THE PREVALENCE OF CHRONIC occupational musculoskeletal

Transcription

1 778 ORIGINAL ARTICLE Patients With Chronic Disabling Occupational Musculoskeletal Disorder Failing to Complete Functional Restoration: Analysis of Treatment-Resistant Personality Characteristics Krista J. Howard, MS, Tom G. Mayer, MD, Brian R. Theodore, MS, Robert J. Gatchel, PhD, ABPP ABSTRACT. Howard KJ, Mayer TG, Theodore BR, Gatchel RJ. Patients with chronic disabling occupational musculoskeletal disorder failing to complete functional restoration: analysis of treatment-resistant personality characteristics. Arch Phys Med Rehabil 2009;90: Objective: To identify the risk factors for noncompletion of a functional restoration program for patients with chronic disabling occupational musculoskeletal disorders. Design: Prospective cohort study. Setting: Consecutive patients undergoing functional restoration treatment in a regional rehabilitation referral center. Participants: A sample of 3052 consecutive patients, classified as either completers (n 2367) or noncompleters (n 685), who entered a functional restoration program. Interventions: Not applicable. Main Outcome Measures: The measures used included medical evaluations, demographic data, Diagnostic and Statistical Manual of Mental Disorders psychiatric diagnoses, the Minnesota Multiphasic Personality Inventory, and validated questionnaires evaluating pain, depression, and occupational factors. Results: The findings revealed that patients who did not complete the program had a longer duration of total disability between injury and admission to treatment (completers 20mo vs noncompleters 13mo; P.001). Furthermore, patients who were opioid-dependent were 1.5 times more likely to drop out of rehabilitation, and patients diagnosed with a socially problematic Cluster B Personality Disorder were 1.6 times more likely to drop out. Conclusions: Although some risk factors associated with program noncompletion may be addressed in treatment, socially maladaptive personality disorders, long-neglected disability, and chronic opioid dependence are the major barriers to successful treatment completion. The patients identified with personality disorders may display resistance to treatment and may be difficult for the treatment staff to deal with. Early recognition of these treatment-resistant personality characteristics in the functional restoration process may assist the treatment team in developing more effective strategies to help this dysfunctional group. Key Words: Personality disorders; Rehabilitation; Risk factors by the American Congress of Rehabilitation Medicine THE PREVALENCE OF CHRONIC occupational musculoskeletal disorders is both a medical and an economic concern. In fact, it is estimated that costs for health care and lost productivity relating to chronic pain are approximately $70 to $100 billion annually. 1 Yet common treatments for chronic pain disorders, including surgery or medication maintenance, often lack evidence of success. 2-4 Furthermore, patients experiencing chronic pain are faced with multiple barriers to recovery, including the physical injury itself (and the loss of mobility and strength associated with it), general deconditioning, and a variety of psychosocial and financial issues. For such patients for whom early intervention is not effective, an interdisciplinary functional restoration program is often recommended. 5,6 Chronic pain patients admitted into a functional restoration program are afforded physical and occupational therapy, psychosocial assessments and counseling, assistance with workrelated factors, and education on health-related issues. 7 Evidence-based outcome measures not only include physical and psychosocial improvement but also take into account posttreatment outcomes, such as return to work, work retention, and decreasing both unnecessary health care use and surgeries. It has repeatedly been shown that patients completing the prescribed regimen within a functional restoration program yield positive outcomes. 8,9 However, those who drop out of the treatment prematurely are highly likely to experience poorer outcomes. 8,10 Mayer et al 8 showed that patients who completed the functional restoration program were more successful at returning to work and retaining work relative to those who did not complete the program. This foundational study determined that only 13% of the noncompleters successfully returned to work, compared with 85% of the program completers who continued work 1-year posttreatment. In a subsequent study published in Archives, Proctor et al 10 assessed the outcomes of completers and noncompleters, and they found that patients who dropped out of a functional restoration program were 9.7 times less likely to return to work compared with those who successfully completed the prescribed treatment. Additionally, at the 1-year follow-up, of those who did initially return to From the Productive Rehabilitation Institute of Dallas for Ergonomics (PRIDE), Research Foundation, Dallas (Howard, Theodore); Department of Orthopedic Surgery, University of Texas Southwestern Medical Center at Dallas, Dallas (Mayer); and Department of Psychology, University of Texas at Arlington, Arlington (Gatchel), TX. No commercial party having a direct financial interest in the results of the research supporting this article has or will confer a benefit on the authors or on any organization with which the authors are associated. Correspondence to Tom G. Mayer, MD, 5701 Maple Ave #100, Dallas, TX 75235, tgmayer@pridedallas.com. Reprints are not available from the author /09/ $36.00/0 doi: /j.apmr BDI DSM-IV MMPI MVAS OR PD List of Abbreviations Beck Depression Inventory Diagnostic and Statistical Manual of Mental Disorders, 4th Edition Minnesota Multiphasic Personality Inventory million visual analog scale odds ratio personality disorder

