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

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

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

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

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

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

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

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

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

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

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

5th visit: foto assessment

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

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?

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

FOTO REPORT: Patient #2

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

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

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

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

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

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

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

Biopsychosocial Classifications Mechanical Psychosocial

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)

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

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

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%

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

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

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

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

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

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

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

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%

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 2007-2012 Secondary aim: Examine association level of MDT postgraduate training & patient outcomes Hypothesis Diplomats achieve better outcomes compared to those certified

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

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

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

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

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

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

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)

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

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

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)

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)

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

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.

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

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

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

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.

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)

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.

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.  

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.

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.

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

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

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

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

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)

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

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

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.

Results

Summary Tell a clinical story Foundation of clinical research Clinical Observations

Thank you