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Volatility and change in chronic pain severity predict outcomes of treatment for prescription opioid addiction

Matthew J. Worley1,2, Keith G. Heinzerling3, Steven Shoptaw3 & Walter Ling4

Department of Psychiatry, University of California, San Diego, CA, USA,1 Veterans Affairs San Diego Healthcare System, San Diego, CA, USA,2 Department of Family Medicine, University of California, Los Angeles, CA, USA3 and Department of Psychiatry, University of California, Los Angeles, CA, USA4

ABSTRACT

Background and aims Buprenorphine–naloxone (BUP–NLX) can be used to manage prescription opioid addiction among persons with chronic pain, but post-treatment relapse is common and difficult to predict. This study estimated whether changes in pain over time and pain volatility during BUP–NLX maintenance would predict opioid use during the taper BUP–NLX taper. Design Secondary analysis of a multi-site clinical trial for prescription opioid addiction, using data obtained during a 12-week BUP–NLX stabilization and 4-week BUP–NLX taper. Setting Community clinics affiliated with a national clinical trials network in 10 US cities. Participants Subjects with chronic pain who entered the BUP–NLX taper phase (n = 125) with enrollment occurring from June 2006 to July 2009 (52% male, 88% Caucasian, 31% married). Measurements Outcomes were weekly biologically verified and self-reported opioid use from the 4-week taper phase. Predictors were estimates of baseline severity, rate of change and volatility in pain from weekly self-reports during the 12-week maintenance phase. Findings Controlling for baseline pain and treatment condition, increased pain [odds ratio (OR) = 2.38, P = 0.02] and greater pain volatility (OR = 2.43, P = 0.04) predicted greater odds of positive opioid urine screen during BUP–NLX taper. Increased pain (IRR = 1.40, P = 0.04) and greater pain volatility [incidence-rate ratio (IRR) = 1.66, P = 0.009] also predicted greater frequency of self-reported opioid use. Conclusions Adults with chronic pain receiving out-patient treatment with buprenorphine-naloxone (BUP–NLX) for prescription opioid addiction have an elevated risk for opioid use when taperingoff maintenance treatment. Those with relative persistence in pain over time and greater volatility in pain during treatment are less likely to sustain abstinence during BUP–NLX taper.

Keywords Buprenorphine–naloxone, chronic pain, multilevel modeling, prediction, prescription opiates, treatment outcomes.

Correspondence to: Matthew J. Worley, Department of Psychiatry, University of California, San Diego, 9500 Gilman Drive, MC 0930, La Jolla, CA 92037, USA.

E-mail: [email protected] Submitted 9 August 2016; initial review completed 14 November 2016; final version accepted 1 February 2017

INTRODUCTION

Prescription opioid addiction in adults with chronic pain has become increasingly common and problematic in many developed nations [1,2]. At the national level, both opioid prescribing and opioid-related overdoses have accelerated drastically in the last 15–20 years [1,3]. Chronic pain patients are now prescribed opioids for longer durations and at higher doses than in previous decades, which places them at greater risk for physiological tolerance and potential addiction [4]. Treatment of this population is complicated by complex medical and psychi- atric problems that often intensify upon opioid withdrawal and prompt relapse [5]. The optimization of treatment for this population is currently a national priority across

diverse areas of interest, including addiction treatment, pain management and primary care [6].

Both clinical recommendations and empirical studies suggest that buprenorphine–naloxone (BUP–NLX) is a viable pharmacotherapy for chronic pain patients with prescription opioid addiction. Compared to full opioid agonists, BUP–NLX offers improved safety and diminished abuse liability [7–10]. Although not currently Food and Drug Administration (FDA)-approved for pain indications, the analgesic benefits of BUP–NLX in patients with opioid addiction have been described [11–13]. Empirical studies also suggest that BUP–NLX maintenance can reduce pain significantly in this population [14–16], with one random- ized trial finding no differences in pain between patients receiving 6 months of BUP–NLX versus low-dose

RESEARCH REPORT doi:10.1111/add.13782

methadone [17]. These studies suggest that BUP–NLX can be used to manage pain sufficiently in opioid-dependent populations. However, little research to date has examined whether individual differences in pain control during BUP–NLX might impact opioid use outcomes.