2 RISK FACTORS FOR NONCOMPLETION, Howard 779 work, the noncompleters were 7 times less likely to retain work than were the completers. That study also showed that the noncompleters were more likely to exhibit excessive treatmentseeking behaviors, and were more likely to undergo surgery to the same injured area (both of which are seen as poor treatment outcomes). Studies evaluating risk factors for completion status of effective tertiary treatment programs for patients with chronic pain are rare. Similar studies that have focused on negative treatment outcomes have provided inconclusive or contradictory determinants with respect to demographic and injuryspecific factors related to poor outcomes Work-related factors, such as the relationship between the patient and the employer, job characteristics and demands, and the availability of the current position postinjury, have been examined in outcome studies. 10,22-26 The general findings support that patients who reported a positive relationship with their respective employers, and a desire to return to work, were more likely to have positive treatment outcomes. Furthermore, patients who continued to work postinjury, or knew that their original job was still available, were also more likely to exhibit better outcomes. Aside from the demographic, injury-specific, and occupational factors characteristic of chronic pain patients, measures of physical pain and disability are also strongly correlated with treatment outcomes. 5,27-31 Furthermore, self-report ratings indicating higher levels of depression have been found to be predictive of poor treatment outcomes, specifically completion status. 10,11,16,32-37 Although most patients entering a functional restoration program report elevated levels of depression, the Proctor et al 10 study showed that patients who prematurely dropped out of the program had reported significantly higher levels of depression at admission than did those who completed the program. Chronic pain populations also tend to exhibit higher rates of substance use and PDs than the general population The DSM-IV uses a multiaxial system to classify psychiatric diagnoses. 38 Axis I diagnoses involve clinical disorders, such as major depressive disorder, anxiety disorders, and substance use disorders. Axis II diagnoses involve PDs, which are divided into 3 specific clusters: Cluster A includes paranoid, schizoid, and schizotypal PDs; Cluster B includes antisocial, borderline, histrionic, and narcissistic PDs; and Cluster C includes avoidant, dependent, and obsessive-compulsive PDs. Axis I and Axis II diagnoses have been shown to be prevalent within the chronic pain populations, and patients with these disorders have repeatedly been found to exhibit poor posttreatment outcomes and are more likely to drop out of treatment. 32 When considering the Axis I clinical disorders, substance use is identified to be highly prevalent in chronic pain populations. Dersh et al 32 found that opioid dependence was strongly associated with poor treatment outcomes. Patients with this diagnosis were found to be 2.7 times less likely to return to work, and 2.6 times less likely to retain work, at 1-year posttreatment. Furthermore, Proctor 10 found a correlation between patients identified as highly at risk for opioid dependence and program noncompletion. While the prevalence of PDs in the general population, as assessed by the DSM-IV Axis II classification, is estimated at 9%, 39 PDs have a greater representation in chronic pain populations. 32 Various PDs have also been tied to poor treatment outcomes. Dersh et al 40 found an association between PDs and treatment completion status. Patients exhibiting no PDs were significantly more likely to complete the rehabilitation program than those who were diagnosed with at least 1 PD. Furthermore, Proctor 10 identified a significant difference in completion status of a functional restoration program for schizotypal, antisocial, borderline, histrionic, narcissistic, and dependent PDs, along with significant differences comparing Clusters A, B, and C. Although substance use and PDs fall into separate axes in the DSM-IV classification system, there appears to be a high level of comorbidity between these diagnostic categories. 39,41-45 The purpose of the present study was to examine simultaneously whether an array of potential risk factors, individually found to be associated with chronic pain in the review by Proctor, 10 was associated with noncompletion of a tertiary functional restoration program. Historically, treatment noncompletion has been relatively low (20% 25%), yet it has been shown that patients who complete the program exhibit better posttreatment outcomes than those who drop out prematurely. The intent of this study was to compare the outcome groups (completers and noncompleters) across 4 dimensions: (1) demographic and injury-specific variables, (2) work-related variables, (3) pain and depression self-reports, and (4) personality inventories and DSM-IV diagnoses. A logistic regression analysis was planned to help determine the combination of key variables among these levels that may be highly associated with predicting noncompletion. METHODS Participants The study consisted of a consecutive cohort of 3052 patients presenting with a chronic disabling occupational