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The 0 to 10 numeric rating scale (NRS) 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 (the NRS, irrespective of concurrent analgesic use), and “underlying” pain intensity (what the NRS would be had concurrent 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 (LESI). 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 observed 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.

Because osteoporosis is under-recognized in patients with vertebral fractures, we evaluated characteristics associated with osteoporosis identification. Most patients with vertebral fractures did not receive evaluation or treatment for osteoporosis. Black, younger, and male participants were particularly unlikely to have had recognized osteoporosis, which could increase their risk of negative outcomes.Vertebral fractures may be identified on imaging but fail to prompt evaluation for osteoporosis. Our objective was to evaluate characteristics associated with clinical osteoporosis recognition in patients who had vertebral fractures detected on their thoracolumbar spine imaging reports.We prospectively identified individuals who received imaging of the lower spine at primary care clinics in 4 large healthcare systems who were eligible for osteoporosis screening and lacked indications of osteoporosis diagnoses or treatments in the prior year. We evaluated characteristics of participants with identified vertebral fractures that were associated with recognition of osteoporosis (diagnosis code in the health record; receipt of bone mineral density scans; and/or prescriptions for anti-osteoporotic medications). We used mixed models to estimate adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs).A total of 114,005 participants (47% female; mean age 65 (interquartile range: 57-72) years) were evaluated. Of the 8579 (7%) participants with vertebral fractures identified, 3784 (44%) had recognition of osteoporosis within the subsequent year. In adjusted regressions, Black participants (OR (95% CI): 0.74 (0.57, 0.97)), younger participants (age 50-60: 0.48 (0.42, 0.54); age 61-64: 0.70 (0.60, 0.81)), and males (0.39 (0.35, 0.43)) were less likely to have recognized osteoporosis compared to white participants, adults aged 65 + years, or females.Individuals with identified vertebral fractures commonly did not have recognition of osteoporosis within a year, particularly those who were younger, Black, or male. Providers and healthcare systems should consider efforts to improve evaluation of osteoporosis in patients with vertebral fractures.

Background Chronic low back pain (cLBP) is widespread, costly, and burdensome to patients and health systems. Little is known about non-pharmacological treatments for the secondary prevention of cLBP. There is some evidence that treatments addressing psychosocial factors in higher risk patients are more effective than usual care. However, most clinical trials on acute and subacute LBP have evaluated interventions irrespective of prognosis. Methods We have designed a phase 3 randomized trial with a 2 × 2 factorial design. The study is also a Hybrid type 1 trial with focus on intervention effectiveness while simultaneously considering plausible implementation strategies. Adults (n = 1000) with acute/subacute LBP at moderate to high risk of chronicity based on the STarT Back screening tool will be randomized in to 1 of 4 interventions lasting up to 8 weeks: supported self-management (SSM), spinal manipulation therapy (SMT), both SSM and SMT, or medical care. The primary objective is to assess intervention effectiveness; the secondary objective is to assess barriers and facilitators impacting future implementation. Primary effectiveness outcome measures are: (1) average pain intensity over 12 months post-randomization (pain, numerical rating scale); (2) average low back disability over 12 months post-randomization (Roland-Morris Disability Questionnaire); (3) prevention of cLBP that is impactful at 10–12 months follow-up (LBP impact from the PROMIS-29 Profile v2.0). Secondary outcomes include: recovery, PROMIS-29 Profile v2.0 measures to assess pain interference, physical function, anxiety, depression, fatigue, sleep disturbance, and ability to participate in social roles and activities. Other patient-reported measures include LBP frequency, medication use, healthcare utilization, productivity loss, STarT Back screening tool status, patient satisfaction, prevention of chronicity, adverse events, and dissemination measures. Objective measures include the Quebec Task Force Classification, Timed Up & Go Test, the Sit to Stand Test, and the Sock Test assessed by clinicians blinded to the patients’ intervention assignment. Discussion By targeting those subjects at higher risk this trial aims to fill an important gap in the scientific literature regarding the effectiveness of promising non-pharmacological treatments compared to medical care for the management of patients with an acute episode of LBP and the prevention of progression to a severe chronic back problem. Trial registration ClinicalTrials.gov Identifier: NCT03581123.

