back pain outcomes using longitudinal data (bold ... slides...mr stadnik/ 1998 17-60 61-71 52% 80%...

Back pain Outcomes using Longitudinal Data (BOLD):

Lessons for LIRE (and Other Pragmatic Trials)

Jeffrey (Jerry) Jarvik, M.D., M.P.H.Professor of Radiology and Neurological Surgery

Adjunct Professor Health Services and Pharmacy

Director, Comparative Effectiveness, Cost and

Outcomes Research Center (CECORC)

University of Washington

Disclosures• Physiosonix (ultrasound company)

– Founder/stockholder• Healthhelp (utilization review)

– Consultant• Evidence-based Neuroradiology (Springer)

– Co-Editor

• AHRQ: R01 HS019222-01

•NIH 1UH2AT007766-01

Acknowledgements-BOLD

Key PeopleUW

• Jerry Jarvik, MD,MPH- PI• Katie James, PA-C, MPH-Proj

Dir• Bryan Comstock, MS- Biostats• Nick Anderson, PhD-

Biomedical Informatics• Brian Bresnahan, PhD- Health

Economist• Patrick Heagerty, PhD- Biostat• Judy Turner, PhD-

Psychologist/Pain expert

Non-UW• Rick Deyo, MD, MPH-OHSU• Dan Cherkin, PhD-GHRI• Heidi Berthoud MPH- GHRI• Safwan Halabi, MD-HFHS• Dave Nerenz, PhD- HFHS• Dave Kallmes, MD- Mayo• Jyoti Pathak, PhD- Mayo• Patrick Luetmer, MD- Mayo• Andy Avins, MD MPH-KPNC

Inappropriate Imaging• 30-40% of imaging studies in the

U.S. may be inappropriatePicano E. Sustainability of medial imaging. BMJ. 2004;328:578-580

Background and Rationale• Lumbar spine imaging frequently

reveals incidental findings• These findings may have an

adverse effect on:–Subsequent healthcare utilization–Patient health related quality of life

Prevalence of Disc Degeneration in Normals

Modality Author/ Year

Age Range

Prev

MR Boden/ 1990

20-60 60-80

44% 93%

MR Stadnik/ 1998

17-60 61-71

52% 80%

MR Weishaupt/ 1998

20-50 72-100%

MR Jarvik/ 2001

35-70 91%

Disc Degeneration

Back pain Outcomes using Longitudinal Data (BOLD)

• CER for seniors with back pain• AHRQ funded- part of $1.1 billion

American Recovery and Reinvestment Act (ARRA)

BOLD CHOICE (Clinical and Health Outcomes Initiative in CE)

• Overall goal: establish registry to evaluate effectiveness, safety, and cost-effectiveness of interventions for pts > 65 with back pain

• Setting: HMO Research Network• Sites

– Kaiser Northern CA: Andy Avins– Henry Ford Health System Detroit: Dave Nerenz– Harvard Pilgrim/Vanguard Boston: Srdj Nedeljkovic

BOLD CHOICE: 3 Aims1. Establish BOLD registry2. Conduct observational cohort

study of early imaging3. Conduct RCT of epidural steroid

injections plus local anesthetic (LA) vs. LA alone

BOLD Aim 1: Registry Measures• 1) Roland-Morris Questionnaire• 2) 0-10 pain NRS-avg pain past 7d• 3) pain interference with activity (BPI)• 4) patient expectation re recovery• 5) PHQ-4 Depression/Anxiety• 6) EQ-5D• 7) Brief fall screen

BOLD Aim 2: Early Imaging Cohort• Observational cohort • Compare early to no early imaging in

elderly with new visit for LBP • Outcomes: Disability (RMDQ), pain,

subsequent resource utilization• Propensity score matching to control for

variables that affect receiving imaging

BOLD Aim 2: Early Imaging Cohort• Observational cohort • Compare early to no early imaging in

elderly with new visit for LBP • Outcomes: Disability (RMDQ), pain,

subsequent resource utilization• Propensity score matching to control for

variables that affect receiving imaging

Lumbar Imaging with Reporting of Epidemiology (LIRE) Proposed Study Flow

Primary Care Clinics With LBP Patients

Randomize Clinics

Macro with prevalence info

Outcomes Assessment-

Resource Utilization

No macro with prevalence info

Outcomes Assessment-

Resource Utilization

LIRE Primary Aim• To determine whether inserting age-

specific prevalence of imaging findings among asymptomatic subjects into lumbar spine imaging reports decreases back-related interventions (imaging, injections, surgeries, etc.) over the subsequent year

