Group Members: Jake Crist, Paige Downing, Jadyn Flores, Katie Kempf, Troy Smith

Purpose

To find clinical prediction rules that yield the best outcomes for pain and function in patients with SI/lumbar pain/dysfunction

Clinical Question

What factors can be identified to help predict a clear pathway to treatment in patients with SI/lumbar pain/dysfunction?

Background Information

Sources of Pain

  • Lumbar Pain
    • Lumbar vertebral bodies
    • Intervertebral discs
    • Facet joints
    • Spinal nerves
    • Surrounding trunk muscles
    • Surrounding ligaments
  • Sacroiliac Joint Dysfunction
    • Facet joints


  • Chronic LBP
    • Coexisting pathology

Symptoms

  • The symptoms of sacroiliac joint dysfunction may be misdiagnosed as a herniated disc so it is important to know the specific symptoms.
  • Sacroiliac joint pain usually manifests as pain in the lower back or buttocks, but pain can spread to surrounding muscles that may spasm in response to joint dysfunction.

Prevalence

  • Lumbar Pain
    • Lifetime prevalence: 84- 90%
    • 5 year recurrence rate: up to 69%
    • Becomes chronic in nature: 2-10%
    • Persisting pain: almost 19%
  • Sacroiliac Joint Dysfunction
    • Patients with LBP: 15%-30%

Medical Cost

  • UK-1998
    • NHS (national health service)treatment: £1.1 billion
    • private treatment: £0.6 billion
    • informal care: £1.6 billion,
    • employment-related productivity loss costs: £3.4 billion

Inclusion/Exclusion Criteria:

  • PubMed Advanced search for CPRs
  • Search terms included:
    • Prediction
    • Rule
    • Low back pain
    • Lumbar dysfunction
    • SI pain
  • Access to full text in English online
  • Both RCTs and nonrandomized controlled designs were included
  • Case studies were excluded

Implementing the Keele stratified care model for patients with low back pain: an observational impact study (1)

  • Study Design: observational cohort study
  • Objective: the objective of this study is to assess the use of the stratified model (Keele) for patients with low back pain following its introduction in an acute hospital physiotherapy department setting.
    • Low back pain (LBP) affects large number of people each year. Back pain is a leading cause of work absence and economic loss.
    • The type of treatment for which there is most economic evidence is combined physical and psychological interventions.
    • The Keele stratified care model (used for management of low back pain) integrates the use of the prognostic STarT Back Screening Tool.
      • Patients are divided into one of three risk-defined categories followed by delivery of associated risk-specific treatment pathways
        • High-risk patients receive enhanced treatment and more sessions than medium- and low-risk patients.
      • Intention of the Keele model
        • Change the pattern of treatment so that it targets appropriate interventions and improves patient outcomes
    • The acute hospital physiotherapy service introduced the Keele stratified care model in Spring 2013, when patients presented with LBP at one of the two acute hospital sites.
    • 201 patients treated with Keele’s model
    • Patients complete the prognostic STarT Back Screening Tool to determine their risk-rating at the first physiotherapy appointment.
    • An audit was performed for 15 weeks (2013) and repeated for 8 weeks (2014)
      • The GHT audit data allow an observational cohort design for the study.
        • Comparison study to assess two of the study’s aims
          • Whether the introduction of the stratified care model was associated with the anticipated treatment of low back pain
          • What the potential consequences of rolling out the model in an acute hospital PT department setting would be for NHS and private treatment costs, QALYs, and societal productivity losses.
    • Patients received significantly more treatment sessions as the risk-rating increased, using the Keele model.
    • The potential impact (annually) of rolling out the model across an acute hospital PT department setting is a gain in approximately 30 QALYs (quality adjusted life years).
    • Reduction in productivity losses valued at £1.4 million
    • Almost no change to NHS (national health services) costs.
    • The Keele model was implemented and successfully used for patients presenting with LBP for physiotherapy.
    • The therapist’s feedback on using the screening tool was very positive.
      • Health and productivity outcomes would be associated with implementation of the Keele model, as it would also be cost-neutral for the NHS.

