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Development of a risk stratification and prevention index for stratified care in chronic low back pain. Focus

yellow flags (MiSpEx network)

Wippert, Pia-Mariaa,*; Puschmann, Anne-Katrina; Drießlein, Davidb; Arampatzis, Adamantiosc; Banzer, Winfriedd; Beck, Heidrune; Schiltenwolf, Marcusf; Schmidt, Hendrikg; Schneider, Christianh,i,j; Mayer, Frankk

doi: 10.1097/PR9.0000000000000623
Musculoskeletal
Open
SDC

Introduction: Chronic low back pain (LBP) is a major cause of disability; early diagnosis and stratification of care remain challenges.

Objectives: This article describes the development of a screening tool for the 1-year prognosis of patients with high chronic LBP risk (risk stratification index) and for treatment allocation according to treatment-modifiable yellow flag indicators (risk prevention indices, RPI-S).

Methods: Screening tools were derived from a multicentre longitudinal study (n = 1071, age >18, intermittent LBP). The greatest prognostic predictors of 4 flag domains (“pain,” “distress,” “social-environment,” “medical care-environment”) were determined using least absolute shrinkage and selection operator regression analysis. Internal validity and prognosis error were evaluated after 1-year follow-up. Receiver operating characteristic curves for discrimination (area under the curve) and cutoff values were determined.

Results: The risk stratification index identified persons with increased risk of chronic LBP and accurately estimated expected pain intensity and disability on the Pain Grade Questionnaire (0–100 points) up to 1 year later with an average prognosis error of 15 points. In addition, 3-risk classes were discerned with an accuracy of area under the curve = 0.74 (95% confidence interval 0.63–0.85). The RPI-S also distinguished persons with potentially modifiable prognostic indicators from 4 flag domains and stratified allocation to biopsychosocial treatments accordingly.

Conclusion: The screening tools, developed in compliance with the PROGRESS and TRIPOD statements, revealed good validation and prognostic strength. These tools improve on existing screening tools because of their utility for secondary preventions, incorporation of exercise effect modifiers, exact pain estimations, and personalized allocation to multimodal treatments.

Two new screening tools identify increased risk of chronic low back pain and potentially treatment-modifiable prognostic indicators in 4 yellow flag domains.

aSociology of Health and Physical Activity, University of Potsdam, Potsdam, Germany

bDepartment of Statistics, Ludwig-Maximilians-University, Munich, Germany

cDepartment of Training and Movement Sciences, Humboldt-University Berlin, Berlin, Germany

dDepartment of Sports Medicine, Goethe University Frankfurt, Frankfurt am Main, Germany

eUniversity Hospital Carl Gustav Carus at Technical University Dresden, Dresden, Germany

fDepartment of Orthopaedics and Trauma Surgery, Heidelberg University Hospital, Heidelberg, Germany

gJulius Wolff Institute, Charité—Universitätsmedizin Berlin, Berlin, Germany

hInstitute of Sports Orthopaedics, Schön Klinik München Harlaching, Munich, Germany

iOrthopaedic Center Theresie, Munich, Germany

jParacelsus Medizinische Privatuniversität Salzburg, Salzburg, Austria

kUniversity Outpatient Clinic, Centre of Sports Medicine, University of Potsdam, Potsdam, Germany

Corresponding author: Sociology of Health and Physical Activity, University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany. Tel.: +49 331 9771051. E-mail address: wippert@uni-potsdam.de (P.-M. Wippert).

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.painjournalonline.com).

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

Received March 22, 2017

Received in revised form August 15, 2017

Accepted August 16, 2017

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1. Introduction

The lifetime prevalence of nonspecific chronic low back pain (LBP) in the general population is considerably high, 84%.1 Many back pain episodes last only days and resolve spontaneously, but 44% to 78% of patients report renewed complaints within 1 year.1 This results in higher health care costs,10 which might have been preventable with early and individualized treatment strategies.

Cochrane Collaboration reviews show treatments such as exercise, physical therapy, and cognitive behavioral techniques are more successful in reducing pain and disability than usual care.13,18,19,21 Multimodal treatments including psychosocial interventions are also more efficient than unimodal approaches25 because they address such factors as distress, subjective pain experience, social environment, and medical care. These factors have been categorized as yellow, orange, black, or blue flags according to their estimated impact on prognosis32,56 and play a critical role in the chronification of LBP.4,6,32,38,58 Furthermore, they have been found to moderate treatment effects.47 However, because of their interindividual variability, these factors are difficult to integrate into routine clinical practice. Therefore, screening of patients' psychosocial risk factors and stratified allocation to treatment are required for evidence-based LBP guidelines.53

Existing screening tools in primary care either (1) classify patients into existing risk groups (eg, HKF-R 1037 and INTERMED49) or (2) predict the risk of chronic pain or disability development (eg, RISC-BP,16 PICKUP,51,52 and ÖMPSQ3). The Keele STarT Back Decision Tool22 is the only tool predicting risk, while designating patients to subgroups (high, medium, and low risk) for stratified care pathways.26 It has shown noteworthy results in primary care interventions14,23 but focuses solely on (1) yellow flag factors, (2) the risk of future chronicity (stratification based on group values), and (3) validation in primary care physical therapy settings.32 Interventions for secondary prevention (eg, in persons with recurrent pain) are still lacking. Although especially exercise seems a promising treatment for this target group, prognostic flag factors may be modifying exercise treatment effects. These interactions were shown for pain,9,12,50 depression,35 distress,33,59 and fear avoidance,35 and should be respected in screening tool development.

