Mortality with concurrent treatment with gabapentin and opioids among people with spine diagnoses in the U.S. Medicare population: a propensity-matched cohort study.

IMPORTANCE: Given the negative impact of opioid use on population health, prescriptions for alternative pain-relieving medications, including gabapentin, have increased. Concurrent gabapentin and opioid prescriptions are commonly reported in retrospective studies of opioid-related overdose deaths. OBJECTIVE: To determine whether people who filled gabapentin and opioid prescriptions concurrently ('gabapentin + opioids') had greater mortality than those who filled an active control medication (tricyclic antidepressants [TCAs] or duloxetine) and opioids concurrently ('TCAs/duloxetine + opioids'). We hypothesized that people treated with gabapentin + opioids would have higher mortality rates compared to people treated with TCAs/duloxetine + opioids. DESIGN: Propensity score-matched cohort study with an incident user, active control design. The median (maximum) follow-up was 45 (1093) days. SETTING: Population-based. PARTICIPANTS: Medicare beneficiaries with spine-related diagnoses 2017-2019. The primary analysis included those who concurrently (within 30 days) filled at least 1 incident gabapentin + at least 1 opioid or at least 1 incident TCA/duloxetine + at least 1 opioid. EXPOSURES: People treated with gabapentin + opioids (n=67,133) were matched on demographic and clinical factors in a 1:1 ratio to people treated with TCAs/duloxetine + opioids (n=67,133). MAIN OUTCOMES AND MEASURES: The primary outcome was mortality at any time. A secondary outcome was occurrence of a major medical complication at any time. RESULTS: Among 134,266 participants (median age 73.4 years; 66.7% female), 2360 died before the end of follow-up. No difference in mortality was observed between groups (adjusted hazard ratio (HR) and 95% confidence interval (CI) for gabapentin + opioids was 0.98 (0.90, 1.06); p=0.63). However, people treated with gabapentin + opioids were at slightly increased risk of a major medical complication (1.02 (1.00, 1.04); p=0.03) compared to those treated with TCAs/duloxetine + opioids. Results were similar in analyses (a) restricted to less than or = 30-day follow-up and (b) that required at least 2 fills of each prescription. CONCLUSIONS AND RELEVANCE: When treating pain in older adults taking opioids, the addition of gabapentin did not increase mortality risk relative to addition of TCAs or duloxetine. However, providers should be cognizant of a small increased risk of major medical complications among opioid users initiating gabapentin compared to those initiating TCAs or duloxetine.

Cost-Effectiveness of Artificial Intelligence-Based Opportunistic Compression Fracture Screening of Existing Radiographs.

PURPOSE: Osteoporotic vertebral compression fractures (OVCFs) are a highly prevalent source of morbidity and mortality, and preventive treatment has been demonstrated to be both effective and cost effective. To take advantage of the information available on existing chest and abdominal radiographs, the authors' study group has developed software to access these radiographs for OVCFs with high sensitivity and specificity using an established artificial intelligence deep learning algorithm. The aim of this analysis was to assess the potential cost-effectiveness of implementing this software. METHODS: A deterministic expected-value cost-utility model was created, combining a tree model and a Markov model, to compare the strategies of opportunistic screening for OVCFs against usual care. Total costs and total quality-adjusted life-years were calculated for each strategy. Screening and treatment costs were considered from a limited societal perspective, at 2022 prices. RESULTS: In the base case, assuming a cost of software implantation of $10 per patient screened, the screening strategy dominated the nonscreening strategy: it resulted in lower cost and increased quality-adjusted life-years. The lower cost was due primarily to the decreased costs associated with fracture treatment and decreased probability of requiring long-term care in patients who received preventive treatment. The screening strategy was dominant up to a cost of $46 per patient screened. CONCLUSIONS: Artificial intelligence-based opportunistic screening for OVCFs on existing radiographs can be cost effective from a societal perspective.

Factors Affecting Post-trial Sustainment or De-implementation of Study Interventions: A Narrative Review.

