Introduction
Methods
Design
Identifying the Research Question
Identifying Relevant Studies
Selecting Studies for Analysis
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Topic of the article A CDS tool for selecting interventions.
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Population Patients with any painful MSK disorder (e.g., regional pain disorders of the back, neck, knee, shoulder, etc.). Our review included all MSK conditions available in the literature and all terms referring to MSK conditions were included in the searches. We excluded articles on non-MSK disorders including metabolic/endocrine disorders (i.e. osteoporosis, diabetic ulcers), rheumatic disorders (i.e. ankylosing spondylitis, rheumatoid arthritis, fibromyalgia) and other general medical conditions.
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Outcome Functional and work-related outcomes, including return to work, disability, performance, and absenteeism. Functional recovery is a crucial outcome in regional pain disorders. From the perspective of the various stakeholders involved (i.e. workers, insurers, employers and health care providers), recovery from pain is important; however, functional recovery—such that the patient can return to work and participate in normal daily living—is equally important and has important career and quality-of-life implications [26, 27]. Functional recovery is also often easier to measure. For these reasons, we focused primarily on interventions aimed at improving function or facilitating return to work and other activities of daily living.
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Study type Any design describing or evaluating a CDS tool. Systematic reviews were excluded but references within those located were searched for further articles.
Consultation with Knowledge Users
Data Analysis
Charting the Data
Collating, Summarizing and Reporting Results
Results
General Description of Articles
Number (%) | |
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Source of evidence | |
Peer-review journal article | 75 (61) |
Commentary/editorial/article summary | 19 (15) |
Conference proceeding | 9 (7) |
Review | 9 (7) |
Study protocol | 6 (5) |
Thesis | 5 (4) |
Discipline of lead authors | |
Health care | 121 (98) |
Computing science | 2 (2) |
Geographic location of lead authors | |
North America | 70 (57) |
Europe | 21 (17) |
Australasia | 9 (7) |
Asia | 4 (3) |
Multiple locations | 19 (15) |
Year of publication | |
2006–2014 | 101 (82) |
2000–2005 | 18 (15) |
Before 2000 | 4 (3) |
Type of tool discussed in the article | |
Clinical prediction rule/classification system | 79 (64) |
Questionnaire | 15 (12) |
Treatment algorithm | 15 (12) |
Theoretical/empirical model | 6 (5) |
Computer-based tool | 8 (7) |
Condition aimed at by tool | |
Low back pain | 69 (56) |
Neck/shoulder/arm pain | 21 (17) |
General MSK disorders | 17 (14) |
Knee/ankle pain | 6 (5) |
Upper extremity pain | 3 (2) |
Serious pathology (fractures, etc.) | 5 (4) |
Thoracolumbar injury | 2 (2) |
Reasoning method | |
Rule-based | 109 (89) |
Other (e.g., neural network, decision tree) | 8 (7) |
Unclear | 6 (5) |
Study design of peer-review studies located (n = 75) | |
Experimental | |
Randomized controlled trial | 12 (16) |
Quasi-experimental | 4 (5) |
Observational | |
Cohort study | 31 (41) |
Case control/case report/case series | 15 (20) |
Cross-sectional study | 6 (8) |
Secondary analysis | 5 (7) |
Methodological study | 2 (3) |
Computer-Based Tools/Questionnaires
Name of CDSS | Purpose | Description | Stage of Development | Hardware and Software | Data Input Requirements | Output(s) | TargetRecipient of Output | Limitations |
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Keele STarT Back Screening Tool (SBST) [34] | The SBST allocates low back pain patients into three risk groups and is intended to assist clinicians in their decisions about choice of treatment in primary care settings | Questionnaire consisting of nine items covering aspects of fear avoidance beliefs, depression, disability and presence of leg pain and neck/shoulder pain. Patients are allocated into one of three subgroups (low, medium or high risk of chronicity) based on the obtained score. Treatments are targeted based on score | The developers have conducted one RCT to compare treatment informed by the SBST to usual care. Patients managed using the SBST had better functional outcomes at 1-year, but this effect was mainly seen in the medium and high risk groups. Acceptable concurrent validity has been demonstrated with the OMPQ [162‐164] The presence of high depression, fear avoidance beliefs, or catastrophizing scores increase significantly from the SBST low risk group, over the medium group to the high-risk group [165] Fair agreement between SBST tool (K = 0.22) and clinicians classification of patients into risk groups [33] StarT Back Tool has been translated and cross culturally validated into Danish [166], Spanish [167],French [168], German [155], Swedish [163], Chinese [156], and Finnish [169] The test–retest reliability of the SBST total score was excellent (intraclass correlation coefficient = 0.78; [169] 0.93 [156]; and 0.90 [164]) and good for the psychosocial subscale (0.68) [169] The StarT Back Tool demonstrated acceptable to outstanding discrimination to determine individuals with different levels of disability as indicated by widely accepted questionnaires such as the Roland Morris Disability Questionnaire, Tampa Scale of Kinesiophobia among others (AUC ranged from 0.79 to 0.91 [155], and 0.75–0.89 [156] | Not needed | 9 questions are answered with a Likert-scale regarding pain and activities of daily living. The tool has been translated into several languages | Treatments recommendations based on risk categories: low, medium or high risk. The authors suggest that the low risk group only needs a ‘light’ intervention with e.g., analgesics and advice, the medium group requires treatments involving elements such as exercises or manual therapy, and that a combination of physical and cognitive-behavioral approaches should be considered for the high risk groups | Primary care providers | The tool has not been validated via clinical trial outside the United Kingdom There is also a need for a large RCT designed to test whether treatment effects differ across the SBST subgroups |