Because persistent pain is often associated with relapse following addiction treatment [18,19], unresponsive pain during BUP–NLX maintenance could trigger a return to opioid use during or following treatment. In our prior analysis of data from a large clinical trial involving BUP–NLX maintenance and counseling, we found signifi- cant variability in patterns of pain during treatment that corresponded to treatment outcome [20]. Among baseline pain severity, rate of change in pain over time and volatility in pain, only pain volatility predicted outcomes at end of treatment, with greater volatility in pain during treatment related to reduced probability of opioid abstinence. Such fine-grained and dynamic aspects of pain may have unique predictive value for substance use outcomes, as the presence of chronic pain alone did not predict treatment outcomes in the same trial or other BUP–NLX treatment samples [21–24]. Continued identification and validation of such predictive markers is a vital step towards identifying processes linked to individual relapse risk developing related strategies for improving treatment.

The aim of this study was to estimate whether individual patterns of pain during BUP–NLX maintenance treatment would predict prospectively both biologically verified and self-reported opioid use during the BUP–NLX taper. The focus on BUP–NLX taper phase was motivated by awareness of this transitional stage as associated with increased risk for opioid relapse [5], as well as an attempt to extend prior work that predicted end of treatment outcome [20]. Additionally, knowledge of factors related to post-treatment opioid use among patients with chronic pain is scarce, despite the high prevalence of chronic pain among BUP–NLX patients [25]. Using estimates of baseline pain, rate of change in pain over time and weekly volatility in pain as primary predictors, we hypothesized that having more severe baseline pain, greater persistence in pain over time and greater volatility in pain during BUP–NLX maintenance would predict greater probability and frequency of opioid use during the BUP–NLX taper.

METHODS

Study design

This institutional review board-exempt study was a secondary analysis of publicly available data from the Prescription Opioid Addiction Treatment Study (POATS), a multi-site clinical trial for treatment of prescription opioid dependence conducted in 10 U.S. cities in the National Drug Abuse Treatment Clinical Trials Network (clinicaltrials.gov identifier NCT00316277). Full details

and main findings of the main study are in previous reports [23,26]. In brief, phase 1 of POATS (n = 653) randomized participants to an enhanced counseling condition or standard medical management counseling during 4-week BUP–NLX detoxification. In phase 2 (n = 360) participants who did not sustain abstinence in phase 1 were re- randomized to standard or enhanced counseling during 12 weeks of BUP–NLX maintenance followed by a 4-week BUP–NLX taper. This current study uses data from phase 2 only. Data were selected in order to measure multiple fea- tures of pain during BUP–NLX maintenance, and to use these indices to predict future opioid use prospectively when participants were tapered off medication. Data from baseline assessments were tested as covariates. The primary predictors were features of pain obtained from the 12 weeks of BUP–NLX stabilization in phase 2, while the outcomes were measures of opioid use assessed during the subsequent 4-week taper phase.

Sample

Participants met the inclusion criteria for the POATS sample as described in prior reports [26]; they were aged at least 18 years, met DSM-IV criteria for current prescrip- tion opioid dependence, were physiologically dependent upon prescription opioids and had no unstable medical or psychiatric conditions. Any participants currently prescribed opioids for pain were cleared for opioid detoxifi- cation from their prescribing physician prior to enrollment. Exclusion criteria included use of heroin on ≥ 4 days in the past month, any prior injection of heroin and current physiological dependence on other substances, such as alcohol, sedatives or stimulants. From the original full POATS sample (n = 653), 360 participants entered phase 2 and 149 participants (41%) in the phase 2 sample had chronic pain. The current sample includes phase 2 partic- ipants with chronic pain who completed at least one outcome assessment during the taper phase (n = 125). Chronic pain was assessed during initial screening by pa- tient self-report of having ‘greater than usual aches and pains’ for at least 3 months and confirmed during medical screening. Aside from reporting greater current pain at baseline, the sample for the current study did not differ significantly from the remaining phase 2 sample (i.e. those without chronic pain) on any demographic or clinical variables obtained at baseline (see Table 1 for descriptive statistics).