In contrast to traditional randomized controlled trials, embedded pragmatic clinical trials (ePCTs) are conducted within healthcare settings with real-world patient populations. ePCTs are intentionally designed to align with health system priorities leveraging existing healthcare system infrastructure and resources to ease intervention implementation and increase the likelihood that effective interventions translate into routine practice following the trial. The NIH Pragmatic Trials Collaboratory, funded by the National Institutes of Health (NIH), supports the conduct of large-scale ePCT Demonstration Projects that address major public health issues within healthcare systems. The Collaboratory has a unique opportunity to draw on the Demonstration Project experiences to generate lessons learned related to ePCTs and the dissemination and implementation of interventions tested in ePCTs. In this article, we use case studies from six completed Demonstration Projects to summarize the Collaboratory's experience with post-trial interpretation of results, and implications for sustainment (or de-implementation) of tested interventions. We highlight three key lessons learned. First, ineffective interventions (i.e., ePCT is null for the primary outcome) may be sustained if they have other measured benefits (e.g., secondary outcome or subgroup) or even perceived benefits (e.g., staff like the intervention). Second, effective interventions-even those solicited by the health system and/or designed with significant health system partner buy-in-may not be sustained if they require significant resources. Third, alignment with policy incentives is essential for achieving sustainment and scale-up of effective interventions. Our experiences point to several recommendations to aid in considering post-trial sustainment or de-implementation of interventions tested in ePCTs: (1) include secondary outcome measures that are salient to health system partners; (2) collect all appropriate data to allow for post hoc analysis of subgroups; (3) collect experience data from clinicians and staff; (4) engage policy-makers before starting the trial.

Frailty and Risk of Serious Infections in Patients With Rheumatoid Arthritis Treated With Biologic or Targeted-Synthetic Disease-Modifying Antirheumatic Drugs.

OBJECTIVE: It remains unknown whether frailty status portends an increased risk of adverse outcomes in patients with rheumatoid arthritis (RA) initiating biologic or targeted-synthetic (b/ts) disease-modifying antirheumatic drugs (DMARDs). The objective of our study was to evaluate the association between frailty and serious infections in a younger population of patients (<65 years old) with RA who initiated b/tsDMARDs. METHODS: Using MarketScan data, we identified new users of tumor necrosis factor inhibitors (TNFi), non-TNFi biologic DMARDs, or Janus kinase inhibitors (JAKi) between 2008 and 2019 among those with RA. Patients' baseline frailty risk score was calculated using a Claims-Based Frailty Index (≥0.2 defined as frail) 12 months prior to drug initiation. The primary outcome was time to serious infection; secondarily, we examined time-to-any infection and all-cause hospitalizations. We used Cox proportional hazards to estimate adjusted hazard ratios and 95% confidence intervals (95% CIs) and assessed the significance of interaction terms between frailty status and drug class. RESULTS: A total of 57,980 patients, mean (±SD) age 48.1 ± 10.1 were included; 48,139 (83%) started TNFi, 8,111 (14%) non-TNFi biologics, and 1,730 (3%) JAKi. Among these, 3,560 (6%) were categorized as frail. Frailty was associated with a 50% increased risk of serious infections (adjusted hazard ratio [95% CI] 1.5, 1.2-1.9) and 40% higher risk of inpatient admissions (1.4 [1.3-1.6]) compared with nonfrail patients among those who initiated TNFi. Frailty was also associated with a higher risk of any infection relative to nonfrail patients among those on TNFi (1.2 [1.1-1.3]) or non-TNFi (1.2 [1.0-1.4]) or JAKi (1.4 [1.0-2.0]). CONCLUSION: Frailty is an important predictor for the risk of adverse outcomes among patients with RA treated with biologic or targeted-synthetic DMARDs.

Red flags to screen for vertebral fracture in people presenting with low back pain.