musculoskeletal disorder. These patients consented to, and started, treatment at a functional restoration treatment facility. The criteria for participation in this treatment program were as follows: (1) the duration between date of injury and treatment was at least 3 months, (2) primary acute care and/or secondary care failed or were determined to be unnecessary, (3) surgery was either not an option or did not produce relief from the injury, (4) severe pain and functional limitations remained, and (5) the patient was able to communicate in English or Spanish. The participants in this study were patients discharged during the period of January 1996 through December This cohort was divided into the following 2 groups: the noncompleter group consisted of 685 patients who were admitted to the functional restoration program and underwent the initial evaluations. Noncompletion status was determined by failure to complete the full prescribed treatment regimen. The completer group consisted of 2367 patients who successfully completed the prescribed functional restoration treatment program. Procedure The participants consented to collection of information for treatment management and research purposes at the time of admission. The medically supervised treatment program consisted of quantitatively directed exercise progression, which was under the supervision of certified physical and occupational therapists. In addition, patients participated in other activities aimed at disability management, such as counseling, stress management, biofeedback, and coping skills training. Furthermore, education support and assistance was provided for injury prevention and occupational factors. 2,8 Several measures were used to assess the demographic, injury-specific, and occupational factors, along with the perceived levels of pain, disability, depression, and Axis I and Axis II diagnoses. Although many of these measures were administered multiple times throughout treatment and at posttreatment, the measures considered in this study were assessed at the initial phase of treatment. At the initial interview, demo-

3 780 RISK FACTORS FOR NONCOMPLETION, Howard graphic data were collected and physical and functional capacity measurements were performed by appropriate staff members. The psychosocial instruments administered at admission to the program included the Quantified Pain Drawing, which is a self-report of perceived pain; the MVAS, 46 which is a visual analog questionnaire measuring disability; the BDI 47 ; the MMPI 48 ; and the Structured Clinical Interview for DSM-IV Axis I and Axis II diagnoses. In particular for the MMPI, the Disability Profile was considered, which is represented by an elevation of 4 or more of the 10 scales assessed. The Structured Clinical Interview for DSM Diagnosis Non-Patient Form 49 is a structured interview that yields Axis I diagnoses that correspond with the DSM-IV criteria. The diagnoses considered in this study included major depressive disorder, generalized anxiety disorder, and substance use disorders. The Structured Clinical Interview for DSM Diagnosis II 50 is a structured interview that identifies Axis II PDs defined with the DSM-IV criteria. The 10 PDs, each assessed as a dichotomous variable, are grouped into 3 clusters. 38 Cluster A includes paranoid, schizoid, and schizotypal PDs. Cluster B includes antisocial, borderline, histrionic, and narcissistic PDs. Cluster C includes avoidant, dependent, and obsessive-compulsive PDs. Statistical Analyses The initial univariate analyses were used to develop a profile of the typical noncompleter. This was accomplished by comparing the completer group with the noncompleter group on the basis of demographic, injury-specific, and occupational variables, along with self-report measures of pain, disability, depression, and Axis I and Axis II diagnoses. In this study, sample sizes varied for certain variables because of either procedural changes in assessments administered over the 9-year span or issues with missing data. Many noncompleters dropped out prior to completing all the initial assessments. For the categorical variables, tests of association were conducted based on the Pearson chi-square test statistic for all analyses of the differences between the completer and noncompleter groups, with effect sizes reported as the OR. Independent t tests were conducted on all analyses of the differences between the completer and noncompleter groups on continuous variables, with effect sizes reported as the Cohen d. A Holm-Bonferroni step-down method was used to correct for any potential type I errors. A multivariate logistic regression model was then created based on the attributes found at the univariate level that determined the variables most associated with noncompletion status. A sequential logistic regression analysis was performed in this study with the intent to identify the specific variables most associated with noncompletion. The variables considered for the logistic regression model were based on those found significant at the univariate level. The first block contained the demographic and injury-specific variables, along with selfreport measures of depression (BDI) and perceived disability (MVAS), followed by the occupational variables in block 2. The third block assessed the Axis I diagnoses. Finally, Axis II PDs were added in block 4. The total number of patients used in the logistic regression analysis was reduced to 1845 (completer group, n 1545; noncompleter group, n 300) because any patient with missing or invalid data was eliminated. (Valid data on all