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Background: Modifying physician behavior to more closely align with guideline-based care can be challenging. Few effective strategies resulting in appropriate spine-related health care have been reported. The Lumbar Imaging With Reporting of Epidemiology (LIRE) intervention did not result in reductions in spine care but did in opioid prescriptions written., Objectives: To estimate organizational resource needs and costs associated with implementing a pragmatic, decision support-type intervention that inserted age- and modality-matched prevalence information for common lumbar spine imaging findings, using site-based resource use data from the LIRE trial., Research Design: Time and cost estimation associated with implementing the LIRE intervention in a health organization., Subjects: Providers and patients assessed in the LIRE trial., Measures: Expected personnel costs required to implement the LIRE intervention., Results: Annual salaries were converted to daily average per person costs, ranging from $400 to $2,200 per day (base case) for personnel (range: $300-$2,600). Estimated total average cost for implementing LIRE was $5,009 (range: $2,651-$12,020), including conducting pilot testing with providers. Costs associated with a small amount of time for a radiologist (6-12 hours) and imaging-ordering providers (1-8 hours each) account for approximately 75% of the estimated total cost., Conclusions: The process of implementing an intervention for lumbar spine imaging reports containing age- and modality-appropriate epidemiological benchmarks for common imaging findings required radiologists, imaging-ordering providers, information technology specialists, and limited testing and monitoring. The LIRE intervention seems to be a relatively low-cost, evidence-based, complementary tool that can be easily integrated into the reporting of spine imaging., (Copyright © 2021. Published by Elsevier Inc.)

Importance: Lumbar spine imaging frequently reveals findings that may seem alarming but are likely unrelated to pain. Prior work has suggested that inserting data on the prevalence of imaging findings among asymptomatic individuals into spine imaging reports may reduce unnecessary subsequent interventions., Objective: To evaluate the impact of including benchmark prevalence data in routine spinal imaging reports on subsequent spine-related health care utilization and opioid prescriptions., Design, Setting, and Participants: This stepped-wedge, pragmatic randomized clinical trial included 250 401 adult participants receiving care from 98 primary care clinics at 4 large health systems in the United States. Participants had imaging of their backs between October 2013 and September 2016 without having had spine imaging in the prior year. Data analysis was conducted from November 2018 to October 2019., Interventions: Either standard lumbar spine imaging reports (control group) or reports containing age-appropriate prevalence data for common imaging findings in individuals without back pain (intervention group)., Main Outcomes and Measures: Health care utilization was measured in spine-related relative value units (RVUs) within 365 days of index imaging. The number of subsequent opioid prescriptions written by a primary care clinician was a secondary outcome, and prespecified subgroup analyses examined results by imaging modality., Results: We enrolled 250 401 participants (of whom 238 886 [95.4%] met eligibility for this analysis, with 137 373 [57.5%] women and 105 497 [44.2%] aged >60 years) from 3278 primary care clinicians. A total of 117 455 patients (49.2%) were randomized to the control group, and 121 431 patients (50.8%) were randomized to the intervention group. There was no significant difference in cumulative spine-related RVUs comparing intervention and control conditions through 365 days. The adjusted median (interquartile range) RVU for the control group was 3.56 (2.71-5.12) compared with 3.53 (2.68-5.08) for the intervention group (difference, -0.7%; 95% CI, -2.9% to 1.5%; P = .54). Rates of subsequent RVUs did not differ between groups by specific clinical findings in the report but did differ by type of index imaging (eg, computed tomography: difference, -29.3%; 95% CI, -42.1% to -13.5%; magnetic resonance imaging: difference, -3.4%; 95% CI, -8.3% to 1.8%). We observed a small but significant decrease in the likelihood of opioid prescribing from a study clinician within 1 year of the intervention (odds ratio, 0.95; 95% CI, 0.91 to 1.00; P = .04)., Conclusions and Relevance: In this study, inserting benchmark prevalence information in lumbar spine imaging reports did not decrease subsequent spine-related RVUs but did reduce subsequent opioid prescriptions. The intervention text is simple, inexpensive, and easily implemented., Trial Registration: ClinicalTrials.gov Identifier: NCT02015455.