BACKGROUND: Low back pain is a common presentation across different healthcare settings. Clinicians need to confidently be able to screen and identify people presenting with low back pain with a high suspicion of serious or specific pathology (e.g. vertebral fracture). Patients identified with an increased likelihood of having a serious pathology will likely require additional investigations and specific treatment. Guidelines recommend a thorough history and clinical assessment to screen for serious pathology as a cause of low back pain. However, the diagnostic accuracy of recommended red flags (e.g. older age, trauma, corticosteroid use) remains unclear, particularly those used to screen for vertebral fracture. OBJECTIVES: To assess the diagnostic accuracy of red flags used to screen for vertebral fracture in people presenting with low back pain. Where possible, we reported results of red flags separately for different types of vertebral fracture (i.e. acute osteoporotic vertebral compression fracture, vertebral traumatic fracture, vertebral stress fracture, unspecified vertebral fracture). SEARCH METHODS: We used standard, extensive Cochrane search methods. The latest search date was 26 July 2022. SELECTION CRITERIA: We considered primary diagnostic studies if they compared results of history taking or physical examination (or both) findings (index test) with a reference standard test (e.g. X-ray, magnetic resonance imaging (MRI), computed tomography (CT), single-photon emission computerised tomography (SPECT)) for the identification of vertebral fracture in people presenting with low back pain. We included index tests that were presented individually or as part of a combination of tests. DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data for diagnostic two-by-two tables from the publications or reconstructed them using information from relevant parameters to calculate sensitivity, specificity, and positive (+LR) and negative (-LR) likelihood ratios with 95% confidence intervals (CIs). We extracted aspects of study design, characteristics of the population, index test, reference standard, and type of vertebral fracture. Meta-analysis was not possible due to heterogeneity of studies and index tests, therefore the analysis was descriptive. We calculated sensitivity, specificity, and LRs for each test and used these as an indication of clinical usefulness. Two review authors independently conducted risk of bias and applicability assessment using the QUADAS-2 tool. MAIN RESULTS: This review is an update of a previous Cochrane Review of red flags to screen for vertebral fracture in people with low back pain. We included 14 studies in this review, six based in primary care, five in secondary care, and three in tertiary care. Four studies reported on 'osteoporotic vertebral fractures', two studies reported on 'vertebral compression fracture', one study reported on 'osteoporotic and traumatic vertebral fracture', two studies reported on 'vertebral stress fracture', and five studies reported on 'unspecified vertebral fracture'. Risk of bias was only rated as low in one study for the domains reference standard and flow and timing. The domain patient selection had three studies and the domain index test had six studies rated at low risk of bias. Meta-analysis was not possible due to heterogeneity of the data. Results from single studies suggest only a small number of the red flags investigated may be informative. In the primary healthcare setting, results from single studies suggest 'trauma' demonstrated informative +LRs (range: 1.93 to 12.85) for 'unspecified vertebral fracture' and 'osteoporotic vertebral fracture' (+LR: 6.42, 95% CI 2.94 to 14.02). Results from single studies suggest 'older age' demonstrated informative +LRs for studies in primary care for 'unspecified vertebral fracture' (older age greater than 70 years: 11.19, 95% CI 5.33 to 23.51). Results from single studies suggest 'corticosteroid use' may be an informative red flag in primary care for 'unspecified vertebral fracture' (+LR range: 3.97, 95% CI 0.20 to 79.15 to 48.50, 95% CI 11.48 to 204.98) and 'osteoporotic vertebral fracture' (+LR: 2.46, 95% CI 1.13 to 5.34); however, diagnostic values varied and CIs were imprecise. Results from a single study suggest red flags as part of a combination of index tests such as 'older age and female gender' in primary care demonstrated informative +LRs for 'unspecified vertebral fracture' (16.17, 95% CI 4.47 to 58.43). In the secondary healthcare setting, results from a single study suggest 'trauma' demonstrated informative +LRs for 'unspecified vertebral fracture' (+LR: 2.18, 95% CI 1.86 to 2.54) and 'older age' demonstrated informative +LRs for 'osteoporotic vertebral fracture' (older age greater than 75 years: 2.51, 95% CI 1.48 to 4.27). Results from a single study suggest red flags as part of a combination of index tests such as 'older age and trauma' in secondary care demonstrated informative +LRs for 'unspecified vertebral fracture' (+LR: 4.35, 95% CI 2.92 to 6.48). Results from a single study suggest when '4 of 5 tests' were positive in secondary care, they demonstrated informative +LRs for 'osteoporotic vertebral fracture' (+LR: 9.62, 95% CI 5.88 to 15.73). In the tertiary care setting, results from a single study suggest 'presence of contusion/abrasion' was informative for 'vertebral compression fracture' (+LR: 31.09, 95% CI 18.25 to 52.96). AUTHORS' CONCLUSIONS: The available evidence suggests that only a few red flags are potentially useful in guiding clinical decisions to further investigate people suspected to have a vertebral fracture. Most red flags were not useful as screening tools to identify vertebral fracture in people with low back pain. In primary care, 'older age' was informative for 'unspecified vertebral fracture', and 'trauma' and 'corticosteroid use' were both informative for 'unspecified vertebral fracture' and 'osteoporotic vertebral fracture'. In secondary care, 'older age' was informative for 'osteoporotic vertebral fracture' and 'trauma' was informative for 'unspecified vertebral fracture'. In tertiary care, 'presence of contusion/abrasion' was informative for 'vertebral compression fracture'. Combinations of red flags were also informative and may be more useful than individual tests alone. Unfortunately, the challenge to provide clear guidance on which red flags should be used routinely in clinical practice remains. Further research with primary studies is needed to improve and consolidate our current recommendations for screening for vertebral fractures to guide clinical care.

Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria.

RATIONALE AND OBJECTIVES: Spinal osteoporotic compression fractures (OCFs) can be an early biomarker for osteoporosis but are often subtle, incidental, and underreported. To ensure early diagnosis and treatment of osteoporosis, we aimed to build a deep learning vertebral body classifier for OCFs as a critical component of our future automated opportunistic screening tool. MATERIALS AND METHODS: We retrospectively assembled a local dataset, including 1790 subjects and 15,050 vertebral bodies (thoracic and lumbar). Each vertebral body was annotated using an adaption of the modified-2 algorithm-based qualitative criteria. The Osteoporotic Fractures in Men (MrOS) Study dataset provided thoracic and lumbar spine radiographs of 5994 men from six clinical centers. Using both datasets, five deep learning algorithms were trained to classify each individual vertebral body of the spine radiographs. Classification performance was compared for these models using multiple metrics, including the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and positive predictive value (PPV). RESULTS: Our best model, built with ensemble averaging, achieved an AUC-ROC of 0.948 and 0.936 on the local dataset's test set and the MrOS dataset's test set, respectively. After setting the cutoff threshold to prioritize PPV, this model achieved a sensitivity of 54.5% and 47.8%, a specificity of 99.7% and 99.6%, and a PPV of 89.8% and 94.8%. CONCLUSION: Our model achieved an AUC-ROC>0.90 on both datasets. This testing shows some generalizability to real-world clinical datasets and a suitable performance for a future opportunistic osteoporosis screening tool.

Mortality among older adults with osteoporotic vertebral fracture.

We evaluated whether older adults who received kyphoplasty had reduced risk of mortality compared to those who did not. In unmatched analyses, those receiving kyphoplasty were at reduced risk of death but after matching on age and medical complications, patients who received kyphoplasty were at increased risk of death. PURPOSE: In previous observational studies, kyphoplasty for treatment of osteoporotic vertebral fractures has been associated with decreased mortality compared to conservative management. The purpose of this research was to determine whether older adults who received kyphoplasty had reduced risk of mortality compared to matched patients who did not. METHODS: Retrospective cohort study of US Medicare enrollees with osteoporotic vertebral fractures between 2017-2019 comparing patients who underwent kyphoplasty to those who did not. We identified 2 control groups a priori: 1) non-augmented patients who met inclusion criteria (group 1); 2) propensity-matched patients on demographic and clinical variables (group 2). We then identified additional control groups using matching for medical complications (group 3) and age + comorbidities (group 4). We calculated hazard ratios (HRs) and 95% confidence intervals (95% CIs) associated with mortality. RESULTS: A total of 235,317 patients (mean (± standard deviation) age 81.1 ± 8.3 years; 85.8% female) were analyzed. In the primary analyses, those who received kyphoplasty were at reduced risk of death compared to those who did not: adjusted HR (95% CI) in group 1 = 0.84 (0.82, 0.87); and in group 2 = 0.88 (0.85, 0.91). However, in post hoc analyses, patients who received kyphoplasty were at increased risk of death: adjusted HR (95% CI) in group 3 = 1.32 (1.25, 1.41) and 1.81 (1.58, 2.09) in group 4. CONCLUSION: An apparent benefit of kyphoplasty on mortality among patients with vertebral fractures was not present after rigorous propensity matching, illustrating the importance of comparing similar individuals when evaluating observational data.

