Clinical and Demographic Predictors of Interdisciplinary Chronic Pain Rehabilitation Program Treatment Response

ARTICLE IN PRESS The Journal of Pain, Vol 00, No 00 (), 2019: pp 1−16 Available online at www.jpain.org and www.sciencedirect.com

Clinical and Demographic Predictors of Interdisciplinary Chronic Pain Rehabilitation Program Treatment Response Kelly L. Huffman,* Darcy Mandell,y,x Jennifer K. Lehmann,z Xavier F. Jimenez,y,x and Brittany R. Lapin{,f Zayed University, Abu Dhabi, United Arab Emirates, yCleveland Clinic Center for Comprehensive Pain Recovery, Neurological Institute, Cleveland, Ohio, zCase Western Reserve University, Cleveland, Ohio, xCleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio, {Cleveland Clinic Neurological Institute Center for Outcomes Research and Evaluation, Cleveland, Ohio, fCleveland Clinic Lerner Research Institute Department of Quantitative Health Science, Cleveland, Ohio *

Abstract: Patients treated in interdisciplinary chronic pain rehabilitation programs show long-term improvements in symptoms; however, outcomes may vary across heterogenous patient subpopulations. This longitudinal retrospective study characterizes the influence of opioids, mood, patient characteristics, and baseline symptoms on pain and functional impairment (FI) in 1,681 patients 6-months to 12-months post-treatment in an interdisciplinary chronic pain rehabilitation program incorporating opioid weaning. Linear mixed models showed immediate and durable treatment benefits with nonuniform worsening at follow up which slowed over time. Latent class growth analysis identified three post-treatment trajectories of pain and FI: mild symptoms and durable benefits, moderate symptoms and durable benefits, and intractable symptoms. A fourth pain trajectory showed immediate post-treatment improvement and worsening at follow up. Whether a patient was weaned from opioids was not predictive of treatment trajectory. Racial ethnic minority status, higher levels of post-treatment depression, and lower perceived treatment response were associated with less resolution (moderate symptoms) or intractable symptoms. Not having a college education was predictive of intractable or worsening pain and a moderate course of FI. Older age and male gender was associated with intractable FI. Treatment outcomes may be improved by the development of targeted interventions for patients at risk of poor recovery and/or deteriorating long-term course. Perspective: This study examined predictors of treatment response in 1,681 patients treated in an interdisciplinary chronic pain rehabilitation program incorporating opioid weaning. Opioid weaning did not predict outcome. Higher levels of symptoms, lower levels of education, and being a racial-ethnic minority were associated with a less salubrious long-term treatment response. © 2019 by the American Pain Society Key Words: Chronic noncancer pain, opioid use, interdisciplinary chronic pain rehabilitation, latent class growth analysis, health disparities.

A

pproximately 25 million adults in the United States are afflicted with chronic noncancer pain (CNCP).70 CNCP is associated with significant

Received January 6, 2019; Revised April 24, 2019; Accepted May 24, 2019. Conflict of interest statement: Dr.’s Huffman, Mandell, Lehmann, Jimenez and Lapin certify that they have no conflict of interest. This research was funded by the Cleveland Clinic Neurological Institute Research Project Pilot Funding as a result of the generous contributions made by the Wuliger Foundation. Address reprint requests to Kelly Huffman, PhD, MS, Zayed University, College of Natural Health Sciences, Department of Psychology, PO Box 14453, Abu Dhabi, United Arab Emirates. E-mail: [email protected] 1526-5900/$36.00 © 2019 by the American Pain Society https://doi.org/10.1016/j.jpain.2019.05.014

individual suffering as well as social and economic cost. A 2012 study estimated the total annual social cost of CNCP to be between $560 and $635 billion; this is 30% higher than the cost of diabetes ($188 billion) and cancer ($243 billion) combined.32 Due to the profound impact of CNCP on the individual and society, the Interagency Pain Research Coordinating Committee was charged with developing a National Pain Strategy outlining priorities for public health intervention.49 This strategy calls for research focused on identifying safe, effective, empirically supported treatments for CNCP that consider individual differences in treatment 1

ARTICLE IN PRESS 2 The Journal of Pain

Clinical and Demographic Predictors of Interdisciplinary Chronic Pain Rehabilita

response and approach treatment from a biopsychosocial and interdisciplinary perspective Interdisciplinary chronic pain rehabilitation programs (ICPRPs) meet the priorities outlined by the Interagency Pain Research Coordinating Committee. ICPRPs are low risk,88 empirically robust,31,33 and cost effective.33,74,82,88 Patients report clinically and statistically significant improvements in pain severity,10,25,36,44,67,75,87,89 affective distress,75 psychological well-being,10 and pain-related functional impairment,36,44,60,61,75,87 while minimizing medication use.10,31,75 Improvements are sustained over time10,25,61,78,89 and patients report long-term success in remaining opioid free.47,48,87 While ICPRPs are an effective treatment for many patients, there is variability in outcomes22,92 and there is growing recognition of the need to identify the factors that affect treatment response.24 Existing research suggests that patient characteristics,6,7,13,14,15,23,30,43,46,65,70,73,77,80,93 psychological and emotional constructs5,11,50,54,63,90 and baseline symptom severity53 may differentially influence treatment response. For example, females are more likely to have CNCP,7,70 and demonstrate higher levels of clinical pain severity7,77 and experimental pain sensitivity.29,30,73 Lower SES is associated with increased risk of CNCP,80 higher levels of functional impairment, and less favorable treatment prognosis.46 Depression has been shown to predict the onset of CNCP5 and diminish the treatment response in psychological63,90 and surgical interventions.11,54 Research which examines the feasibility and utility of opioid tapering is also priority. Existing research suggests that factors such as depression,40,48 higher baseline opioid dose, higher baseline pain severity,40,53 and higher baseline levels of functional impairment47 increase the likelihood of a failed opioid taper and subsequent opioid relapse.53 The current study leveraged a large data set and robust statistical methods to identify predictors of treatment response in patients with CNCP completing an ICPRP treatment incorporating opioid weaning. Our primary aim was to determine whether sociodemographic variables, baseline symptomology, psychological constructs, and opioid weaning during treatment influenced immediate and long-term treatment outcomes. Pain severity and pain related functional impairment were used as primary and secondary indicators of treatment response. Regression models were used to evaluate the associations of age, gender, marital status, socioeconomic status, ethnicity, depression, anxiety, opioid use and pain severity, and functional impairment over time. A secondary aim was to identify subpopulations of patients with distinct treatment trajectories and unique predictors of their treatment outcomes. Identifying factors that predict treatment durability, nonresponsiveness or long-term relapse allows clinicians to better target their efforts and distinguish clinically-relevant subgroups that may need additional services during treatment or after discharge. The regression models traditionally used1 for these types of analyses are limited by a high type I error rate, especially in exploratory studies with many predictors, and their parameters reflect average treatment

effects across all participants. This makes it difficult to examine higher order interactions (eg, the treatment response of married, White, males) however treatment outcomes actually may vary across specific subpopulations of patients.69 Thus, latent class growth analysis (LCGA) was utilized to explore trajectories of treatment response from discharge through 6- and 12-months following treatment completion, as a function of age, gender, marital status, socioeconomic status, ethnicity, depression, and post-treatment opioid use.

Methods Study Design Study participants were patients with treatment refractory CNCP treated within an ICPRP in an academic medical center between 2007 and 2014. Prior to admission, patients typically had attempted and failed extensive outpatient management, such as pharmacotherapy, surgeries, and interventions. Patients often presented not only with marked pain and functional impairment, but also significant affective distress. This study utilized data from multiple sources including: 1) an IRB approved data registry tracking the outcomes of clinical care, 2) the Knowledge Program (KP),52 and 3) manual review of the electronic medical record (EMR). IRB registry data are gathered as part of routine clinical care at four time points: admission, discharge, 6months, and 12-months post-treatment. Admission and discharge data are collected via paper and pencil measures administered to ICPRP participants by ICPRP staff on the day of admission to treatment and on the day of discharge from treatment. Longitudinal follow-up data are collected via self-report paper and pencil questionnaires sent via the U.S. postal service, with a prepaid return envelope and a small amount of remuneration (»$25 USD) for survey completion and return. The KP52 is an institution wide electronic data capture system of patient reported outcomes that patients complete prior to the onset of their provider visit. KP52 data on pain severity and functional impairment were available at post-treatment outpatient follow-up visits from patients who continued to receive care at the Cleveland Clinic (CC) post-treatment in the CPRP but did not return follow-up surveys. Review of the EMR was used to gather additional information for patients who provided data either verbally or on paper, but did not enter the data electronically into the KP.52 Follow-up data (both 6months and 12-months) was considered usable if data were available within 30 days of the actual 6-month or 12-month anniversary date of the patients discharge from treatment.