Rationale and Objectives: To evaluate a natural language processing (NLP) system built with open-source tools for identification of lumbar spine imaging findings related to low back pain on magnetic resonance and x-ray radiology reports from four health systems., Materials and Methods: We used a limited data set (de-identified except for dates) sampled from lumbar spine imaging reports of a prospectively assembled cohort of adults. From N = 178,333 reports, we randomly selected N = 871 to form a reference-standard dataset, consisting of N = 413 x-ray reports and N = 458 MR reports. Using standardized criteria, four spine experts annotated the presence of 26 findings, where 71 reports were annotated by all four experts and 800 were each annotated by two experts. We calculated inter-rater agreement and finding prevalence from annotated data. We randomly split the annotated data into development (80%) and testing (20%) sets. We developed an NLP system from both rule-based and machine-learned models. We validated the system using accuracy metrics such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC)., Results: The multirater annotated dataset achieved inter-rater agreement of Cohen's kappa > 0.60 (substantial agreement) for 25 of 26 findings, with finding prevalence ranging from 3% to 89%. In the testing sample, rule-based and machine-learned predictions both had comparable average specificity (0.97 and 0.95, respectively). The machine-learned approach had a higher average sensitivity (0.94, compared to 0.83 for rules-based), and a higher overall AUC (0.98, compared to 0.90 for rules-based)., Conclusions: Our NLP system performed well in identifying the 26 lumbar spine findings, as benchmarked by reference-standard annotation by medical experts. Machine-learned models provided substantial gains in model sensitivity with slight loss of specificity, and overall higher AUC., (Copyright © 2018 The Association of University Radiologists. All rights reserved.)

Electronic medical record (EMR) systems provide easy access to radiology reports and offer great potential to support quality improvement efforts and clinical research. Harnessing the full potential of the EMR requires scalable approaches such as natural language processing (NLP) to convert text into variables used for evaluation or analysis. Our goal was to determine the feasibility of using NLP to identify patients with Type 1 Modic endplate changes using clinical reports of magnetic resonance (MR) imaging examinations of the spine. Identifying patients with Type 1 Modic change who may be eligible for clinical trials is important as these findings may be important targets for intervention. Four annotators identified all reports that contained Type 1 Modic change, using N = 458 randomly selected lumbar spine MR reports. We then implemented a rule-based NLP algorithm in Java using regular expressions. The prevalence of Type 1 Modic change in the annotated dataset was 10%. Results were recall (sensitivity) 35/50 = 0.70 (95% confidence interval (C.I.) 0.52-0.82), specificity 404/408 = 0.99 (0.97-1.0), precision (positive predictive value) 35/39 = 0.90 (0.75-0.97), negative predictive value 404/419 = 0.96 (0.94-0.98), and F1-score 0.79 (0.43-1.0). Our evaluation shows the efficacy of rule-based NLP approach for identifying patients with Type 1 Modic change if the emphasis is on identifying only relevant cases with low concern regarding false negatives. As expected, our results show that specificity is higher than recall. This is due to the inherent difficulty of eliciting all possible keywords given the enormous variability of lumbar spine reporting, which decreases recall, while availability of good negation algorithms improves specificity.

