Abstract
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
Supplementary Information
The online version contains supplementary material available at 10.1007/s11606-021-06627-6.
KEY WORDS: analgesics, non-narcotic; radiology; spine; health services research
INTRODUCTION
Low back pain is the fifth most frequent reason for office visits in the USA and is a leading cause of years lived with disability globally and in the USA.1,2 The use of healthcare services for chronic low back pain has been increasing over time.3 Moreover, spine imaging is over-used in the USA, despite national guidelines recommending it only for those patients with low back pain who have certain clinical indications such as signs or symptoms indicating serious underlying conditions.4–6 Common degenerative spine imaging findings (e.g., disc height loss, bulging disks) are only weakly associated with symptoms;7,8 nevertheless, incidental, non-clinically meaningful imaging findings can result in patients and providers pursuing costly, ineffective, and potentially harmful treatments. Prior studies suggest that including information on the prevalence of common imaging findings among patients without back pain in spine imaging reports may reduce potentially unnecessary subsequent treatments in people with back pain, plausibly by reminding providers that such findings are common in those without back pain and are only weakly linked to symptoms.9,10
Our prior observational work found that patients whose physicians received epidemiologic text providing age- and modality-specific benchmarks of specific findings in their thoracic or lumbar spine magnetic resonance (MR) imaging reports were less likely to receive an opioid prescription at their follow-up clinic visit than were patients whose physicians had not received epidemiologic information.11 In addition, a large, prospective randomized controlled trial, the Lumbar Imaging with Reporting of Epidemiology (LIRE) trial, found a small but statistically significant reduction (odds ratio (OR), 0.95; 95% confidence interval (CI), 0.90–0.99) in the likelihood of receiving an opioid prescription within 90 days among patients with spine imaging whose providers were randomized to receive the epidemiologic text versus patients whose providers were not randomized to receive the text.12 A total of 29% of patients overall received an opioid prescription within 90 days in the LIRE trial.
However, it is unknown how insertion of such epidemiologic text in spine imaging reports might affect prescribing of medications other than opioids for pain. On one hand, after viewing the epidemiologic text in the spine imaging report, primary care providers (PCPs) could adopt a more conservative global approach to the prescribing of all pain-related medications. On the other hand, it is possible that a corollary to a reduction in opioid prescribing—as reported in the primary outcomes of the LIRE trial—might be an increase in the prescribing of non-opioid medications for pain. This could be due to the intervention resulting in a reduction in providers’ appraisal of patients’ pain as a serious problem warranting treatment with opioid therapy but at the same time providers wanting to provide some pharmacological treatment in response to the patient’s concerns. Our objective in this post hoc, hypothesis-generating analysis of the LIRE trial was to evaluate the effect of inserting epidemiologic text in spine imaging reports on prescribing non-opioid medications for pain in primary care patients.
METHODS
Setting and Data Sources
We conducted a post hoc analysis of data obtained from the LIRE study. LIRE was a pragmatic, multi-center, stepped-wedge, cluster-randomized trial conducted by assigning primary care clinics within four large healthcare systems to different start dates for receiving imaging reports containing several additional lines of text describing age- and modality-appropriate epidemiologic benchmarks for the prevalence of common degenerative imaging findings in individuals without back pain. Clinics received standard imaging reports prior to their assigned intervention date. The study protocol was published previously.9
Study Participants and Eligibility Criteria
Clinics and their patients at four integrated healthcare systems were enrolled: Kaiser Permanente, Northern California; Henry Ford Health System, Detroit, MI; Kaiser Permanente Washington, formerly Group Health Cooperative, Seattle, WA; and Mayo Clinic Health System, Rochester, MN. These systems have comprehensive electronic medical record (EMR) systems for capture of healthcare utilization and prescribing data.