A Clinical Prediction Rule To Identify Patients with Low Back Pain Most Likely To Benefit from Spinal Manipulation: A Validation Study (2)

  • Study Design: multicenter RCT
  • Objective: To validate a manipulation clinical prediction rule
  • Methods
    external image Zf1eAOONLoiIb_AnvuGL6CtnlXanfyNWBSfbrf7w2U8BvWIU-_KIkhn4--yP0zrmSFCyew9R0ZiqGWRNf8J2v-c-fHsENZzrzMoSCMmkeexUSItVnGMwYyGLszwF38zBfVep4om8
    • 14 PTs, 8 clinics in the US
    • Patients 18-60 years old with primary symptom of low back pain with or without referral into the LE, and an Oswestry disability questionnaire score of at least 30%.
    • Patients that met 4/5 criteria or higher were positive and therefore likely to respond to manipulation. 3/5 or fewer as negative.
    • After evaluation patients were randomly assigned to 1 of 2 groups: 1. spinal manipulation plus an exercise program. 2. An exercise program alone.
    • Spinal Manipulation: “The therapist stood opposite the side to be manipulated. The patient was passively side-bent away from the therapist. The therapist passively rotated the patient and then delivered a quick posterior and inferior thrust through the anterior superior iliac spine “
    • Screen Shot 2017-04-20 at 11.28.05 AM.png

  • Results
    • The overall 3-way, clinical prediction rule X treatment group X time interaction, for the repeated measures multivariate ANOVA was statistically significant, indicating that the outcome depended on both the patient’s treatment group and status on the rule.
    • Patients who were positive on the rule and received manipulation experienced greater improvement in 1 and 4 week disability than patients who were negative and received manipulation. This difference remained at the 6 month follow up.
    • At the 6 month follow-up, patients in the exercise group deomonstrated statistically signficantly greater medication use, health care utilization, and lost work time due to back pain than patients in the manipulation group. Among patients who were positive on the rule, a greater proportion in the exercise group were currently seeking other treatment for their back pain.
    • The effect of manipulation on disability and pain among these patients was statistically significantly greater than the effect of manipulation the whole group.
    • A patient who was positive on the rule and received manipulation has a 92% chance of a successful outcome.
    • Odds of successful outcome (vs negative on rule and exercise)
      • Positive rule and received manipulation = 60.8 (95% CI, 5.2 to 704.7)
      • Negative rule and received manipulation = 2.4 (CI, 0.83 to 6.9)
      • Positive rule and received exercise = 1.0 (CI, 0.28 to 3.6)
    • external image JefU0uebqTRz3v8_NurL-ksi5OFMUApIZWQ54rxKz8E6SbYPvs9a-5K5mbOjHomvsd00ObaaWQ1x-s0-qNB8HadEmXFfyy3D52_4fUg5ozbr6xP3XgwT2qx4usHY0zOPGd8cIhcQ
  • Conclusion
    • Patients were most likely to benefit from spinal manipulation if they met 4 of the 5 criteria: symptom duration less than 16 days, no symptoms distal to knee, score less than 19 on a fear avoidance measure, at least 1 hypomobile lumbar segment, and at least 1 hip with more than 35 degrees of internal rotation.
    • Clinicians can use the criteria to identify which patients will be good candidates for spinal manipulation.


Preliminary Development of a Clinical Prediction Rule for Determining Which Patients With Low Back Pain Will Respond to a Stabilization Exercise Program (5)