Measures

Pain severity

Current pain severity was assessed weekly during the 12- week BUP–NLX maintenance phase with a single self- report rating (range = 0–10) on the full or abbreviated

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http://clinicaltrials.gov

Brief Pain Inventory–Short Form [27]. Weekly pain scores were used to obtain individualized estimates of pain intercept, pain slope and pain volatility in analyses described below, which were then used as predictors of opioid use outcomes in predictive models.

Opioid use

Opioid use was assessed weekly with a urine drug screen (UDS) and a calendar-assisted self-report interview [28]. Results of each UDS were aggregated across all tested opioids (i.e. analgesics, illicit opioids, methadone) to provide a single dichotomous indicator of opioid use (0 = negative, 1 = positive) for each visit. Self-reported use data were used to measure opioid use frequency, coded as the number of days of opioid use in the past week, standardized into 7-day segments for analysis (range = 0–7). Outcome variables in this study were UDS-confirmed opioid use and self-reported opioid use frequency assessed during the BUP–NLX taper (i.e. at weekly visits in weeks 13–16 post-randomization in phase 2).

Baseline demographic and clinical characteristics

Baseline demographic and clinical covariates were selected according to previous literature and prior studies of this sample. A brief demographics questionnaire and the Addiction Severity Index–Lite [29] assessed demographics. The Pain and Opiate Analgesic Use History [26] captured current and historical measures of opioid use, pain and opioid dependence treatment, while the Composite International Diagnostic Interview [30] assessed life-time major depression. Demographic covariates tested were sex, race and marital status, while clinical covariates were

phase 1 treatment condition, phase 2 treatment condition, historyof heroin use, history of non-oral prescription opioid use, history of opioid dependence treatment and life-time major depression.

Statistical analyses

Model-based estimates of pain trajectories and volatility in this sample were described in detail in our previous work [20] and are reviewed here. A multi-level growth curve model was fitted to weekly pain scores during BUP–NLX stabilization, controlling for fixed effects of time, sex and weekly opioid use, and random effects for person (intercepts) and time (slope). Individual estimates of pain intercepts and time slopes were extracted from the model for subsequent use as predictor variables. Each individual’s intercept and time slope, respectively, reflect baseline level and degree of change over time in pain. As shown in Fig. 1 for conceptual illustration, participants with ‘low’ esti- mated time slopes (25th percentile) had pain that decreased over time, while those with ‘high’ slopes (75th percentile) had little overall change or a slight increase in pain. From the multi-level pain score model, we also extracted an index of pain volatility, which captures the extent of each individual’s week-to-week instability in pain. Following the methods of previous similar studies in smoking [31], we collected residuals of each individual’s pain growth curve, converted the residuals to absolute values and computed the average for a single pain volatility score. Because this score is derived from the absolute residuals of the multi-level growth curve model, it captures the extent to which a given subject had pain scores that deviated either far above or far below their typical trajec- tory of pain during BUP–NLX stabilization. To illustrate volatility, Fig. 1 displays the residuals from the pain score growth curves separately for participants with ‘low’ volatil- ity (≤ 25th percentile) and ‘high’ volatility (≥ 75th percen- tile). Because the residuals reflect remaining variability in pain after accounting for each individual’s pain growth curve, the plotted residuals display weekly ‘unaccounted for’ variation in pain, as if each individual’s pain growth curve was constant at 0. Figure 1 illustrates that individ- uals with lower pain volatility scores (lower quartile) had pain scores that adhered closely to their individual trajec- tory, while those with high pain scores (upper quartile) had pain scores that deviated more drastically from their individual pain trajectory. Standardized, continuous measures of pain intercept, time slope and volatility were used as predictors in the primary analyses.

Separate multi-level models were used to examine the two outcome variables, opioid UDS and opioid use fre- quency, which were both assessed weekly during the BUP–NLX taper phase and nested within individuals. Multi-level logistic regression examined opioid UDS and

Table 1 Demographic and clinical characteristics of adults with chronic pain who received 12 weeks of buprenorphine–naloxone and counseling for prescription opioid dependence and completed at least one follow-up visit during a 4-week taper (n = 125).