Other criticisms of existing instruments include their accuracy (eg, sensitivity and specificity) and practical utility (eg, length),26,32,56 which are limited by outdated actuarial and clinical methods used to develop them. Newer statistical methods are more applicable to high-dimensional data sets and allow combinations of actuarial and clinical methods,30,38,51 assisting practitioners to predict future pain values and allocate individuals to stratified treatment pathways.

The study objectives were to develop 2 screening tools using modern statistical methods suitable for secondary prevention, whereby: (1) 1 tool should allow the prediction of the chronic LBP risk at 1-year follow-up (risk stratification index, RSI) and the other tool, a detection of modifiable prognostic indicators in 4 risk factor domains (risk prevention indices, RPI-S); further (2) the prognosis errors (internal validation) of the tools and (3) their optimal classification thresholds should be evaluated.

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2. Material and methods

2.1. Development of risk stratification and prevention index

Objectives were pursued through a longitudinal multicenter study conducted by the German Network for Medicine in Spine Exercise (MiSpEx). The RSI and RPI-S were developed, a priori, with the Prognosis Research Strategy framework24,42,48 and guidelines from the Transparent Reporting of a multivariable prediction model for Individual Prognosis and Diagnosis.8 Risk stratification index and the risk prevention index are subsections of a planned final screening, which will also include biomechanical and functional parameters.

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2.2. Design and procedure

Risk stratification index and RPI-S development was based on data collected over a 2-year multicentre longitudinal study (without treatment) conducted at 4 sites across Germany. Participants were invited to participate in 7 measurements: baseline (M1), 1-month (M2), 3-month (M3), 6-month (M4), 12-month (M5), 18-month (M6), and 24-month follow-ups (M7). At each measurement, trained study nurses administered a comprehensive questionnaire consisting of predictor and outcome variables. Furthermore, a physician or a physiotherapist performed a clinical examination with measurements of anthropometric and orthopedic data. For this reason, participants were asked to visit the same clinic at every measurement to receive the same assessment (Fig. 1).

Figure 1

Figure 1

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2.3. Participants

Persons between the ages of 18 and 65 years were considered eligible if they fulfilled the following inclusion criteria: at least 1 episode (≥4 days) of nonspecific LBP in the past 12 months; able to understand the meaning of the study; and able to answer a questionnaire without help. Exclusion criteria were pregnancy, acute pain in the past 7 days, inability to stand upright, inability to share information regarding sick leave, or signs of red flag factors (inflammatory, traumatic, or systemic processes). Participants were referred to the study before their first consultation with a health care provider. All participants gave informed consent after receiving both written and oral information about the project.

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2.4. Instruments

The main outcome (chronic) pain status was assessed using subscales from the Chronic Pain Grade questionnaire (CPG55), which measures subjective pain intensity (characteristic pain intensity [CPI]: 0 = “no pain” to 100 = “the worst pain imaginable”) and pain disability (DISS: 0 = “no disability” to 100 = “I was incapable of doing anything”) during the past 3 months. The predictors were modifiable psychosocial risk factors for pain recommended from the flag catalogue,32,38,56 as well as protective factors (eg, social environment), which can also be of relevance within exercise treatment settings. The final predictor set depicts 4 domains: distress, pain experience, social environment, and medical care environment. The assessment uses standardized, psychometrically sound and validated German questionnaires: (1) Pain experience: anxiety and depression (HADS-D, Hospital Anxiety and Depression Scale—German version20), avoidance–endurance behavior (AEQ-PPS, Avoidance–Endurance-Questionnaire, Pain Persistence Scale17), self-efficacy (I-SEE, Inventory for the Measurement of Self-Efficacy and Externality28), and pain-related cognition (FABQ-D40). (2) Stress: chronic stress (TICS, Trier Inventory for Chronic Stress44), critical life events, perceived stress (PSS, Perceived Stress Scale7), vital exhaustion (VE, Maastricht Vital Exhaustion Questionnaire), and self-efficacy (I-SEE). (3) Social environment: social support (BSSS45,46), relationships (RQ-2, Relationship Questionnaire-22), sociodemography (CASMIN Index29), lifestyle (alcohol, smoking, medication, sleep and health status), and physical activity (regular exercise per week). (4) Medical care environment: health insurance, urbanization level, distance to hospitals, and preventive medical check-ups. To reduce time burden during assessment, questionnaires were rotated in short form (Fig. 1).