Epidermolysis bullosa (EB) is a devastating genetic condition caused by mutations in genes that give rise to aberrant proteins. There are 16 different such proteins implicated in EB that are important in maintaining the integrity of the dermo-epidermal junction. It is classified into 4 major subtypes: 1) EB Simplex (EBS) 2) Junctional EB (JEB) 3) dystrophic EB (DEB) and 4) Kindler EB (KEB) . Renal disease is a recognized complication of EB and the aetiology is complex. We describe our experience of managing 5 patients with EB and IgA nephropathy. We recommend that patients with RDEB and JEB routinely have the following monitored: renal function, urinary ACR, urine analysis, serum albumin levels and immunoglobulins; specifically serum IgA. Management of IgA nephropathy in the context of EB should be tailored to the individual and be carried out within a specialist MDT. Our case series provides important insights into the treatment of IgA nephropathy in patients with EB and will help inform treatment in this rare genetic disease. Case series and reports like ours, are key in gaining real life data to quantify the actual risk of morbidity and mortality from each of the treatment modalities discussed.

Purpose: To determine if gene expression signature of invasive oral squamous cell carcinoma (OSCC) can subclassify OSCC based on survival.Experimental Design: We analyzed the expression of 131 genes in 119 OSCC, 35 normal, and 17 dysplastic mucosa to identify cluster-defined subgroups. Multivariate Cox regression was used to estimate the association between gene expression and survival. By stepwise Cox regression, the top predictive models of OSCC-specific survival were determined and compared by receiver operating characteristic analysis.Results: The 3-year overall mean ± SE survival for a cluster of 45 OSCC patients was 38.7 ± 0.09% compared with 69.1 ± 0.08% for the remaining patients. Multivariate analysis adjusted for age, sex, and stage showed that the 45 OSCC patient cluster had worse overall and OSCC-specific survival (hazard ratio, 3.31; 95% confidence interval, 1.66-6.58 and hazard ratio, 5.43; 95% confidence interval, 2.32-12.73, respectively). Stepwise Cox regression on the 131 probe sets revealed that a model with a term for LAMC2 (laminin γ2) gene expression best identified patients with worst OSCC-specific survival. We fit a Cox model with a term for a principal component analysis-derived risk score marker and two other models that combined stage with either LAMC2 or PCA. The area under the curve for models combining stage with either LAMC2 or PCA was 0.80 or 0.82, respectively, compared with 0.70 for stage alone (P = 0.013 and 0.008, respectively).Conclusions: Gene expression and stage combined predict survival of OSCC patients better than stage alone.

Supplementary Data from A Genetic Expression Profile Associated with Oral Cancer Identifies a Group of Patients at High Risk of Poor Survival

Our study demonstrates that severity scores do not correlate well with the impact that localized epidermolysis bullosa simplex (EBS) has on quality of life. There is a paucity of literature exploring correlations between disease severity and patient-reported outcome measures in EB. Both the Birmingham EB Severity (BEBS) score and EB Disease Activity and Severity Index (EBDASI) consist of weighted factors including body surface area, scalp, mucous membrane, nail and systemic involvement, as well as complications of longstanding disease such as cutaneous squamous cell carcinoma, which is more commonly seen in recessive dystrophic EB. Current scoring systems fail to account for the palmoplantar blistering and keratoderma which is the most significant source of morbidity in the EBS cohort.