To assess the presence or absence of potential risk factors for the establishment of risk profiles related to neck, shoulder and arm symptoms in computer workers and potentially determine targeted treatment | Computer-based survey aimed at identifying workers’ at risk of arm, shoulder and neck symptoms. Based on score results, recommendations are made to the worker to reduce risk of symptoms. In total, the questionnaire consists of 81 items, divided over two categories and 11 subcategories. A description of the actual questions can be found at: www.compufitquickscan.com/ne/quickscan/
| The tool has been tested in a cluster randomized control trial and associated cost-effectiveness evaluation. Use of the tool did not reduce work disability and the tool was not found to be cost-effective The tool does have acceptable internal consistency, reliability and concurrent validity. Cronbach’s alpha was mostly between 0.40 and 0.85. Six scales scored 0.70 or higher. Concurrent validity of the RSI with original questionnaires was acceptable [170] The concurrent validity of the questionnaire symptom items with the observations of 2 physicians was defined as poor to moderate with kappa values between 0.16 and 0.53 [171] Predictive Validity of the RSI QuickScan questionnaire was tested. High scores of the RSI QuickScan on 9 out of 13 scales, including previous symptoms, were significantly related to arm, shoulder and neck symptoms at follow-up [172] | Internet-based RSI QuickScan survey/questionnaire (https://www.compufitquickscan.com/ne/quickscan) | Items are answered following a web platform | Interventions can be targeted at each of the factors in the RSI QuickScan, with a total of 16 interventions aimed at reducing the associated risk [36, 52]. A score of 30 % or less of the maximum on a scale was classified as a low risk, colour-coded “green”. A score of 31 % to 60 % of the maximum on a scale was classified as a medium risk, colour-coded “amber”. A score of 61 % or more of the maximum on a scale was classified as a high risk, colour-coded “red”. | Primary care providers and ergonomists | The RSI QuickScan appears to have a modest effect and was not cost-effective. However, this might have been due to problems with implementation of expensive ergonomic interventions, which were sold at regular commercial prices during the trial. This was despite commitment from all participating organizations prior to starting the study that they were prepared to invest in the necessary preventive measures | |
Pain Recovery Inventory of Concerns and Expectations (PRICE) [36] | Brief screening questionnaire to triage return-to-work strategies among patients with low back pain | Questionnaire consisting of 46 items measuring, depressive symptoms (12 items), pain catastrophizing (2 items), lack of organizational support (7 items), activity limitation (15 items), fear of movement (4 items), perceiving grave life impacts (3 items) poor expectations for recovery (2 items), and pain intensity (1 item) [36] | A confirmatory cluster analysis replicated previous findings of three risk subgroups: distressed, avoidant, and lacking employer support Validity of the PRICE screening was supported by its prospective association with the 3-month disability outcome measures (return to work, functional limitation, and clinical case rating) [36] | Not needed | Subjects are asked to respond to each of the 46 items on different Likert-type scales scale (i.e. “strongly disagree” to “strongly agree.”; “not at all” to “all the time”) | PRICE can be used to identify early intervention needs among working adults with low back pain based on the group classifications It provides an indication of whether attention should be focused on workplace coordination, physical activation, or psychological coping, and this may improve the ability to provide more patient-centered strategies for early disability prevention | Primary care providers | This questionnaire is at an early stage of development. Future trials should be conducted to validate the classification and targeted management approach |
Orebro Musculoskeletal Pain Questionnaire (OMPQ) [39] | Screening tool aimed at identifying high-risk patients with MSK pain in need of early intervention | Questionnaire consisting of 24-items that allocates patients into three different risk categories related to work absenteeism and guides potential interventions for those with low (reassurance and advice), moderate (physical therapy) or high risk (psychologically-informed care) | The OMPQ was initially developed as a screening tool and has been evaluated in several settings and translated into several languages for this purpose. However, it has recently been evaluated as a potential CDS tool for selecting interventions for patients with MSK pain. One study is underway in Germany that evaluates the OMPQ as a CDS tool [44] | Not needed | 24 items with various response options for different sections of the tool | After OMPQ administration and scoring, the questionnaire categorized patients into one of three risk level categories: low, medium and high risk. Various cut-points have been recommended for the categorization, with the developers stating the cut-off scores are related to the population studied | Primary care providers | Has only been evaluated as a CDS tool in one student thesis, with negative results. The OMPQ was not explicitly developed as a CDS tool, although early risk stratification implies different approaches for different categories |
Pain Management Advisor (PMA) [35] | To enhance primary care providers’ management of chronic pain | Computer-based tool that relies on rule-based algorithms derived from expert knowledge of pain specialists User asked a series of questions to refine the diagnosis and determine appropriate therapy Interactive capability (e.g., for explanations, therapeutic rationales, therapy guidelines) | Working version developed: some field testing conducted | Computer program PMA written in MicroSoft Visual Basic, v. 5.0, run as an expert application in XpertRule Algorithms stored in MicroSoft Access database MicroSoft Help Utility used for explanations and queries | Patient demographics Diagnosis Pain characteristics Laboratory tests and imaging studies Current medications Prior therapies Concurrent disease conditions Allergies Psychological status | A prioritized list of recommendations: (1) medical management (pharmacologic and nonpharmacological management, physical, psychosocial modalities); (2) invasive procedures; (3) referrals | Primary care providers. | This software was only tested qualitatively in one study. No further testing has been published |