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2.5. Statistics and data analysis

After descriptive analysis using IBM SPSS Statistics 23.0, development and validation of the RSI and RPI-S were performed in 3 steps with the R-package penalized15: (1) least absolute shrinkage and selection operator (LASSO) regression analysis to select predictors with high potential; (2) root-mean-squared error (RMSE) analysis to calculate RSI and RPI-S error between predicted and observed values; and (3) receiver operating characteristic (ROC) curve analysis to determine discriminant validity and establish RSI and RPI-S cutoff values. Two-sided tests were used with significance level set to p = 0.05. The screening tools were designed for a 1-year follow-up time, which is different from other tools focusing on shorter follow-ups (3–6 months). Psychosocial risk increases with the duration of LBP (>1 month, prevalence = 5%; >3 months, prevalence = 35%), meaning the positive predictive value of the test increases with prevalence.27 Furthermore, persons affected by intermittent pain show high variability in intensity during episodes and pain-free phases, up to months and years later.54 If the aim is secondary prevention, a longer-term prognosis would then seem more appropriate than those constructed for primary and clinical care management.

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2.5.1. Step 1: selection and development of risk stratification index and risk prevention index

High-potential predictors from the LASSO regression analysis (10-fold cross-validation) were selected. This method was chosen because it enables calculation of more predictors within a small sample, while avoiding an over fitting of the data. After LASSO calculation (and refitting of biased coefficients with linear regression models), the unbiased coefficients served as weights for the calculation of RSI and RPI-S values. Finally, a subgroup, n = 588 participants, having no missing values in predictor and outcome variables (each item) at baseline and 1-year follow-up were available for LASSO calculation.

The RSI was derived through 1 LASSO calculation model containing predictors from all flag domains (full model; number of predictors [P] = 208). As control variables, baseline pain, age, sex, and study center were included unpenalized. The RPI-S was derived within 4 LASSO calculation models (partial models) for each flag domain (pain experience MP, P = 88; distress MS, P = 116; social environment/life context MSE, P = 41; and medical care environment MMC, P = 31). Self-efficacy is known to play a role both in stress management and pain experience and was therefore incorporated in both calculations. For the RPI-S, baseline pain, age, sex, study center, physical activity, and lifestyle (eg, smoking, alcohol, sleep, and medications) were included as control variables (unpenalized) in each model. Although the RPI-S should be appropriate for stratified exercise treatment allocation, the physical activity status before as well as its interaction with flag factors during the intervention are respected as treatment effect modifiers.

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2.5.2. Step 2: internal validity and prognosis error of risk stratification index and risk prevention index

Although the instruments allow for accurate future estimation of CPG-CPI and CPG-DISS, it is necessary to quantify the prognosis error between predicted and observed values using the mean squared error, ie, its RMSE.43 This internal validity check was performed for a 1-year prognosis (baseline to 1-year follow-up) using the subset of n = 588.

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2.5.3. Step 3: discriminant validity

After construction of the basic RSI and RPI-S, each tool was tested for discriminant validity using ROC curves36 by calculating the area under the curve (AUC) of overall screening tool cutoff values. Optimal classification thresholds of the RPI-S were essential for treatment allocation, but not necessary for the RSI, which allowed an accurate CPG estimation while still respecting all risk factor domains. These analyses were derived from the same data set as Step 2. Optimal cutoffs by means of ROC analysis require previous sorting of subjects according to a specific criterion, thus these analyses were geared to risk subgroups based on both CPG scales (CPI and DISS).55 Finally, optimal cutoffs for both RSI and RPI-S were determined with the Youden's index.60 In line with Metz,34 a strength of discrimination between 0.7 and 0.8 was classified as acceptable discrimination, from 0.8 to 0.9 as excellent, and more than 0.9 as outstanding.

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3. Results

3.1. Sample

At baseline, n = 1071 (age: mean = 40.4 years, SD = 13.4 years, f = 57%) participants were enrolled over a period of 18 months; n = 25 persons declined participation (participation rate: 97%). Of those who participated at baseline, n = 677 (65%) completed questionnaires at 1-year follow-up (Figure 2). There were no differences between participants who completed and those who did not. Reported reasons for dropout were, eg, upcoming pregnancy, illness, or relocation (sample characteristics; Table 1). Instrument development was derived from a subset of n = 588 subjects with no missing data at baseline and follow-up.

Figure 2

Figure 2

Table 1

Table 1

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3.2. Step 1: selection and development of risk stratification index and risk prevention index

3.2.1. Risk stratification index (full model)

Least absolute shrinkage and selection operator selection reduced the number of predictors from 205 to 17 for pain intensity and from 205 to 8 for pain disability. Thus, care providers would need 17 predictors for a 1-year prognosis of expected pain intensity and 8 predictors for expected pain disability. The screening tool contains questions concerning pain at baseline, pain endurance, sleep problems, unhappiness, chronic worry, misfortune, work dissatisfaction, social support, social status, and health care–related topics (eg, pharmaceutical and physical therapy). Figure 3 shows the immense reduction of predictors achieved by applying LASSO. The remaining coefficients from the CPI and DISS predictions are listed in Table 2.