Background In newborns with hypoxic-ischemic encephalopathy (HIE), the correlation between neonatal neuroimaging and the degree of neurodevelopmental impairment (NDI) is unclear. Methods Infants with HIE enrolled in a randomized controlled trial underwent neonatal MRI/MR spectroscopy (MRS) using a harmonized protocol at 4–6 days of age. The severity of brain injury was measured with a validated scoring system. Using proportional odds regression, we calculated adjusted odds ratios (aOR) for the associations between MRI/MRS measures of injury and primary ordinal outcome (i.e., normal, mild NDI, moderate NDI, severe NDI, or death) at age 2 years. Results Of 451 infants with MRI/MRS at a median age of 5 days (IQR 4.5–5.8), outcomes were normal (51%); mild (12%), moderate (14%), severe NDI (13%); or death (9%). MRI injury score (aOR 1.06, 95% CI 1.05, 1.07), severe brain injury (aOR 39.6, 95% CI 16.4, 95.6), and MRS lactate/n-acetylaspartate (NAA) ratio (aOR 1.6, 95% CI 1.4,1.8) were associated with worse primary outcomes. Infants with mild/moderate MRI brain injury had similar BSID-III cognitive, language, and motor scores as infants with no injury. Conclusion In the absence of severe injury, brain MRI/MRS does not accurately discriminate the degree of NDI. Given diagnostic uncertainty, families need to be counseled regarding a range of possible neurodevelopmental outcomes. Impact Half of all infants with hypoxic-ischemic encephalopathy (HIE) enrolled in a large clinical trial either died or had neurodevelopmental impairment at age 2 years despite receiving therapeutic hypothermia. Severe brain injury and a global pattern of brain injury on MRI were both strongly associated with death or neurodevelopmental impairment. Infants with mild or moderate brain injury had similar mean BSID-III cognitive, language, and motor scores as infants with no brain injury on MRI. Given the prognostic uncertainty of brain MRI among infants with less severe degrees of brain injury, families should be counseled regarding a range of possible neurodevelopmental outcomes.

Stepped wedge cluster randomized trials are often analysed using linear mixed effects models that may include random effects for cluster, time and/or treatment. We investigate the impact of misspecification of the random effects structure of the model. Specifically, we considered two cases of misspecification of the random effects in a cross-sectional stepped wedge cluster randomized trials model – fit a linear mixed effects model with random time effects but the true model includes random treatment effects (case 1) or fit a linear mixed effects model with random treatment effect but the true model includes random time effects (case 2) – and derived the variance of the estimated treatment effect under misspecification. We defined two measures of the effect of misspecification: validity and efficiency. Validity is the ratio of the model-based variance of the treatment effect from the mis-specified model divided by the true variance of the treatment effect from the mis-specified model (based on a sandwich estimate of the variance). Efficiency is the ratio of the model-based variance of the treatment effect from the correctly specified model divided by the true variance of the treatment effect from the mis-specified model. We found that validity is less than 1.0 (anti-conservative) in almost all situations investigated with the exception of case 1 with two sequences, when validity could be greater than 1.0. Efficiency is less than 1 in all cases and depends on the intracluster correlation coefficient, the relative magnitude of the variance of the misclassified variance component, and the number of sequences. In general, there is no universal recommendation as to the most robust approach except for the case of a classic stepped wedge cluster randomized trial with only 2 sequences, where fitting a random time model is less likely to lead to anti-conservative inference compared with fitting a random intervention model.