Decision Support Software (DSS) [38] | To determine whether the use of software as a decision support system can help with evaluation and control of physical job stresses and prevent re-injury of workers who have experienced or are concerned about work-related musculoskeletal disorders The ergonomists used the database as a decision support tool in the control of work-related MSK disorders (WMSDs) | Computer-based tool consisting of decision support software is a spreadsheet-based database program written in Microsoft Excel. It has a graphical user interface (GUI) in the WindowsTM environment, and contains video clips of representative cycles of the selected job and in some cases, multiple videos showing multiple views It was designed specifically for the site, but is adaptable to other manufacturing plants with relatively stable work patterns. The software program is a database that stores detailed job information such as standardized work data, videos, and upper-extremity physical stress ratings for over 400 jobs in the plant. Additionally, the database users were able to record comments about the jobs and related control issues | Testing usability and effectiveness to prevent worker injuries No further testing The database seems to be useful to facilitate the quality of job evaluation. This improvement in quality can lead to better intervention and control of MSK problems [38] | Pentium-based PCs Ideally a portable computer Windows 95 Excel Program Visual interface For video import, mpg images are needed | Upper extremity exposure ratings (evaluated by the research team) for repetition, posture, contact stress, and force Standard data: work elements and times for selected job, obtained from the company standard data system -Text box that allows users to store and retrieve comments about selected job -Menu to search for a job by department, section, line position, and by date Videos of the work environment | Information from database was used to make recommendations for injury prevention and management strategies by the ergonomists | Ergonomists | This software was only tested qualitatively in one study. No further testing has been published [38] |
Soft Tissue Continuum of Care Model [37] | The model was designed as a high-level, decision-making tool or “roadmap” to promote a consistent, evidence-based approach to manage soft tissue injuries | The model with computer-based tool that involves 3 main components: (1) Staged application of rehabilitation services; (2) Case management protocols and case planning checkpoints; and (3) Contracted services with providers | A population-based, quasi-experimental, before-and-after design with concurrent control groups was used to evaluate the model’s impact and effectiveness | Computer-based prompts were given to workers’ compensation case managers via a custom-built program | Data on type of injury and time since injury is used from within the workers’ compensation administrative database to generate prompts for case managers | Based on type of injury and time of recovery, claimants are referred to different assessment and treatment programs | Workers’ compensation case managers | Further validation of this model is recommended through the implementation of experimental design such as RCT |
Work Assessment Triage Tool (WATT) [32] | The classification algorithm and accompanying computer-based CDS tool help categorize injured workers toward optimal rehabilitation interventions based on unique worker characteristics | Computer-based tool comprised of 18 variables related to: injury duration, occupation, job attachment and working status at time of RTW assessment, availability of modified work, National Occupational Classification Code, ICD9 diagnostic group, calendar days injury to assessment, the ‘Occupation’ item from the PDI Pain VAS out of 10, and 9 items from the SF36 (items 2, 4, 5, 7, 12, 14, 18, 21, 25) | The algorithm used by the WATT was developed using machine learning techniques and demonstrated high accuracy for correct classifications during internal validation [32, 41] Concurrent validity of WATT with clinician’s recommendations was tested. Percent agreement between clinician and WATT recommendations was low to moderate. The WATT did not improve upon clinician recommendations, but was more likely to recommend evidence-based interventions [42] | HTML-based computer program that can run on any computer system with access to the Internet | Data entered into WATT involves 18 items related to injury duration, occupation, job attachment and working status at time of RTW assessment, availability of modified work, National Occupational Classification Code, ICD9 diagnostic group, calendar days injury to assessment, the ‘Occupation’ item from the PDI, Pain VAS out of 10, and9 items from the SF36 (items 2, 4, 5, 7, 12, 14, 18, 21, 25) | The rehabilitation options available to clinicians were: physical therapy, interdisciplinary functional restoration, workplace-based rehabilitation, ‘hybrid’ functional restoration/workplace-based rehabilitation; complex interdisciplinary bio-psychosocial rehabilitation and no further rehabilitation | Primary care providers and case managers | This tool is at the early stages of validation. Findings do not provide evidence of concurrent validity of the WATT against clinician recommendations. WATT appeared more likely than clinicians to recommend treatments supported by current evidence such as workplace-based interventions. Further validation is needed |
Authors (ID) | Year | Study design | Population | Body part | Context | Tool mentioned | Properties tested | Methods | Outcome | Results |