Treatment The CC ICPRP is an intensive interdisciplinary outpatient program for adults with treatment refractory CNCP. Treatment is 3 to 4 weeks from 7:30 AM to 5:00 PM Monday to Friday, and includes medication management, individual and group psychotherapy, cognitive-behavioral group

ARTICLE IN PRESS Huffman et al interventions, psychoeducation, physical and occupational therapy, substance use education, weaning from habituating medications, and optional free monthly aftercare. The CC ICPRP has been in existence since the late 1970s and treats approximately 300 patients per year. The program is located within a tertiary care center. The majority of patients are referred from within the state (»70%), but others are referred regionally, nationally, and rarely internationally. Patients admitted to this program typically have treatment refractory chronic pain and have failed extensive outpatient treatment such as medication management, physical therapy, occupational therapy, surgery, and psychological interventions. Patients typically report significant affective symptoms and marked functional impairment, in addition to chronic debilitating pain. This level of care is often reserved for the most refractory of pain syndromes and patients admitted to this program may not be representative of patients with chronic pain presenting in other settings, such as primary care. There is a however a substantial heterogeneity in comorbidity observed, including accompanying depression, anxiety, insomnia, addiction, excessive healthcare utilization, and complex family/environmental stressors. The CC ICPRP is staffed by interdisciplinary clinical staff including pain psychiatry, psychologists, physical and occupational therapists, nurses and counselors. All staffs regardless of specialization have training in the behavioral management of CNCP and its aforementioned comorbidities. About 80% of patients complete the program47 and treated patients demonstrate clinically and statistically significant immediate and longterm treatment benefits in pain severity,47,97 functional impairment,47,97 affective distress,47,97 and reductions in opioid use.47,48

Participants A total of 2,089 patients were admitted to the CC ICPRP between 2007 and 2014. Of these, 80.47% (n = 1,681) completed treatment and 59.49% (n = 1,000) provided follow-up data at either one or both subsequent time points. Participants completing treatment were predominately non-Hispanic white (83.25%, n = 1,739), married (61.08%, n = 1,276), females (65.29%, n = 1,364) ranging in age from 18 to 92 with a mean age of 46.61 (§13.65). Six hundred and eighty patients (32.55%) had 4-year college or postgraduate degrees. The median income of participants completing the program was $52,072 USD (interquartile range: $41,875−$64,676 USD) with a median of 10% (interquartile range: 6.2−17%) with incomes below the poverty level. The majority of patients presented with more than one chronic pain concern (»79%). The most common conditions reported among participants were low back pain (59.42%, n = 999), headache (46.76%, n = 786), neck pain (36.64%, n = 616), fibromyalgia (32.96%, n = 554) and joint pain (25.64%, n = 431). Table 1 details additional characteristics participants completing treatment.

The Journal of Pain 3

Measures Treatment Outcomes Primary outcome: The primary outcome of clinical pain severity was measured using the Numeric Rating Scale-11 (NRS-11).38 The NRS-11 is a commonly used clinical assessment measure which asks patients to rate their pain severity on an 11-point Likert scale ranging from 0 (no pain) to 10 (worst pain imaginable). The NRS-11 is a preferred method of assessing pain as it is responsive to changes in pain severity,19,28,42 and presents advantages in administration including ease of use and compliance.42 A recent study demonstrated that the test−retest reliability of the NRS-11 was .95 (intraclass correlation coefficient) in adults with knee osteoarthritis when pain severity was assessed twice in two separate visits over a 24-hour time span.2 The NRS11 has been found to be highly correlated with other measures of pain severity, including the visual analogue scale (r = .941 VAS)96 and the verbal rating scale (r = .93, VAS96) Secondary outcome: Pain-related functional impairment was assessed using the pain disability index (PDI).84 The PDI measures self-reported pain-related functional impairment across seven domains of daily living, including family/home responsibilities, recreation, social activities, occupation, sexual behavior, self-care, and life support activity. Participants rate the degree of painrelated interference in each domain on a Likert scale ranging from 0 (no pain-related interference) to 10 (total interference). Scores are summed and range from 0 to 70, with higher scores reflecting greater levels of functional impairment. Factor analyses have suggested a 1-factor solution, with this factor accounting for 49 to 59%18,84 of the variance and significant loadings for each of the seven items. The PDI has good internal consistency (a = .86)18,84 and moderate-high test−retest reliability (r = .44, P < .001,84 and ICC = .8335). The construct validity of the instrument is good, with higher scores being associated with higher levels of pain intensity35, higher levels of affective distress,84 greater restriction in activity,84 and higher scores on the Oswestry Disability Questionnaire (r = .06227,35).

Predictors of Treatment Response Patient characteristics: Patient characteristics examined included gender, age, marital status, ethnicity, and socioeconomic status. Participant ethnicity was based on self-report. Due to the relatively small number of participants in the sample who identified as members of racial ethnic minority groups, for analytic purposes, ethnicity was dichotomized as non-Hispanic white versus all others. Marital status was dichotomized as married/partnered versus all other. Socioeconomic status was operationalized at both an individual and community level. On an individual level, education was used as a proxy for socioeconomic status; education was dichotomized as bachelor’s degree or higher degree versus less than bachelor’s degree. At a community level, socioeconomic

ARTICLE IN PRESS 4 The Journal of Pain

Table 1.

Clinical and Demographic Predictors of Interdisciplinary Chronic Pain Rehabilita

Participant Characteristics of Treatment Completers and Non-Completers, N = 2,089

Total patients, n (%) Male, n (%) Age, Mean § SD Range Ethnicity, n (%) Non-Hispanic white Black/African-American Other Missing Marital status, n (%) Married/Partner Single Divorced/Separated Widowed Education, n (%)
TOTAL

STUDY COHORT TREATMENT COMPLETERS

EXCLUDED NONCOMPLETERS

2089 725 (34.7) 46.6 § 13.6 18-92

1681 (80.5) 583 (34.7) 46.7 § 13.6 18-92

408 (19.5) 142 (34.8) 46.1 § 14.0 19-85

1739 (83.2) 220 (10.5) 66 (3.2) 64 (3.1)

1426 (84.8) 153 (9.1) 53 (3.2) 49 (2.9)

313 (76.7) 67 (16.4) 13 (3.2) 15 (3.7)

1276 (61.1) 475 (22.7) 289 (13.8) 49 (2.4)

1059 (63.0) 363 (21.6) 221 (13.1) 38 (2.3)

217 (53.2) 112 (27.4) 68 (16.7) 11 (2.7)

111 (5.3) 462 (22.1) 828 (39.6) 442 (21.2) 238 (11.4) 8 (0.4) 10.0 (6.2, 17.0) 5.21 (4.19, 6.47)

76 (4.5) 353 (21.0) 670 (39.9) 378 (22.5) 200 (11.9) 4 (0.2) 9.9 (6.2, 16.3) 5.23 (4.24, 6.48)

35 (8.6) 109 (26.7) 158 (38.7) 64 (15.7) 38 (9.3) 4 (1.0) 10.5 (6.2, 17.9) 5.08 (4.01, 6.42)

43.3 § 12.2 6.7 § 2.0 19.3 § 12.2 13.5 § 9.9 1308 (62.6)

42.9 § 12.0 6.6 § 2.0 19.2 § 12.2 13.2 § 9.9 1027 (61.1)

45.1 § 12.7 7.1 § 2.0 19.6 § 12.5 14.7 § 9.9 281 (68.9)

P

.96 .39

<.001

.004

<.001

.011 .07 .001 <.001 .53 .008 .004

q, quartile; PDI, pain-related functional impairment assessed by the Pain Disability Index; SD, standard deviation; *based on ZIP code, n = 2,057; Study cohort included patients who completed the interdisciplinary chronic pain rehabilitation program. Total scores for depression range from normal (0−9), mild (10−13), moderate (14−20), severe (21−27), to extremely severe (28+). Anxiety scores range from normal (0−7), mild (8−9), moderate (10−14), severe (15−19) to extremely severe (20+).

status was captured using the median household income of the patient’s zip code (based on 2010 census data). This method has been used successfully86 in previous studies examining health outcomes.51,57,93 Physical and emotional symptoms: Symptomology assessed included clinical pain severity, pain-related functional impairment, depression, and anxiety. All four of these measures were collected at admission, discharge, and 6- and 12-month follow-up. Pain severity and pain-related functional impairment were captured using the NRS-1138 and PDI84 as previously described. Depression and anxiety was assessed using the depression, anxiety and stress inventory (DASS).58 There are two versions of the DASS; the 42item full version and 21 item short form. Items on both forms are equally divided among three subscales (depression, anxiety, and stress) and scored by summing the subscales. On the short form, the scores are doubled to obtain the final score. Total scores for depression range from normal (0−9), mild (10−13), moderate (14−20), severe (21−27), to extremely severe (28+). Anxiety scores range from normal (0−7), mild (8−9), moderate (10−14), severe (15−19) to extremely severe (20+). DASS data from 2007 to 2009 were collected using the 42-item scale and data from 2009 to 2015 were collected using the 21-item version.

Both the DASS-42 and the DASS-21 have been demonstrated to have high internal consistency reliabilities with Cronbach’s alphas of .97 and .94 for depression and .92 and .87 for anxiety respectively.4 The concurrent validity of both versions of the DASS is also acceptable; the depression subscales are highly correlated with the Beck Depression Inventory (r = .77, DASS-42 and r = .79, DASS-218) and the anxiety subscales are moderately correlated with the Beck Anxiety Inventory (r = .84, DASS42 and r = .62, DASS-219). Perceived treatment benefit: Patients’ own perceptions of the benefit of ICPRP treatment may have contributed to the durability of any treatment-related gains. Patient global impression of change (PGIC) was assessed using a 7-point Likert scale question that asked patients to rate their perception of their own improvement as a result of ICPRP treatment. Anchors ranged from 1 (very much improved), 4 (no change), to 7 (very much worse). Perceived treatment response was defined as a response of 1 (very much improved) or 2 (much improved). Opioid use: Participant’s opioid use at program admission and discharge was recorded in accordance with the patient’s EMR. Participants’ opioid use at follow up was based on their self-reported use at 6- or 12-months and/ or the patients’ EMR if available. Participants were considered using opioids if their use met the criteria for