Rationale and Objectives The use of natural language processing (NLP) in radiology provides an opportunity to assist clinicians with phenotyping patients. However, the performance and generalizability of NLP across healthcare systems is uncertain. We assessed the performance within and generalizability across four healthcare systems of different NLP representational methods, coupled with elastic-net logistic regression to classify lower back pain-related findings from lumbar spine imaging reports. Materials and Methods We used a dataset of 871 X-ray and magnetic resonance imaging reports sampled from a prospective study across four healthcare systems between October 2013 and September 2016. We annotated each report for 26 findings potentially related to lower back pain. Our framework applied four different NLP methods to convert text into feature sets (representations). For each representation, our framework used an elastic-net logistic regression model for each finding (i.e., 26 binary or “one-vs.-rest” classification models). For performance evaluation, we split data into training (80%, 697/871) and testing (20%, 174/871). In the training set, we used cross validation to identify the optimal hyperparameter value and then retrained on the full training set. We then assessed performance based on area under the curve (AUC) for the test set. We repeated this process 25 times with each repeat using a different random train/test split of the data, so that we could estimate 95% confidence intervals, and assess significant difference in performance between representations. For generalizability evaluation, we trained models on data from three healthcare systems with cross validation and then tested on the fourth. We repeated this process for each system, then calculated mean and standard deviation (SD) of AUC across the systems. Results For individual representations, n-grams had the best average performance across all 26 findings (AUC: 0.960). For generalizability, document embeddings had the most consistent average performance across systems (SD: 0.010). Out of these 26 findings, we considered eight as potentially clinically important (any stenosis, central stenosis, lateral stenosis, foraminal stenosis, disc extrusion, nerve root displacement compression, endplate edema, and listhesis grade 2) since they have a relatively greater association with a history of lower back pain compared to the remaining 18 classes. We found a similar pattern for these eight in which n-grams and document embeddings had the best average performance (AUC: 0.954) and generalizability (SD: 0.007), respectively. Conclusion Based on performance assessment, we found that n-grams is the preferred method if classifier development and deployment occur at the same system. However, for deployment at multiple systems outside of the development system, or potentially if physician behavior changes within a system, one should consider document embeddings since embeddings appear to have the most consistent performance across systems.

OBJECTIVES: We propose a framework of health outcomes modeling with dynamic decision-making and real-world data (RWD) to evaluate the potential utility of novel risk prediction models in clinical practice. Lung transplant (LTx) referral decisions in cystic fibrosis offer a complex case study. METHODS: We used longitudinal RWD for a cohort of adults (n = 4,247) from the Cystic Fibrosis Foundation patient registry to compare outcomes of an LTx referral policy based on machine learning (ML) mortality risk predictions to referral based on (1) forced expiratory volume in one second (FEV(1)) alone, and (2) heterogenous usual care (UC). We then developed a patient-level simulation model to project number of patients referred for LTx and 5-year survival, accounting for transplant availability, organ allocation policy, and heterogenous treatment effects. RESULTS: Only 12% (95% CI: 11%, 13%) of patients were referred for LTx over 5 years under UC, compared to 19% (18%, 20%) under FEV(1) and 20% (19%, 22%) under ML. Of 309 patients who died before LTx referral under UC, 31% (27%, 36%) would have been referred under FEV(1) and 40% (35%, 45%) would have been referred under ML. Given a fixed supply of organs, differences in referral time did not lead to significant differences in transplants, pre- or post- transplant deaths, or overall survival in 5 years. CONCLUSIONS: Health outcomes modelling with RWD may help to identify novel ML risk prediction models with high potential real-world clinical utility, and rule out further investment in models that are unlikely to offer meaningful real-world benefits.

Mixed models are commonly used to analyze stepped wedge trials (SWTs) to account for clustering and repeated measures on clusters. One critical issue researchers face is whether to include a random time effect or a random treatment effect. When the wrong model is chosen, inference on the treatment effect may be invalid. We explore asymptotic and finite-sample convergence of variance component estimates when the model is misspecified and how misspecification affects the estimated variance of the treatment effect. For asymptotic results, we rely on analytical solutions rather than simulation studies, which allows us to succinctly describe the convergence of misspecified estimates, even though there are multiple roots for each misspecified model. We found that both direction and magnitude of the bias associated with model-based standard errors depends on the study design and magnitude of the true variance components. We identify some scenarios in which choosing the wrong random effect has a large impact on model-based inference. However, many trends depend on trial design and assumptions about the true correlation structure, so we provide tools for researchers to investigate specific scenarios of interest. We use data from a SWT on disinvesting from weekend services in hospital wards to demonstrate how these results can be applied as a sensitivity analysis, which quantifies the impact of misspecification under a variety of settings and directly compares the potential consequences of different modeling choices. Our results will provide guidance for pre-specified model choices and supplement sensitivity analyses to inform confidence in the validity of results.

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.

