1. Mark Werneke PT, MS, Dip MDT Annual User FOTO Conference April 2016
Using FOTO Data to Assist Clinicians Managing Patients with Low Back Pain: MDT Research
Mark Werneke PT, MS, Dip MDT
Annual User FOTO Conference
April 2016

2. Introduction Latest research projects
Clinical research team - MDT trained
Clinical scientists
Standardized FOTO data
Clinical exam results
Granular treatment taxonomy
Psychosocial & RX-based classifications
Latest research projects

3. Clinical Observations Essential!!!
Keep an open mind
&
Ask questions
Foundation of clinical research

4. Evidence-Based Medicine
“Integration of the best research evidence with clinical expertise applied to patient values to optimize patient outcomes and quality of life to achieve the highest level of excellence in practice.”
Sackett et al 2000
patient values
The answer to whose value is embedded in Sacket’s operational definition of EBM: read
so the definition is clearly linking our performance to patient values using PRO

5. Evidence-Based Practice
Revised APTA vision
Value = improved function achieved efficiently
Science
Clinical expertise

6. Evidence-Based Practice
Systematic patient feedback (PRO)
Enhances RX effectiveness
Value
To ultimately achieve this vision integrating systematic patient feedback i.e. PRO throughout the treatment process is essential to becoming a competent rehab professional or expert clinician and I will be going over 2 case studies to highlight this next
Science
Clinical expertise

7. Systematic patient feedback (PRO)
Two Case Studies
Systematic patient feedback (PRO)
Enhances RX effectiveness
Value
Science
Clinical expertise

8. Centralization Stages of treatment 1. Mechanical Reduction
2. Maintenance of
mechanical problem
3. Recovery of function
4. Prevention

9. Treatment First 3 visits Fourth visit Fifth visit
Mechanical reduction = extension principles
Confirm initial diagnosis
Maintenance of mechanical problem
Fourth visit
Recovery of function
Fifth visit
FOTO lumbar CAT (PRO) status assessment

10. Patient: #1 FOTO system report Intake FS = 68/100
Risk adjustment model
Predicted FS change= 3
Predicted Visits = 8
65 year old retiree with
chronic low back pain (>9 months)
Medical Hx
Enjoys dancing
Good health
Advil prn for pain

11. PATIENT #2 FOTO System report Medical Hx 50 year old with
Intake FS = 47/100
FOTO RA model
Predicted FS change= 18
Visits =8
Medical Hx
Good health
Motrin daily
50 year old with
subacute LBP (2 months)
Self employed

12. 5th visit: foto assessment

13. PATIENT #1: 5th visit FOTO system report FS change = 24 (3)
Lumbar FS score = 92/100
FS change = 24 (3)
# Visits = 5 (8)
Patient GROC = 7/7
Discharged

14. PATIENT #2: 5th visit FOTO system report FS change = 8 (18)
Lumbar FS score = 55/100
FS change = 8 (18)
# Visits = 5 (8)
What – I didn’t expect that?

15. WHAT IS PATIENT #2 TELLING ME?
FOTO System Report = Usable data
Clarification of patients perceptions
Essential information for enhancing recovery of function & effective patient management
regarding her ability performing various physical activities

16. FOTO REPORT: Patient #2

17. WHAT IS PATIENT #2 TELLING ME?
FOTO System Report = Usable data
Elevated fear avoidance beliefs
Centralization & fear characteristics impact outcomes
Consider both factors when managing patients with low back in an effort to optimize rehabilitation outcomes
Werneke MW et al. APMR 2009

18. PATIENT #2: PATIENT FEEDBACK
Know your data
Education
DP stretches
Manual techniques prn
Strengthening
Recovery of Function
Cognitive Behavioral
Reassurance
Physical activities
Problem solving

19. PATIENT #2: 8th visit FOTO system report FS change = 25(18)
FS score = 72/100
FS change = 25(18)
# Visits = 8 (8)
Patient GROC = 6/7
Discharged

20. Value of Case Studies? Clinical observations Each case = limited piece
of isolated information
Together tell us a story
and form a foundation
for clinical knowledge
& research gathered at
the patient bedside

21. Clinical Research Stories
Story #1
Story # 2
Effect of adding classification ratings to a robust baseline risk- adjusted prediction model for functional status outcomes at discharge
Change in self-efficacy and STarT levels when patients with low back are managed by physical therapists trained in McKenzie methods