  • Study Design: prospective cohort study
  • Objective: develop a CPR to predict treatment response to a stabilization exercise program for patients with low back pain (LBP)
  • Background
    • Patients with LBP are not homogenous - they need to be classified into subgroups with similar characteristics (age, symptom duration, distribution) in order to guide diagnosis and treatment.
      • A classification system would improve overall decision making in the management of LBP
    • Recent research has shown that stabilization exercises targeting the muscles of the spine appear to be effective in certain subgroups of patients with LBP.
      • This study aimed to develop a valid method for identifying which subgroup will benefit from stabilization exercises in addition to which subgroup will not.
  • Methods
    • 54 patients with non-radicular LBP
    • ODQ - Modified Oswestry Low Back Pain Disability Questionnaire
      • Disease-specific measure of disability in LBP patients
      • Has been widely used in RCTs and has shown excellent reliability and good construct validity
      • Assessed at baseline and after 8 weeks of treatment (served as the reference standard for determining success/failure of the treatment program)
    • All subjects underwent the same stabilization program for 8 weeks, 2x/week
      • The program challenged and encouraged stabilizing motor patterns for the following muscles: rectus abdominis, transversus abdominus, internal oblique, erector spinae, multifidus, and quadratus lumborum
      • Focused on encouraging repeated submaximal efforts to mimic the function of these muscles in spine stabilization
      • Screen Shot 2017-04-18 at 4.29.54 PM.png
    • Data Analysis
        • Individual variables were tested for a significant univariate association with stabilization success or failure
          • Independent sample t tests for continuous variables
          • Chi-square tests for categoric variables
          • Variables were retained as potential predictors at a p level < 0.10
            • A more liberal significance level was used because the researchers wanted to filter variables with no association with outcome and they didn’t want to exclude any potentially useful predictors
        • Variables that showed a significant univariate association to treatment outcome were then used to develop multivariate CPRs
          • If > 5 variables had a significant association to an outcome, they were entered into a forward stepwise logistic regression equation to reduce the number of predictors
            • P of 0.15 was required to enter
            • P value of 0.20 was used to remove
            • More liberal significance levels were used because this is the first approximation of a CPR
  • Results
    • Definitions of success, improvement, and failure Success: 50% or > improvement in ODQ score come from the literature and previous experience with the ODQ.
      • The minimum clinically important difference (MCID) in ODQ score has been calculated as 5-6 points, so subjects who didn't even achieve the MCID were considered to have failed the intervention.


Success
Improvement
Failure
Definition
50% or > improvement in ODQ score
at least a 6-point improvement but less than 50% improvement in ODQ score
< a 6-point improvement in ODQ score
Number of subjects
18
21
15
Percentage
33.3
38.9
27.8
    • Four variables significantly related to success were considered for the multivariate CPR
    • 1. Age (cutoff < 40 yrs)
      • Had the greatest positive likelihood ratio (3.7)
      • A subject < 40 had a 3.7 higher odds of succeeding with the stabilization program
    • 2. Average SLR (cutoff > 91°)
      • Decreased SLR is related to radiculopathy and worse prognosis
    • 3. Presence of abnormal movement during lumbar ROM
      • May represent the inability to control lumbar motion, indicating a need for stabilization exercises
    • 4. Positive prone instability test
      • The idea behind this variable is that if pain is present on passive testing of the vertebral levels but disappears when the spinal extensors are active, then muscle activity may be able to stabilize the spinal segment and reduce pain
    • Best rule for predicting success was the presence of ≥ 3 of the 4 variables

    • Out of nine variables retained as potential predictors of failure, four were kept in the final model
    • Best rule for predicting failure was the presence of ≥ 2 of the 4 variables
    • 1. FABQ score > 8 (Fear Avoidance Beliefs Questionnaire)
      • Best individual screening test for prediction of failure
    • 2. Aberrant movements absent
    • 3. Negative prone instability test
    • 4. No hypermobility during lumbar spring testing
  • Conclusions
    • This study was successful in deriving CPRs for the use of stabilization exercises in patients with LBP. However, they still need to be validated.
    • Clinical decision making could be improved if clinicians had a CPR for identifying individuals who would respond successfully to a stabilization intervention.
    • The ability to predict patients who would fail a stabilization exercise program would allow clinicians to consider alternative interventions.
    • The results from this study need to be replicated in another sample for confirmation and examined in a RCT before they can be recommended for widespread use.