ARTICLE IN PRESS Huffman et al chronic opioid therapy. Chronic opioid therapy was defined as legal, prescription use for at least 5 days per week for 3 or more months, in keeping with previous empirical literature.3,39 Discharge opioid use: Discharge opioid use was captured in three levels − those who were not on opioids at either admission or discharge, those who were fully weaned from opioids during treatment, and those who were discharged on opioids. Opioid use over time: Opioid use over time was evaluated by categorizing participants into one of four groups (1 − remaining opioid free; 2 − initiating use; 3 − remaining on opioids; 4 − discontinuing opioids) based on the comparison of their opioid use at 6- and 12-months to their discharge status. Participants considered “remaining opioid free” were not on opioids at discharge or any subsequent time point. Participants considered to have “initiated use,” were participants who were opioid free at discharge and were using chronic opioid therapy at either one or both longitudinal time points. Participants who “remained on opioids” were discharged on opioids and were still on opioids at longitudinal follow up. Participants discontinuing opioids were on opioids at discharge but not at the last available follow-up time point. In analyses which captured opioid use over time as one variable, in order to avoid inflating estimates of participants remaining opioid free, participants were considered on opioids at follow up if they reported opioid use at only one of the two follow-up time points. For example, a participant fully weaned from opioids at discharge, on chronic opioid therapy at 6-months, but not at 12-months, would be considered to have “initiated opioids.” Pro re nata usage: A small number of participants were using prescribed opioids on a PRN basis at the time of discharge from treatment (n = 28). This usage was verified by the prescribing physician in the EMR. These participants included 14 who were not on opioids at admission and 14 who were weaned from chronic opioid therapy (COT). Because their usage was truly PRN and did not meet the criteria for COT, these participants were classified as “opioid free” at discharge. Their inclusion or exclusion as part of this group did not impact findings. When follow-up data were based alone on self-report on a written survey that participants returned via the U.S. postal service (with no information available to confirm in the EMR), it was not possible to determine whether patients who said they were using opioids on a PRN basis were indeed doing so. For example, a patient may respond in writing that they are taking opioids “as needed.” In these cases, usage was assumed to be chronic, in order to avoid inflating estimates of patients remaining off COT.

Analysis Preliminary Analysis Baseline descriptive statistics were calculated for all patient characteristics and clinical variables, including

The Journal of Pain 5 means with standard deviations, medians with interquartile ranges, frequency counts, and percentages, as applicable. Group differences were compared between patients completing versus not completing the program, patients who responded versus did not respond to the program, and patients who did versus did not return follow-up surveys, using chi-square test for categorical variables and t-test or (nonparametric) Mann−Whitney U test for continuous variables, as appropriate.

Nonresponse Bias As it is possible that patients were not equally likely to provide longitudinal follow-up data, nonresponse bias was examined as a function of patient demographics, admission and discharge symptomology, admission and discharge opioid use and PGIC as reported at discharge. Sensitivity analyses of patient characteristics for individuals who did versus did not provide 6- and 12-month follow-up data were conducted. Patients were classified as treatment responders (clinically significant change), treatment nonresponders (no clinically significant change), or having deteriorated (clinically significant worsening), based on changes in PGIC.

Overall Treatment Success and Durability of Response First, the immediate and long-term durability of improvements in pain severity and pain-related functional impairment were assessed using either a paired t-test or nonparametric Wilcoxon signed-rank test, as appropriate, to compare discharge, 6-, and 12-month scores to admission scores. Similarly, t-tests or nonparametric Wilcoxon signed-rank tests were used to compare 6- and 12-month follow-up scores to discharge scores. McNemar’s test was used to compare opioid use over the same time points. The clinical significance of changes in all treatment outcomes measures were evaluated using Norman’s method for assessing minimally clinically significant differences (1/ 2 standard deviation).71 This method was recommended as a “reasonable criterion” in the IMMPACT consensus meeting.24

Primary and Secondary Aim Two different types of analyses were used to address the study aims. First, to assess the primary aim two linear mixed models were built to identify factors which predicted the durability of immediate treatment gains in both pain severity and functional impairment. To address the secondary aim, LCGA and multinomial logistic regression were utilized to determine if there were specific subpopulations of patients with distinct treatment trajectories and identify any unique predictors of treatment outcomes for these groups.

Step 1: Linear Mixed Effects Models Two linear mixed effects models with random intercept were used to evaluate predictors of pain severity and pain-related functional impairment over time from discharge through 12-months following completion of

ARTICLE IN PRESS 6 The Journal of Pain

Clinical and Demographic Predictors of Interdisciplinary Chronic Pain Rehabilita

ICPRP. A number of structures were considered for the variance−covariance matrix and, based on model fit criteria, an unstructured covariance matrix provided the best fit. Linear mixed model methodology was chosen in order to accommodate some of the methodological difficulties inherent in longitudinal design such as repeated measures, missing data, and observations collected at different time points.17,94 Linear mixed models, unlike other types of repeated measures analyses, do not eliminate missing data on a list wise basis. This was advantageous in this analysis, as many participants provided follow-up data at only one of the two time points. The two constructed multivariable models included time, patient characteristics, PGIC, depression and anxiety over time, opioid use patterns over time, and either pain or PDI at admission as covariates (in accordance with the dependent variable). All possible interaction effects were assessed at P <.05. Time trends were modeled using both linear and quadratic functions. Time was included as a quadratic function (in addition to linear) in order to capture a plateau or relapse in response to treatment over time.

Step 2: LCGA and Multinominal Logistic Regression LCGA is used to detect underlying (eg, latent) subpopulations within a sample based on similar patterns in the outcome variable observed in each participant. This approach has previously been used to classify longitudinal profiles.69 In this case, LCGA was useful not only to identify subpopulations based on their pain and functional impairment trajectories over time but also to determine whether predictors of long-term treatment success differ between these subpopulations. Parameters derived from conventional analysis, such as linear mixed models, reflect the averages of effects across potentially heterogeneous subpopulations, meaning that it is possible for the magnitude of association to be negligible for some subpopulations and large for others.69 These differences are important yet would remain undetected in conventional analyses. LCGA was utilized to explore the trajectories of pain and functional impairment from discharge through 6-months and 12-months following completion of ICPRP. First, the optimal number of trajectories for each outcome variable was determined. Akaike Information Criterion, Bayesian Information Criterion, Vuong-Lo-Mendell-Rubin Likelihood Ratio test, Lo-Mendell-Rubin Likelihood Ratio test, and the proportion of patients per group were used as statistical fit criteria for the determination of the optimal number of subgroups.16,56 Next, multinomial logistic regression analysis was used to characterize the trajectory-based groups based on demographic and clinical predictors. Multinomial logistic regression allowed for the examination of the individual contribution of each predictor variable in the context of all other predictor variables included in the model. Predictors were determined a priori and included: age, gender, ethnicity, marital status, college education, median income, PGIC, depression at

discharge, and discharge opioid use. Baseline pain severity or functional impairment at admission was included as a covariate in the model with the corresponding dependent variable. Sensitivity analysis was conducted to replicate the findings within the subset of patients whose data were available at all three time points (discharge, 6- and 12-month follow-up). Depression was included in the models instead of anxiety due to multicollinearity. As patient-reported outcomes are subjective and vary widely following ICPRP, missing data were not imputed. Statistical significance was established throughout at P < .05. Data analyses were conducted using SAS version 9.481 and Mplus version 7.4.68

Sample Size Justification A priori sample size calculation for the primary aim was based on the general rule of thumb that it is necessary to include 20 participants for each degree of freedom. Our predictors, including possible interaction terms, would constitute a maximum of 30 degrees of freedom. Thus, it is estimated that this study would require approximately 600 participants to reach a statistical power of .80. This is well within our sample size of 1,681 patients with 1,000 participants reporting follow-up data.

Results Preliminary Analyses There were 408 participants who dropped out prior to ICPRP treatment completion (19.5% of patients admitted; see Table 1 for statistical comparisons of program completers vs noncompleters on demographic and primary clinical variables). Completers were significantly more likely to be white, married, and college-educated, and were significantly less likely to be below the poverty level than noncompleters (all P < .05). Noncompleters reported significantly higher levels of baseline pain-related functional impairment, pain severity, and anxiety, and were more likely to be on COT at admission (all P < .01). Differences in baseline symptomology (pain severity, depression, anxiety and functional impairment while statistically significant did not meet the thresholds for clinical significance, as assessed by Norman’s method.71)

Nonresponse Bias Sensitivity analyses of patient characteristics for individuals who did versus did not provide 6- and 12-month follow-up data can be found in Supplementary Table S1. Patients who were lost to follow-up were less likely to be married (58.6% versus 66.0%, P < .01) or less likely to have completed college (31.1% versus 36.8%, P = .02) as compared to patients with follow-up data. Patients lost to follow-up were more likely to be using opioids at admission, and reported worse psychological symptoms, pain, and greater levels of functional impairment at admission and discharge. They were also less likely to have a perceived treatment response to the ICPRP (81.2% versus 86.4%, P < .01).

ARTICLE IN PRESS Huffman et al Table 2.

ICPRP

The Journal of Pain 7

Patient-Reported Outcomes and Opioid Use Over Time in 1,681 Patients Who Completed ADMISSION

PATIENT REPORTED

N

MEAN § SD

DISCHARGE N

MEAN § SD

MONTH 6 A

N

D

MEAN § SD

MONTH 12

D

A

, DB

N

MEAN § SD

A

D

, DB, DC

OUTCOMES

Pain PDI Depression Anxiety Opioid use On COT at admission Remained opioid free Resumed Remained on opioids Discontinued

1,680 6.6 § 2.0 1,645 42.9 § 12.0 1,681 19.2 § 12.2 1,680 13.2 § 9.9 N % (n) 1,680 61.1 (1027)

1681 3.4 § 2.5* 1635 17.8 § 12.7* 1670 6.4 § 7.8* 1670 6.4 § 6.6* N % (n) 1681 7.4 (125)*

1.4 2.0 1.3 0.8

792 4.3 § 2.4*y 770 22.3 § 16.0*y 736 9.8 § 10.1*y 737 6.3 § 7.3*y N %(n) 982 20.4 (200)*y 982 76.3 (749) 982 14.6 (143) 982 5.8 (57) 982 3.4 (33)

1.0, 0.4 1.5, 0.3 0.8, 0.4 0.8, 0.0

636 4.6 § 2.5*y 610 23.9 § 16.5*y 580 10.5 § 10.9*y 580 6.8 § 7.7*y N % (n) 982 76.3 (749) 982 14.6 (143) 982 5.8 (57) 982 3.4 (33) 982 76.3 (749)

0.9, 0.5, 0.1 1.3, 0.4, 0.1 0.8, 0.4, 0.1 0.7, 0.1, 0.1

COT, chronic opioid therapy, defined as use for 5 or more days per week for longer than 90 days; da, Cohen’s d, calculated in reference to status at admission, db, Cohen’s d, calculated in reference to status at discharge, dc, Cohen’s d, calculated in reference to status at 6-months; PDI, pain-related functional impairment as measured by the pain disability index; SD, standard deviation; *P < .05, significant improvement from admission; yP < .05, significant change from discharge.

