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).
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
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.
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.
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).
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.
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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