Contexte : Il existe peu de recherches sur l’efficacité à long terme des injections péridurales de corticostéroïdes (IPC) chez les personnes âgées, malgré la prévalence élevée des douleurs au dos et au membre inférieur dans ce groupe d’âge. Nous avons testé les hypothèses selon lesquelles les adultes plus âgés recevant une IPC, comparés aux patients n’en recevant pas : 1) présentent avant l’IPC une douleur et une impotence fonctionnelle plus graves et une qualité de vie moindre (« critères cliniques ») ; 2) présentent une amélioration des critères cliniques après l’IPC et 3) sont améliorés grâce à un effet spécifique de l’IPC. Méthodes : Nous avons étudié prospectivement des patients de plus de 65 ans consultant en soins primaires dans trois systèmes de santé étatsuniens (registre BOLD), pour nouvel épisode de lombalgie. Les critères de jugement étaient l’intensité de la douleur du/des membre(s) inférieur(s) et lombaire, l’impotence fonctionnelle et la qualité de vie, évalués au départ et lors des suivis à 3, 6, 12 et 24 mois. Nous avons classé les participants comme suit : groupe 1, IPC dans les six mois suivant la visite de référence (n = 295) ; groupe 2, pas d’IPC dans les six mois (n = 4 809) ; groupe 3, pas d’IPC dans les six mois, appariés sur score de propension au groupe 1 (n = 483). Nous avons analysé les données au moyen d’une régression linéaire avec équations d’estimation généralisées. Résultats : L’intensité de la douleur, l’impotence fonctionnelle et la qualité de vie au départ étaient significativement plus défavorables chez les patients IPC (groupe 1) que chez ceux du groupe 2. L’amélioration entre l’initiation et le 24e mois de tous les critères était statistiquement significative dans le groupe 1. Cependant, aucune différence statistiquement significative n’a été observée entre les trajectoires des critères des groupes 1 et 3, appariés sur score de propension. Conclusions : Les personnes âgées traitées par IPC présentent une amélioration à long terme. Cependant, il est peu probable que cette amélioration soit le résultat d’un effet spécifique de l’IPC. Importance : Dans ce grand suivi prospectif de deux ans chez des sujets âgés présentant un nouvel épisode de lombalgie, la douleur lombaire et la douleur du/des membre(s) inférieur(s), l’impotence fonctionnelle et la qualité de vie ont évolué favorablement après IPC ; toutefois, l’appariement sur score de propension a montré que cette amélioration n’était probablement pas due à un effet spécifique des injections, ce qui indique que les corticostéroïdes en péridural sont peu susceptibles de procurer des bénéfices à long terme aux sujets âgés présentant un nouvel épisode de douleur lombaire et de douleur du/des membre(s) inférieur(s).

Additional file 2. Type I error rate tables for analytic models.

Background Multiple-period parallel group randomized trials (GRTs) analyzed with linear mixed models can represent time in mean models as continuous or categorical. If time is continuous, random effects are traditionally group- and member-level deviations from condition-specific slopes and intercepts and are referred to as random coefficients (RC) analytic models. If time is categorical, random effects are traditionally group- and member-level deviations from time-specific condition means and are referred to as repeated measures ANOVA (RM-ANOVA) analytic models. Longstanding guidance recommends the use of RC over RM-ANOVA for parallel GRTs with more than two periods because RC exhibited nominal type I error rates for both time parameterizations while RM-ANOVA exhibited inflated type I error rates when applied to data generated using the RC model. However, this recommendation was developed assuming a variance components covariance matrix for the RM-ANOVA, using only cross-sectional data, and explicitly modeling time × group variation. Left unanswered were how well RM-ANOVA with an unstructured covariance would perform on data generated according to the RC mechanism, if similar patterns would be observed in cohort data, and the impact of not modeling time × group variation if such variation was present in the data-generating model. Methods Continuous outcomes for cohort and cross-sectional parallel GRT data were simulated according to RM-ANOVA and RC mechanisms at five total time periods. All simulations assumed time × group variation. We varied the number of groups, group size, and intra-cluster correlation. Analytic models using RC, RM-ANOVA, RM-ANOVA with unstructured covariance, and a Saturated random effects structure were applied to the data. All analytic models specified time × group random effects. The analytic models were then reapplied without specifying random effects for time × group. Results Results indicated the RC and saturated analytic models maintained the nominal type I error rate in all data sets, RM-ANOVA with an unstructured covariance did not avoid type I error rate inflation when applied to cohort RC data, and analytic models omitting time-varying group random effects when such variation exists in the data were prone to substantial type I error inflation unless the residual error variance is high relative to the time × group variance. Conclusion The time × group RC and saturated analytic models are recommended as the default for multiple period parallel GRTs.