A majority of patients (88.4%) perceived themselves as having improved, while 10.9% perceived no change and 0.7% felt they had gotten worse (data not shown). Demographic characteristics for ICPRP program responders and nonresponders (quantified as PGIC, patients missing PGIC (n = 78) were classified as nonresponders) can be found in Supplementary Table S2. Responders were more likely than nonresponders to have a college degree (35.7% vs 27.8%, P = .01), and had higher baseline pain severity (P = .02) and higher levels of functional impairment (P < .01) at admission.

Overall Treatment Success and Durability Matched pairs t-tests showed that participants completing treatment showed significant pre- to post-

treatment improvements in pain severity (pre: 6.6 § 2.0; post: 3.4 § 2.5; P < .001), functional impairment (pre: 42.9 § 12.0; post: 17.8 § 12.7; P < .001), depression (pre: 19.2 § 12.2; post: 6.4 § 7.8; P < .001), and anxiety (pre: 13.2 § 9.9; post: 6.4 § 6.6; P < .001) (Table 2; Fig 1). Significant improvements were maintained at 6- and 12-month follow-up (all P < .05; Table 2; Fig 1). Improvements were statistically as well as clinically significant (as defined by Norman’s method71). Effect sizes for immediate and long-term benefits were calculated using Cohen’s d with admission status as a reference point. All were uniformly moderate-large (ranging from .7 to 2.0). Matched pairs t-tests also showed evidence of longitudinal decay in treatment benefit. While patients maintained a net improvement in pain severity in

Figure 1. Pain-related functional impairment and pain severity at admission to ICPRP, discharge, month 6, and month 12.Patientreported pain-related functional impairment (PDI) and pain severity at admission into ICPRP, at discharge, at 6-months and at 12-months following completion of ICPRP. Compared to admission, scores were significantly improved at discharge, 6-months, and 12-months (P < .01 for all). Compared to discharge, scores were significantly improved at 6-months and 12 months (P < .05 for all).

ARTICLE IN PRESS 8 The Journal of Pain

Clinical and Demographic Predictors of Interdisciplinary Chronic Pain Rehabilita

comparison to admission status, pain severity increased by 26% from discharge to 6-months (d = .4) and by 35% from discharge to 12-months (d = .5). Functional impairment and depression showed smallmoderate21 statistically, but not clinically, significant declines effect from discharge to 6-months (d = .3; d = .4, respectively) and discharge to 12-months (d = .4; d = .4). Fig 1 shows mean changes in primary outcome measures from admission through 12-month follow-up.

Predictors of Pain Severity From Discharge Through 12-Months In a multivariable linear mixed-effects model predicting pain severity from discharge through 12months, pain severity increased over time. The combination of the significant positive slope of the linear time trend (time) combined with the negative slope of the quadratic trend (time2) indicated that the rate of the increase slowed over time (time (estimate (se): 2.17 (0.30), P < .01)) and time2 (estimate (se): 0.42 (0.08), P < .01) (Table 3). Being non-Hispanic white was protective against worsening pain scores (estimate (se): 0.42 (0.18), P = .02), as was being married (estimate (se): 0.31 (0.12), P < .01), having a college degree (estimate (se): 0.53 (0.12), P < .01), and perceived treatment benefit at discharge (estimate (se): 1.63 (0.18), P < .01). Higher pain at admission (estimate (se): 0.28 (0.03), P < .01), worsening depression over time (estimate (se): 0.06 (0.01), P < .01), and worsening anxiety over time (estimate (se): 0.04 (0.01) P < .01) were associated with increased pain over time. Compared to

patients who were successfully weaned from opioids during ICPRP and remained opioid free, patients who initiated use or who were not weaned but then later discontinued reported worse pain (estimate (se): 0.98 (0.15), P < .01; 0.91 (0.31), P < .01, respectively). There were no significant interactions over time.

Latent Class Growth Analysis: Trajectories of Pain Severity In an LCGA comparing the different indices of fit, four distinct trajectories were determined to best fit pain severity treatment response in this population. (See Supplementary Table S3 for 1- through 5-class solutions). The four trajectory groups included 1) mild pain: patients whose pain was mild at discharge and remained mild and stable from discharge through 12-months (mean pain over time = 1.51, §0.92, 37.7%), 2) moderate pain: patients whose pain was moderate at discharge and remained moderate and stable over time (mean pain over time = 4.28 § 0.59, 19.3%), 3) worsening: patients who showed initial treatment benefit but demonstrated a worsening in pain severity over time (+20.8%), and 4) severe pain: patients whose pain did not respond to treatment and was persistently high over time (mean pain over time = 7.10 § 1.01, 22.3%). Patients in the worsening pain trajectory group reported average pain scores of 2.64 § 1.17 at the time of discharge from treatment, 5.61 § 1.59 at 6-months, and 6.77 § 1.09 at 12-months post-treatment. Fig 2a shows the four trajectory groups. In a multivariable multinomial regression model, the mild pain trajectory group was chosen as the reference

Multivariable Longitudinal Linear Mixed-Effects Model for Predicting Pain Severity and Pain-Related Functional Impairment Over Time (Discharge Through 1 Year), n = 1,681

Table 3.

PAIN SEVERITY

PAIN-RELATED FI

EFFECT

ESTIMATE (SE)

P VALUE

ESTIMATE (SE)

P VALUE

Time Time2 Age (per decade) Male Non-Hispanic White (vs all other) Married (vs all other) ≥ College (vs
2.17 (0.30) 0.42 (0.08) 0.02 (0.04) 0.14 (0.12) 0.42 (0.18) 0.31 (0.12) 0.53 (0.12) 0.02 (0.03) 1.63 (0.18) 0.28 (0.03) 0.06 (0.01) 0.04 (0.01)

<.001 <.001 .60 .22 .020 .009 <.001 .55 <.001 <.001

<.001 .007 <.001 .032 <.001 .08 .024 .42 <.001

<.001 <.001

7.04 (1.70) 1.16 (0.43) 1.02 (0.23) 1.36 (0.63) 5.38 (0.98) 1.15 (0.65) 1.40 (0.62) 0.12 (0.16) 6.03 (0.84) 0.25 (0.02) 0.46 (0.03) 0.31 (0.04)

<.001 .18 .004

Reference 4.20 (0.81) 3.27 (1.32) 2.90 (1.75)

Reference 0.98 (0.15) 0.32 (0.24) 0.91 (0.31)

<.001 <.001 <.001

<.001 .013 .10

FI, pain related functional impairment; PDI, pain related functional impairment as measured by the pain disability index; SE, standard error; Models include a linear (Time) and quadratic time (Time)2 trends over all three estimates; *based on ZIP code; Perceived treatment response defined based on responses of “very much improved” or “much improved.”

ARTICLE IN PRESS Huffman et al

The Journal of Pain 9

Figure 2. Trajectories of patient reported (a) pain severity and (b) pain-related functional impairment from discharge through 1 year by class membership. (a) Four distinct subgroups of pain trajectories as determined by latent class growth analysis: Group 1 (mild pain, n = 633) includes patients who do well at discharge and remain low pain; Group 2 (moderate pain, n = 324) has patients who have moderate scores at discharge and remain moderate; Group 3 (relapsing pain, n = 349) has patients who do well at discharge and worsen over time; Group 4 (severe pain, n = 375) includes patients who do poorly and remain with high pain. (b) Three distinct subgroups of pain-related impairment trajectories as determined by latent class growth analysis: Group 1 (low PDI, n = 805) includes patients who do well at discharge and remain with low pain-related functional impairment; Group 2 (intermediate PDI, n = 619) has patients with moderate scores at discharge and get slightly worse over time; Group 3 (high PDI, n = 229) has patients who do poorly and remain with high PDI.

category (Table 4a). Patients in this trajectory reported a mean pain at admission of 5.9 § 2.2 and a mean depression score of 4.4 § 6.3. Comparatively, patients in the moderate pain trajectory group were less likely to be non-Hispanic white (OR: 0.59, 95% CI: 0.37 −0.94), less likely to be married/partnered (OR: 0.57, 95% CI: 0.42−0.78), less likely to report perceived treatment benefit (OR: 0.32 (95% CI: 0.20−0.51), reported higher pain at admission (m = 6.5 § 1.9; OR: 1.17, 95% CI: 1.09−1.26) and reported more depression at discharge (m = 7.4 § 7.9; OR: 1.06, 95% CI: 1.03−1.08). Patients in the worsening pain trajectory were less likely have a college degree (OR: 0.64, 95% CI: 0.48 −0.86) and reported higher pain at admission (m = 6.7 § 1.8; OR: 1.21, 95% CI: 1.13−1.30). Patients in the severe pain trajectory were less likely to be non-Hispanic white (OR: 0.54, 95% CI: 0.34−0.88), have a

college degree (OR: 0.59, 95% CI: 0.42−0.83), or to report perceived treatment benefit (OR: 0.14, 95% CI: 0.09−0.21). These patients reported higher pain at admission (m = 7.8 § 1.5; OR: 1.76, 95% CI: 1.60, 1.93) and more depression at discharge (m = 5.3 § 6.7; OR: 1.09, 95% CI: 1.06−1.10). Gender and opioid use at discharge (whether or not a patient was weaned from opioids during treatment) were not associated with pain trajectory group membership.