22. Risk-Adjusted Model Classification
Background
Project #1
“Effect of Adding McKenzie Syndrome, Centralization, Directional Preference, and Psychosocial Classification Variables to a Risk-Adjusted Model Predicting Functional Status Outcomes for Patients with Lumbar Impairment”
Risk-Adjusted
Model
Classification

23. Classification Treatment-based classification paradigms
Clinically relevant & enhance patient outcomes
Recommended by recent clinical practice guidelines
Contemporary topic in today’s physical therapy literature

24. Biopsychosocial Classifications
Mechanical
Psychosocial

25. Psychosocial Models Patients referred to physical therapy
30-50% show clear signs of depressive symptoms (Haggman Phys Ther 2004, Werneke JOSPT 2011)
50% classified as high risk based on fear of physical and/or work activities (Werneke. Spine 2001)
Nearly 20% classified at high risk based on psychosocial distress (Werneke JOSPT 2011)

26. FOTO’s Risk-Adjusted Model
Based on sophisticated multivariate linear regression analyses: 8 important variables
Non modifiable (6)
age, duration of symptoms, surgery, payer source, gender, and number of functional comorbidities
Modifiable (2)
Patient’s intake functional status (i.e., severity)
Fear-avoidance beliefs of physical activities

27. Risk Adjusted Model Power
Model’s power or ability at intake to improve prediction outcomes & estimate patient’s rehabilitation potential

28. FOTO’s Model Power
Best models have moderate ability to predict outcomes R sq = 25% to 51%
Hilfiker et al. Systematic review. Eur Spine J 2007
FOTO = 35%

29. Increasing Model Power > 35%
FOTO Research
Hart Medicare 2006
Sample N= 106,000
Rsq = 35%
Deutscher JOSPT 2014
Hierarchical linear mixed model
Sample N = 20,000
R sq = 36%
Difficult in rehab setting!!!
Complexity of patients (diverse case-mix)
Not all known variables are understood nor included in physical therpy RA models

30. Complexity Risk-Adjustment Levels Playing Field
Risk adjustment levels the playing field so therapists treating more difficult patients are not penalized –
Levels Playing Field

31. Example: Why Risk Adjust?
Age
Acuity
Gender
Age
Acuity
Gender
Payer
Surgery
Complexity
Severity
Age
Acuity
Gender
Payer
Age
Acuity
Gender
Payer
Surgery
Age
Acuity
Gender
Payer
Surgery
Complexity
Age
Age
Acuity
Important statistical method
Considers factors not related to your interventions which influence your patient functional outcomes
Two well trained & experienced credentialed PTs
Clinician A better outcomes compared to clinician B
Important variables influencing
patient outcomes

32. Purpose: Project #1
Examine the effect of adding clinically relevant biopsychosocial classification variables to a robust risk adjusted model to see if model power to predict functional outcomes would improve
purpose of our study was therefore to determine the effect of adding classification variables to an intake risk-adjusted prediction model for FS outcomes at discharge from rehabilitation by adding (1) McKenzie classification system i.e., derangement, dysfunction, posture, and other, (2) Pain Pattern Classification subgroups, i.e., directional preference and/or centralization, and (3) psychosocial risk using the SCL BPPM, FABQ-W and FABQ-PA while controlling for important, recommended, and patient characteristics known for their association with FS outcomes

33. Research Hypotheses Clinically relevant classifications
1. Enhance model’s predictive power &
2. Assist clinicians in improving estimates about the patient’s likelihood for a good or poor outcome

34. Methods
Study Design: Retrospective analysis of longitudinal cohort of 2066 patients with lumbar impairments attending physical therapy.
Inclusion criteria
All patients (n=2066):
Completed FOTO LCAT survey at intake
Evaluated by participating therapists following McKenzie assessment methods

35. Methods Exclusion criteria Incomplete mechanical classifications (n=9)
McKenzie classification
Centralization classification
Incomplete psychosocial classifications (n=994)
FABQ-W sub-score
FABQ-PA sub-score
Depression symptoms
Somatization symptom

36. Methods The reality of everyday practice Psychosocial data:
It is difficult to gather psychosocial data despite best attempts to control for data documentation in our study
Psychosocial data:
FABQ-PA = 98%
FABQ-W = 70%,
Depression = 71%
Somatization= 70%