Clinical prediction rules in the physiotherapy management of low back pain: A systematic review (4)

  • Study Design: systematic review
  • Objective: Identify, evaluate and determine CPR in physical therapy for treating LBP.
  • Background
    • There has been a recent increase in studies involving development and application of CPRs in relation to treating LBP. The outcomes of some studies may be unique to the study population or other characteristics involved in that study. Criteria for a CPR cannot be validated from a single study. Thus, a series of studies that test both internal and external validity of the rule is required and needs to be applied across a broad range of clinical environments.
  • Methods
    • A systematic literature search was conducted from 1990 to January 2010 using MEDLINE, EMBASE, CINAHL, AMED and the Cochrane Database of Systematic Reviews. All research was gathered from two independent researchers who selected data using a two-phase procedure. Of the 7453 studies screened, 23 were chosen to be included in this systematic review. No restriction was placed upon criteria regarding clinical location, patient demographics or type of potential predictor variables used in treatment. The criteria for inclusion consisted upon the following requirements:
      1. Studies need to explicitly aim to develop one or more CPRs involving physiotherapy management of LBP. For this review, CPR was defined as containing two or more predictor variables.
      2. Providers need to have evident variation in practice when treating patients with LBP. This is required in order to remove the assumption that selection and assessment of potential predictor rules can be applied across a general population. Validation of their treatment strategy needs to be derived from current research and not solely dependent upon personal experience.
      3. Consisted with the definition of CPR and predictor variables were required to be independently meaningful.
      4. Diagnostic, prescriptive and prognostic studies investigating CPRs at any stage of their development (McGinn et al., 2000), derivation, validation, or impact-analysis, were included.
      • Screen Shot 2017-04-20 at 12.34.28 PM.png

  • Results
table 1.png
table 2.png
  • Conclusion
    • 25 unique rules were identified across 15 derivation and 8 validation studies. As a result of this systematic study there was no evidence found that enables any direct application of the identified CPRs. Further studies must be conducted in order to determine a valid clinical application for CPRs that can be applicable among generalized populations and setting for treatment of LBP.

Derivation and validation phase for the development of clinical prediction rules for rehabilitation in chronic nonspecific low back pain patients: study protocol for a randomized controlled trial (3)

  • Study Design: RCT
  • Objective: Objectives for this trial are: the derivation of CPRs to predict treatment response to three forms of exercise therapy for patients with nonspecific chronic low back pain (CLBP). Secondly, to validate a CPR for the three forms of exercise therapy for patients with nonspecific CLBP.
  • Background
    • Based on the European guidelines [2] nonspecific CLBP is defined as pain and discomfort localised below the costal margin and above the inferior gluteal folds, with or without referred leg pain, persisting for at least 12 weeks. Nonspecific LBP is not attributable to a known specific pathology.
    • There is a consensus that exercise therapy should be used as a therapeutic approach in CLBP but little consensus has been reached about the preferential type of therapy.
      • Due to the heterogeneity of the population no clear effect of specific therapy interventions are found.
      • A specific subgroup of the investigated population will benefit from the intervention and another subgroup will not benefit, looking at the total investigated population no significant effects can be found.
    • The question is the development (derivation) and validation of a CPR for the choice of an exercise therapy type (motor control therapy, general active exercise therapy and isometric training therapy) in nonspecific CLBP patients. Based on the derived CPR, subgroups will be made, ultimately leading to detection of patients with a high chance for success with a certain type of exercise therapy. The CPR will be developed according to the correct methodological phases.
  • Methods
    • There are three phases when developing a CPR. This study is only conducting research on the first two phases. They are the derivation phase and validation phase.
      • Derivation phase: CPR is derived from a number of variables that have predictive potential for therapy outcome. These variables are obtained from baseline measurements and have a multidimensional character. Data analysis is being used to calculate the most powerful combination of these variables to finally form the new derived CPR
      • external image ttF-wWFoaP89BN9FR3aIwMogUvGNsZGO7nQqAbr2FINbNCOgZfh3gHa1hd-tKPdYc5ftG71qe_0SRfUKhd8oANshGb6Eh212ve7Nmbi1xyMK1QF8fCJAu5oEqdx3xScq86TLbwG0
      • Validation phase: CPR will be applied in a new study population. This application will result in matched patients and in unmatched patients. The treatment success of both groups will be compared. The hypothesis is that treatment success will be higher in the matched group.
      • external image HELscvZ9N2wHxbwUiUQkuD7fL6ZNqRdlwmLmtgkroQqwzU9E5lDsjoAJrRVgfMhGG7k_i3SujoXaHMKIVVBgmdcIKvwitj1Zghpm0jzuZ7a8RAwUCkM7RJKiaLCJFOx51Z0smP_J
    • Patient criteria:
Inclusion Criteria
Rationale
18-65 years old
Chronic low back pain in older adults is more likely to have specific causes (e.g., spinal canal stenosis)
Current nonspecific low back pain persisting ≥ 3 months
Condition studies is specifically chronic
Consulted a doctor during the last month for persistent low back pain
Back pain severe enough and motivation from the patient themself to seek treatment
Dutch fluency sufficient to follow treatment instructions and answer survey questions
Fully informed consent and data collection