Predictors of Pain-Related Functional Impairment From Discharge Through 12-Months In a multivariable linear mixed-effects model predicting PDI from discharge through 12-months, PDI scores

Multivariable Multinomial Regression* Model for Predicting Pain Severity Trajectory Group Membership, n = 1,681

Table 4a.

GROUP 2: INTERMEDIATE PAIN TRAJECTORY (VS GROUP 1: LOW PAIN) VARIABLE Age (per decade) Male Non-Hispanic White (vs all other) Married (vs all other) ≥ College (vs
GROUP 3: WORSENING TRAJECTORY (VS GROUP 1: LOW PAIN)

GROUP 4: HIGH PAIN TRAJECTORY (VS GROUP 1: LOW PAIN)

ODDS RATIO (95% CI)

P

ODDS RATIO (95% CI)

P

ODDS RATIO (95% CI)

P

0.99 (0.98, 1.01) 1.00 (0.74, 1.35) 0.59 (0.37, 0.94) 0.57 (0.42, 0.78) 1.01 (0.75, 1.37) 1.03 (0.96, 1.11) 0.32 (0.20, 0.51) 1.17 (1.09, 1.26) 1.06 (1.03, 1.08) 1.20 (0.69, 2.11)

.28 .98 .026 <.001 .93 .39 <.001 <.001 <.001 .52

1.00 (0.99, 1.01) 0.86 (0.64, 1.16) 0.68 (0.43, 1.08) 0.93 (0.68, 1.25) 0.64 (0.48, 0.86) 1.01 (0.94, 1.09) 0.61 (0.37, 1.01) 1.21 (1.13, 1.30) 1.02 (0.99, 1.04) 1.21 (0.71, 2.08)

.49 .32 .10 .62 .003 .72 .053 <.001 .14 .48

0.99 (0.98, 1.00) 1.22 (0.88, 1.68) 0.54 (0.34, 0.88) 0.78 (0.56, 1.08) 0.59 (0.42, 0.83) 1.01 (0.93, 1.10) 0.14 (0.09, 0.21) 1.76 (1.60, 1.93) 1.09 (1.06, 1.10) 1.37 (0.77, 2.44)

.06 .23 .014 .13 .002 .77 <.001 <.001 <.001 .29

Dc, discharge; CI, confidence interval; *Predictors were included in a multinomial logistic regression model determined a priori: age, gender, ethnicity, married, college educated, median income, ICPRP perceived treatment responder, pain severity at admission, depression at discharge, and opioid use at discharge. Perceived treatment response defined based on responses of “very much improved” or “much improved.”

ARTICLE IN PRESS 10

The Journal of Pain

Clinical and Demographic Predictors of Interdisciplinary Chronic Pain Rehabilita

Table 4b. Multivariable Multinomial Regression* Model for Predicting Pain-Related Functional Impairment Trajectory Group Membership, n = 1,653 GROUP 2: INTERMEDIATE PDI TRAJECTORY (VS GROUP 1: LOW PDI) VARIABLE Age (per decade) Male Non-Hispanic White (vs all other) Married (vs all other) ≥ College (vs
GROUP 3 HIGH PDI TRAJECTORY (VS GROUP 1: LOW PDI)

ODDS RATIO (95% CI)

P

ODDS RATIO (95% CI)

P

1.01 (0.99, 1.02) 1.08 (0.85, 1.37) 0.48 (0.32, 0.71) 0.95 (0.74, 1.22) 0.78 (0.61, 0.99) 0.99 (0.94, 1.05) 0.50 (0.35, 0.72) 1.03 (1.02, 1.04) 1.05 (1.04, 1.07) 1.39 (0.88, 2.18)

.08 .54 <.001 .70 .040 .80 <.001 <.001 <.001 .16

1.02 (1.01, 1.04) 2.05 (1.38, 3.05) 0.20 (0.11, 0.35) 0.75 (0.50, 1.13) 0.89 (0.59, 1.37) 0.96 (0.86, 1.07) 0.12 (0.07, 0.19) 1.12 (1.10, 1.14) 1.13 (1.10, 1.15) 1.80 (0.89, 3.66)

.009 <.001 <.001 .17 .61 .47 <.001 <.001 <.001 .10

CI, confidence interval; FI n = 1,653 as 28 patients are missing scores at all 3 time points. *Predictors were included in a multinomial logistic regression model determined a priori: age, gender, ethnicity, married, college educated, median income, ICPRP perceived treatment responder, pain related functional impairment at admission, depression at discharge, and opioid use at discharge. Perceived treatment response defined based on responses of “very much improved” or “much improved.”

worsened over time. This deterioration, like that of the deterioration in pain severity, slowed over time: time (estimate (se): 7.04 (1.70), P < .01) and time2 (estimate (se): 1.16 (0.43), P <. 01) (Table 3). Predictors of worsening PDI scores included being older (estimate (se): 1.02 (0.23) per decade, P < .01), male (estimate (se): 1.36 (0.63), P = .03), having a higher PDI at admission (estimate (se): 0.25 (0.02), P < .01), worsening anxiety (estimate (se): 0.31 (0.04), P < .01), and worsening depression (estimate (se): 0.46 (0.03), P < .01). Compared to patients who remained opioid free, patients who initiated opioid use post-treatment or remained on opioids had worse PDI over time (estimate (se): 4.20 (0.81), P < .01; 3.27 (1.32), P = .01, respectively). Being non-Hispanic white was protective against worsening PDI scores (estimate (se): 5.38 (0.98), P < .01), as was having a college degree (estimate (se): 1.40 (0.62), P = .02), and having perceived treatment benefit at discharge (estimate (se): 6.03 (0.84), P < .01). There were no significant interactions over time.

Latent Class Growth Analysis: Trajectories of Pain-Related Functional Impairment Three trajectories were determined to best fit posttreatment pain-related functional impairment in this population. (See Supplementary Table S3 for = indices of fit for 1- through 5-class solutions). Fig 2b shows the three trajectories from this model, including 1) low functional impairment: patients whose PDI scores were low at discharge and remained low over time (mean pdi over time = 9.25 § 5.40, 48.7%), 2) worsening functional impairment: patients who were discharged with moderate PDI scores that worsened over time (37.4%), and 3) high functional impairment: patients who had persistently high PDI scores throughout (mean PDI over time = 42.7 § 6.42, 13.9%). Those with worsening scores were discharged with mean PDI scores of 20.6 § 7.99 at

the time of discharge from treatment, 30.9 § 9.71 at 6-months, and 35.3 § 9.75 at 12-months post-treatment. In a multinomial logistic regression model, the low functional impairment trajectory was chosen as the reference category (Table 4b). Comparatively, patients in the worsening functional impairment trajectory were less likely to be non-Hispanic white (odds ratio (OR): 0.48, 95% CI: 0.32 −0.71), to have a college degree (OR: 0.78, 95% CI: 0.61 −0.99), or to report perceived treatment benefit (OR: 0.50, 95% CI: 0.35−0.72); they reported higher levels of functional impairment at admission (OR: 1.03, 95% CI: 1.02−1.04) and more depression at discharge (OR: 1.05, 95% CI: 1.04−1.07). Patients in the high functional impairment trajectory group were also more likely to be older (OR: 1.02, 95% CI: 1.01−1.04, per decade) more likely to be male (OR: 2.05, 95% CI: 1.38−3.05), less likely to be non-Hispanic white (OR: 0.20, 95% CI: .0.11−0.35) and less likely to report perceived treatment benefit (OR: 0.12, 95% CI: 0.07−0.19); they reported higher levels of functional impairment at admission (OR: 1.12, 95% CI: 1.10−1.14) and more depression at discharge (OR: 1.13, 95% CI: 1.10−1.15). Opioid use at discharge was not associated with pain-related functional impairment trajectory group membership. Sensitivity analyses of LCGA within the subset of patients with complete data across discharge, 6- and 12-months resulted in similar findings (data available upon request).

Discussion The present study examined socio-demographic and clinical variables as predictors of ICPRP treatment response as well as 12-month treatment trajectories. Consistent with previous research, participants showed immediate and durable statistically and clinically significant benefits in pain severity, functional impairment, depression, and anxiety.75,79,87 Patients remained

ARTICLE IN PRESS Huffman et al improved over time, with »50% in trajectories reporting ongoing pain relief (eg, mild/moderate pain severity) and functional improvements. There was decay in benefit over time with a »25% increase in symptoms from discharge to 12-months. This increase was small compared to the magnitude of long-term gains (d ≥ .8 from admission through 12-months), and consistent with previous research.83,87

Predictors of Durable Treatment Response Our primary aim was to identify factors predicting patterns of treatment response maintenance in the year following discharge from ICPRP treatment. Multivariable regression models showed that several variables predicted more durable treatment effects for pain severity and functional impairment following discharge. These included lower baseline levels of each respective measure (eg, lower levels of pain and functional impairment (FI) at admission), lower levels of anxiety and depression over time, better perceived treatment response, ethnicity (eg, non-Hispanic white), education (eg, college educated), and staying off opioid medications. Younger age and female gender also predicted better long-term outcomes in functional impairment, but not pain severity.