37. Methods Participating therapists characteristics Practice settings
Hospital-based (4)
Private practice (7)
Military (1)
Advanced McKenzie postgraduate education
7 diplomats
5 basic certification
Not all therapists collected data during the entire study period between
Secondary aim: Examine association level of MDT postgraduate training & patient outcomes Hypothesis Diplomats achieve better outcomes compared to those certified

38. Mechanical Classification Levels
McKenzie syndromes
Derangement
Reducible
Irreducible
Dysfunction
Other (stenosis, SI, chronic pain syndrome, surgery etc)
Pain patterns
Centralization
Directional preference

39. Psychosocial Classification Levels
Distress risk (low, medium, high)
SCL-90R subscales
Depression
Somatization
Fear avoidance risk (low vs. high)
FABQ
Physical activity
Work
Dionne’s risk algorithm

40. Building Our Baseline Model
Pain intensity (NRS)
Use of medication
FOTO’s current model (8)
Exercise history
Prior RX history
Treating therapist
Postgraduate MDT education

41. Treating Therapist Best Patient Outcomes
It is plausible that therapists who pursue advanced training may also have a stronger desire to professionally excel & develop a better patient-therapist alliance

42. Therapeutic Alliance Systematic review (13 papers) Results
Patient-therapist relationship was (+) associated with RX adherence, patient satisfaction, improved physical function both in geriatrics and those with chronic LBP
Hall et al Phys Ther 2012

43. Therapeutic Alliance The patient is comfortable approaching you
Collaborative relationship
Good communication
Patient has trust & confidence in your ability to function effectively on their behalf
We did not measure TA but used treating therapist as a proxy for TA
Vong et al APMR 2011
Resnik et al Phys Ther 2003

44. Treatment Guided by the patient’s symptomatic and mechanical responses
If no directional preference, an individualized active rehabilitation plan including CBT was prescribed at the discretion of the treating therapist.
Intervention taxonomy (72 items)
Standardized operational definitions
Chance-corrected agreement among McKenzie raters using this taxonomy was good to excellent (Werneke et al JMMT 2011)

45. Outcome measure Outcome
Change in functional status at discharge from therapy
FOTO’s LCAT

46. Data Analyses Missing data 3 sets of analyses
Compared those with & without all classification intake data
Compared those with complete intake/discharge data vs. those without discharge data
Distribution of completion rates by therapists

47. Data Analyses Prediction models
A series of stepwise R2 linear regression analyses
Examined change in R-sq value by adding each of the classification variables to our baseline model
Final models were cross-validated using the predicted residual sum of squares estimate (PRESS)

48. Results Completion rates: Missing data analyses: Outcomes:
Entire sample (n=2066) = 71%
Final sample (n=723) = 68%
Missing data analyses:
No evidence for systematic selection bias
Outcomes:
Average visits = 6 (mean 5.86, range 1 – 27)
FS change scores improved on average 22 points (4x > MCID)

49. Results: Model R-Square
Baseline model without classification = 40%
Important intake variables (7)
Intake FS
Age
Duration of symptoms
Surgical history
Number of medical comorbidities
Payer
Treating therapist

50. Results: Model Power Classification
McKenzie and psychosocial classification variables explained 18% and 26% respectively of the variability in outcomes without considering baseline variables
Classification data only resulted in small (3-4%) improvement in explaining variance in outcome after controlling for all baseline variables
Final model R-square = 44%
When classification data were analyzed without considering the contribution of our baseline variables, McKenzie and psychosocial classification variables explained 18% and 26% respectively of the variability in outcomes. Yet, classification data only resulted in small (3-4%) and non-significant improvement in explaining variance in functional status after controlling for all other important variables in the model.

51. Increasing Model Power > 35%
Final model’s R-square compared to previously published risk adjusted prediction models for physical therapy improved by 8-9%
Robust baseline model
Added effect of classification

52. Increasing Model Power > 35%
Model R-square = 50%
Gold standard
Robust prediction models
Rehabilitation setting
Diverse patient case-mix
Diverse practice settings
Large sample size
Gold standard in rehab setting – 50% in robust models using diverse patient case mix in rehab settings so achieving 44% R2 is noteworthy

53. Results: Within Classification
McKenzie classification
Reducible derangement (reference standard)
Patients classified as irreducible derangement, chronic pain syndrome, mechanically inconclusive), and other reported 5, 14, 5, and 7 less discharge functional status units, respectively (P<0.001)
First study demonstrating discriminative ability for certain patient subgroups using McKenzie classification

54. Results: Within Classification
Pain pattern classification
Centralization/DP (reference standard)
Patients classified as directional preference/non-centralization or no-directional preference/non-centralization reported 3 and 8 less discharge functional status units, respectively (P<0.001)
Findings consistent previously published data
Physical therapists trained in MDT methods could use either treatment-based classification method to help estimate rehabilitation prognosis and guide RX.