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Hill et al. [34] | 2011 | RCT | 851 adults aged ≥18 years with low back pain with or without radiculopathy | Low Back | Ten general practice clinics in England | Questionnaire: Keele STarT Back Screening Tool that stratifies patients into low, medium or high risk, requiring different interventions | Validity of a stratified/classification approach to primary care Sensitivity/specificity for identifying treatments | Eligible patients were randomly assigned to intervention (use of SBST to inform management) or control group (usual care). Disability, cost and quality of life were evaluated | Results indicate a classification approach using the tool significantly improves patient outcomes and is associated with substantial economic benefits | Positive |
Hill et al. [33] | 2010 | Methodological study | 12 consecutively consulting patients with primary care back pain | Low Back | 8 General Practices in the United Kingdom | Questionnaire: Keele STarT Back Screening Tool that stratifies patients into low, medium or high risk, requiring different interventions | Agreement between clinicians and STarT Back tool | 12 patients underwent a video-recorded clinical assessment. The SBST was completed on the same day. Clinical experts reviewed the videos and categorized subjects to low, medium or high-risk | Clinicians make inconsistent risk estimations for primary care patients with back pain when using intuition alone, with little agreement with the STarT Back tool | Unclear |
Spekle et al. [40] | 2010 | Cluster RCT | 741 computer workers from 7 Dutch organisations in various work branches (e.g., health care, local government, nature conservation, engineering, education and regulatory affairs), located throughout the Netherlands The population consisted of office staff, local government officials, engineers, consultants, teachers, health care personnel, nature conservation professionals, researchers and managers | Arm, shoulder and neck pain | Employees of a large occupational health service in the Netherlands | Questionnaire: RSI QuickScan intervention program | Effectiveness of the intervention program for reducing symptoms and sick leave | The participants were assigned to either an intervention or usual care group by means of cluster randomization. At baseline and after 12 months of follow-up, participants completed the RSI QuickScan questionnaire to determine exposure to the risk factors and prevalence of arm, shoulder and neck symptoms. A tailor-made intervention program was proposed to participants with high-risk profiles at baseline. Examples of implemented interventions are an individual workstation check, a visit to the occupational health physician and an education program on the prevention of arm, shoulder and neck symptoms | There were no significant differences in changes in the prevalence of arm, shoulder and neck symptoms or sick leave between the intervention and usual care group | Negative |
Spekle et al. [52] | 2010 | Economic evaluation alongside a cluster RCT | 638 computer users with and without shoulder, arm and neck symptoms | Arm, shoulder and neck | Workers from seven Dutch companies | Questionnaire: RSI QuickScan intervention program | Cost–benefit of the RSI QuickScan program | Workers were randomized to either the intervention or usual care group. The intervention consisted of a tailor-made program based on the RSI-QuickScan program. Usual care group did not receive elaborate advice. The participants completed the questionnaire at baseline and 12-month follow-up. Effectiveness and cost were compared | The RSI QuickScan intervention program did not prove to be cost-effective. However, with a relatively small investment, the program increased the number of workers who received information on healthy computer use and improved their work posture and movement | Negative |
Shaw et al. [36] | 2013 | Cohort study | 496 workers with acute (fewer than 14 days) work-related low back pain | Low Back | A private network of occupational health clinics in the USA with eight participating clinics located in various states | Questionnaire: The Pain Recovery Inventory of Concerns and Expectations (PRICE) measure. Designed to subgroup patients within the first 2 weeks of an episode of back pain to determine needed treatment depending on whether disability is related to pain beliefs, emotional distress, or workplace concerns | Sensitivity analysis conducted to reduce the number of items while maintaining scale reliability, then classification accuracy was tested using a confirmatory cluster analysis | Patients were recruited from the consecutive caseload of patients reporting low back pain, and volunteer patients completed a brief demographic questionnaire and a 10-page psychosocial test battery. Participants were then followed-up at 3-months to determine pain, function, and work status | The reduced PRICE measure is a 46-item screening measure that can be used to identify early intervention needs of working adults with low back pain | Unclear Not testing effectiveness, only development |
Aravena-Paez [39] | 2014 | Retrospective cohort study | 2046 workers compensation claimants with back disorders | Low Back | Rehabilitation facilities in Alberta, Canada with contracts to treat workers’ compensation claimants | Questionnaire: OMPQ. Screening tool aimed at identifying high-risk patients with MSK pain in need of early intervention | Tested level of agreement between clinician recommendations and OMPQ categories. Also examined whether a match between OMPQ categories and actual programs were associated with better RTW outcomes | Secondary analysis of a dataset used for developing a CDS tool. Examined whether a match between OMPQ categories, clinician recommendations and actual rehab program undertaken was related to a better return to work outcome | The OMPQ had limited agreement with clinician recommendations suggesting other measures or factors are considered when making treatment recommendations. Finally, concordance of OMPQ categorization and actual rehabilitation undertaken did not appear to favorably impact outcomes | Negative |