variables entered into the model were required for each patient to be considered in the multivariate analysis.) A Pearson chi-square statistic was assessed after the addition of each block in the sequential logistic regression model to evaluate the association of each set of variables with noncompletion status. The significance criterion for the logistic regression analysis was set at alpha equal to.05. RESULTS The demographic and injury-specific data are presented in table 1. There was a significant difference between the 2 comparison groups for both the length of disability (t 4.933; P.001) and the temporary-total disability (t 6.602; P.001), which indicated that noncompleters took longer to get into the rehabilitation program, and that they were not working for longer periods than the completers. Furthermore, noncompleters were more likely to have undergone surgery ( ; P.004; OR 1.3) and were more likely to have settled their workers compensation case ( ; P.009; OR 1.3) prior to treatment. Table 2 depicts the findings for the occupational variables considered in this study. Although there were no differences between the 2 groups in terms of the type of work and physical demands required, the groups did differ significantly with respect to their work status at admission and the knowledge of the availability of their original job at postrehabilitation. Patients currently working at admission to the functional restoration program were found to be 2.2 times more likely to complete the program than those not working ( ; P.001). Additionally, patients who knew their original jobs were still available were 1.9 times more likely to complete the program than those who had been replaced in their jobs ( ; P.001). The various psychologic and social variables considered in this study are presented in tables 3 and 4. Patients in the noncompleter group displayed significantly higher scores on the BDI (t 7.730; P.001) and the MVAS (t 7.981; P.001) relative to the completers. Patients displaying the disability profile on the MMPI were 1.6 times more likely to drop out prematurely than those with fewer than 3 scale elevations ( ; P.001). Axis I and Axis II DSM-IV diagnoses also differed significantly between the 2 groups. The noncompleters were identified to be 1.6 times more likely to be diagnosed with an anxiety disorder ( ; P.004) and were twice as likely to be diagnosed with a substance use disorder ( ; P.001). More specifically, patients entering the rehabilitation program with a diagnosis of opioid dependence were 2.1 times more likely to drop out of the program than patients not dependent on opioids ( ; P.001). The 2 groups were also compared on the prevalence of Axis II PDs, and 74.7% of the noncompleters were diagnosed with an Axis II PD, compared with 59.8% of the completers ( ; P.001; OR 2.0). In addition, patients diagnosed with any Cluster B PD (antisocial, borderline, histrionic, narcissistic) were twice as likely to drop out of the program than patients without a Cluster B PD ( ; P.001). The multivariate logistic regression analysis (table 5) was found to be significant with the addition of each block, and revealed the following key risk factors associated with noncompletion status: (1) length of disability, (2) high score on the MVAS, (3) not working at admission to treatment, (4) opioid dependence, and (5) any Cluster B PD. The overall classification rate of this model was 78.5%. DISCUSSION The present study represents a comprehensive examination of patients with chronic disabling occupational musculoskeletal (specifically spinal) disorders who were admitted to a tertiary functional restoration program. The purpose of this study was to identify key risk factors associated with noncompletion of a functional restoration treatment program, then create a logistic regression model that would predict patients who were less likely to complete the program based on selected criteria at

4 RISK FACTORS FOR NONCOMPLETION, Howard 781 Variables Table 1: Demographic Characteristics and Injury-Specific Variables (N 3052) Completers ( ) n % Noncompleters ( ) n % P ( 2 or t test) OR and 95% CI or Cohen d Sex (% male) NS Age (y)* NS Ethnicity (%) Black NS White Hispanic Asian Other Education (y)* NS Length of disability (mo) d mo mo mo mo mo mo mo Temporary-total disability (mo)* d.374 Pretreatment surgeries (%) ( ) Attorney retained (%) NS Case settlement, pretreatment (%) ( ) Valid n 2987 n 565/2317 n 197/670 Compensable body parts* NS Area of injury NS Cervical (%) Thoracic/lumbar Multiple spinal Multiple-musculoskeletal Upper extremity Lower extremity Upper and lower (no spine) Other Abbreviations: CI, confidence interval; NS, nonsignificant. *Values are mean SD. Values are mean SD; percent breakdown. admission. As highlighted, a psychosocial risk factor profile was identified. Because it has been shown that patients who complete a functional restoration program have much higher success rates of work return, work retention, and other positive outcomes, 10 the ultimate future goal would be to devise and implement appropriate interventions that would assist these Variables Completers ( ) n % Table 2: Occupational Variables (N 3052) Noncompleters ( ) n % P ( 2 ) OR (95% CI) Currently working (%) ( ) (for completion) Original job available (% yes) ( ) (for completion) Job satisfaction (%) 1: Very satisfied NS 2: Satisfied : Neither : Dissatisfied : Very dissatisfied Job code (% blue collar) NS Job demand (%) 1: Sedentary/light NS 2: Light/medium : Medium/heavy : Heavy/very heavy Abbreviations: CI, confidence interval; NS, nonsignificant.