Trajectory Group Membership The secondary aim of this study was to identify specific subpopulations of patients with distinct treatment trajectories. Higher levels of baseline symptoms were associated with less resolution of symptoms (moderate symptoms with durable benefits), or intractable symptoms. Lower levels of education were associated with pain was either intractable or worsened after treatment and a moderate course of functional impairment. Racial ethnic minority status and higher levels of depression, lower perceived treatment benefit was associated with increased likelihood of intractable symptoms (both pain and functional impairment) PDI, or less resolution of functional impairment (moderate trajectory group).

Baseline and Affective Symptoms Consistent with previous research higher levels of baseline symptomology (more severe pain and worse FI at admission) were associated with worse outcomes on those respective measures at discharge and 6- and 12months following treatment.41,91 Previous studies showed that treatment-related changes in depression are linked to positive long-term clinical outcomes following multidisciplinary treatments for chronic pain.12,63 In this study, increased depression symptoms at discharge were indeed associated with increased likelihood of less resolution of pain and FI (intermediate trajectories) or intractable symptoms. Our findings support the value of assessing patients’ depression throughout treatment and suggest targeted treatment of depression (for example addition of depression

The Journal of Pain

11

specific psychological interventions) may assist with enhancement and maintenance of treatment response.

Patient Characteristics Ethnicity: Ethnicity (in the present study not being non-Hispanic white), was predictive of poor initial treatment response, decay in treatment benefits and lack of follow up across the two study time points. These disparities were independent of other predictors such as education and income. In this study, racial ethnic minority participants were 54% more likely and 20% more likely to belong to treatment trajectories characterized by intractable symptoms of pain and functional impairment respectively and were less likely to complete treatment. Racial/ethnic disparities in care are pervasive across pain treatment. The etiology of these differences is multifactorial and believed to be a complex interplay of different factors such as differences in pain processing,14,64 socio-cultural factors15,85 systemic social factors (eg, social inequality, discrimination, bias).13,95 In addition, socioeconomic barriers may disproportionately affect potential racial/ethnic minority participants. Research, primarily focused on opioid prescribing, shows that provider clinical decision making in pain treatment is biased by patient race.34 Additionally, biopsychosocial interventions may be unintentionally constructed/delivered in a manner more likely to benefit non-Hispanic white participants.13 Education: Lower levels of education were significantly associated with treatment outcomes, including increased likelihood (59%) of having intractable pain and increased likelihood (64%) of post-treatment worsening of pain. Future research should examine whether these socioeconomic status related differences in trajectory group membership are related to financial and employment barriers in accessing post-treatment follow-up care and resulting differences in ability to adhere to post-treatment lifestyle changes. Ethnicity * Education: Research shows that racial/ethnic minorities15,59,62 and individuals from lower socioeconomic backgrounds59 report higher levels of baseline pain severity, pain interference, and disability. One possibility, requires further investigation, is that chronic stress induced by systematic social inequalities may alter pain processing. Individuals may attribute different meanings to pain or utilize different coping styles15,85 dependent upon sociocultural context. Individuals with lower levels of education and racial ethnic minorities have been shown to be more likely to engage in maladaptive coping strategies,15 and to have higher levels of pain catastrophizing.76 Clinicians are encouraged to make note of these discrepancies and correspondingly adapted programming (eg, adding additional emphasis on education, coping, cognition, catastrophizing) to meet the needs of these patient groups. Age and gender: In this study, younger age was associated with less pain-related functional impairment; however, there were no significant differences in pain

ARTICLE IN PRESS 12

The Journal of Pain

Clinical and Demographic Predictors of Interdisciplinary Chronic Pain Rehabilita

severity. Research which suggests that pain threshold increases with age55 may explain these findings. There were no gender differences in outcomes related to pain severity, however male participants were twice as likely to be in the trajectory of intractable functional impairment. This is consistent with previous studies showing that females report greater functional improvements than males, but equivalent improvements in pain.26,72 These findings are not yet fully understood but may perhaps be linked to research which suggests that women not only have higher pain sensitivity and are also more likely to report pain. Perhaps the pain severity ratings provided by men in this sample are underestimates of their actual physical pain. Future research may want to gender related pain treatment gains using objective measures of pain, such as pain tolerance and pain threshold.

Opioid Use The United States is in the midst of an “opioid epidemic” with recent statistics indicating over 11.5 million Americans have misused prescription opioids and 1.9 million have a prescription opioid use disorder.37 Of those misusing, 62% reported doing so to relieve physical pain.37 In this context, it is a moral imperative to identify, develop, and support empirically validated, effective, nonopioid treatments for chronic noncancer pain. In this study, 61% of patients were weaned from COT during treatment in an ICPRP. The vast majority did not resume use. Further, whether a patient was weaned from opioids during treatment was not a predictor of long-term treatment trajectory suggesting that for many patients COT is not essential for chronic pain management and can be discontinued in the context of an ICPRP. This is also line with our previous findings which show that patients can be weaned from even high doses of opioids with outcomes equivalent to those of peers not on or on low doses of opioids.47 While this study found that remaining off opioids is associated with better long-term outcomes, resuming opioids and the associated consequences remain to be better understood. In this study, it is not known whether patients resuming opioids may have resumed them for reasons unrelated to chronic pain (eg, cancer pain, or for postoperative pain management). Thus, future research should examine the long-term treatment outcomes of patients who resume opioids for the treatment of chronic pain, with one goal of identifying risk factors for opioid resumption. Finally, it is important to note that a small number of patients were not weaned from opioids. This was predictive of an increase in symptoms over the course of year following discharge from treatment. Many patients discharged from the ICPRP treatment program on COT, were discharged on Suboxone for the management of co-occurring opioid dependence. These patients are likely to have a poorer long-term prognosis. In the context of the current opioid crisis, it is imperative to further explore the long-term outcomes for patients with co-morbid pain and opioid addiction.

Strengths and Limitations Our study builds upon literature citing demographic and baseline clinical variables as predictors of treatment response to identify subgroups of patients with similar change trajectories. The large sample size and advanced statistical methods are strengths of the study. These results highlight the need to account for heterogeneity in the post-treatment course of key clinical outcomes. Our examination of group-based trajectories allows for the identification of characteristics predictive of unresponsiveness to treatment or a relapsing long-term clinical course. In addition, our sample is composed of patients who typically report severe symptoms that are treatment resistant and have tried multiple outpatient single-modality treatments before presenting for ICPRP care. Thus, these findings are likely representative of the “sickest” patients with CNCP. This is a strength and limitation as it limits the generalizability of our findings. In addition, patients in this program are only a subset of those who would be appropriate candidates for such treatment, due to declining referral, or quitting the treatment. In this study, patients who dropped out had higher levels of pain (§ .5 SD) and were more likely (7.8%) to be on COT. Noncompleters were also statistically more likely to have higher levels of functional impairment and anxiety; these differences were not clinically significant, and the statistical significance of these differences may be a byproduct of sample size. Another significant limitation is missing longitudinal data; 40.5% of study participants did not return follow-up surveys. This was addressed by utilizing data from multiple sources (eg, the EMR and the KP52) and statistically by assessing for nonresponse bias, adjusting for covariates different within the population lost to follow-up, and utilizing statistical methods allowing inclusion of all available data. Additionally, sensitivity analyses conducted within the subset of patients with data at all three time points and replicated our findings.

Implications and Future Research The findings of this research, highlight two pressing issues in pain care, that of race and socioeconomicrelated health disparities and the need to develop safe and efficacious nonopioid treatments for chronic pain. The predictive factors shown here to influence treatment outcomes (eg, ethnicity and education) are implicated in systematic disparities across the health care system as well as structural, social, and institutional inequalities, which extend outside the health care sector. Adequately addressing the impact of these inequalities requires addressing disparities at a systemic level. The National Pain Strategy provides a long-term high level plan for addressing disparities through improving pain care for vulnerable populations.49 Due to various limitations, there are important aspects that the current study cannot examine, and they all warrant further examination with a goal of adapting treatments in a way that would benefit patients in

ARTICLE IN PRESS Huffman et al at-risk trajectory groups. These include specific treatment components that may differ in relative importance for individual patients or patients in at risk trajectory groups. There also may be what is referred to as “common factors” in psychotherapy underlying treatment benefits (eg, patient expectancies, patient−physician relationship). These common factors also include social support as it has been demonstrated to be related pain processing,66 responsiveness to painful stimuli20,66 and neural response to threat of electric shock.20 While not measured in this study, social support is a hallmark of the ICPRP experience. Participants who are demographically underrepresented in this sample (men, racial ethnic minorities, patients with lower levels of education) may feel less connected to their peers and receive less social support from the other participants. Future research may also consider whether any of these variables differ as a function of demographic characteristics, such as gender, racial/ethnic background and socioeconomic status and whether this is linked to program outcomes. Another factor not examined is treatment duration. Patients remained in treatment for varying lengths of time dependent upon their symptomology within the range of 3 to 4 weeks. Future research should examine the “dosage” of ICPRP treatment needed for optimal treatment outcomes. In psychotherapy research, there is a well-established “dose effect,” with one study demonstrating that 50% of patients improved within 8 sessions and 75% within 26.45 Extended treatment duration may be beneficial for patients with higher levels of affective

References 1. Aiken LS, West SG: Multiple Regression: Testing and Interpreting Interactions. Newbury Park, CA, Sage, 1991 2. Alghadir AH, Anwer S, Iqbal A, Iqbal ZA: Test-retest reliability, validity, and minimum detectable change of visual analog, numerical rating, and verbal rating scales for measurement of osteoarthritic knee pain. J Pain Res 11:851-856, 2018 3. Andrade SE, Kahler KH, Frech F, Chan KA: Methods for evaluation of medication adherence and persistence using automated databases. Pharmacoepidemiol Drug Saf 15:565-574, 2006 4. Antony MM, Bieling PJ, Cox BJ, Enns MW, Swinson RP: Psychometric properties of the 42-item and 21-item versions of the Depression Anxiety Stress Scales in clinical groups and a community sample. Psychol Assess 10:176181, 1998 5. Ashina S, Serrano D, Lipton RB, et al. Depression and risk of transformation of episodic to chronic migraine. J Headache Pain 13:615-624, 2012

The Journal of Pain

13

symptoms, more baseline severe symptoms, or those at greatest risk of post-treatment worsening. Finally, our data did not evaluate patients’ use of postdischarge treatments or attendance at monthly “aftercare” meetings. The ICPRP program has a free monthly aftercare program and local patients sometimes choose to follow up with program staff on an outpatient basis, for additional individual or group psychotherapy, physical therapy, or on-going medication management. Patients at the greatest risk of posttreatment worsening, or with less symptom resolution, may benefit from continued treatment focused on relapse prevention, or continued less intensive personalized treatment focused on maximizing treatment benefits. Telemedicine may also be an important addition for those who are at risk of relapse but face barriers prohibiting in person follow up. Future research should examine if these “aftercare” components have the potential to at least partially alleviate some disparities in outcomes seen in this research.