Knab et al. [35] | 2001 | Quasi-experimental study | 100 patients with chronic pain referred for treatment at a chronic pain clinic | All | VA San Diego Healthcare System Pain Clinic in the United States | Computerized: Pain management advisor (PMA) | Validity and acceptability of recommendations made based on a computerized tool | A pain specialist used a decision support system to determine appropriate pain therapy and sent letters to the referring physicians outlining these recommendations. Separately, five board-certified PCPs used a CBDS system to “treat” the 50 cases. Patients were followed up 1-year later | The use of a Computer-Based Decision-Support system may improve the ability of primary care physicians to manage chronic pain and may facilitate screening of consults to optimize specialist utilization | Positive |
Womack and Armstrong [38] | 2005 | Quasi-experimental study | Workers in an automobile assembly plant conducting over 400 jobs in the plant | Upper extremity | Worksite-based study. The plant built small trucks in a 2.1 million square foot facility. There were over 500 on- and offline assembly jobs and a workforce of ~2580 union employees | Computerized: Decision support system (DSS) for helping ergonomists better match workers with the work environment | Utility of the tool over a 20-month period | Evaluation of qualitative comments regarding utility of the tool as well as 1-on-1 semi-structured interviews with users | Of 197 comments entered by users, 25 % pertained to primary prevention, 75 % pertained to secondary prevention, and 94 comments (47.7 %) described ergonomic interventions. Use of the software tool improved the quality and efficiency of the ergonomic job analysis process | Positive |
Stephens and Gross [37] | 2007 | Quasi-experimental study | 171,736 workers’ compensation claimants with any type of MSK injury aged 18–65 years | All | Rehabilitation facilities in Alberta, Canada with contracts to treat workers’ compensation claimants | Soft Tissue Injury Continuum of Care Model with computerized prompts for case managers | Effectiveness of the tool compared to usual care | A population-based, quasi-experimental, before-and-after design with concurrent control groups was used to evaluate the model’s impact. Data were extracted from the main WCB-Alberta administrative database from 2 years before model implementation to 5 years after | Implementation of a soft tissue injury continuum of care involving staged application of various types of rehabilitation services appears to have resulted in more rapid and sustained recovery | Positive |
Gross et al. [32] | 2013 | Cohort study | 8611 injured Canadian workers’ compensation claimants with any type of MSK injury between 18 and 65 years old | All | Rehabilitation facilities in Alberta, Canada with contracts to treat workers’ compensation claimants | Computerized: Work Assessment Triage Tool | Classification accuracy of the tool | Data were extracted from a workers’ compensation database and machine-learning techniques were used to generate and test a tool | A CDS tool was developed for selecting rehabilitation interventions for injured workers. Preliminary validation was also conducted | Not testing effectiveness, only tool development |
Zhang et al. [41] | 2013 | Methodological study (rule-based classifiers) | 8611 injured Canadian workers’ compensation claimants with any type of MSK injury between 18 and 65 years old | All | Rehabilitation facilities in Alberta, Canada with contracts to treat workers’ compensation claimants | Computerized: Work Assessment Triage Tool | Accuracy of various rule-based classifiers | Data were extracted from a workers’ compensation database and various machine-learning techniques and rule-based classifiers were tested | This paper presents a description of the algorithm development from a computer science/machine learning perspective | Not testing effectiveness, only tool development |
Qin et al. [42] | 2015 | Cross sectional | 434 injured Canadian workers’ compensation claimants with any type of MSK injury between 18 and 65 years old | All | Workers’ compensation rehabilitation facility in Alberta, Canada | Computerized: Work Assessment Triage Tool (WATT). Designed to categorize injured workers to various programs including functional restoration, workplace-based intervention, or chronic pain programs | Concurrent validity of the tool’s recommendations | Level of agreement was examined between the WATT and clinical recommendations by therapists participating in a clinical trial | Percent agreement between clinician and WATT recommendations was low to moderate. The WATT did not improve upon clinician recommendations | Unclear |
Tool | Internal consistency | Face validity | Content validity | Criterion validity* | Construct validity | Reproducibility (agreement/reliability) |
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StarT Back | + | + | + | +* | + | + |
RSI Quick Scan | + | + | + | – | + | + |
PRICE | + | + | + | – | + | – |
PMA | – | + | + | + | + | – |
DSS | – | + | + | – | – | – |
Soft Tissue Model | – | + | + | – | + | – |
WATT | – | + | + | + | + | – |
Treatment Algorithms/Decision-Models
Authors (ID) | Year | Study design | Body part | Algorithm/model mentioned | Population | Methods | Outcome/conclusion | Results |
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Hurd et al. [63] | 2008 | Cohort study | Knee | Algorithm for managing subacute anterior ligament (ACL) injuries was created using clinical information on: concomitant injury, unresolved impairments, and results of a screening examination | 345 highly active adults (216 men, 129 women) with subacute anterior cruciate ligament injury aged 18–65 years presenting to an orthopedic surgeon | Prospective follow-up study. Patients presenting within 7 months of their injury were treated using a decision-making algorithm. Algorithm was used as criteria to guide management and classify individuals as ‘noncopers’ (poor potential) or potential ‘copers’ (good potential) for non-operative care. Patients were followed up for the duration of care (up to 10 PT sessions over 5 weeks) | 199 subjects classified as ‘noncopers’ and 146 as potential ‘copers’. 63 of 88 potential ‘copers’ successfully returned to pre-injury activities without surgery, with 25 of these not undergoing ACL reconstruction at follow-up. The algorithm should be considered as an alternative to management based on anterior knee laxity, age, and preinjury activity levels | Positive |