Treatment With Opioids Is Not Associated With Poor Outcomes Among Older Adults With Lumbar Spinal Stenosis Receiving Epidural Injections.

STUDY DESIGN: Secondary analysis of a randomized controlled trial. OBJECTIVE: To assess how baseline treatment with opioids is associated with pain and function in older adults with lumbar spinal stenosis who receive epidural injections. SUMMARY OF BACKGROUND DATA: Data were obtained from the Lumbar Epidural Steroid injections for Spinal Stenosis trial, a double-blind, multisite, randomized controlled trial. METHODS: Baseline treatment with opioids was assessed from electronic medical record prescription pharmacy data or from health utilization records collected from patients. We calculated adjusted changes in back pain numerical rating scale, leg pain numerical rating scale, and back-related disability (Roland Morris Disability Questionnaire scores) from baseline to three weeks and to six weeks among patients treated and not treated with opioids at baseline using generalized linear regression. RESULTS: Baseline treatment with opioids was not significantly associated with back pain intensity (adjusted difference in means at three weeks of follow-up between patients treated with opioids at baseline versus not [±95% CI, 0.1 (-0.7, 0.7)], leg pain intensity [-0.2 (-0.9, 0.4)], or back-related function [-0.8 (-2.1, 0.4)]. We found similar results at six weeks of follow-up. CONCLUSIONS: Among older adults with lumbar spinal stenosis who are receiving epidural injections, those treated with opioids at baseline had similar outcomes to those who were not.

Predicting decompression surgery by applying multimodal deep learning to patients' structured and unstructured health data.

BACKGROUND: Low back pain (LBP) is a common condition made up of a variety of anatomic and clinical subtypes. Lumbar disc herniation (LDH) and lumbar spinal stenosis (LSS) are two subtypes highly associated with LBP. Patients with LDH/LSS are often started with non-surgical treatments and if those are not effective then go on to have decompression surgery. However, recommendation of surgery is complicated as the outcome may depend on the patient's health characteristics. We developed a deep learning (DL) model to predict decompression surgery for patients with LDH/LSS. MATERIALS AND METHOD: We used datasets of 8387 and 8620 patients from a prospective study that collected data from four healthcare systems to predict early (within 2 months) and late surgery (within 12 months after a 2 month gap), respectively. We developed a DL model to use patients' demographics, diagnosis and procedure codes, drug names, and diagnostic imaging reports to predict surgery. For each prediction task, we evaluated the model's performance using classical and generalizability evaluation. For classical evaluation, we split the data into training (80%) and testing (20%). For generalizability evaluation, we split the data based on the healthcare system. We used the area under the curve (AUC) to assess performance for each evaluation. We compared results to a benchmark model (i.e. LASSO logistic regression). RESULTS: For classical performance, the DL model outperformed the benchmark model for early surgery with an AUC of 0.725 compared to 0.597. For late surgery, the DL model outperformed the benchmark model with an AUC of 0.655 compared to 0.635. For generalizability performance, the DL model outperformed the benchmark model for early surgery. For late surgery, the benchmark model outperformed the DL model. CONCLUSIONS: For early surgery, the DL model was preferred for classical and generalizability evaluation. However, for late surgery, the benchmark and DL model had comparable performance. Depending on the prediction task, the balance of performance may shift between DL and a conventional ML method. As a result, thorough assessment is needed to quantify the value of DL, a relatively computationally expensive, time-consuming and less interpretable method.

Intervention delivery for embedded pragmatic clinical trials: Development of a tool to measure complexity.