Variable % or mean (SD)

Sex [% (n) male] 52% (64) Race (% (n) white] 88% (109) Years of education: mean (SD) 12.8 (2.4) Marital status [% (n) currently married] 31% (38) Baseline pain severity [rated 0–10): mean (SD)] 4.5 (3.0) Days of prescription opioid use in past 30: mean (SD)

27.9 (3.8)

Heroin use history [% (n) ever used] 24% (29) Prescription opioid route [% (n) ever used non-orally]

87% (108)

Prescription opioid treatment [% (n) ever received treatment]

30% (37)

Life-time major depression [% (n) with diagnosis] 40% (50)

SD = standard deviation.

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multi-level Poisson regression examined opioid use fre- quency. Random intercepts accounted for person-level clustering of observations, while random time slopes allowed individual heterogeneity in the rate of change in opioid use over time during the taper phase. All available data were included, as missing data analyses revealed no significant differences on study variables between patients who completed a taper-phase visit and those who did not, supporting the missing-at-random assumption and the use of maximum-likelihood estimation. Preliminary models tested time, demographics, baseline clinical severity variables and treatment condition as covariates, with any statistically significant covariates (P < 0.05) retained for subsequent models. Pain intercept, pain time slope and pain volatility were then added to the model together as a set of person-level predictors, to test the independent predictive effects of pain intercept, pain time slope and pain volatility. For the multi-level logistic and Poisson regression models, each coefficient estimate is expressed with an odds ratio (OR) or incidence-rate ratio (IRR), respectively. With standardized continuous predictors, these estimates reflect the differential probability of the outcome associated with a ± standard deviation (SD) difference in the predictor variable, expressed as difference in odds (positive drug

screen) or incidence rate (days using opioids). All analyses were conducted in Stata version 13.0 [32].

RESULTS

Descriptives of opioid use during BUP–NLX taper

During the 4-week BUP–NLX taper phase, 407 observa- tions of opioid UDS and opioid use frequency (each) were provided. Almost the entire sample (91%) provided opioid use measures in week 13 (114 of 125), but retention declined to 65% at week 16 (81 of 125). Opioid use increased during the 4-week taper, from 22% of screens positive in week 13 to 31% in week 16. Opioid use fre- quency also increased over time during the 4-week taper, from a mean of 0.28 (SD = 0.68) days/week at week 13 to 0.64 (SD = 1.49) at week 16.

Covariate models of opioid use outcomes

Covariates tested for both opioid UDS and opioid use frequency included demographics, clinical covariate and treatment condition. Only the phase 2 treatment condition predicted opioid UDS [OR = 0.11, P = 0.01, 95% confi- dence interval (CI) = 0.02, 0.58]. Receiving enhanced

Figure 1 (a) Estimated pain trajectories for participants in upper and lower quartile of pain time slope. Individual trajectories were estimated as a function of time, sex and weekly opioid use, random person-level intercepts and random time slopes. (b) Pain score deviations for participants in upper and lower quartile of pain volatility. BUP-NLX = buprenorphine-naloxone

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counseling reduced the probability of a positive UDS; participants in this condition had a lower overall propor- tion of positive UDS (18%) than those in standard counsel- ing (32%). No other demographic or clinical covariates predicted opioid UDS. For the model of opioid use fre- quency, time was the only statistically significant covariate (IRR = 1.42, P = 0.001, 95% CI = 1.15, 1.74), indicative of a significant increase over time in opioid use frequency during the taper phase. Significant variance in random time slopes for both outcomes revealed individual heteroge- neity in the rate of change over time. Treatment condition and linear time (fixed and random effects) were included as covariates in all subsequent models.

Prediction of opioid use outcomes from pain variables

Pain intercept, time slope and volatility during BUP–NLX stabilization were tested as predictors of opioid use during BUP–NLX taper. Pain time slope (95% CI = 1.13, 5.02) and pain volatility (95% CI = 1.03, 5.76) both predicted opioid UDS (see Table 2). As shown in Fig. 2 for illustration,

the group of participants with at least one positive UDS duringtaper had greater levels of pain over timeand greater pain volatility scores during BUP–NLX maintenance, compared to the group of participants without a positive UDS during taper. These independent effects indicated that both pain slopes and greater pain volatility predicted the likelihood of opioid use during BUP–NLX taper when controlling for the other factor. These effects also appeared to be clinically significant. The ORs, which were estimated on standardized coefficients, indicated that a two-SD differ- ence in pain volatility or pain time slope was associated, respectively, with 4.86 and 4.76 greater odds of opioid use during the taper. Treatment condition also remained statistically significant (95% CI = 0.04, 0.80), indicating that enhanced medicalmanagement reduced thelikelihood of opioid use during the taper in this sample with chronic pain, regardless of pain intercept, slope and volatility.