At each healthcare system, we identified adult primary care clinics and their associated providers. We defined a LIRE provider as a PCP who was based at one clinic providing primary care and ordered at least one qualifying index imaging examination during the study period. We enrolled patients ≥ 18 years old if a PCP from an eligible clinic ordered a diagnostic imaging test of the thoracolumbar, lumbar, or lumbosacral spine between October 1, 2013, and September 30, 2016. Unless the patient had signed a declaration opting out of all research studies, we included all patients receiving eligible thoracolumbar, lumbar, or lumbosacral spine imaging studies at participating clinics who had not had spine imaging within the prior 12 months. For the current analysis, we were interested in the effect of the intervention on incident (new) non-opioid prescribing. Patients were excluded if they received a prescription (from any provider) for any non-opioid within 120 days before the date that the imaging report was finalized. Patients were not excluded based on prescription of any opioid. Prescriptions written between the date the image occurred and prior to the image report being finalized were not counted as prescriptions in the 120 days prior to index nor were they counted as outcomes. Prescriptions written on the same day as the image report being finalized were counted as outcomes.
All participating institutional review boards agreed that our study was minimal risk and granted waivers of both consent and Health Insurance Portability and Accountability Act (HIPAA) authorization.
LIRE Trial Randomization
We used a stepped-wedge randomization scheme, randomly assigning clinics at each system to begin receiving the intervention at one of five calendar times: April 2014, October 2014, April 2015, October 2015, and April 2016. We created tertiles of the number of PCPs in the clinics and from each tertile we randomly selected clinics using urn-based randomization (without replacement) stratified by system and clinic size. Thus, clinics of small, medium, and large sizes were equally represented in each randomization wave.
Intervention
The intervention text consisted of age- and modality-specific epidemiologic benchmarks indicating the prevalence of common findings in people without back pain (Online Supplemental Methods).12–14 Using a fully automated approach through either the radiology information system or EMR, the intervention text was inserted into thoracolumbar, lumbar, or lumbosacral spine imaging reports at intervention clinics. PCPs in control clinics received the usual imaging reports without the intervention text.
Baseline Measures
From the EMR, we assembled diagnosis (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] and Tenth Revision [ICD-10-CM]) and procedure (Current Procedural Terminology; CPT) data for patients from the 12 months prior to index imaging. We collected the following baseline variables: patient age category (18–39, 40–60, and ≥61 years), Charlson comorbidity index category (0, 1, 2, and ≥3),15,16 sex, index imaging modality (X-ray, CT, or MRI), study site, and clinic size. To assess trends over time in the rates of new prescriptions for non-opioid pain medications, overall, and for each of the non-opioid sub-classes, we also measured calendar time in 6-month intervals. Furthermore, we also extracted findings on the index images from radiology text reports using a validated natural language processing approach.17 These were categorized into three mutually exclusive groups:1 no findings;2 findings referenced in the intervention text that were likely clinically unimportant (e.g., disc bulge, disc space narrowing, annular fissure); and3 findings that were likely to be clinically important (e.g., moderate-severe spinal canal stenosis, nerve root compression, disc extrusion).
Outcome Measures
Each health system provided data from their electronic pharmacy databases for all prescription orders for study patients throughout the study period. A pharmacist (ZAM) reviewed the prescription data and identified each non-opioid medication of interest using drug information databases (IBM Micromedex® and UpToDate®) and categorized the non-opioid medications into their respective sub-classes (Appendix Table 1). Oral and topical dosage forms were included for analysis.
Our primary outcomes were non-opioid pain medication prescriptions (none versus any) by LIRE providers (although not necessarily the same provider who ordered the patient’s index image) for a study patient within the first 90 days after the index imaging report for the patient was finalized, overall as well as by sub-class. We did not assess whether prescriptions were filled because the intervention targeted physicians, not patients. Moreover, incomplete prescription fill data across sites prevented analysis of such outcomes. We defined non-opioid, pain-related medications as skeletal muscle relaxants, non-steroidal anti-inflammatory drugs (NSAIDs), gabapentinoids, tricyclic antidepressants, benzodiazepines, and duloxetine. Our secondary outcomes were the total counts of the number of non-opioid prescriptions by LIRE providers within the first 90 days, overall as well as by sub-class.
Statistical Analysis
We calculated descriptive statistics for prescriptions for each non-opioid medication by demographic and clinical variables. We also examined the number and percent of patients who had 1, 2, or >2 prescriptions for each non-opioid medication within 90 days after the imaging report finalization date.