5 782 RISK FACTORS FOR NONCOMPLETION, Howard Variables Table 3: Pretreatment BDI, Pain Intensity, MVAS, and MMPI (N 3052) Completers ( ) Noncompleters ( ) P ( 2 or t test) OR and 95% CI or Cohen d BDI (pretreatment) d.326 No depression (%) Mild (%) Moderate (%) Severe (%) Extreme (%) Pain intensity (pretreatment)* NS MVAS (pretreatment) d.353 Mildly disabling (%) Moderately disabling (%) Severely disabling (%) MMPI Normal profile (0 scale elevations) (%) ( ) (for completion) MMPI Disability profile (4 or more scale elevations) (%) ( ) Abbreviations: CI, confidence interval; NS, nonsignificant. *Values are mean SD. Values are mean SD; percent breakdown. predetermined dropouts in completing the prescribed treatment. In comparing the completers to the noncompleters, it was evident that the noncompletion group had a greater length of disability than the completer group. Although the specific reasons there was a greater delay for treatment for the noncompleter group is unknown with this sample, it can be speculated that some patients might encounter difficulties getting funding for the multidisciplinary treatment program; some patients might resist treatment because of fear of reinjury or secondary gains; and some patients may develop conditions, such as drug dependency, that could interfere with proper medical treatment. Regardless of the reason, though, a greater duration between injury and treatment has been associated with poor outcomes in numerous studies. 7,10,12,51 Because most patients in this study had experienced a workrelated injury, the occupational variables taken into consideration when comparing the risk factors for noncompletion included whether patients were working at admission to the treatment, whether patients had insight that their original jobs were still available, and whether patients were satisfied with their employers. The present analysis identified that patients working at admission (specifically working full-time) were more likely to complete the treatment. Furthermore, those patients whose original jobs were still available were more likely to complete the program. For employers, keeping the injured employee at work, or at least keeping the prospect of allowing the employee to return to the original position posttreatment, is an important consideration to increase the likelihood of positive treatment outcomes. Physical and psychosocial factors that have been found to be associated with poor treatment outcomes include higher levels of depression, 10,11,33-36 higher levels of pain intensity, 16,28,29,31 and higher levels of perceived disability. 5,52 The findings from the current study align with prior research with respect to both depression and perceived disability. Noncompleters were more likely to report severe/extreme depression symptoms on the BDI (as indicated by a score of 25), and were more likely to rate themselves as severely disabled on the MVAS (as indicated by a score of 85). This coincides with the MMPI analysis, for which noncompleters were significantly more likely to be diagnosed with disability profile than the completers. Psychosocial factors, such as depression and perceived disability, are seen to impact treatment dropout regardless of the level of pain associated directly with the injury. Thus, psychosocial factors seem to be an integral part of determining completion status regardless of the physical condition itself. Postinjury psychopathologies (Axis I), along with PDs (Axis II), were also found to be risk factors for noncompletion of treatment. A prior study has linked Axis I disorders to negative treatment outcomes. 5 In this present study, incidences of major depressive disorder did not differ significantly between the completers and noncompleters, compared with the depression symptoms assessed by the BDI. However, more than 50% of the patients in the entire cohort studied (both completers and noncompleters) were diagnosed with major Variables Table 4: Postinjury Axis I and Axis II Diagnoses (n 2395) Completers ( ) Noncompleters ( ) P ( 2 or t test) OR and 95% CI Major depressive disorder NS Anxiety disorder ( ) Substance use disorder ( ) Opioid dependency ( ) Any Axis II diagnosis ( ) Any Cluster A diagnosis at admission ( ) Any Cluster B diagnosis at admission ( ) Any Cluster C diagnosis at admission ( ) NOTE: Values are percentages or as otherwise indicated. Abbreviations: CI, confidence interval; NS, not significant.