Exclusion Criteria
Rationale
Spinal canal stenosis
Back pain possibly due to specific disease
Spondylolisthesis

Spondylitis

Large herniated disc sciatica

Radiating pain below the knee

Previous back surgery

History of vertebral fracture

Malignancy

Muscle, nerve, skin, or joint diseases

Known pregnancy
Pregnancy-related low back pain is different in etiology and time course than the target condition for the study (nonspecific chronic low back pain)
Lack of consent
Research policy
    • Measurements
      • Measurement of impairment
      • Measurement of limitations in activities and participation
      • Measurement of contextual factors
    • Follow ups
      • There will be short term and long term follow ups. The short term follow ups will be conducted at baseline and at nine weeks. The long term follow up will be conducted at 6 months and a year.

Measures
0 weeks
9 weeks
6 months
1 year
Primary outcome:




MODI
X
X
X
X
Secondary outcome




Impairments:




Duration low back pain*
X
X
X
X
Pelvis impairments*
X
X


Respiratory impairments*
X
X


PIT
X
X


SLR
X
X


Beighton scale
X
X


ASLR
X
X


SKET
X
X


Waiters bow
X
X


Pelvic tilt
X
X


Side support test
X
X


Extensor endurance test
X
X


Active sit up
X
X


VAS*
X
X
X
X
Secondary outcome




Activities and participation:




RMDQ
X
X


SF-36
X
X


Hours physical activity/week*
X
X
X
X
Secondary outcome




contextual factors:




Tampa scale
X
X
X
X
FABQ
X
X


Gender*
X



Age*
X
X
X
X
Height*
X



Weight*
X
X
X
X
BMI*
X
X


Smoking*
X
X


Profession*
X
X
X
X
Previous therapy*
X



Comorbidity*
X
X



    • Intervention
      • Patients will be randomly assigned to be treated twice a week for nine weeks in one of three categories: motor control therapy, general active exercise therapy and isometric training therapy. A treatment protocol was developed for each category to ensure therapist provide the same treatment to each patient. They will also have to fill out a diary after each session. This treatment protocol will be used for both derivation and validation.
      • Motor Control Therapy: Focuses on stabilizing muscles and global muscles that help with spinal stability. Patients with impaired spinal stability are at risk for developing LBP. There are three levels within this intervention patients must pass throughout their 9 weeks. Level one focuses on stabilizing muscles level two emphasizes global muscles and level three is a combination of both with a functional component.
      • General Active Exercise Therapy: Generic exercise program for LBP. Doing aerobic exercises for the UE and LE while stretching and strengthening the back and abdominals
      • Isometric Training Therapy: This will follow the Tergumed™ training protocol. It is a progressive resistance isometric training program that targets the lumbar trunk muscles in all ROM.