BACKGROUND: Conducting an embedded pragmatic clinical trial in the workflow of a healthcare system is a complex endeavor. The complexity of the intervention delivery can have implications for study planning, ability to maintain fidelity to the intervention during the trial, and/or ability to detect meaningful differences in outcomes. METHODS: We conducted a literature review, developed a tool, and conducted two rounds of phone calls with NIH Pragmatic Trials Collaboratory Demonstration Project principal investigators to develop the Intervention Delivery Complexity Tool. After refining the tool, we piloted it with Collaboratory demonstration projects and developed an online version of the tool using the R Shiny application (https://duke-som.shinyapps.io/ICT-ePCT/). RESULTS: The 6-item tool consists of internal and external factors. Internal factors pertain to the intervention itself and include workflow, training, and the number of intervention components. External factors are related to intervention delivery at the system level including differences in healthcare systems, the dependency on setting for implementation, and the number of steps between the intervention and the outcome. CONCLUSION: The Intervention Delivery Complexity Tool was developed as a standard way to overcome communication challenges of intervention delivery within an embedded pragmatic trial. This version of the tool is most likely to be useful to the trial team and its health system partners during trial planning and conduct. We expect further evolution of the tool as more pragmatic trials are conducted and feedback is received on its performance outside of the NIH Pragmatic Trials Collaboratory.

Estimating individualized treatment effects using a risk-modeling approach: an application to epidural steroid injections for lumbar spinal stenosis.

ABSTRACT: Conventional "1-variable-at-a-time" analyses to identify treatment effect modifiers are often underpowered and prone to false-positive results. This study used a "risk-modeling" approach guided by the Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement framework: (1) developing and validating a multivariable model to estimate predicted future back-related functional limitations as measured by the Roland-Morris Disability Questionnaire (RMDQ) and (2) stratifying patients from a randomized controlled trial (RCT) of lumbar epidural steroid injections (LESIs) for the treatment of lumbar spinal stenosis into subgroups with different individualized treatment effects on RMDQ scores at the 3-week follow-up. Model development and validation were conducted in a cohort (n = 3259) randomly split into training and testing sets in a 4:1 ratio. The model was developed in the testing set using linear regression with least absolute shrinkage and selection regularization and 5-fold cross-validation. The model was then applied in the testing set and subsequently in patients receiving the control treatment in the RCT of LESI. R2 values in the training set, testing set, and RCT were 0.38, 0.32, and 0.34, respectively. There was statistically significant modification ( P = 0.03) of the LESI treatment effect according to predicted risk quartile, with clinically relevant LESI treatment effect point estimates in the 2 quartiles with greatest predicted risk (-3.7 and -3.3 RMDQ points) and no effect in the lowest 2 quartiles. A multivariable risk-modeling approach identified subgroups of patients with lumbar spinal stenosis with a clinically relevant treatment effect of LESI on back-related functional limitations.

Improving Power and Accuracy in Randomized Controlled Trials of Pain Treatments by Accounting for Concurrent Analgesic Use.

The 0 to 10 numeric rating scale of pain intensity is a standard outcome in randomized controlled trials (RCTs) of pain treatments. For individuals taking analgesics, there may be a disparity between "observed" pain intensity (pain intensity with concurrent analgesic use) and pain intensity without concurrent analgesic use (what the numeric rating scale would be had analgesics not been taken). Using a contemporary causal inference framework, we compare analytic methods that can potentially account for concurrent analgesic use, first in statistical simulations, and second in analyses of real (non-simulated) data from an RCT of lumbar epidural steroid injections. The default analytic method was ignoring analgesic use, which is the most common approach in pain RCTs. Compared to ignoring analgesic use and other analytic methods, simulations showed that a quantitative pain and analgesia composite outcome based on adding 1.5 points to pain intensity for those who were taking an analgesic (the QPAC1.5) optimized power and minimized bias. Analyses of real RCT data supported the results of the simulations, showing greater power with analysis of the QPAC1.5 as compared to ignoring analgesic use and most other methods examined. We propose alternative methods that should be considered in the analysis of pain RCTs. PERSPECTIVE: This article presents the conceptual framework behind a new quantitative pain and analgesia composite outcome, the QPAC1.5, and the results of statistical simulations and analyses of trial data supporting improvements in power and bias using the QPAC1.5. Methods of this type should be considered in the analysis of pain RCTs.

Patient-centred outcomes of imaging tests: recommendations for patients, clinicians and researchers.