6 RISK FACTORS FOR NONCOMPLETION, Howard 783 Table 5: Logistic Regression Analysis of Noncompletion Rate Variables SE Wald P OR 95% CI for OR Lower 95% CI for OR Upper Length of disability Pretreatment surgery Case settlement (pretreatment) BDI (pretreatment) MVAS (pretreatment) Work status (pre) Job availability Anxiety disorder Opioid dependency Any Cluster A Dx Any Cluster B Dx Any Cluster C Dx Any Cluster D Dx Constant NOTE. Classification table: overall 78.5%; specificity 87.1%; sensitivity 34.3%. Abbreviations:, beta; CI, confidence interval; Dx, diagnosis. depressive disorder. With respect to anxiety disorders, although the prevalence rate is not very high overall, there is a significant difference between the 2 groups, with noncompleters 1.6 times more likely to have an anxiety disorder than the completer group. These psychosocial factors are important to consider when assessing and treating patients with chronic pain conditions. Substance use disorders have been associated with poor outcomes in numerous studies. 39,41,42,53-54 Specifically, opioid dependence is of particular interest in chronic pain populations. Although this diagnosis includes any type of opioid drug, most patients report a dependency on prescription narcotics. Considering that the patients entering a tertiary rehabilitation setting are being treated for chronic pain conditions, it makes sense that they may develop an addiction or dependence to these types of pain medications. In this study, there was a strong distinction between completers and noncompleters related to opioid dependence at admission. In fact, a patient entering the treatment program who was diagnosed with opioid dependency was twice as likely to drop out of the program as one who was not diagnosed with opioid dependency. Even though attempts are made to help the patient detoxify during the program, the drug dependency is often so overwhelming that it interferes with treatment. It was also found that program noncompleters were more likely to be diagnosed with any Axis II PD. Unlike depression and anxiety, PDs cannot easily be treated in a short period. However, knowing that a patient has a particular PD may help with treatment. By identifying these disorders at admission to the treatment program, the interdisciplinary team can adopt various methods or strategies to treat these particular individuals better with their specific vagaries. Study Limitations The main study limitation to identifying the risk factors for noncompletion was that not all of the patients who dropped out prematurely completed all of the initial assessments. If all the data for each patient who failed to complete the program were available, this would help develop an even better profile of the typical noncompleter of functional restoration. CONCLUSIONS The purpose of this study was to identify the key risk factors for noncompletion of a functional restoration program for patients with chronic disabling occupational musculoskeletal disorders. The study started by comparing the 2 subgroups (completers, noncompleters) on various physical, psychologic, and social factors at the univariate level. The results from the subsequent logistic regression analysis indicated that the main risk factors for noncompletion were (1) having an extended length of disability, (2) indicating higher ratings of perceived disability on the MVAS, (3) not currently working at time of admission to treatment, (4) being diagnosed with an opioid dependence disorder, and (5) being diagnosed with any Cluster B PD. It is also important to note that the measurement for depression symptoms (BDI) remained significant in the sequential logistic regression model until the fourth block, when the Axis II PD clusters were added. For practical purposes, if a proper DSM-IV evaluation is not administered at admission to a functional restoration program, then it would be appropriate to consider an elevation in the BDI as a risk factor for noncompletion. Knowing that individuals differ on every domain, it is implausible to integrate every possible physiologic, psychologic, social, and behavioral indicator into a single model. However, by assessing the characteristics that reliably identify a particular behavior, such as dropping out of a treatment program, it is possible to flag these patients with treatment-resistant personality characteristics at admission and provide an intervention tailored to their specific needs that will foster positive treatment outcomes. References 1. Gatchel RJ. Comorbidity of chronic mental and physical health disorders: the biopsychosocial perspective. Am Psychol 2004;59: Mayer TG, Gatchel RJ, Kishino N, et al. Objective assessment of spine function following industrial injury: a prospective study with comparison group and one-year follow-up. Spine 1985;10: Mayer TG, Polatin PB. Tertiary nonoperative interdisciplinary programs: the functional restoration variant of the outpatient chronic pain management program. In: Mayer TG, Gatchel RJ, Polatin PB, editors. Occupational musculoskeletal disorders: function, outcomes and evidence. Philadelphia: Lippincott, Williams & Wilkins; p Glenn BA. Process and outcome of multidisciplinary pain treatment: an evaluation of a stage of change model. Dissertation