Acknowledgment I/We acknowledge the Knowledge Program Data Registry of Cleveland Clinic, Cleveland, OH for providing data used in these analyses.

Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.jpain.2019.05.014. 8. Beck AT, Rush AJ, Shaw BF, Emery G: Cognitive Therapy of Depression: A Treatment Manual. New York, Guilford Press, 1979 9. Beck AX, Steer RA: Beck Anxiety Inventory Manual. San Antonio, TX, Psychological Corporation, 1990 10. Becker N, Sjøgren P, Bech P, Olsen AK, Eriksen J: Treatment outcome of chronic non-malignant pain patients managed in a Danish multidisciplinary pain centre compared to general practice: A randomised controlled trial. Pain 84:203-211, 2000 11. Burchiel KJ, Anderson VC, Wilson BJ, Denison DB, Olson KA, Shatin D: Prognostic factors of spinal cord stimulation for chronic back and leg pain. Neurosurgery 36:1101-1111, 1995 12. Burns JW, Kubilus A, Bruehl S, Harden RN, Lofland K: Do changes in cognitive factors influence outcome following multidisciplinary treatment for chronic pain? A crosslagged panel analysis. J Consult Clin Psychol 71:81-91, 2003 13. Campbell CM, Edwards RR: Ethnic differences in pain and pain management. Pain Manage 2:219-230, 2012

6. Barry DT, Glenn CP, Hoff RA, Potenza MN: Pain interference, psychopathology, and general medical conditions among black and white adults in the US general population. J Addict Med 11:98-105, 2017

14. Campbell CM, France CR, Robinson ME, Logan HL, Geffken GR, Fillingim RB: Ethnic differences in diffuse noxious inhibitory controls. J Pain 9:759-766, 2008

7. Bartley EJ, Fillingim RB: Sex differences in pain: A brief review of clinical and experimental findings. Br J Anaesth 111:52-58, 2013

15. Cano A, Mayo A, Ventimiglia M: Coping, pain severity, interference, and disability: The potential mediating and moderating roles of race and education. J Pain 7:459-468, 2006

ARTICLE IN PRESS 14

The Journal of Pain

Clinical and Demographic Predictors of Interdisciplinary Chronic Pain Rehabilita

16. Celeux G, Chauveau D, Diebolt J: Stochastic versions of the em algorithm: An experimental study in the mixture case. J Stat Comput Simul 55:287-314, 1996 17. Chakraborty HG: Hong: A Mixed Model Approach for Intent-to-Treat Analysis in Longitudinal Clinical Trials With Missing Values. Research Triangle Park, NC, RTI International, 2009. RTI Press publication No. MR-0009-0903Retrieved June 3rd, 2016 from http://www.rti.org/rtipress 18. Chibnall JT, Tait RC: The pain disability index: Factor structure and normative data. Arch Phys Med Rehabil 75:1082-1086, 1994 19. Chien CW, Bagraith KS, Khan A, Deen M, Strong J: Comparative responsiveness of verbal and numerical rating scales to measure pain intensity in patients with chronic pain. J Pain 14:1653-1662, 2013 20. Coan JA, Schaefer HS, Davidson RJ: Lending a hand: Social regulation of the neural response to threat. Psychol Sci 17:1032-1039, 2006

comprehensive pain programs for chronic nonmalignant pain. J Pain 7:779-793, 2006 34. Green CR, Anderson KO, Baker TA, et al. The unequal burden of pain: Confronting racial and ethnic disparities in pain. Pain Med 4:277-294, 2003 35. Gronblad M, Hupli M, Wennerstrand P, et al. Intercorrelation and test-retest reliability of the pain disability index (PDI) and the Oswestry disability questionnaire (ODQ) and their correlation with pain intensity in low back pain patients. Clin J Pain 9:189-195, 1993 n J, Esmail R, Karjalainen K, Malmivaara A, Irvin 36. Guzma E, Bombardier C: Multidisciplinary rehabilitation for chronic low back pain: Systematic review. BMJ 322:1511-1516, 2001 37. Han B, Compton WM, Blanco C, Crane E, Lee J, Jones CM: Prescription opioid use, misuse, and use disorders in U.S. adults: 2015 National Survey on Drug Use and Health. Ann Intern Med 167:293-301, 2017

21. Cohen J: A power primer. Psychol Bull 112:155-159, 1992

38. Hartrick CT, Kovan JP, Shapiro S: The numeric rating scale for clinical pain measurement: A ratio measure? Pain Pract 3:310-316, 2003

22. Cormier S, Lavigne GL, Choiniere M, Rainville P: Expectations predict chronic pain treatment outcomes. Pain 157:329-338, 2016

39. Hausmann LR, Gao S, Lee ES, Kwoh CK: Racial disparities in the monitoring of patients on chronic opioid therapy. Pain 154:46-52, 2013

23. Day MA, Thorn BE: The relationship of demographic and psychosocial variables to pain-related outcomes in a rural chronic pain population. Pain 151:467-474, 2010

40. Heiwe S, Lonnquist I, Kallmen H: Potential risk factors associated with risk for drop-out and relapse during and following withdrawal of opioid prescription medication. Eur J Pain 15:966-970, 2011

24. Dworkin RH, Turk DC, Wyrwich KW, et al. Interpreting the clinical importance of treatment outcomes in chronic pain clinical trials: IMMPACT recommendations. J Pain 9:105-121, 2008 25. Dworkin SF, Turner JA, Mancl L, et al. A randomized clinical trial of a tailored comprehensive care treatment program for temporomandibular disorders. J Orofac Pain 16:259-276, 2002 26. Edwards RR, Doleys DM, Lowery D, Fillingim RB: Pain tolerance as a predictor of outcome following multidisciplinary treatment for chronic pain: Differential effects as a function of sex. Pain 106:419-426, 2003 27. Fairbanks JC, Couper J, Davies JB, O’Brien JP: Oswestry low back pain disability questionnaire. Physiotherapy 66:271-273, 1980 28. Ferreira-Valente MA, Pais-Ribeiro JL, Jensen MP: Validity of four pain intensity rating scales. Pain 152:2399-2404, 2011 29. Fillingim RB: Sex, gender, and pain: Women and men really are different. Curr Rev Pain 4:24-30, 2000 30. Fillingim RB, King CD, Ribeiro-Dasilva MC, Rahim-Williams B, Riley JL 3rd: Sex, gender, and pain: A review of recent clinical and experimental findings. J Pain 10:447-485, 2009 31. Flor H, Fydrich T, Turk DC: Efficacy of multidisciplinary pain treatment centers: A meta-analytic review. Pain 49:221-230, 1992 32. Gaskin DJ, Richard P: The economic costs of pain in the United States. J Pain 13:715-724, 2012 33. Gatchel RJ, Okifuji A: Evidence-based scientific data documenting the treatment and cost-effectiveness of

41. Hill JC, Lewis M, Sim J, Hay EM, Dziedzic K: Predictors of poor outcome in patients with neck pain treated by physical therapy. Clin J Pain 23:683-690, 2007 42. Hjermstad MJ, Fayers PM, Haugen DF, et al. Studies comparing numerical rating scales, verbal rating scales, and visual analogue scales for assessment of pain intensity in adults: A systematic literature review. J Pain Symptom Manage 41:1073-1093, 2011 43. Hooten WM, Knight-Brown M, Townsend CO, Laures HJ: Clinical outcomes of multidisciplinary pain rehabilitation among African American compared with Caucasian patients with chronic pain. Pain Med 13:1499-1508, 2012 44. Hooten WM, Townsend CO, Sletten CD, Bruce BK, Rome JD: Treatment outcomes after multidisciplinary pain rehabilitation with analgesic medication withdrawal for patients with fibromyalgia. Pain Med 8:8-16, 2007 45. Howard KI, Kopta SM, Krause MS, Orlinsky DE: The dose −effect relationship in psychotherapy. Am Psychol 41:159164, 1986 46. Huffman KL, Davin SA, Sweis G: The impact of race and educational attainment on chronic pain rehabilitation program outcomes. J Pain 12(Suppl 1):80, 2011 47. Huffman KL, Rush TE, Fan Y, et al. Sustained improvements in pain, mood, function and opioid use post interdisciplinary pain rehabilitation in patients weaned from high and low dose chronic opioid therapy. Pain 158:1380-1394, 2017 48. Huffman KL, Sweis GW, Gase A, Scheman J, Covington EC: Opioid use 12 months following interdisciplinary pain rehabilitation with weaning. Pain Med 14:1908-1917, 2013

ARTICLE IN PRESS Huffman et al

The Journal of Pain

15

49. Interagency Pain Research Coordinating Committee (IPRCC): National pain strategy: A comprehensive population health-level strategy for pain.