Figure 3

Figure 3

Table 2-a

Table 2-a

Table 2-b

Table 2-b

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3.2.2. Risk prevention index (partial models)

To summarize the different RPI-S domains, a minimum of 3 up to a maximum of 16 predictors would allow health care providers to individualized treatment allocation, while respecting the heterogeneity of their patients. The selected predictors partially overlap with RSI predictors and therefore refer to similar items. In detail, 12 CPI predictors and 6 DISS predictors were selected for the domain pain experience (RPI-SP). Ten CPI predictors and 9 DISS predictors were selected for the domain distress (RPI-SS). Fourteen CPI predictors and 16 DISS predictors were selected for the domain social environment (RPI-SSE). Seven CPI predictors and 3 DISS predictors were selected for the domain medical care environment (RPI-SMC). In all models, baseline pain values showed strong influence.

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3.3. Step 2: internal validity and prognosis error of risk stratification index and risk prevention index

3.3.1. Risk stratification index

The validity check of the RSI model for pain intensity resulted in a prognosis error (RMSE) of 16.87. The model for pain disability showed an all-observation RMSE = 15.45. This means that a 1-year prognosis of estimated disability from pain value can vary per patient an average of 15 points on the 0- to 100-point CPG scales.

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3.3.2. Risk prevention index

The 4 partial models had a prediction validity for pain intensity ranging from RMSE = 15.44 to 17.05, with similar results for RSI pain intensity prediction. The RMSE for pain disability was found to be between 13.02 and 16.20 (see all values in Table 3).

Table 3

Table 3

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3.4. Step 3: discriminant validity

The evaluation of each screening tools' discriminant validity extracted from the development sample is presented in Tables 4 and 5. In all approaches, the discriminant power decreased with increasing severity of chronic pain. This resulted in small sample sizes for patients with high pain intensity and disability. Tables 6 and 7 contain sensitivity, specificity, and negative as well as positive likelihood ratios (LRs) obtained for the RSI and the 4 RPI-S based on pain intensity and pain disability (baseline to 1-year follow-up).

Table 4

Table 4

Table 5

Table 5

Table 6

Table 6

Table 7

Table 7

For example, a RSI <28 would indicate subjects with low CPI (low risk) 12 months later, ie, with low risk. Subjects characterized by a RSI ≥28 and <29 would be at risk of a slightly increased CPI (medium risk) after 12 months. Risk stratification index scores ≥29 and <32 would characterize an increased CPI (high risk) after 12 months. Subjects with RSI ≥32 would be identified and predicted for high CPI (very high risk) after 12 months. The RSI for a 1-year prognosis of increased CPI (high risk) obtained an AUC = 0.81 (95% confidence interval, 0.76–0.86) and AUC = 0.74 (95% confidence interval, 0.63–0.85) for DISS. The results of the 4 RPI-S are listed in Tables 6 and 7. The negative LRs for CPI ranged from 0.28 to 0.42 and from 0.22 to 0.47 for DISS, indicating small differences. Positive LRs for CPI ranged from 2.60 to 5.13 and from 1.94 to 8.2 for DISS, indicating moderate differences and substantial aid for clinical decision making (Table 6).

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4. Discussion

In this study, a screening tool for the 1-year prognosis of persons at high risk of LBP chronification (risk prevention index, RSI), as well as a screening tool to identify persons with treatment-modifiable prognostic indicators from 4 risk factor domains (risk prevention index, RPI-S), were developed and internally validated. The major strengths of the presented screening tool development are the methods, which are in accordance with the Prognosis Research Strategy for clinical outcomes (PROGRESS).24,42,48 First, the screening tools were derived within the presented 2-year longitudinal study. Then, they were externally validated in 2 further currently conducted randomized exercise treatment studies of 6 and of 12 months (randomized controlled trials reported here39,57), where additional domain-specific biopsychosocial education modules (in the 4 RPI-S domains) were developed and combined with exercise treatment.57 This allowed an evaluation of the treatment response and the effectiveness of the individual treatment allocation because of the RPI-S. These steps: (1) the development of a prognostic model,48 (2) the defining of modifiable risk factors42 and their screening, (3) the designing of specific and stratified intervention modules,24 and (4) the transparent reporting according to the TRIPOD statements8 (see supplemental digital content, available at http://links.lww.com/PR9/A12), were all completed in high-quality data sets within 1 research network. This procedure enabled the extension of the presented type-3 prognostic study to implement new statistical methods as is required for stratified model research.5 For LASSO selected predictors p-values were calculated via the LDPE approach,11,61 which addresses the research gap of statistical inference in high-dimensional data settings. For comparability to other screening tools, we calculated ROC curves to determine cutoff classes.

The RSI provided a precise estimation of the expected individual CPG-DISS and CPI values for persons up to 1 year later with an average prognosis error (RMSE) of 15 points (on a 100-point scale). The brief 5-minute screening tool contained 8 items for pain disability and 17 items for pain intensity. It displayed a performance of AUC = 0.81 for the risk of developing greater CPI and AUC = 0.74 for developing greater DISS. The LRs exhibited substantial improvement in clinical decision making, especially when predicting increased pain. Values above the critical cutoff indicated an 8-fold increase in probability of more severe CPI or DISS after 1 year.