55. Results: Within Classification
Psychosocial classification
Low fear avoidance beliefs (reference standard)
No significant differences found
Fear-avoidance beliefs of physical activity
Fear-avoidance beliefs of work
Low distress risk classification (reference standard)
Patients classified as high risk reported 5 less discharge functional status units (P<0.001)

56. McKenzie Postgraduate Education
Patients treated by diplomats achieved significantly better FS outcomes compared to those therapists with certification (P<0.001)
Difference < MCID (5 points)
Collinearity between training level & treating therapist
Treating therapist stronger predictor of outcome
Treating therapist was a stronger predictor of change in function than level of McKenzie postgraduate education and only treating therapist was retained as IV in our models.

57. Key Points
Findings
Classification rating results support small (3-4%) improvement in explaining variance in functional outcomes after controlling for all other important variables in our baseline model.

58. Key Points Implications
Robust baseline predictive models capturing diverse patient and therapist characteristics are recommended for future research examining the additive predictive value of advanced postgraduate training & classification data.

59. Key Points Caution Impact of our predictive models require:
Validation in different clinical settings
Larger and different patient populations
Physical therapists trained and not trained in McKenzie methods.

60. Change Psychosocial data
Background
Project #2
Change Psychosocial data
“Does StarT Classification and Self Efficacy Improve When Patients with Low Back Pain Impairments Are Managed by Physical Therapists Trained in McKenzie Methods”
STarT
Self-efficacy

61. STarT Classification High Medium Low Main et al. Physiotherapy 2012
Psychosocial informed
practice based on
cognitive behavioral
principles
Medium
Tailored management plan using patient classification approach supported by evidence
Low
1 visit
intervention plan: education & self care: Back Book & education video
Main et al. Physiotherapy 2012

62. Background Self-Efficacy
“Describes a patient’s confidence in carrying out his or her normal & usual physical activities despite the pain experienced”
“Prominent psychological factor both as a predictor & mediator to understand the relationship between pain and disability
Foster et al Pain 2010

63. Background Foster et al Pain 2010 Depression Fear Catastophizing
Psychological cause
Somatizing
Illness perceptions
Anxiety
Self-efficacy
distress
Accident/chance
Diversion
Foster et al Pain 2010 reported that pain self efficacy was a better predictor of long term disability in primary care compared to 20 other psychosocial risk factors.
Beliefs of pain cause
immunity
Re-interpreting
Cognitive coping

64. Background FOTO optional surveys
Self-Efficacy (Anderson et al Pain 1995)
3 subscales
Physical (9 items)
Coping with symptoms (8 items)
Pain management of symptoms (5 items)
FOTO optional surveys
Self-Efficacy (Anderson et al Pain 1995)
3 subscales
Physical (9 items)
Coping with symptoms (8 items)
Pain management of symptoms (5 items)

65. Call for Research Self-efficacy
Identifying effective interventions to improve patients self-efficacy are required
Self-efficacy
Science
Given the evidence suggests that patients with higher self efficacy experience better quality of life, less disability, and lower health care use. In contrast, patients with low self efficacy report increased psychological distress, poor coping skills, more pain, and low tolerance for physical activities

66. Background McKenzie methods Evidence Distinct philosophical features
A behavioral modification approach
Patient education
Empowerment
Self-care
Biopsychosocial perspective on managing LBP
Takasaki et al J Ther & Rehab 2014
Outcomes between MDT & CBT interventions had similar favorable results
Moffett et al Rheum 2006
Al-Obaidi et al Phys Med Rehab 2011

67. Purposes
Determine:
1) the effect of managing patients with lumbar impairment by physical therapists credentialed in McKenzie methods on patients’ self-efficacy for coping & managing pain
2) the association between change in patients’self-efficacy & STarT classification between baseline and discharge from therapy and functional outcomes.

68. Results

69. Summary Tell a clinical story Foundation of clinical research
Clinical Observations

70. Thank you