Background: Diagnostic imaging is often the first step in evaluating patients with back pain and likely functions as a "gateway" to a subsequent cascade of interventions. However, lumbar spine imaging frequently reveals incidental findings among normal, pain-free individuals suggesting that treatment of these "abnormalities" may not be warranted. Our prior work suggested that inserting the prevalence of imaging findings in patients without back pain into spine imaging reports may reduce subsequent interventions. We are now conducting a pragmatic cluster randomized clinical trial to test the hypothesis that inserting this prevalence data into lumbar spine imaging reports for studies ordered by primary care providers will reduce subsequent spine-related interventions., Methods/design: We are using a stepped wedge design that sequentially randomizes 100 primary care clinics at four health systems to receive either standard lumbar spine imaging reports, or reports containing prevalence data for common imaging findings in patients without back pain. We capture all outcomes passively through the electronic medical record. Our primary outcome is spine-related intervention intensity based on Relative Value Units (RVUs) during the following year. Secondary outcomes include subsequent prescriptions for opioid analgesics and cross-sectional lumbar spine re-imaging., Discussion: If our study shows that adding prevalence data to spine imaging reports decreases subsequent back-related RVUs, this intervention could be easily generalized and applied to other kinds of testing, as well as other conditions where incidental findings may be common. Our study also serves as a model for cluster randomized trials that are minimal risk and highly pragmatic., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background and Purpose: Intravertebral clefts have long been considered as pathognomonic for avascular necrosis and as a rare cause of compression fracture. We have observed unsuspected clefts opacifying frequently during vertebroplasty. Our purpose in this study was to determine the incidence of these clefts in symptomatic osteoporotic compression fractures, assess the sensitivity of MR imaging and conventional radiography in the detection of these clefts, and determine whether there is any prognostic significance of these clefts in patients treated with vertebroplasty., Methods: Retrospective chart reviews were conducted of 135 vertebroplasty procedures performed during a 2-year period. MR images and conventional radiographs were reviewed for the presence of clefts defined as fluid-filled cavities on MR images or gas-filled cavities on conventional radiographs. Digital radiographs obtained at the time of the procedure were inspected for the presence of opacified clefts. Imaging findings were correlated with subjective pain scores documented before the procedure and at 1 week, 1 month, 6 months, and 12 months after vertebroplasty., Results: Two hundred thirty-six osteoporotic compression fractures were treated with polymethylmethacrylate in 125 patients. Thirty-one and eight-tenths percent of the fractures were noted to contain clefts at the time of vertebroplasty. Fluid-filled clefts were detected on preoperative MR images in only 52.8% of the fractures with opacified clefts at vertebroplasty. Gas-filled clefts were evident on preoperative conventional radiographs in only 11.4% of the fractures with opacified clefts at vertebroplasty. No significant difference was noted in numerical pain scores between the two populations at baseline or 1 week or 1 month after the procedure. Pain scores at 6 and 12 months after vertebroplasty showed a trend toward greater pain relief in patients with clefts, although the difference was not statistically significant. A sustained, statistically significant decrease in pain scores after treatment (P <.01) was noted in both groups., Conclusion: Intravertebral clefts are much more common than previously described and probably represent fracture nonunions. Imaging is not sensitive in detecting these clefts before vertebroplasty. We advocate complete filling of the cleft with cement during vertebroplasty to maximize stabilization of the fracture fragments. There is a trend toward greater pain relief being achieved 6 and 12 months after the procedure in patients with clefts that are opacified at the time of vertebroplasty.

Background: Information on the prevalence of common imaging findings among patients without back pain in spine imaging reports might affect pain medication prescribing for patients with back pain. Prior research on inserting this text suggested a small reduction in opioid prescribing.Objective: To evaluate the effect of epidemiologic information in spine imaging reports on non-opioid pain medication prescribing for primary care patients with back pain.Design: Post hoc analysis of the Lumbar Imaging with Reporting of Epidemiology cluster-randomized trial.Participants: A total of 170,680 patients aged ≥ 18 years from four healthcare systems who received thoracolumbar, lumbar, or lumbosacral spine imaging from 2013 to 2016 and had not received a prescription for non-opioid pain medication in the preceding 120 days.Intervention: Text of age- and modality-specific epidemiologic benchmarks indicating the prevalence of common findings in people without back pain inserted into thoracolumbar, lumbar, or lumbosacral spine imaging reports at intervention clinics.Main Measures: Primary outcomes: any non-opioid prescription within 90 days after index imaging, overall, and by sub-class (skeletal muscle relaxants, NSAIDs, gabapentinoids, tricyclic antidepressants, benzodiazepines, duloxetine).Secondary Outcomes: count of non-opioid prescriptions within 90 days, overall, and by sub-class.Key Results: The intervention was not associated with the likelihood of patients receiving at least one prescription for new non-opioid pain-related medications, overall (adjusted OR, 1.02; 95% CI, 0.97-1.08) or by sub-class. The intervention was not associated with the number of prescriptions for any non-opioid medication (adjusted incidence rate ratio [IRR], 1.02; 95% CI, 0.99-1.04). However, the intervention was associated with more new prescriptions for NSAIDs (IRR, 1.12) and tricyclic antidepressants (IRR, 1.11).Conclusions: Inserting epidemiologic text in spine imaging reports had no effect on whether new non-opioid pain-related medications were prescribed but was associated with the number of new prescriptions for certain non-opioid sub-classes.Trial Registration: ClinicalTrials.gov identifier: NCT02015455. [ABSTRACT FROM AUTHOR]