The RPI-S (with 3 up to 16 items, duration time = 15 minutes, for all domains) will assist health care providers when deciding whether their patients could benefit from additional biopsychosocial treatment or education within the 4 risk factor domains (pain experience, distress, social environment, and medical care environment). As physical activity was included in its development, the RPI-S may be helpful in identifying patients who would not respond to unimodal exercise treatments but rather to a multimodal with additional psychosocial treatment. Estimation errors (RMSE) of the RPI-S models are similar, suggesting strong influence of baseline pain on dependent variable variation and supporting the chosen follow-up time for screening in secondary prevention.54

Both screening tools cover mainly yellow, black, and blue flag factors, as well as demographic and protective factors.32 For the RSI, these included pain at baseline, unhappiness, social isolation/social support, social status, distress (chronic worries), work dissatisfaction, claims for indemnity, misfortune, pain persistence, sleep problems, and other health care–related topics including medication, insurance status, and physical treatments. In the RPI-S models, pain persistence, avoidance behavior, fatigue, irritability, relationships, and feelings of lack of control over one's own life were also included. The domains stress and pain experience were more strongly associated with future pain disability than pain intensity, whereas social environment affected both. Within the ROC analyses, both instruments achieved better results using pain intensity models, which could be explained by a greater CPI stability in the sample and a better-balanced number of subjects in the CPI subgroups.

In contrast to other instruments, the RSI evolved from 205 predictors and showed a good performance (AUC for CPI = 0.81 and AUC for DISS = 0.74) and economy (reduced from 205 DISS predictors to only 8). The recently published and shortest screening tool for the prediction of pain intensity, PickUP,52 was extracted from 20 predictors, and the final version contains 5 predictors with a performance of AUC = 0.66. Other tools, such as the StarT Back22 and the Örebro Musculoskeletal Pain Screening Questionnaire, contain 9 predictors for pain disability (AUC = 0.92) and 24 predictors for disability and return to work, respectively. Most of these screening tools were developed using a different strategy, stepwise regression models,22,23,30 which prohibits a direct prediction of pain (CPG) and the inclusion of different risk factors because of the risk of over fitting. The lack of these important prognostic indicators in such screening tools is criticized by the authors themselves.52 When controlling for so many various influencing factors, high-dimensional methods are necessary because they allow for the new approaches presented here that focus on modifiable risk factors.9,33,59 One benefit of the RPI-S is the identification of individual risk profiles relating to 4 domains avoids “screening out” from 1 treatment and leads to a “screening in” for appropriate treatment.32 This should enable health care providers to individualize treatment as suggested for future screening developments in personalized medicine.26

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4.1. Limitations

Although our approach produced good validity and generalizability and uses advanced actuarial and clinical methods, there are some limitations to consider: (1) In prognosis research, a follow-up rate of >80% is desired31; this study reached a 1-year follow-up rate of only 65%, which could bias results. (2) Each screening is population dependent, hence trade-offs between sensitivity and specificity depend on the purpose of the screening tool. Therefore, the generalizability to other populations must be evaluated in further studies. (3) In prediction quality, it should be noted that the results could have been influenced by single extreme deviations in the predictors, which may have distorted RMSE. (4) The decrease in discriminant power with increased severity of chronic pain is a result of the small number of subjects in risk subgroups 2, 3, and 4. Thus, results could be affected by outliers. The discriminant validity of both screening tools, as well as the effectiveness of the individual treatment allocation and treatment response, should be evaluated in an external-balanced study population to fix the final screening tools ranges. This was currently conducted in 2 further MiSpEx-exercise randomized controlled trials.39,57 (5) Finally, the screening instruments still need to be converted into categorized questionnaires with standardized answer formats.

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5. Conclusions

This multidimensional approach aimed to develop 2 screening tools for the identification of modifiable psychosocial risk factors that can be applied to upcoming stratified care in secondary prevention, as requested for innovative concepts of prevention.4,32 The brief RSI (∼5 minutes) provides medical practitioners with a quick estimation of prognostic pain and chronicity risk because of psychosocial risk variables in the patients' pain history. A high RSI-profile would indicate the practitioner to investigate if a specific additional psychosocial treatment within the 4 flag domains could be rewarding for the patient, for which the RPI-S can be used. The RPI-S (∼15 minutes) identifies patients with specific needs in 4 flag domains, enabling health care providers to better stratify allocation to additional biopsychosocial treatment and education.

Both screening tools were developed in line with modern concepts of secondary prevention and based on a wide range of risk predictors to avoid “screening in” or “screening out” of treatments as well as under and overtreatment of patients. Although the RSI outperforms other screening tools because of its precise estimation of future pain, the RPI-S exceeds other screening tools because of its respect of exercise treatment effect modifiers and its estimation of individual needs allowing for more complex allocations to treatment.

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Disclosures

The authors have no conflict of interest to declare.

This study was funded by the German Federal Institute of Sport Science on behalf of the Federal Ministry of the Interior of Germany. It was realized within MiSpEx–the National Research Network for Medicine in Spine Exercise (grant number: 080102A/11-14). All sources of funding for the research reported are declared. The funder did not influence data collection, analysis, interpretation, or writing of the manuscript.

All clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki. Final ethical approval was provided on January 25, 2012 from the major institutional ethics review board of the University of Potsdam, Germany (number 36/2011).