To evaluate the impact of inserting epidemiologic data into an imaging report, we used generalized linear mixed effects models with robust standard errors for our primary outcome measures of whether patients had prescriptions for each of the non-opioids within 90 days after the imaging report finalization date. We used zero-inflated Poisson regression mixed effects models for our secondary outcome measures of the counts (including 0’s) of how many prescriptions were written within 90 days. All models were adjusted for patient age category, Charlson comorbidity index category, sex, index imaging modality, study site, clinic size, type of findings on index image, and calendar time. We also assessed secular trends in prescribing non-opioids overall and by non-opioid sub-class using the variable, calendar time. We included random effects for the study clinic and for the provider who ordered the index image. All analyses employed the intention-to-treat principle of analyzing each participant according to their clinic’s randomization status. SAS software version 9.4 (Cary, NC, USA) was used for all analyses.
RESULTS
Study Sample
Table 1 shows characteristics of patients and image-ordering providers in each study arm. The intervention and control groups were similar for these characteristics. Of the 238,886 patients who met the eligibility criteria for the LIRE trial, 170,680 patients did not receive a prescription for a non-opioid pain medication in the 120 days prior to their index image finalization date and thus were included in the analyses of the primary outcomes (Appendix Table 2). Of these, 48,697 (29%) were prescribed at least one new non-opioid pain-related medication within 90 days after the index image (Appendix Table 3). The most common non-opioid pain medication sub-class prescribed within 90 days was skeletal muscle relaxants (14% of patients), followed by prescription NSAIDs (8%), gabapentinoids (6%), tricyclic antidepressants (4%), and benzodiazepines (4%).
Table 1.
Characteristics of Patients and Image-Ordering Providers Stratified by Presence of Intervention Text on Index Lower Back Imaging Report
| Intervention text present N=86,984 |
Intervention text absent N=83,696 |
|
|---|---|---|
| Patient characteristics | ||
| Age, years | ||
| 18–39 | 16,438 (19%) | 15,654 (19%) |
| 40–60 | 31,107 (36%) | 30,854 (37%) |
| >60 | 39,439 (45%) | 37,188 (44%) |
| Comorbidity | ||
| 0 | 58,116 (67%) | 55,693 (67%) |
| 1 | 14,091 (16%) | 13,579 (16%) |
| 2 | 8043 (9%) | 7876 (9%) |
| 3+ | 6734 (8%) | 6548 (8%) |
| Female | 47,784 (55%) | 46,536 (56%) |
| Index image type | ||
| X-ray | 73,589 (85%) | 69,699 (83%) |
| CT | 265 (0.3%) | 272 (0.3%) |
| MRI | 13,130 (15%) | 13,725 (16%) |
| Site | ||
| Site (A) | 5238 (6%) | 4897 (6%) |
| Site (B) | 72,311 (83%) | 68,507 (82%) |
| Site (C) | 5617 (6%) | 5885 (7%) |
| Site (D) | 3818 (4%) | 4407 (5%) |
| Race | ||
| Asian | 10,712 (12%) | 10,643 (13%) |
| Black/African-American | 8006 (9%) | 8362 (10%) |
| Native Hawaiian/Pacific Islanders | 516 (0.6%) | 666 (0.8%) |
| Native American/Alaska Native | 551 (0.6%) | 525 (0.6%) |
| Multiracial | 386 (0.4%) | 326 (0.4%) |
| White | 55,430 (64%) | 52,924 (63%) |
| Unknown | 11,383 (13%) | 10,250 (12%) |
| Hispanic | 13,287 (15%) | 12,865 (15%) |
| Insurance status | ||
| Medicare | 33,020 (38%) | 31,415 (38%) |
| Medicaid/state-subsidized | 4005 (5%) | 3405 (4%) |
| Commercial (including Medicare supplements) | 48,410 (56%) | 47,268 (56%) |
| Veterans Administration | 96 (0.1%) | 78 (0.1%) |
| Self-pay | 444 (0.5%) | 556 (0.7% |
| Unknown/missing | 1009 (1%) | 974 (1%) |
| Image finding status | ||
| None | 20,861 (24%) | 20,872 (25%) |