Kodama et al. [66] | 2013 | Review and retrospective study | Wrist | Scoring system for selecting treatment for distal radius fractures. Includes a variety of clinical factors related to the fracture, as well as dominant hand, high occupational or recreational activity, age, and supplemental factors (Table 2 in paper) | 164 patients with distal radius fracture who were 50 years or older presenting to a surgeon. | Development of the decision-making guide was described, and then a retrospective study was used to evaluate the guide in patients. Comparison was made on clinical outcomes (DASH questionnaire scores) between patients where recommendations of the guide were followed and not followed | 164 patients were divided into 4 groups using the tool: conservative care, relative conservative care, relative surgical care, and surgical care. Clinical outcomes of those that followed the recommendation were better than those not following the recommendation. The present scoring system is an easy-to-use decision-making tool for choosing conservative or surgical treatment for distal radius fractures | Positive |
Murphy et al. [76] | 2007 | Cohort study | Low back | The approach is based on 3 questions: (1) Are the symptoms reflective of a visceral disorder or a serious/potentially life-threatening disease? (2) From where is the patient’s pain arising? (3) What has gone wrong with this person as a whole that would cause the pain experience to develop and persist? | 264 patients with moderate to severe low back pain over 18 years old presenting to a private practice physical therapy clinic | Cross-sectional feasibility study. Demographic, diagnostic and baseline outcome measure data were gathered on a cohort of low back pain patients examined by one of three examiners trained in the application of a diagnosis-based clinical decision rule that guided subsequent treatment | The guide can be applied in a private practice setting It appears that patients with low back pain can be distinguished on the basis of this approach, and treatment plans can be formulated utilizing this strategy | Development article |
Sonnabend [64] | 1994 | Case Series | Shoulder | Treatment algorithm was based on presence of fracture location, weeks in a sling, presence of pain and weakness, and arthrogram or ultrasound results | 53 patients with primary traumatic anterior shoulder dislocation older than 40 years of age presenting to an orthopedic surgeon | Patients were classified into 3 groups according to an algorithm based on signs and symptoms. This algorithm was used to determine treatment. Clinical outcomes in the different groups were described after the treatment | The algorithm is suggested as an approach to treatment of primary traumatic dislocation | Development article |
Spiegl et al. [65] | 2013 | Retrospective case series | Shoulder | Treatment algorithm for acute osseous Bankart lesions consisting of a conservative strategy for small defect sizes and a surgical approach for medium-sized and large defects | 25 patients who sustained acute traumatic osseous Bankart lesions after a first time shoulder dislocation from a ski or snowboard accident without rotator cuff tears | Retrospective case series to describe outcomes. Operative therapy was performed in patients with osseous defects of 5 % or more, otherwise conservative therapy was initiated | Applying the treatment algorithm appears to lead to encouraging mid-term results and a low rate of recurrent instability in active patients | Positive |
Stanton et al. [55] | 2011 | Cross sectional study and test–retest reliability for a subset | Low back | Treatment-Based Classification Algorithm based on clinical examination findings for selecting treatments for patients with low back pain. This algorithm was summarized into a decision-making flowchart | 250 patients with acute or sub-acute low back pain recruited from teaching hospitals (Sydney, Australia) and private physical therapy clinics (Australia and United States) | Observational study to determine the prevalence of patients meeting the criteria for each subgroup (i.e. responders to the various treatments in the system). Trained physical therapists performed standardized assessments on all participants. These findings were used to classify participants into subgroups. 31 participants were reassessed to determine inter-rater reliability of the algorithm decision | Reliability of the algorithm is sufficient for clinical use. But 25 % of participants met the criteria for more than 1 subgroup and 25 % did not meet the criteria for any subgroup. This has important implications for validity and potential revisions to the algorithm’s section that guides unclear classification | Development article |
Stanton et al. [56] | 2013 | Cross-sectional secondary analysis from 3 previous studies | Low back | Treatment-Based Classification Algorithm (see above) | 529 patients with low back pain treated at private physical therapy clinics in USA, Australia and the Netherlands, and public hospital physical therapy outpatient departments in Australia | To guide improvements in the algorithm, this study aimed to determine whether people with unclear classifications are different from those with clear classifications. Univariate logistic regression was used to determine which participant variables were related to having an unclear classification | People with unclear classifications appeared to be less affected by low back pain (less disability and fewer fear avoidance beliefs), despite typically having a longer duration of low back pain. Recommendations to the algorithm are suggested, this study provides no evidence that any changes will result in better outcomes | Unclear |
Strong et al. [57] | 1995 | Cohort study | Low back | The Integrated Psychosocial Assessment Model (IPAM), a multidimensional assessment for use with patients with chronic low back pain | 70 consecutive patients with chronic low back pain presenting at the Auckland Regional Pain Service or private practice pain facility in Auckland, New Zealand | Subjects were assessed on pain intensity, disability, coping strategies, depression and illness behavior. Cluster analysis was used to identify homogenous groups of patients. Clusters were named “In Control”, “Depressed and Disabled”, and “High Denial”. The clustered obtained by this study were used to propose management | The IPAM model may be valuable for identifying low back pain subgroups. Treatments corresponding to each subgroup were proposed | Development article |