Treatment regimens:
Motor control therapy
General active exercise therapy
Isometric training therapy
Total intervention time
70 minutes
70 minutes
70 minutes
Warming up
10 minutes
10 minutes
10 minutes
Cooling down
10 minutes
10 minutes
10 minutes
Regime specific intervention time
50 minutes
50 minutes
50 minutes
Intensity
Low load
60% 1RM
30-40% MIS
Number of sets
2
3
2
Number of repetitions
20
20
20
Duration of one repetition
6 seconds
-
5 seconds
Rest between sets
30 seconds
30 seconds
30 seconds
    • Power Analysis
      • Derivation phase: A two-tailed hypothesis was used to calculate power. It’s estimated that ~120 patients will be needed to detect a MCID with the power set at 80%.
      • Validation phase: The same interventions will be used but different patients will be needed. This looks at the success of the matched vs. unmatched treatment. Given the intent of this study a one-tailed hypothesis will be used to calculate power. For this study only 105 patients will be needed to detect a MCID with a power of 80%.
    • Data Analysis
      • Derivation phase: The intent of this is to find predictive variables for successful treatment using baseline measures. Multiple variables are used. Variables with a significant P of <.10 are kept for possible predictive values. The P is higher to avoid discarding any potential variables. Once potential predictive values are determined they are put into a multiple linear regression table to pick out the optimal predictors. Variables are kept if the significance of P is <.05. Variables kept are used to create CPR’s. This statistical analysis is performed on each group.
      • Validation phase: New patients will be randomly placed into an intervention and categorized as “matched” or “unmatched” based on the CPR’s created in the derivation phase. Variables will be compared between groups to verify intergroup differences at baseline. A three-way repeated-measures analysis of variance will be performed with treatment group and classification subgroup as between-subject variables and time as the within-subject variable. The hypothesis for this phase is that outcome over time will not differ based on the randomized treatment group, or the classification subgroup, but will depend on the interaction between the treatment group and classification subgroup, such that patients randomized to matched treatment will have better outcomes than patients randomized to unmatched treatment. This hypothesis will be supported if the three-way interaction is significant, but the two-way interactions are not. Pairwise post-hoc comparisons will be performed at each follow-up period to further explore any significant interaction terms. In both the derivation and validation phase we will use an intention-to-treat analysis
  • Discussion
    • The purpose of this study is the derivation and validation of CPR in exercise therapy for nonspecific CLBP. There are several ways this protocol differs from other studies that have been done. First this study used randomized control trial with several treatment options. They also targets patients specifically with CLBP of at least three months. Lastly they intend to have a significant sample size based on their power analytics. This protocol only includes two of the three phases derivation and validation. The last phase, the impact phase, is where you see the implications and effects of the CPR on the public. The impact phase should be used to further this research.


So what? What is the clinical relevance?

  • There is not one single CPR for our patients who present with LBP, as these patients are heterogenous.
  • More research needs to be done on this topic, specifically on SI joint pain since the current evidence for this area is sparse.
  • As we see patients who present with LBP, we need to reference research that applies to their specific population and their presenting symptoms in order to more effectively manage their LBP.


References

  1. Bamford A, Nation A, Durrell S, Andronis L, Rule E, McLeod H. Implementing the Keele stratified care model for patients with low back pain: an observational impact study. BMC Musculoskeletal Disorders. 2017; 18.
  2. Childs M, Fritz J, Flynn T, Irrgang J, Johnson M, Majkowski M, Delitto A. A Clinical Prediction Rule to Identify Patients with Low Back Pain Most Likely To Benefit from Spinal Manipulation: A Validation Study. Annals of Internal Medicine. 2004; 141: 920-928.
  3. Denteneer L, Stassijns G, Hertogh W, Truijen S, Jansen N, Daele U. Derivation and validation phase for the development of clinical prediction rules for rehabilitation in chronic nonspecific low back pain patients: study protocol for a randomized controlled trial. Trial. 2015; 16.
  4. Haskins R, Rivett D, Osmotherly P. Clinical prediction rules in the physiotherapy management of low back pain: A systematic review. Manual Therapy. 2012; 17: 9-21.
  5. Hicks G, Fritz J, Delitto A, McGill S. Preliminary Development of a Clinical Prediction Rule for Determining Which Patients With Low Back Pain Will Respond to a Stabilization Exercise Program. Archives of Physical Medicine and Rehabilitation. 2005; 86: 1753-1762.
  6. Rimmalapudi V, Kumar S. Lumbar Radiofrequency Rhizotomy in Patients with Chronic Low Back Pain Increases the Diagnosis of Sacroiliac Joint Dysfunction in Subsequnt Follow-Up Visits. Pain Research and Management. 2017.