| Clinically unimportant finding | 56,249 (65%) | 52,559 (63%) |
| Clinically important finding | 9874 (11%) | 10,265 (12%) |
| Image-ordering provider characteristics | ||
| Provider mean age (IQR) | 49.3 (43–55) | 49.5 (43–56) |
| Provider type | ||
| Doctor osteopathy | 6351 (7%) | 5762 (7%) |
| Medical doctor | 77,752 (89%) | 75,157 (90%) |
| Physician assistant/nurse practitioner | 2881 (3%) | 2777 (3%) |
| Provider female | 44,583 (51%) | 44,945 (54%) |
Primary Outcomes
Inserting epidemiologic text in spine imaging reports of primary care patients did not have a significant effect on prescribing non-opioid pain-related medications as measured dichotomously (i.e., any vs. none). This was true for the model including prescription of any non-opioid medication (adjusted OR, 1.02; 95% CI, 0.97–1.08), as well as for each non-opioid medication sub-class (skeletal muscle relaxant: adjusted OR 1.01, 95% CI 0.94–1.07; NSAID: adjusted OR 1.04, 95% CI 0.95–1.13; gabapentinoids: adjusted OR 1.01, 95% CI 0.93–1.11; tricyclic antidepressants: adjusted OR 1.04, 95% CI 0.94–1.15; benzodiazepine: adjusted OR 1.02, 95% CI 0.93–1.12) (Table 2). Of note, the model for duloxetine did not converge due to low numbers so duloxetine was not evaluated as its own sub-class, but was included in the any non-opioid model.
Table 2.
Effect of LIRE Intervention on Incident Non-Opioid Pain-Related Prescriptions Within 90 Days of Index Date, Overall, and by Medication Sub-class (N=170,680)
| Any non-opioid | Skeletal muscle relaxant | NSAID | Gabapentinoid | Tricyclic anti-depressant | Benzodiazepine | |
|---|---|---|---|---|---|---|
| Primary outcomes: Any prescription at 90 daysa | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) |
| LIRE intervention text present | 1.02 (0.97–1.08) | 1.01 (0.94–1.07) | 1.04 (0.95–1.13) | 1.01 (0.93–1.11) | 1.04 (0.94–1.15) | 1.02 (0.93–1.12) |
| Secondary outcome: Count of prescriptions at 90 days | IRR (95% CI) | IRR (95% CI) | IRR (95% CI) | IRR (95% CI) | IRR (95% CI) | IRR (95% CI) |
| LIRE intervention text present | 1.02 (0.99–1.04) | 0.97 (0.94–1.00) | 1.12 (1.07–1.18) | 1.06 (0.99–1.12) | 1.11 (1.03–1.20) | 0.98 (0.91–1.05) |
CI, confidence interval; IRR, incidence rate ratio; OR, odds ratio
All models adjusted for age, comorbidity, sex, index image type, site, clinic size, type (if any) of findings on index image, and time (years). The model for duloxetine did not converge due to low numbers
aOdds ratios and 95% confidence intervals for time (in years) variable: any non-opioid, 0.98 (0.96–0.995); skeletal muscle relaxant, 0.97 (0.95–1.00); NSAID, 1.01 (0.97–1.04); gabapentinoid, 1.11 (1.07–1.16); tricyclic antidepressant, 0.89 (0.85–0.94); benzodiazepine, 0.90 (0.86–0.95)
Secondary Outcomes
Inserting epidemiologic text in spine imaging reports of patients did not have a significant effect on the total number of new non-opioid pain-related prescriptions (adjusted incidence rate ratio [IRR] 1.02, 95% CI 0.99–1.04). However, the intervention had effects on non-opioid sub-classes (Table 2). The intervention was associated with 12% and 11% increases in the number of new prescriptions for NSAIDs (adjusted IRR 1.12, 95% CI 1.07–1.18) and tricyclic antidepressants (adjusted IRR 1.11, 95% CI 1.03–1.20), respectively. The intervention was not significantly associated with the number of new prescriptions for skeletal muscle relaxants (adjusted IRR 0.97, 95% CI 0.94–1.00), gabapentinoids (adjusted IRR 1.06, 95% CI 0.99–1.12), or benzodiazepines (adjusted IRR 0.98, 95% CI 0.91–1.05). Among patients who were prescribed non-opioid pain-related medications within 90 days, a large majority were written only one prescription (Table 3).
