Lumbar Imaging with Reporting of Epidemiology (LIRE) Trial

LIRE Trial Overview

The overall goal of the LIRE Pragmatic Trial was to perform a large, pragmatic, randomized controlled trial to determine the effectiveness of a simple, inexpensive and easy to deploy intervention – of inserting epidemiological benchmarks into lumbar spine imaging reports– at reducing subsequent tests and treatments (including cross-sectional imaging (MR/CT), opioid prescriptions, spinal injections and surgery). This NIH Collaboratory-funded pragmatic clinical trial included 250,401 participants and was conducted at 98 primary care clinics at four healthcare systems across the nation. Inclusion in the trial required that PCPs initiated one of 10 lumbar spine imaging (XR, CT, MR) CPT codes for evaluation of back pain.  The intervention was inserting epidemiologic data into imaging reports using text that varied by imaging modality and patient age bracket.  Intervention deployment was conducted using a stepped-wedge design with 5 randomization waves at the clinic level, spaced 6 months apart.  Outcome data was passively collected via the electronic health record for a period spanning one year prior to index imaging to two years following index imaging. Utilization data include lumbar spine imaging reports, outpatient and inpatient encounters and diagnosis and procedure codes associated with those encounters, and prescriptions.

LIRE Trial Manuscripts

  • Lumbar Imaging with Reporting of Epidemiology (LIRE)- Protocol for a Pragmatic Cluster Randomized Trial (2015)
    PDF | On the Web
  • Systematic Literature Review of Imaging Features of Spinal Degeneration in Asymptomatic Populations (2015)
    PDF | On the Web
  • Use of PRECIS ratings in the National Institutes of Health (NIH) Health Care Systems Research Collaboratory (2016)
    PDF | On the Web
  • Pragmatic clinical trials embedded in healthcare systems: generalizable lessons from the NIH Collaboratory (2017)
    PDF | On the Web
  • Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes (2018)
    PDF | On the Web
  • Comparison of Natural Language Processing Rules-based and Machine-learning Systems to Identify Lumbar Spine Imaging Findings Related to Low Back Pain (2018)
    PDF | On the Web