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Acknowledgments

The authors thank Helmut Küchenhoff, Gerhard Tutz, Sören Matzk, Juliane Müller, Steffen Müller, Jessica Messerschmidt, Daniela Schubert, Hannes Kaplick, Josephine Stoll, Tilmann Engel, Philipp Flössel, Jan Wilke, Andreas Rosenhagen, Tobias Engeroff, Meltem Hacibayramoglu, Martin Handel, Thore Haag, Johanna Vogel, Kristin Kalo, Jonas Newrly, Olga Tjukov, Karsten Dreinhöfer, Monika Hasenbring, Dirk Stengel, Jeronimo Weerts, Jens Kleinert, Michael Kellmann, María Moreno Catalá, Arno Schroll, Ann-Christin Pfeifer, Simone Gantz, and all local principal investigators for their valuable support. They also thank both the technical and medical staff at the study sites for their contributions during the study.

Author contributions: All authors substantially contributed to the conception and realization of the studies. PW and AP wrote the first draft of the manuscript, and all authors critically revised the manuscript for important intellectual content. PW was responsible for methodological design and analysis related to psychosocial factors, and PW, AP, and MS provided all scientific and practical information for the psychosocial content. DD provided the statistical analysis with LASSO and information. CS, WB, HB, HS, AA, and FM provided all scientific information for biomechanical and medical content. FM conceived the study as principal investigator. All authors read and approved the final manuscript.

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Supplemental digital content

Supplemental digital content associated with this article can be found online at http://links.lww.com/PR9/A12.