Table 3.
Number and Percentage of Patients with 0, 1, 2, or >2 Non-Opioid Prescriptions Within 90 Days After Imaging, Stratified by Presence of Intervention Text on Index Lower Back Imaging Report
| Non-opioid medication class | 0 Prescriptions | 1 Prescription | 2 Prescriptions | >2 Prescriptions | ||||
|---|---|---|---|---|---|---|---|---|
| Intervention text present N=86,984 |
Intervention text absent N=83,696 |
Intervention text present N=86,984 |
Intervention text absent N=83,696 |
Intervention text present N=86,984 |
Intervention text absent N=83,696 |
Intervention text present N=86,984 |
Intervention text absent N=83,696 |
|
| Any non-opioid | 61,922 (71%) | 60,061 (72%) | 17,367 (20%) | 16,188 (19%) | 5385 (6%) | 5136 (6%) | 2310 (3%) | 2311 (3%) |
| Skeletal muscle relaxant | 74,570 (86%) | 71,699 (86%) | 10,838 (12%) | 10,250 (12%) | 1229 (1%) | 1368 (2%) | 347 (0.4%) | 379 (0.5%) |
| NSAID | 79,369 (91%) | 76,871 (92%) | 6536 (8%) | 5820 (7%) | 908 (1%) | 851 (1%) | 171 (0.2%) | 154 (0.2%) |
| Gabapentinoid | 82,475 (95%) | 79,952 (96%) | 3691 (4%) | 2955 (4%) | 625 (0.7%) | 610 (0.7%) | 193 (0.2%) | 179 (0.2%) |
| Tricyclic anti-depressant | 83,888 (96%) | 80,683 (96%) | 2511 (3%) | 2370 (3%) | 476 (0.6%) | 527 (0.6%) | 109 (0.1%) | 116 (0.1% |
| Benzodiazepine | 84,271 (97%) | 80,690 (96%) | 2240 (3%) | 2422 (3%) | 343 (0.4%) | 402 (0.5%) | 130 (0.2%) | 182 (0.2%) |
Secular Trends in Non-Opioid Prescribing
Over the years of the study, decreases were observed in new non-opioid prescriptions overall (adjusted OR per year, 0.98; 95% CI, 0.96–0.995), tricyclic antidepressants (adjusted OR per year, 0.89; 95% CI, 0.85–0.94), and benzodiazepines (adjusted OR per year, 0.90; 95% CI, 0.86–0.95) (Table 2, footnote). In contrast, new prescribing of gabapentinoids increased over time (adjusted OR per year, 1.11; 95% CI, 1.07–1.16).
DISCUSSION
In post hoc analyses of the LIRE pragmatic, stepped-wedge, cluster randomized trial of inserting epidemiologic data into thoracolumbar, lumbar, or lumbosacral spine imaging reports, we observed no significant effect of the intervention on new prescriptions for non-opioid medications (measured dichotomously or as a count) within 90 days after imaging report finalization. Furthermore, the intervention did not affect new prescriptions for non-opioid pain-related medication sub-classes as measured dichotomously. However, we observed small but statistically significant effects of the intervention on the number of new prescriptions for certain non-opioid medication sub-classes. Specifically, the intervention was associated with an increase in the number of prescriptions for tricyclic antidepressants and NSAIDs, even after controlling for secular trends in prescribing. The effect on continuous but not dichotomous variables may be due to the increased statistical power to detect differences in continuous outcomes between intervention groups.18 However, it is also possible that the intervention had no effect on the likelihood of prescribing a non-opioid but did affect the prescribing behavior of PCPs who decided to prescribe a non-opioid.