Wang et al. [67] | 2003 | Cohort study | Neck | Clinical reasoning algorithm for treating patients with neck pain. This algorithm was developed before the study by one of the authors. The algorithm consists of 4 categories: (1) radicular arm pain or neck pain; (2) referred arm pain or neck pain; (3) cervicogenic headaches; and (4) neck pain only. There are subcategories formed by different clinical patterns that are used to guide treatment | 57 adults referred from general practitioners for physical therapy treatment of neck pain. All patients had current neck pain with or without radiating pain and no other serious pathology | A quasi-experimental, nonequivalent, pretest-post-test control group design was used to investigate the effects of algorithm-based clinical decision making. Outcomes in a treatment group of 30 patients with neck pain treated based on the algorithm were compared to a control group of convenience formed of 27 subjects who also had neck pain but did not receive treatment for various reasons | After ~4 weeks of physical therapy intervention, patients in the treatment group demonstrated statistically significant increases of cervical range of motion, decreased pain, increases of physical performance measures, and decreases in level of disability. The control group showed no differences in all five outcome variables. Authors conclude that organized and specific physical therapy program was effective in improving the status of patients with neck pain, and the algorithm can help clinicians classify patients with cervical pain into clinical patterns | Positive |
Widerstrom et al. [58] | 2007 | Multiple case pretest–posttest study | Back | Clinical ‘pain modulating’ treatment classification for patients with low back pain that was formed empirically. It is considered for patients with moderate to high irritability and high pain and/or disability scores, and where judgments on spinal mobility were inconclusive and no segmental level could be determined | 16 consecutive adults patients with low back pain, regardless of duration, with or without radiating pain to the lower extremities. Patients were from the waiting list of a primary care physiotherapy clinic in Sweden. All patients but one had chronic low back pain (>3 months) | The first part of the paper was descriptive, resulting in an individualized clinical decision-making algorithm As an illustration of the utility of the presented algorithm, a multiple subject case study was then conducted, using a pretest–posttest design. The 16 patients were classified based on the algorithm, and treated based on the algorithm, then evaluated at discharge from physiotherapy | Two patients were excluded from the study (1 pregnancy and 1 with progressive symptoms). All but 1 of the remaining 14 patients showed improvements in pain intensity scores. The authors interpret study findings to suggest that the presented model may be used when clinical decisions on selecting interventions for patients with chronic low back pain are made | Positive |
Fitzgerald et al. [62] | 2000 | Cohort Study | Knee | Decision-making scheme for returning patients to high-level activity with non-operative treatment after anterior cruciate ligament rupture. The screening exam consists of four 1- legged hop tests, the incidence of knee giving-way, a self-report functional survey, and a self-report global knee function rating | 93 consecutive patients with acute unilateral anterior cruciate ligament rupture | Patients were classified as either candidates (n = 39, 42 %) or non-candidates (n = 54, 58 %) for non-operative management based on the decision-making scheme. Patients were returned to full activity an average of 4 weeks after the screening examination. Successful treatment was defined as the ability to return to preinjury levels of activity without experiencing an episode of giving-way at the knee. Failure was defined as either having at least one episode of givingway at the knee or a reduction in functional status | Of the 39 rehabilitation candidates, 28 chose non-operative management and returned to preinjury activity levels, 22 of whom (79 %) returned to preinjury activity levels without further episodes of instability or a reduction in functional status. The decision-making scheme described in this study shows promise in identifying patients who can safely postpone surgical reconstruction and temporarily return to physically demanding activities | Positive |
Rundell et al. [54] | 2009 | Case series | Back pain | Management of acute and chronic low back pain using the World Health Organization’s International Classification of Functioning. This model provides a method that considers biological, individual, and social contributions that can be used to classify patients | Two patients, 1 with acute and 1 with chronic pain were treated pragmatically using models of clinical reasoning | Manual therapy, exercise, and education interventions were directed toward relevant body structure and function impairments, activity limitations, and contextual factors based on their hypothesized contribution to functioning and disability. Patients were evaluated after a period of 3 and 10 weeks of intervention, respectively | Both patients demonstrated clinically important improvements in pain, disability, and psychosocial factors after intervention. The WHO-ICF model appears to provide an effective framework for physical therapists to better identify each person’s experience with his or her disablements and assists in prioritizing treatment selection | Positive |
Shaw et al. [60] | 2007 | Cohort study | Back pain | A model is developed for discriminating patients with acute back pain into subgroups depending on whether disability is related to pain beliefs, emotional distress, or workplace concerns | 528 patients with work-related back pain seeking treatment for acute back pain at one of 8 community-based occupational health clinics in the New England region of the USA | Patients with back pain completed a 16-item questionnaire of potential disability risk factors before their initial medical evaluation. Outcomes of pain, functional limitation, and work disability were assessed 1 and 3 months later | A K-Means cluster analysis of 5 disability risk factors (pain, depressed mood, fear avoidant beliefs, work inflexibility, and poor expectations for recovery) resulted in 4 sub-groups: low risk (n = 182); emotional distress (n = 103); severe pain/fear avoidant (n = 102); and concerns about job accommodation (n = 141). Pain and disability outcomes at follow-up were superior in the low-risk group and poorest in the severe pain/fear avoidant group | Development article |