BACKGROUND: Imaging tests are one of the most frequently used diagnostic modalities in healthcare, but the benefits of their direct impacts on clinical decision-making have been countered by concerns that they can be overused. Assessing the relative value of imaging tests has largely focused on measures of test accuracy, which overlooks more comprehensive benefits and risks of imaging tests, particularly their impact on patient-centred outcomes (PCOs). We present the findings of the Patient Reported Outcomes of Diagnostics (PROD) research study in response to a methodological gap in the area of diagnostic test comparative effectiveness research. METHODS: Over a 3-year period, the PROD Study engaged with multiple stakeholders to identify existing conceptual models related to PCOs for imaging testing, conducted primary research and evidence synthesis, and developed consensus recommendations to describe and categorise PCOs related to imaging testing. RESULTS: The PROD framework categorises PCOs from imaging studies within four main domains: information or knowledge yielded, physical impact, emotional outcomes and test burden. PCOs interact with each other and influence effects across domains, and can be modified by factors related to the patient, clinical situation, healthcare team and the testing environment. CONCLUSIONS: Using PCOs to inform healthcare decision-making will require ways of collating and presenting information on PCOs in ways that can inform patient-provider decision-making, and developing methods to determine the relative importance of outcomes (including test accuracy) to one another.

The Lumbar Stenosis Prognostic Subgroups for Personalizing Care and Treatment (PROSPECTS) study: protocol for an inception cohort study.

BACKGROUND: Lumbar spinal stenosis (LSS) is a common degenerative condition that contributes to back and back-related leg pain in older adults. Most patients with symptomatic LSS initially receive non-operative care before surgical consultation. However, there is a scarcity of data regarding prognosis for patients seeking non-surgical care. The overall goal of this project is to develop and evaluate a clinically useful model to predict long-term physical function of patients initiating non-surgical care for symptomatic LSS. METHODS: This is a protocol for an inception cohort study of adults 50 years and older who are initiating non-surgical care for symptomatic LSS in a secondary care setting. We plan to recruit up to 625 patients at two study sites. We exclude patients with prior lumbar spine surgeries or those who are planning on lumbar spine surgery. We also exclude patients with serious medical conditions that have back pain as a symptom or limit walking. We are using weekly, automated data pulls from the electronic health records to identify potential participants. We then contact patients by email and telephone within 21 days of a new visit to determine eligibility, obtain consent, and enroll participants. We collect data using telephone interviews, web-based surveys, and queries of electronic health records. Participants are followed for 12 months, with surveys completed at baseline, 3, 6, and 12 months. The primary outcome measure is the 8-item PROMIS Physical Function (PF) Short Form. We will identify distinct phenotypes using PROMIS PF scores at baseline and 3, 6, and 12 months using group-based trajectory modeling. We will develop and evaluate the performance of a multivariable prognostic model to predict 12-month physical function using the least absolute shrinkage and selection operator and will compare performance to other machine learning methods. Internal validation will be conducted using k-folds cross-validation. DISCUSSION: This study will be one of the largest cohorts of individuals with symptomatic LSS initiating new episodes of non-surgical care. The successful completion of this project will produce a cross-validated prognostic model for LSS that can be used to tailor treatment approaches for patient care and clinical trials.

Long-term effectiveness of epidural steroid injections after new episodes of low back pain in older adults.

BACKGROUND: There is limited research on the long-term effectiveness of epidural steroid injections (ESI) in older adults despite the high prevalence of back and leg pain in this age group. We tested the hypotheses that older adults undergoing ESI, compared to patients not receiving ESI: (1) have worse pain, disability and quality of life ('outcomes') pre-ESI, (2) have improved outcomes after ESI and (3) have improved outcomes due to a specific ESI effect. METHODS: We prospectively studied patients ≥65 years old presenting to primary care with new episodes of back pain in three US healthcare systems (BOLD registry). Outcomes were leg and back pain intensity, disability and quality of life, assessed at baseline and 3-, 6-, 12- and 24-month follow-ups. We categorized participants as: (1) ESI within 6 months from the index visit (n = 295); (2) no ESI within 6 months (n = 4809); (3) no ESI within 6 months, propensity-score matched to group 1 (n = 483). We analysed the data using linear regression and Generalized Estimating Equations. RESULTS: Pain intensity, disability and quality of life at baseline were significantly worse at baseline in ESI patients (group 1) than in group 2. The improvement from baseline to 24 months in all outcomes was statistically significant for group 1. However, no statistically significant differences were observed between outcome trajectories for the propensity-score matched groups 1 and 3. CONCLUSIONS: Older adults treated with ESI have long-term improvement. However, the improvement is unlikely the result of a specific ESI effect. SIGNIFICANCE: In this large, two-year, prospective study in older adults with a new episode of low back pain, back pain, leg pain, disability and quality of life improved after epidural steroid injections; however, propensity-score matching revealed that the improvement was unlikely the result of a specific effect of the injections, indicating that epidural steroids are unlikely to provide long-term benefits in older adults with new episodes of back and leg pain.

Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria.

RATIONALE AND OBJECTIVES: Osteoporosis affects 9% of individuals over 50 in the United States and 200 million women globally. Spinal osteoporotic compression fractures (OCFs), an osteoporosis biomarker, are often incidental and under-reported. Accurate automated opportunistic OCF screening can increase the diagnosis rate and ensure adequate treatment. We aimed to develop a deep learning classifier for OCFs, a critical component of our future automated opportunistic screening tool. MATERIALS AND METHODS: The dataset from the Osteoporotic Fractures in Men Study comprised 4461 subjects and 15,524 spine radiographs. This dataset was split by subject: 76.5% training, 8.5% validation, and 15% testing. From the radiographs, 100,409 vertebral bodies were extracted, each assigned one of two labels adapted from the Genant semiquantitative system: moderate to severe fracture vs. normal/trace/mild fracture. GoogLeNet, a deep learning model, was trained to classify the vertebral bodies. The classification threshold on the predicted probability of OCF outputted by GoogLeNet was set to prioritize the positive predictive value (PPV) while balancing it with the sensitivity. Vertebral bodies with the top 0.75% predicted probabilities were classified as moderate to severe fracture. RESULTS: Our model yielded a sensitivity of 59.8%, a PPV of 91.2%, and an F1 score of 0.72. The areas under the receiver operating characteristic curve (AUC-ROC) and the precision-recall curve were 0.99 and 0.82, respectively. CONCLUSION: Our model classified vertebral bodies with an AUC-ROC of 0.99, providing a critical component for our future automated opportunistic screening tool. This could lead to earlier detection and treatment of OCFs.

Reliability and validity of subjective radiologist reporting of temporal changes in lumbar spine MRI findings.

BACKGROUND: The importance of lumbar findings on magnetic resonance imaging (MRI) remains controversial. Changes in lumbar MRI findings over time may provide important insights into the causes of low back pain. However, the reliability and validity of temporal changes are unknown. OBJECTIVE: To (1) investigate the interrater reliability of subjective radiologist reporting of temporal changes in lumbar spine MRI findings and (2) determine how commonly temporal changes are reported when two scans are conducted 30 minutes apart (considered false positives). DESIGN: Cross-sectional study. SETTING: Radiology clinic. PARTICIPANTS: Forty volunteers (mean age 40; 53% female) with current (n = 31) or previous (n = 9) low back pain underwent initial lumbar MRI on a single 3T scanner. Participants then lay on a bed for 30 minutes before undergoing an identical MRI. In addition, we purposely selected five participants from a previous study with repeat lumbar MRI scans where temporal changes were reported in at least one MRI finding (1-12 weeks after initial scan) and another five participants where no temporal change was reported. The 10 participants were included in analyses for aim 1 only. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Two blinded radiologists reported on temporal changes between the baseline and repeat scan for 12 different MRI findings (eg, disk herniation, annular fissure) at five levels. RESULTS: The interrater reliability of subjective reporting of temporal changes was poor for all MRI findings based on Kappa values (≤ 0.24), but agreement was relatively high (≥ 90.8%). This is explained by the low prevalence of temporal changes as demonstrated by high values for Prevalence and Bias Adjusted Kappa (≥ 0.82). "False positive" temporal changes were reported by at least one radiologist for most MRI findings, but the rate was generally low. CONCLUSIONS: Caution is required when interpreting temporal changes in lumbar MRI findings owing to low reliability and some false positive reporting.