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References

[1]. Airaksinen O, Brox JI, Cedraschi C, Hildebrandt J, Klaber-Moffett J, Kovacs F, Mannion AF, Reis S, Staal JB, Ursin H, Zanoli G; COST B13 Working Group on Guidelines for Chronic Low Back Pain. Chapter 4. European guidelines for the management of chronic nonspecific low back pain. Eur Spine J 2006;15(suppl 2):S192–300.
[2]. Bartholomew K, Horowitz LM. Attachment styles among young adults: a test of a four-category model. J Personal Social Psychol 1991;61:226–44.
[3]. Boersma K, Linton SJ. Early assessment of psychological factors: the Örebro Screening Questionnaire of pain. In: Linton SJ, editor. New avenues for the prevention of chronic musculoskeletal pain and disability: pain research and clinical management, Vol. 12. Amsterdam: Elsevier, 2002:205–13.
[4]. Burton AK, McClune TD, Clarke RD, Main CJ. Long-term follow-up of patients with low back pain attending for manipulative care: outcomes and predictors. Man Ther 2004;9:30–5.
[5]. Chou R, Shekelle P. Will this patient develop persistent disabling low back pain? JAMA 2010;303:1295–302.
[6]. Clays E, De Bacquer D, Leynen F, Kornitzer M, Kittel F, De Backer G. The impact of psychosocial factors on low back pain: longitudinal results from the Belstress study. Spine (Phila Pa 1976) 2007;32:262–8.
[7]. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav 1983;24:385–96.
[8]. Collins GS, Reitsma JB, Altman DG, Moons KGH. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMC Med 2015;13:1.
[9]. Cook DB, Stegner AJ, Ellingson LD. Exercise alters pain sensitivity in Gulf War veterans with chronic musculoskeletal pain. J Pain 2010;11:764–72.
[10]. Dagenais S, Caro J, Haldeman S. A systematic review of low back pain cost of illness studies in the United States and internationally. Spine J 2008;8:8–20.
[11]. Drießlein D, Küchenhoff H, Tutz G, Wippert P-M. Variable Selection and Inference in a follow-up Study on Back Pain. Technical Report, LMU Munich, 2017. https://epub.ub.uni-muenchen.de/40387/1/trHK.pdf.
[12]. Ellingson LD, Koltyn KF, Kim JS, Cook DB. Does exercise induce hypoalgesia through conditioned pain modulation? Psychophysiology 2014;51:267–76.
[13]. Engers AJ, Jellema P, Wensing M, van der Windt DAWM, Grol R, van Tulder MW. Individual patient education for low back pain. Cochrane Database Syst Rev 2008:CD004057.
[14]. Foster NE, Mullis R, Hill JC, Lewis M, Whitehurst DGT, Doyle C, Konstantinou K, Main CJ, Somerville S, Sowden G, Wathall S, Young S, Young J, Hay EM. Effect of stratified care for low back pain in family practice (IMPaCT back): a prospective population-based sequential comparison. Ann Familiy Med 2014;12:102–11.
[15]. Goeman JJ. L1 penalized estimation in the Cox proportional hazards model. Biom J 2010;52:70–84.
[16]. Hallner D, Hasenbring M. Classification of psychosocial risk factors for the development of chronic low back and leg pain using artificial neural network. Neurosci Lett 2004;361:151–4.
[17]. Hasenbring MI, Hallner D, Rusu AC. Fear-avoidance- and endurance-related responses to pain: development and validation of the Avoidance-Endurance Questionnaire (AEQ). Eur J Pain 2009;13:620–8.
[18]. Hayden JA, van Tulder MW, Malmivaara A, Koes BW. Exercise therapy for treatment of non-specific low back pain. Cochrane Database Syst Rev 2005:CD000335.
[19]. Henschke N, Ostelo RW, von Tulder MW, Vlaeyen JW, Morley S, Assendelft WJ, Main CJ. Behavioural treatment for chronic low-back pain. Cochrane Database Syst Rev 2010:CD002014.
[20]. Herrmann C, Buss U, Snaith RP. Hospital anxiety and depression scale—deutsche version (HADS-D): manual. Bern: Hans Huber, 1995.
[21]. Heymans M, van Tulder MW, Esmail R, Bombardier C, Koes BW. Back schools for non-specific low-back pain. Cochrane Database Syst Rev 2004:CD000261.
[22]. Hill JC, Dunn KM, Lewis M, Mullis R, Main CJ, Foster NE, Hay EM. A primary care back pain screening tool: identifying patient subgroups for initial treatment. Arthritis Rheum 2008;59:632–41.
[23]. Hill JC, Whitehurst DGT, Lewis M, Bryan S, Dunn KM, Foster NE, Konstantinou K, Main CJ, Mason E, Somerville S, Sowden G, Vohora K, Hay EM. Comparison of stratified primary care management for low back pain with current best practice (STarT Back): a randomised controlled trial. Lancet 2011;378:1560–71.
[24]. Hingorani AD, van der Windt DA, Riley RD, Abrams K, Moons KG, Steyerberg EW, Schroter S, Sauerbrei W, Douglas GA, Hemingway H. Prognosis research strategy (PROGRESS) 4: stratified medicine research. Br Med J 2013;346.
[25]. Kamper SJ, Apeldoorn AT, Chiarotto A, Smeets RJ, Ostelo RWJG, Guzman J, van Tulder MW. Multidisciplinary biopsychosocial rehabilitation for chronic low back pain. Cochrane Database Syst Rev 2014:CD000963.
[26]. Karran EL, McAuley JH, Traeger AC, Hillier SL, Grabherr L, Russek LN, Moseley GL. Can screening instruments accurately determine poor outcome risk in adults with recent onset low back pain? A systematic review and meta-analysis. BMC Med 2017;15:13.
[27]. Kendall NAS, Burton AK, Main CJ, Watson P. Musculoskeletal problems: a guide for the clinic and workplace: identifying obstacles using the psychosocial flags framework. London: Stationery Office, 2009.
[28]. Krampen G. Fragebogen zu kompetenz- und kontrollüberzeugungen (FKK) (inventory on competence and control beliefs). Göttingen: Hofgrefe, 1991.
[29]. Lechert Y, Schroedter J, Lüttinger P. Die umsetzung der bildungsklassifikation CASMIN für die volkszählung 1970, die mikrozensus-zusatzerhebung 1971 und die mikrozensen 1976–2004, Vol. Mannheim: ZUMA, 2006.
[30]. Lentz TA, Beneciuk JM, Bialosky JE, Zeppieri G Jr, Dai Y, Wu SS, George SZ. Development of a yellow flag assessment tool for orthopaedic physical therapists: results from the optimal screening for prediction of referral and outcome (OSPRO) cohort. J Orthop Sports Phys Ther 2016;46:327–43.
[31]. Linton SJ, Gross D, Schultz IZ, Main C, Côté P, Pransky G, Johnson W. Prognosis and the identification of workers risking disability: research issues and directions for future research. J Occup Rehabil 2005;15:459–74.
