.

DISCUSSION

To the best of our knowledge, this study is the first to report on the effect of CPAP on pain intensity and opioid consumption among opioid-treated veterans with OSA. Previous studies conducted in non-opioid users revealed that experimental sleep fragmentation and sleep deprivation enhance sensitivity to pain,7,17 augment spontaneous pain, and decrease pain threshold.18 In clinical practice, patients frequently attribute their sleep disturbance to pain and pain intensity scores have been found to predict sleep disturbances.19,20 Two long-term prospective studies have suggested that prolonged nighttime awakenings in patients with rheumatoid arthritis are associated with increased joint pain severity, and that subjective sleep problems increased risk for development of widespread pain within 15 mo in the general population.21,22 Different theories have been advanced to explain the mechanisms underlying sleep and pain, including repeated mircroarousals and disruption of restorative slow-wave sleep.23,24 However, not all studies have identified a significant association between pain severity and sleep.25,26 Most studies investigating the relationship between these two entities have focused on sleep disturbances in patients with pain, and few studies have investigated pain in opioid-treated patients with OSA. In our patient population, we did not find a significant association between either pain intensity score or opioid consumption and AHI. Attended polysomnography revealed that approximately 60% of our patients had a mean nocturnal nadir SaO2 ≤ 80% and 9% of sleep time spent at SaO2 < 90%. This recurrent nocturnal arterial desaturation may be responsible for the increased sensitivity to the analgesic effects of opioids through upregulation of μ-opioid receptors as previously documented in developing rats.27,28 Furthermore, insulin growth factor binding protein-1 (IGFBP-1), a serum marker of hypoxia,29 is associated with hypoalgesia to experimental heat, as well as increased potency of opioid analgesia.30 Consistent with these findings, Doufas and colleagues30 have shown that nocturnal hypoxemia in subjects at high risk for OSA was associated with an increased potency of opioid analgesia.

The results of our study suggest that CPAP adherence in opioid-treated veterans with OSA is lower than in veterans on no opioid treatment. Many factors are associated with adherence to CPAP, and include patient characteristics, psychological factors, side effects, cost of treatment, expertise available, and implementation of educational strategies to improve compliance with treatment.3133 In our analysis, underlying comorbid illnesses and psychiatric ailments were predominant features in opioid-treated patients compared to controls. Knowing that posttraumatic stress disorders and depression score have been linked to poor compliance with CPAP therapy,3436 it is plausible that the co-occurrence of these conditions with chronic pain may limit acclimatization to CPAP.

One noticeable finding of our study is the lack of improvement in pain intensity following 12 mo of CPAP treatment in opioid-treated veterans. In contrast to our findings, a randomized, double-blind crossover study of low versus high CPAP pressure (4 cm versus 5–10 cm H2O) in elderly patients with moderate OSA showed that electrical tolerance score was significantly augmented only under high CPAP treatment.37 Another trial of 12 patients with severe OSA demonstrated that CPAP treatment reduced pain sensitivity by improving finger withdrawal latency to a radiant heat stimulus.8 Withdrawing CPAP treatment returned finger withdrawal latency to baseline. Although the exact mechanism and specificity of this analgesic effect of CPAP is unknown, fragmented sleep was advanced as a potential modifiable risk factor underlying the increased hyperalgesia.17 However, there was strikingly little sleep fragmentation in our study, as demonstrated by the arousal index in opioid-treated patients. An alternative explanation is that previous studies evaluated the effects of sleep-disordered breathing in seemingly intact sensorineural system while most of our patients had chronic neuropathic pain. Reversing sleep disturbances may improve pain sensitivity following an acute nerve injury. Whether this amelioration occurs in a chronic state of pain or chronic nerve injury is unsupported by our findings. Further, had pain intensity shown significant amelioration after CPAP therapy, opioid consumption would have dropped substantially in those patients adherent to CPAP therapy. Yet, our data showed a decrease in opioid utilization across the entire spectrum irrespective of CPAP adherence, coinciding with a national campaign to reduce the overuse and overdose of prescription pain medications in veterans.

Overall, a number of qualifications and limitations should be highlighted when interpreting the findings of the current study. First, the patient population included may limit the external validity of the study because the majority of patients were white males. Second, these findings are based on a single-item measure of pain. Although many epidemiological studies have used a similar item, it would be preferable to obtain multidimensional measures of pain in future studies; for example, items assessing not only the frequency but also the severity, quality, source, and spatial extent of pain. Third, pharmacological interventions such as steroid injections, or non-pharmacologic therapy such as physical therapy, all of which may also influence pain intensity, were not included when determining changes in treatment.38 Although none of our patients were receiving cognitive behavioral therapy for pain reduction, complementary and alternative medicine including psychological interventions, such as cognitive behavioral therapy and self-regulatory treatments, have been found to be efficacious in decreasing pain intensity and improving physical functioning although military veterans are underrepresented in these studies.39 Fourth, we were not able to assess the use of illicit opioid because of the retrospective design of our study; however, our institutional policy prohibits the prescription of any opioids for patients who are actively using illicit drugs or who tested positive on routine urine drug testing.

In conclusion, the link between opioid-treated chronic pain and OSA is multidimensional in nature. Although OSA is the prevalent sleep-disordered breathing in opioid-treated chronic pain, sleep-disordered breathing may not greatly contribute to excessive daytime sleepiness in this patient group in whom there are potentially multiple causes of subjective sleepiness. CPAP adherence was lower in opioid-treated veterans with OSA compared to veterans with OSA alone. Pain intensity and underlying comorbid conditions were predictive of poor CPAP adherence. Our findings underscore the importance of integrating behavioral health and pain management as a part of multidisciplinary approach to improve CPAP adherence.

DISCLOSURE STATEMENT

This was not an industry supported study. Support was provided by a Merit Review Grant (CX000478) from the Department of Veterans Affairs (AES) and the American Sleep Medicine Foundation (PJ). This project is the result of work supported with resources and the use of facilities at the VA Western New York Health Care System. The contents of this project do not represent the views of the Department of Veterans Affairs or the United States Government. The authors have indicated no financial conflicts of interest.

ABBREVIATIONS

AHI

apnea-hypopnea index

BMI

body mass index

CPAP

continuous positive airway pressure

ESS

Epworth Sleepiness Scale

OSA

obstructive sleep apnea

MEDD

morphine equivalent daily dose

PTSD

posttraumatic stress disorder

TST

total sleep time

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