It is not clear why the LIRE intervention was associated with an increase in the number of prescriptions for tricyclic antidepressants and NSAIDs. The LIRE trial’s main results included a small but statistically significant reduction in opioid prescribing. In the context of a small reduction in opioid prescribing, our finding of a small increase in the number of prescriptions for tricyclic antidepressants and NSAIDs suggests a more liberal treatment approach with certain non-opioids. However, it is important to note that we did not formally test a substitution hypothesis. These results should be interpreted in the context of the incidence of multiple prescriptions within 90 days for each of these sub-classes; only 1.2% (N=2084) and 0.7% (N=1226) of patients received 2+ prescriptions for an NSAID and TCA, respectively, within 90 days. While uncommon, these results suggest that PCPs might have prescribed a shorter days supply, with closer monitoring. Yet, we do not have data on provider rationale to support this hypothesis so further research is needed. Our finding of no effect of the intervention on benzodiazepine prescriptions might be due to the fact that they are primarily prescribed for non-pain conditions (e.g., anxiety) and thus less likely to be prescribed for pain that may result from spine abnormalities. Finally, although we did not find a significant effect of the intervention on gabapentinoid prescribing, our analysis of secular trends in prescribing non-opioids showed an 11% increase per year in new prescribing of gabapentinoids, which is consistent with other research showing increasing use of these medications.19,20
This study has several limitations. First, we were unable to measure patient-reported outcomes (e.g., pain and function). Thus, we are unable to infer whether any effects of the intervention on prescribed medications affected patient symptoms. We were also unable to measure whether pain-related medications were prescribed for pain or for a different indication (e.g., anxiety). In addition, we assessed only prescriptions for NSAIDs; some PCPs might have recommended to their patients use of over-the-counter NSAIDs or did not write a prescription for an NSAID because they knew their patient was already taking an over-the-counter NSAID. The observed intervention effect could be biased in either direction by this limitation, and our findings might have differed if we had been able to incorporate over-the-counter medications. Furthermore, because we were interested in the effect of the intervention on new prescribing, we did not examine changes in prescribing for patients who had prior prescriptions of non-opioid pain-related medications (as these patients were excluded). It is also important to note that because we were interested in the effect of the intervention on prescribing, we did not measure whether patients filled their prescriptions. Finally, we did not have access to data that would enable us to examine whether the LIRE intervention affected PCPs’ recommendations for non-pharmacological treatments such as physical therapy, acupuncture, and cognitive-behavioral therapy.
In conclusion, inserting epidemiologic text in spine imaging reports did not affect new prescriptions for non-opioid pain-related medications or the total number of new non-opioid prescriptions at 90 days after the index imaging report was finalized. However, the intervention was associated with increases in the number of new prescriptions for NSAIDs and tricyclic antidepressants. In light of the LIRE trial’s main results, including a small but significant reduction in the likelihood of prescribing an opioid, these findings suggest that the intervention might have resulted in substitution of non-opioid pain-related medications for opioids. To better understand effects of inserting epidemiologic text in spine imaging reports on prescribing, future research should explore physician-level decision making for analgesic prescribing to patients receiving thoracolumbar, lumbar, or lumbosacral spine imaging in primary care. Another potentially fruitful area for future research is the insertion of guideline-based treatment information tailored to spine imaging findings combined with other important clinical characteristics for certain patients.
Supplementary Information
(DOCX 18 kb)
Acknowledgements
Contributors: N/A
Funding
This work was supported within the National Institutes of Health (NIH) Health Care Systems Research Collaboratory by the NIH Common Fund through cooperative agreement U24AT009676 from the Office of Strategic Coordination within the Office of the NIH Director and cooperative agreements UH2AT007766 and UH3AR066795 from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS). It was also supported by the UW Clinical Learning, Evidence And Research (CLEAR) Center for Musculoskeletal Disorders funded by NIH/NIAMS P30AR072572. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Declaration
Conflict of Interest
The authors declare that they do not have a conflict of interest.
Footnotes
Prior Presentation
N/A
Publisher’s Note
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