[32]. Main CJ, Kendall NA, Hasenbring M. Screening of psychosocial risk factors (yellow flags) for chronic back pain and disability. In: Hasenbring M, Rusus AC, Turk DC, editors. From acute to chronic back pain: risk factors, mechanisms and clinical implications. Oxford: University Press, 2012:203–29.
[33]. McEwen BS, Kalia M. The role of corticosteroids and stress in chronic pain conditions. Metabolism 2010;59(suppl 1):S9–15.
[34]. Metz CE. Basic principles of ROC analysis. Semin Nucl Med 1978;8:283–98.
[35]. Moffett JAK, Carr J, Howarth E. High fear-avoiders of physical activity benefit from an exercise program for patients with back pain. Spine 2004;29:1167–72.
[36]. Murphy JM, Berwick DM, Weinstein MC, Borus JF, Budman SH, Klerman GL. Performance of screening and diagnostic tests. Application of receiver operating characteristic analysis. Arch Gen Psychiatry 1987;44:550–5.
[37]. Neubauer E, Junge A, Pirron P, Seemann H, Schiltenwolf M. HKF-R 10-screening for predicting chronicity in acute low back pain (LBP): a prospective clinical trial. Eur J Pain 2006;10:559–66.
[38]. Nicholas MK, Linton SJ, Watson PJ, Main CJ; “Decade of the Flags” Working Group. Early identification and management of psychological risk factors (“yellow flags”) in patients with low back pain: a reappraisal. Phys Ther 2011;91:737–53.
[39]. Niederer D, Vogt L, Wippert PM, Puschmann AK, Pfeifer AC, Schiltenwolf M, Banzer W, Mayer F. Medicine in spine exercise (MiSpEx) for nonspecific low back pain patients: study protocol for a multicentre, single-blind randomized controlled trial. Trials 2016;17:507.
[40]. Pfingsten M, Leibing E, Franz C, Bansemer D, Busch O, Hildebrandt J. Erfassung der “fear-avoidance-beliefs” bei patienten mit rückenschmerzen: deutsche version des “fear-avoidance-beliefs questionnaire” (FABQ-D). Der Schmerz 1997;11:387–95.
[41]. Pfingsten M, Nagel B, Emrich O, Seemann H, Lindena G. Deutscher Schmerz-Fragebogen—Handbuch: Deutsche Gesellschaft zum Studium des Schmerzes DGSS, 2007.
    [42]. Riley RD, Hayden JA, Steyerberg EW, Moons KG, Abrams K, Kyzas PA, Malats N, Briggs A, Schroter S, Altman DG, Hemingway H, Group P. Prognosis Research Strategy (PROGRESS) 2: prognostic factor research. Plos Med 2013;10:e1001380.
    [43]. Rüschendorf L. Mathematische statistik. Berlin, Heidelberg: Springer Verlag, 2014.
    [44]. Schulz P, Schlotz W, Becker P. Trierer inventar zum chronischen stress (TICS). Göttingen: Hofgrefe, 2004.
    [45]. Schulz U, Schwarzer R. Social support in coping with illness: the Berlin Social Support Scales (BSSS) [in German]. Diagnostica 2003;49:73–82.
    [46]. Schwarzer R, Schulz U. BSSS—Berliner Social-Support Skalen: tests info. Berlin: Freie Universität, Abteilung für Gesundheitspsychologie, 2000.
    [47]. Stanton TR, Hancock MJ, Maher CG, Koes BW. Critical appraisal of clinical prediction rules that aim to optimize treatment selection for musculoskeletal conditions. Phys Ther 2010;90:843–54.
    [48]. Steyerberg EW, Moons KG, van der Windt DA, Hayden JA, Perel P, Schroter S, Riley R, Hemingway H, Douglas GA. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 2013;10:e1001381.
    [49]. Stiefel FC, de Jonge P, Huyse FJ, Slaets JP, Guex P, Lyons JS, Vannotti M, Fritsch C, Moeri R, Leyvraz PF, So A, Spagnoli J. INTERMED-an assessment and classification system for case complexity: results in patients with low back pain. Spine (Phila Pa 1976) 1999;24:378–84; discussion 385.
    [50]. Tesarz J, Schuster AK, Hartmann M, Gerhardt A, Eich W. Pain perception in athletes compared to normally active controls: a systematic review with meta-analysis. PAIN 2012;153:1253–62.
    [51]. Traeger AC, Henschke N, Hubscher M, Williams CM, Kamper SJ, Maher CG, Moseley GL, McAuley JH. Development and validation of a screening tool to predict the risk of chronic low back pain in patients presenting with acute low back pain: a study protocol. BMJ Open 2015;5:e007916.
    [52]. Traeger AC, Henschke N, Hubscher M, Williams CM, Kamper SJ, Maher CG, Moseley GL, McAuley JH. Estimating the risk of chronic pain: development and validation of a prognostic model (PICKUP) for patients with acute low back pain. PLoS Med 2016;13:e1002019.
    [53]. van Tulder MW, Becker A, Bekkering T, Breen A, del Real MT, Hutchinson A; COST B13 Working Group on Guidelines for the Management of Acute Low Back Pain in Primary Care. European Guidelines for the management of acute nonspecific low back pain in primary care (chapter 3). Eur J Spine 2006;15:169–91.
    [54]. Von Korff M, Miglioretti DL. A prognostic approach to defining chronic pain. PAIN 2005;117:304–13.
    [55]. Von Korff M, Ormel J, Keefe FJ, Dworkin SF. Grading the severity of chronic pain. PAIN 1992;50:133–49.
    [56]. Waddell G, Burton AK, Main CJ. Screening of DWP clients for risk of long-term incapacity: a conceptual and scientific review. London: Royal Society of Medicine Press, 2003.
    [57]. Wippert PM, de Witt Huberts J, Klipker K, Gantz S, Schiltenwolf M, Mayer F. Development and content of the behavioral therapy module of the MiSpEx intervention: randomized, controlled trial on chronic nonspecific low back pain [in German]. Schmerz 2015;29:658–63.
    [58]. Wippert PM, Fliesser M, Krause M Risk, Protective Factors in the clinical rehabilitation of chronic back pain. J Pain Res 2017;10:1–11.
    [59]. Wippert PM, Wiebking C. Adaptation to physical activity and mental stress in the context of pain: psychobiological aspects [in German]. Schmerz 2016;30:429–36.
    [60]. Youden WJ. Index for rating diagnostic tests. Cancer 1950;3:32–5.
    [61]. Zhang CH, Zhang SS. Confidence intervals for low dimensional parameters in high dimensional linear models. J R Stat Soc Ser B Stat Methodol 2014;76:217–42.
    Keywords:

    Back pain prognosis; Back pain diagnosis; Pain screening; PROGRESS/TRIPOD; Prediction of disability/intensity; Yellow flags; Exercise

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