7 784 RISK FACTORS FOR NONCOMPLETION, Howard Abstracts International: Section B: The Sciences and Engineering 2002;62(11-B): Gatchel RJ, Mayer T, Theodore R. The Pain Disability Questionnaire: relationship to one-year functional and psychosocial rehabilitation outcomes. J Occup Rehabil 2006;16: Mayer TG, Gatchel RJ. Functional restoration for spinal disorders: the sports medicine approach. Philadelphia: Lea & Febiger; Mayer TG. Quantitative physical and functional capacity assessment. In: Mayer TG, Gatchel RJ, Polatin PB, editors. Occupational musculoskeletal disorders: function, outcomes and evidence. Philadelphia; Lippincott, Williams & Wilkins; 2000: p Mayer TG, Gatchel RJ, Mayer H, et al. Prospective two year study of functional restoration in industrial low back injury. JAMA 1987;258: Gatchel RJ, Okifuji A. Evidence-based scientific data documenting the treatment- and cost-effectiveness of comprehensive pain programs for chronic nonmalignant pain. J Pain 2006;7: Proctor TJ, Mayer TG, Gatchel RJ, Theodore B. Failure to complete a functional restoration program for chronic musculoskeletal disorders: a prospective one-year outcome study. Arch Phys Med Rehabil 2005;86: Biller N, Arnstein P, Caudill MA, Federman CW, Guberman C. Predicting completion of a cognitive-behavioral pain management program by initial measures of a chronic pain patient s readiness for change. Clin J Pain 2000;16: Bendix AF, Bendix T, Haestrup C. Can it be predicted which patients with chronic low back pain should be offered tertiary rehabilitation in a functional restoration program? A search for demographic, socioeconomic and physical predictors. Spine 1998; 23: Kerns RD, Rosenberg R. Predicting responses to self-management treatments for chronic pain: application of the pain stages of change model. Pain 2000;84: Dysvik E, Vinsnes AG, Eikeland OJ. The effectiveness of a multidisciplinary pain management programme managing chronic pain. Int J Nurs Pract 2004;10: Gatchel RJ, Mayer TG, Kidner CL, McGeary DD. Are gender, marital status or parenthood risk factors for outcome of treatment for chronic disabling spinal disorders? J Occup Rehabil 2005;15: McGeary DD, Mayer TG, Gatchel RJ, Anagnostis C, Proctor TJ. Gender-related differences in treatment outcomes for patients with musculoskeletal disorders. Spine J 2003;3: Mayer TG, Gatchel RJ, Evans TH. Effect of age on outcomes of tertiary rehabilitation for chronic disabling spinal disorders. Spine 2001;26: Levenstein S, Kaplan GA. Socioeconomic status and ulcer: a prospective study of contributory risk factors. J Clin Gastroenterol 1998;26: Burnett DM, Cifu DX, Kolakowsky-Hayner S, Kreutzer JS. Predicting charge outliers after spinal cord injury: a multicenter analysis of demographics, injury, characteristics, outcomes and rehabilitation charges. Arch Phys Med Rehabil 2001;82: Proctor T, Mayer T, Gatchel R, McGeary DD. Unremitting health care utilization outcomes of tertiary rehabilitation of chronic musculoskeletal disorders. J Bone Joint Surg Am 2004;86-A: Korte SM, Koolhaas JM, Wingfield JC, McEwen BS. Functioncentered rehabilitation increases work days in patients with nonacute nonspecific low back pain: 1-year results from a randomized controlled trial. Arch Phys Med Rehabil 2007;88: Petersen T, Larsen K, Jacobsen S. One-year follow-up comparison of the effectiveness of McKenzie treatment and strengthening training for patients with chronic low back pain: outcome and prognostic factors. Spine 2007;32: Shaw WS, Means-Christensen A, Slater MA, Patterson TL, Webster JS, Atkinson JH. Shared and independent associations of psychosocial factors on work status among men with subacute low back pain. Clin J Pain 2007;23: Anderson PA, Schwaegler PE, Cizek D, Leverson G. Work status as a predictor of surgical outcome of discogenic low back pain. Spine 2006;31: Pransky GS, Benjamin KL, Savageau JA. Early retirement due to occupational injury: who is at risk? Am J Ind Med 2005;47: Williams RA, Pruitt SD, Doctor JN, Epping-Jordan JE, Wahlgren DR, Grant I. The contribution of job satisfaction to the transition from acute to chronic low back pain. Arch Phys Med Rehabil 1998;79: Becker N, Hojsted