Results of models use to predict opioid use frequency were similar. Both pain time slope (95% CI = 1.02, 1.97) and pain volatility (95% CI = 1.20, 2.58) predicted opioid use frequency independently during the taper phase (see

Table 2 Results of multi-level models predicting opioid use during four-week buprenorphine–naloxone taper.

Variables

Opioid UDS Days/week using opioids

Covariate model Full model Covariate model Full model

OR P OR P IRR P IRR P

Taper week 1.35 0.20 1.31 0.27 1.41 0.001 1.40 0.001 Enhanced versus standard counseling 0.13 0.01 0.17 0.03 0.48 0.05 0.58 0.12 Pain intercept 1.13 0.44 1.16 0.42 Pain time slope 2.38 0.02 1.40 0.04 Pain volatility 2.43 0.04 1.66 0.009

OR = odds ratio; IRR = incidence-rate ratio; UDS = urine drug screen.

Figure 2 Participants with relatively greater pain over time and greater pain volatility during buprenorphine–naloxone (BUP-NLX) stabilization were more likely to use opioids during the 4-week taper

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Table 2), with greater levels of pain over time and greater pain volatility during BUP–NLX maintenance predicting greater frequency of opioid use during BUP–NLX taper. As shown in Fig. 3, the group of participants reporting opioid use multiple days per week during taper also had the greatest persistence in pain and the greatest volatility in pain during BUP–NLX maintenance. These effects also appeared to be clinically significant, as the IRRs indicated a two-SD difference in pain volatility or pain time slope was associated, respectively, with 2.80 and 3.32 increase in the rate of opioid use (days using per week). Linear time also remained statistically significant (95% CI = 1.15, 1.73), indicative of the overall increase in opioid use frequency during the taper phase.

DISCUSSION

In this study we identified characteristics of pain during treatment for prescription opioid addiction that predicted post-treatment opioid use in people with chronic pain. Higher levels of pain over time and greater volatility in pain during BUP–NLX maintenance and counseling predicted both biologically verified and self-reported opioid use independently during the BUP–NLX taper. The course of BUP–NLX treatment for opioid dependence often has a fixed duration, with the transition off opioid maintenance associated with elevated rates of relapse [33]. However, the specific factors that contribute to relapse during BUP–NLX treatment are not well understood, especially in patients with chronic pain who comprise substantial and increasing portions of the treatment population [25]. In this study of patients with chronic pain, those with relative persistence in pain and greater volatility in pain during BUP–NLX maintenance had the greatest odds of opioid use and also used opioids more frequently during the BUP–NLX taper. These findings provide preliminary evidence that persistent or erratic pain during prescription opioid addiction treatment may impact risk for opioid use

during withdrawal of opioid maintenance therapy in adults with chronic pain.

Our findings suggest that temporal aspects of pain re- sponse could potentially be used to identify high-risk pa- tients who warrant additional interventions to stabilize pain prior to tapering from opioid maintenance. In prior studies of substance use treatment, patients with chronic pain or severe pain at baseline had the greatest rates of post-treatment relapse [18,19], but findings from similar studies of BUP–NLX for opioid dependence have been mixed [22–24]. Our study revealed that among opioid- dependent adults with chronic pain, the more dynamic as- pects of pain predicted future opioid use more reliably than the static indicator of baseline pain severity. While replica- tion in other samples is necessary, these findings could po- tentially be used to guide treatment of other substance use disorders, given the strong overlap between chronic pain and use of other substances such as alcohol and illicit opiates [34,35].