65. Merry B, Campbell CM, Buenaver LF, et al. Ethnic group differences in the outcomes of multidisciplinary pain treatment. J Musculoskelet Pain 19:24-30, 2011

50. Jensen MP, Turner JA, Romano JM: Changes in beliefs, catastrophizing, and coping are associated with improvement in multidisciplinary pain treatment. J Consult Clin Psychol 69:655-662, 2001

66. Montoya P, Larbig W, Braun C, Preissl H, Birbaumer N: Influence of social support and emotional context on pain processing and magnetic brain responses in fibromyalgia. Arthritis Rheum 50:4035-4044, 2004

51. Jolly M, Mikolaitis RA, Shakoor N, Fogg LF, Block JA: Education, zip code-based annualized household income, and health outcomes in patients with systemic lupus erythematosus. J Rheumatol 37:1150-1157, 2010

67. Murphy JL, Clark ME, Banou E: Opioid cessation and multidimensional outcomes after interdisciplinary chronic pain treatment. Clin J Pain 29:109-117, 2013

52. Katzan I, Speck M, Dopler C, et al. The Knowledge Program: An innovative, comprehensive electronic data capture system and warehouse. AMIA Annu Symp Proc 683692, 2011 53. Krumova EK, Bennemann P, Kindler D, Schwarzer A, Zenz M, Maier C: Low pain intensity after opioid withdrawal as a first step of a comprehensive pain rehabilitation program predicts long-term nonuse of opioids in chronic noncancer pain. Clin J Pain 29:760-769, 2013 54. LaCaille RA, DeBerard MS, Masters KS, Colledge AL, Bacon W: Presurgical biopsychosocial factors predict multidimensional patient: Outcomes of interbody cage lumbar fusion. Spine J 5:71-78, 2005 55. Lautenbacher S, Peters JH, Heesen M, Scheel J, Kunz M: Age changes in pain perception: A systematic-review and meta-analysis of age effects on pain and tolerance thresholds. Neurosci Biobehav Rev 75:104-113, 2017 56. Lo Y, Mendell NR, Rubin DB: Testing the number of components in a normal mixture. Biometrika 88:767-778, 2001 57. Lopez RP: Neighborhood risk factors for obesity. Obesity 15:2111-2119, 2007 58. Lovibond S, Lovibond P: Manual for the Depression Anxiety Stress Scales, 2nd ed. Sydney, Australia, Psychology Foundation of Australia, 1995 59. Martinez KA, Snyder CF, Malin JL, Dy SM: Is race/ethnicity related to the presence or severity of pain in colorectal and lung cancer? J Pain Symptom Manage 48:1050-1059, 2014 60. Mayer T, Gatchel R: Functional Restoration for Spinal Disorders: The Sports Medicine Approach. Philadelphia, PA, Lea & Febiger, 1988 61. Mayer TG, Gatchel RJ, Mayer H, Kishino ND, Keeley J, Mooney V: A prospective two-year study of functional restoration in industrial low back injury. An objective assessment procedure. JAMA 258:1763-1767, 1987 62. McCracken LM, Matthews AK, Tang TS, Cuba SL: A comparison of blacks and whites seeking treatment for chronic pain. Clin J Pain 17:249-255, 2001 63. McCracken LM, Turk DC: Behavioral and cognitivebehavioral treatment for chronic pain: Outcome, predictors of outcome, and treatment process. Spine 27:2564-2573, 2002 64. Mechlin B, Heymen S, Edwards CL, Girdler SS: Ethnic differences in cardiovascular-somatosensory interactions and in the central processing of noxious stimuli. Psychophysiology 48:762-773, 2011

n LK, Muthe n BO: Mplus User’s Guide, 7th ed Los 68. Muthe n & Muthe  n, 1998 Angeles, CA, Muthe 69. Nagin DS, Tremblay RE: Analyzing developmental trajectories of distinct but related behaviors: A group-based method. Psychol Methods 6:18-34, 2001 70. Nahin RL: Estimates of pain prevalence and severity in adults: United States, 2012. J Pain 16:769-780, 2015 71. Norman GR, Sloan JA, Wyrwich KW: Interpretation of changes in health-related quality of life: The remarkable universality of half a standard deviation. Med Care 41:582592, 2003 72. Pieh C, Altmeppen J, Neumeier S, Loew T, Angerer M, Lahmann C: Gender differences in outcomes of a multimodal pain management program. Pain 153:197-202, 2012 73. Riley JL 3rd, Robinson ME, Wise EA, Myers CD, Fillingim RB: Sex differences in the perception of noxious experimental stimuli: A meta-analysis. Pain 74:181-187, 1998 74. Rivero-Arias O, Campbell H, Gray A, Fairbank J, Frost H, Wilson-MacDonald J: Surgical stabilisation of the spine compared with a programme of intensive rehabilitation for the management of patients with chronic low back pain: Cost utility analysis based on a randomised controlled trial. BMJ 330:1239, 2005 75. Rome JD, Townsend CO, Bruce BK, Sletten CD, Luedtke CA, Hodgson JE: Chronic noncancer pain rehabilitation with opioid withdrawal: Comparison of treatment outcomes based on opioid use status at admission. Mayo Clin Proc 79:759-768, 2004 76. Roth RS, Geisser ME: Educational achievement and chronic pain disability: Mediating role of pain-related cognitions. Clin J Pain 18:286-296, 2002 77. Ruau D, Liu LY, Clark JD, Angst MS, Butte AJ: Sex differences in reported pain across 11,000 patients captured in electronic medical records. J Pain 13:228-234, 2012 78. Rudy TE, Turk DC, Kubinski JA, Zaki HS: Differential treatment responses of TMD patients as a function of psychological characteristics. Pain 61:103-112, 1995 79. Rush TE, Sweis GW, Vij B, et al. High dose opioid weaning within the context of a chronic pain rehabilitation program. J Pain 15:S111, 2014 80. Saastamoinen P, Leino-Arjas P, Laaksonen M, Lahelma E: Socio-economic differences in the prevalence of acute, chronic and disabling chronic pain among ageing employees. PAIN 114:364-371, 2005 81. SAS Institute Inc: Base SAS Ò 9.4 Procedures Guide: Statistical Procedures, 2nd ed Cary, NC, SAS Institute Inc., 2013

ARTICLE IN PRESS 16

The Journal of Pain

Clinical and Demographic Predictors of Interdisciplinary Chronic Pain Rehabilita

82. Skouen JS, Grasdal AL, Haldorsen EM, Ursin H: Relative cost-effectiveness of extensive and light multidisciplinary treatment programs versus treatment as usual for patients with chronic low back pain on long-term sick leave: Randomized controlled study. Spine 27:901-909, 2002 83. Stanos S: Focused review of interdisciplinary pain rehabilitation programs for chronic pain management. Curr Pain Headache Rep 16:147-152, 2012 84. Tait R, Pollard C, Margolis R, Duckro P, Krause SJ: The pain disability index: Psychometric & validity data. Arch Phys Med Rehabil 68:438-441, 1987 85. Tan G, Jensen MP, Thornby J, Anderson KO: Ethnicity, control appraisal, coping, and adjustment to chronic pain among black and white Americans. Pain Med 6:18-28, 2005 86. Thomas AJ, Eberly LE, Davey Smith G, Neaton JD: ZIPcode-based versus tract-based income measures as longterm risk-adjusted mortality predictors. Am J Epidemiol 164:586-590, 2006 87. Townsend CO, Kerkvliet JL, Bruce BK, et al. A longitudinal study of the efficacy of a comprehensive pain rehabilitation program with opioid withdrawal: Comparison of treatment outcomes based on opioid use status at admission. Pain 140:177-189, 2008

90. Turner JA, Holtzman S, Mancl L: Mediators, moderators, and predictors of therapeutic change in cognitivebehavioral therapy for chronic pain. Pain 127:276-286, 2007 91. van der Hulst M, Vollenbroek-Hutten MM, Ijzerman MJ: A systematic review of sociodemographic, physical, and psychological predictors of multidisciplinary rehabilitationor, back school treatment outcome in patients with chronic low back pain. Spine 30:813-825, 2005 92. van Hooff ML, Spruit M, O’Dowd JK, van Lankveld W, Fairbank JC, van Limbeek J: Predictive factors for successful clinical outcome 1 year after an intensive combined physical and psychological programme for chronic low back pain. Eur Spine J 23:102-112, 2014 93. Wang L, Haberland C, Thurm C, Bhattacharya J, Park KT: Health outcomes in US children with abdominal pain at major emergency departments associated with race and socioeconomic status. PLoS One 10:e0132758, 2015 94. West BT, WK, Galecki A: Linear Mixed Models: A Practical Guide Using Statistical Software, 2nd ed Boca Raton, Florida, CRC Press, 2008 95. Williams DR, Rucker TD: Understanding and addressing racial disparities in health care. Health Care Financ Rev 21:75-90, 2000

88. Turk DC: Clinical effectiveness and cost-effectiveness of treatments for patients with chronic pain. Clin J Pain 18:355-365, 2002

96. Williamson A, Hoggart B: Pain: A review of three commonly used pain rating scales. J Clin Nurs 14:798-804, 2005

89. Turk DC, Zaki HS, Rudy TE: Effects of intraoral appliance and biofeedback/stress management alone and in combination in treating pain and depression in patients with temporomandibular disorders. J Prosthet Dent 70:158-164, 1993

97. Zheng Y, Tepper SJ, Covington EC, Mathews M, Scheman J: Retrospective outcome analyses for headaches in a pain rehabilitation interdisciplinary program. Headache 54:520-527, 2014