Steenstra et al. [61] | 2010 | Secondary analysis of previous cohort study data | Back pain | Evaluation of the Risk Factor-Based Intervention Strategy Model proposed previously by Shaw et al. The model was developed based on a literature review and classifies patients into 1 of 4 groups that require different forms of intervention | 442 workers with a new, accepted or pending, work related injury lost-time claim for low back pain who were absent from work for at least 5 days within the first 14 calendar days post-injury, and were at least 15 years of age | Claimants (n = 259) who had already returned to work, were categorized as low risk. A latent class analysis was performed on 183 workers absent from work. Groups were classified based on: pain, disability, fear avoidance beliefs, physical demands, people-oriented culture and disability management practice at the workplace, and depressive symptoms | Three classes were identified; (1) workers with ‘workplace issues’, (2) workers with a ‘no workplace issues, but back pain’, and (3) workers having ‘multiple issues’ (the most negative values on every scale, notably depressive symptoms). This study confirms an earlier model theorizing that subgroups of patients can be identified who might benefit from different interventions | Positive but exploratory |
Reme et al. [59] | 2012 | Cohort study | Back pain | Development of a sub-classification of workers with acute back pain. Patterns of early disability risk factors from this study suggest patients have differential needs with respect to overcoming emotional distress, resuming normal activity, and obtaining workplace support | 496 workers seeking treatment for work-related, acute back pain at private occupational medicine clinics in the states of Massachusetts, Rhode Island, or Texas, USA | Workers completed self-report measures comprising 11 possible risk factors for chronicity of pain and disability. Outcomes of pain, function, and return-to-work were assessed at 3-month follow-up. A K-means cluster analysis was used to derive patient subgroups based on risk factor patterns, and then these subgroups were compared with respect to 3-month outcomes | A 4-cluster solution met criteria for cluster separation and interpretability, and the four clusters were labeled: minimal risk (29 %), workplace concerns (26 %); activity limitations (27 %); and emotional distress (19 %). Classifying patients in this manner may improve the cost–benefit of early intervention strategies to prevent long-term sickness absence and disability due to back pain | Development article |
Clinical Prediction Rules/Classification Systems
Condition | Purpose of rule/system | Evaluation status of the rule/system |
---|---|---|
Low back pain | Rule developed empirically Confirmatory testing in additional samples, including workers, has shown mixed results Unsuccessful evaluation via clinical trial Patients positive for the main factors in the rule (symptom duration <16 days and no symptoms distal to the knee) respond to other interventions such as McKenzie therapy | |
Low back pain | Rule developed theoretically Empirical testing has had mixed results Mixed evaluations via clinical trial Reliability of mobilization and stabilization subgroups has been unacceptable in some studies Questionable utility of the system as 25 % of patients fall into multiple subgroups and 25 % remain unclassified | |
Low back pain | Rule developed empirically Confirmatory testing in additional samples has had mixed results Stabilization and manipulation rules do not represent mutually exclusive subgroups | |
Low back pain | Rule developed empirically No confirmatory testing Prevalence testing of McKenzie classifications shows overlap with other prediction rules | |
Low back pain | Identifying non-responders to spinal manipulation [114] | Rule developed empirically No confirmatory testing |
Low back pain | Two separate rules developed empirically Factors in the rules are not consistent No confirmatory testing | |
Low back pain | CBI health classification system [117] | System developed theoretically Empirically tested by the developers No confirmatory testing |
Low back pain | Identifying responders to pilates based exercises [118] | Rule developed empirically No confirmatory testing |
Neck pain | System developed theoretically Empirical testing has been promising (case series, pilot and cohort studies) No evaluation via clinical trial | |
Neck pain | Rule developed empirically Unsuccessful evaluation via clinical trial | |
Neck pain | Rule developed empirically No confirmatory testing | |
Neck pain | Identifying responders to home-based cervical traction [122] | Rule developed empirically No confirmatory testing |
Neck pain | Two separate rules developed empirically Factors in the rules are not consistent No confirmatory testing | |
Neck pain | Identifying responders to cervical manipulation physiotherapy or usual care [138] | Rule developed empirically No confirmatory testing |
Patellofemoral knee pain | Rule developed empirically Confirmatory testing in a separate sample was unsuccessful | |
Patellofemoral knee pain | Two separate rules developed empirically Factors in the rules are not consistent No confirmatory testing | |
Patellofemoral knee pain | Identifying responders to patellar taping [143] | Rule developed empirically No confirmatory testing |
Ankle sprain | Identifying responders to manipulation and exercises [148] | Rule developed empirically No confirmatory testing |
Lateral epicondylalgia | Classification model for tennis elbow [149] | Theoretical model description No empirical testing |
Lateral epicondylalgia | Identifying responders to manual therapy and exercise [147] | Rule developed empirically No confirmatory testing |
Thoracolumbar injury | System developed theoretically Successful reliability testing No confirmatory testing |