GHC Test Template

Intervention Text

Stepped Wedge Design

Stepped Wedge Design

• A one-way cluster, randomized crossover design

• Temporally spaces the intervention • Assures that each participating clinic

eventually receives the intervention• Within site comparison controls for

between site differences (eg- CPT coding)

LIRE Sites• Kaiser Permanente

Northern California

– Andy Avins, MD MPH

• Henry Ford Health System– Safwan Halabi, MD

• Group Health Research Institute/GHC– Dan Cherkin, PhD

• Mayo Clinic Health System– Dave Kallmes, MD

Site CharacteristicsSite #

Primary Care

Clinics

# PCPs # Patients # Back Pain Visits (2011)

# L-spine

Imaging exams

Kaiser PNCA 17 1096 2,430,000 149,300 44790Henry Ford 26 230 187,000 23,900 7170Group Health 24 303 347,000 37,700

11310Mayo Clinic 61 269 1,500,000 106,700 32010Total 128 1,898 4,464,000 317,600 95,280

LIRE Aims/Working Groups and Leaders1. Refinement of benchmark text

Jerry Jarvik, MD MPH2. Implementation of cluster randomization

Bryan Comstock, MS3. Spine intervention intensity measure

Brian Bresnahan, PhD4. Electronic data capture

Nick Anderson, PhD5. IRB, Protocols, Subcontracts

Katie James, PA, MPH

LIRE Aims/Working Groups and Leaders1. Refinement of benchmark text

Jerry Jarvik, MD MPH2. Implementation of cluster randomization

Bryan Comstock, MS3. Spine intervention intensity measure

Brian Bresnahan, PhD4. Electronic data capture

Nick Anderson, PhD5. IRB, Protocols, Subcontracts

Katie James, PA, MPH

LIRE Aim 3• Develop/validate a composite

measure of spine intervention intensity-a single metric of overall intensity of resource utilization for spine care

Aim 3 Progress• Working with site programmers to

pull CPT data• Already established data pulls for 2

sites• Constructed density plots of CPT

–For QC checks–Compare site use of codes

BOLD: CPT Code Frequencies By SiteSite 1Site 2Site 3

BOLD: Density Plot of Radiology CPT Code Frequencies By Site for QC

Site 1Site 2Site 3

BOLD: Density Plot of Surgery CPT Code Frequencies By Site

Site 1Site 2Site 3

Aim 3 (cont.)• Converted CPT codes to RVUs as

our primary metric of back-related utilization–Used total RVU (tech + pro)–Did not use geographic adjuster–Use 2012 values using CMS look-up

files

Converting CPTs to RVUs

• Validate CPT conversion by directly pulling RVUs from one site

Example RVU ValuesHCPCS DESCRIPTION TOTAL 2012

RVUs72100 X-ray exam of

lower spine1.07

99214 Office/outpatient visit level 4

2.26

72131 CT lumbar spine w/o dye

6.27

72148 MRI lumbar spine w/o dye

11.31

63047 Laminectomy 32.89

CPT Proportion of RVUs

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

99214 99233 99223 99213 99285 72148 99215 G0202

Site 1Office level IV

Subsequent hospital care

Initial hospital care Office

level III

ED visit MR Lspine

Office level V

Screening mammo

dig

Challenges• CPT counts seem to differ by site

– Step wedge design helps to address this since before-after comparison is within site

– Using only back-related RVUs improves accuracy/reliability using algorithm developed by Martin et al at Dartmouth

• Different pharmacy data systems (e.g. not all sites have Rx filled data)– Within-system comparisons will be valid

Challenges• System differences will always be present

in large pragmatic trials• When do pragmatic trials become meta-

analysis of parallel trials?

Key People to ThankUW

• Katie James, PA-C, MPH-Proj Dir

• Bryan Comstock, MS- Biostats• Nick Anderson, PhD-

Biomedical Informatics• Brian Bresnahan, PhD- Health

Economist• Patrick Heagerty, PhD- Biostat• Judy Turner, PhD-

Psychologist/Pain expert

Non-UW• Rick Deyo, MD, MPH-OHSU• Dan Cherkin, PhD-GHRI• Heidi Berthoud. MPH- GHRI• Safwan Halabi, MD-HFHS• Dave Nerenz, PhD- HFHS• Dave Kallmes, MD- Mayo• Jyoti Pathak, PhD- Mayo• Patrick Luetmer, MD- Mayo• Andy Avins, MD MPH-KPNC