J, Sjogren P, Eriksen J. Sociodemographic predictors of treatment outcome in chronic non-malignant pain patients: do patients receiving or applying for disability pension benefit from multidisciplinary pain treatment? Pain 1998; 77: Gauthier JP, Ivers H, Carrier S. Investigating risk factors for chronicity: the importance of distinguishing between return-towork status and self-report measures of disability. J Occup Environ Med 2006;48: Sullivan MJ, Feuerstein M, Gatchel RJ, Linton SJ, Pransky G. Secondary prevention of work disability: community-based psychosocial intervention for musculoskeletal disorders. J Occup Rehabil 2005;15: McGeary DD, Mayer TG, Gatchel RJ. High pain ratings predict treatment failure in chronic occupational musculoskeletal disorders. J Bone Joint Surg Am 2006;88: Anagnostis C, Mayer TG, Gatchel RJ, Proctor T. The Million Visual Analog Scale: its utility for predicting tertiary rehabilitation outcomes. Spine 2003;28: Dersh J, Mayer T, Gatchel R, Towns B, Theodore B, Polatin PB. Psychiatric comorbidity in chronic disabling occupational spinal disorders has minimal impact on functional restoration socioeconomic outcomes. Spine 2007;32: Rush A, Polatin P, Gatchel RJ. Depression and chronic low back pain: establishing priorities in treatment. Spine 2000;25: Fishbain DA, Cutler R, Rosomoff HL, Rosomoff RS. Chronic pain-associated depression: antecedent or consequence of chronic pain? A review. Clin J Pain 1997;13: Garrod R, Marshall J, Barley E, Jones PW. Predictors of success and failure in pulmonary rehabilitation. Eur Respir J 2006;27: Buckwalter AE, Callaghan JJ, Liu SS, et al. Initial depression severity and the trajectory of recovery following cognitivebehavioral intervention for work disability. J Occup Rehabil 2006; 16: Kerns RD, Haythornthwaite JA. Depression among chronic pain patients: cognitive-behavioral analysis and effect on rehabilitation outcome. J Consult Clin Psychol 1988;56: American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 4th ed. Washington (DC): APA; Lenzenweger MF, Lane MC, Loranger AW, Kessler RC. DSM-IV personality disorders in the National Comorbidity Survey Replication. Biol Psychiatry 2007;62: Dersh J, Mayer TG, Gatchel RJ, Towns B, Theodore B, Polatin PB. Do psychiatric disorders affect functional restoration outcomes in chronic disabling occupational spinal disorders? Spine 2007;32: Cohen P, Chen H, Crawford TN, Brook JS, Gordon K. Personality disorders in early adolescence and the development of later substance disorders in the general population. Drug Alcohol Depend 2007;88(Suppl 1):S Kay JH, Altshuler LL, Ventura J, Mintz J. Impact of axis II comorbidity on the curse of bipolar illness in men: a retrospective chart review. Bipolar Disord 2002;4:

8 RISK FACTORS FOR NONCOMPLETION, Howard James LM, Taylor J. Impulsivity and negative emotionality associated with substance use problems and Cluster B personality in college students. Addict Behav 2007;32: Bowden-Jones O, Iqbal MZ, Tyrer P, et al. Prevalence of personality disorder in alcohol and drug services and associated comorbidity. Addiction 2004;99: Grunebaum MF, Galfalvy HC, Nichols CM, et al. Aggression and substance abuse in bipolar disorder. Bipolar Disord 2006;8: Million R, Hall W, Nilsen KH, Baker RD, Jayson MI, et al. Assessment of the progress of the back-pain patient Volvo Award in Clinical Science. Spine 1982;7: Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatry 1961;4: Butcher JN, Dahlstrom WG, Graham JR, Tellegen AM, Kaemmer B. MMPI-2: manual for the administration and scoring. Minneapolis: Univ of Minnesota Pr; First MB, Spitzer RL, Gibbon M, Williams JB. Structured clinical interview for DSM-IV Axis I disorders nonpatient edition (SCID-I/NP, Version 2.0). New York: New York State Psychiatric Institute; First MB, Spitzer RL, Gibbon M, Williams JB. Structured clinical interview for DSM-IV personality disorders (SCID-II). Washington (DC): American Psychiatric Pr, Inc; Jordan KD, Mayer TG, Gatchel RJ. Should disability chronicity be an exclusion criterion for tertiary rehabilitation? Socioeconomic outcomes of early vs. late functional restoration in compensation spinal disorders. Spine 1998;23: Gatchel RJ, Mayer TG, Eddington A. The MMPI Disability Profile : the least known, most useful screen for psychopathology in chronic occupational spinal disorders. Spine 2006;31: Dersh J, Mayer TG, Gatchel RJ, Polatin P, Theodore BR, Mayer K. Prescription opioid dependence is associated with poorer outcomes in disabling spinal disorders. Spine 2008;33: Kidner CL, Mayer TG, Gatchel RJ. Higher opioid doses predict poorer functional outcomes in chronic disabling occupational musculoskeletal disorder. J Bone Joint Surg Am. In press.