An additional, unexpected finding was that the enhanced counseling condition produced lower rates of opioid use during the BUP–NLX taper than standard counseling condition. Treatment condition in this sample did not impact rates of multi-week abstinence at treatment end-point or 2-month follow-up [23]. The current study differs from the primary study by (1) including only patients with chronic pain, (2) focusing only on the taper phase and (3) predicting observed measures of opioid use instead of a composite measure of sustained abstinence. Enhanced medical management may have been particu- larly helpful for preventing relapse during the BUP–NLX taper for participants with chronic pain, perhaps by providing additional coping skills for managing aversive symptoms that arise during the taper. People with chronic pain comprise a substantial amount of adults treated for opioid dependence [25], thus research should continue to develop more effective therapies for improving treatment outcomes in this subpopulation.

Figure 3 Patients with greater pain over time and greater pain volatility during 12 weeks of buprenorphine–naloxone (BUP-NLX) stabilization re- ported more days/week using opioids during the subsequent 4-week taper

Volatility and change in pain 1207

Because persistence and volatility in pain predicted future opioid use, our findings provide a rationale to inves- tigate specific mechanisms that explain individual differ- ences in pain response during opioid maintenance treatment for prescription opioid addiction and chronic pain. These individual changes in pain may reflect underly- ing biomedical pathology, life stress and mood or genetic factors that influence pain sensitivity or biological response to opioid maintenance medications [36,37]. Central and peripheral inflammatory mechanisms may play a critical role in both pain perception and substance use, including substance-related reward [38]. Further translational inves- tigations are needed to specify underlying mechanisms and further personalize interventions for patients with chronic pain and prescription opioid addiction.

Our findings should be interpreted in light of several limitations. This study was a secondary analysis in which we tested hypotheses outside the scope of the original clinical trial. As such, these findings are prelim- inary and in need of confirmation through replication or a prospective design. These analyses also involved a subset of the original sample (participants with chronic pain who were retained after BUP–NLX detoxification and maintenance), so these findings may not generalize to the general population of prescription opioid- dependent adults who seek treatment. A potential concern is the accelerated rate of attrition during the taper phase which led to a steep increase in missing data, although confidence in the results is bolstered by our use of modern estimation procedures that are gener- ally more robust to missing data than alternative approaches [39]. The original clinical trial was conducted in community clinics connected to an established clinical trials research network, therefore the frequency and quality of clinical services provided in this study may differ from those typically available to this population. Because this sample was recruited for treatment of prescription opioid addiction, these findings may not generalize to adults with chronic pain patients who do not meet diagnostic thresholds for prescription opioid addiction or are not seeking treatment.

In conclusion, we found that features of individual trajectories of pain during BUP–NLX maintenance pre- dicted opioid use during BUP–NLX taper in patients with prescription opioid addiction and chronic pain. In adults with chronic pain receiving treatment for prescription opi- oid addiction, those who have relatively persistent or volatile pain during BUP–NLX maintenance are at greater risk for resuming opioid use while tapering off BUP–NLX. These findings suggest that stabilizing and/or reducing subjective pain prior to discontinuation of BUP–NLX main- tenance may be a means to improve treatment outcomes in this population. Future research should examine dy- namic aspects of pain response, perhaps while

supplementing opioid maintenance treatment with behav- ioral therapies or medications that target pain, to deter- mine whether stabilizing pain improves longer-term outcomes of prescription opioid addiction treatment in adults with chronic pain.

Declaration of interests

S.S. and K.G.H. have received clinical research supplies from Pfizer and Medicinova. W.L. has served as consultant to Reckitt Benckiser and Titan Pharmaceuticals, and has received unrestricted educational and research grants through UCLA from Reckitt Benckiser.

Acknowledgements

The analysis, interpretation and preparation of this study was supported by National Institute on Drug Abuse (NIDA) grants 5 T32 DA026400, 5R01 DA030577, 5R01 DA035054 and 3U10DA01304. The design, conduct, data collection and management of the original clinical trial providing the data for this study was conducted in the NIDA Clinical Trials Network (CTN) and was sup- ported by NIDA CTN grants 2U10DA015831, 2U10DA013045, 2U10DA015815, 2U10DA013727, 2U10DA020036, 2U10DA013035, 2U10DA013714 and 5U10DA013732. The sponsor of the original clinical trial, the NIDA Center for the CTN, collaborated in the design and conduct of the original trial but was not in- volved in the conceptualization of this study. The NIDA CTN publications committee is acknowledged for their review and feedback provided for this manuscript.

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