Background Patient surveillance using repeated biomarker measurements presents an opportunity to detect and treat disease progression early. Frequent surveillance testing using biomarkers is recommended and routinely conducted in several diseases, including cancer and diabetes. However, frequent testing involves tradeoffs. Although surveillance tests provide information about current disease status, the complications and costs of frequent tests may not be justified for patients who are at low risk of progression. Predictions based on patients’ earlier biomarker values may be used to inform decision making; however, predictions are uncertain, leading to decision uncertainty. Methods We propose the Personalized Risk-Adaptive Surveillance (PRAISE) framework, a novel method for embedding predictions into a value-of-information (VOI) framework to account for the cost of uncertainty over time and determine the time point at which collection of biomarker data would be most valuable. The proposed sequential decision-making framework is innovative in that it leverages the patient’s longitudinal history, considers individual benefits and harms, and allows for dynamic tailoring of surveillance intervals by considering the uncertainty in current information and estimating the probability that new information may change treatment decisions, as well as the impact of this change on patient outcomes. Results When applied to data from cystic fibrosis patients, PRAISE lowers costs by allowing some patients to skip a visit, compared to an “always test” strategy. It does so without compromising expected survival, by recommending less frequent testing among those who are unlikely to be treated at the skipped time point. Conclusions A VOI-based approach to patient monitoring is feasible and could be applied to several diseases to develop more cost-effective and personalized strategies for ongoing patient care. Highlights In many patient-monitoring settings, the complications and costs of frequent tests are not justified for patients who are at low risk of disease progression. Predictions based on patient history may be used to individualize the timing of patient visits based on evolving risk. We propose Personalized Risk-Adaptive Surveillance (PRAISE), a novel method for personalizing the timing of surveillance testing, where prediction modeling projects the disease trajectory and a value-of-information (VOI)–based pragmatic decision-theoretic framework quantifies patient- and time-specific benefit-harm tradeoffs. A VOI-based approach to patient monitoring could be applied to several diseases to develop more personalized and cost-effective strategies for ongoing patient care.

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.

Background: To describe characteristics of patients, providers, and clinics associated with opioid or non-opioid pain medication prescribing patterns for patients who received lower spine imaging in primary care clinics. Methods: In these secondary analyses of the Lumbar Imaging with Reporting of Epidemiology (LIRE) study, a randomized controlled trial conducted in 4 health systems in the United States, we evaluated characteristics associated with receipt of pain medication prescriptions. The outcomes were receipt of prescriptions for opioid or, separately, non-opioid pain medications within 90 days after imaging. Among patients who received opioid or non-opioid prescriptions, we evaluated receipt of multiple prescriptions in the year following imaging. Mixed models were used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Results: Compared with whites, patients identified as Asian (OR, 0.53; 95% CI, 0.51–0.56), Native Hawaiian/Pacific Islander (OR, 0.73; 95% CI, 0.64–0.83), multiracial (OR, 0.84; 95% CI, 0.71–0.98) or Black (OR, 0.92; 95% CI, 0.89–0.96) had significantly reduced odds for receiving prescriptions for opioids within 90 days. Patients identified as Native American/Alaska Native had greater odds for receiving prescriptions for non-opioid pain medications within 90 days (OR, 1.12; 95% CI, 1.01–1.24). Receipt of pain prescriptions 120 days before imaging was strongly predictive of subsequent receipt of pain prescriptions across all categories. Conclusions: After adjusting for factors that could affect prescribing, the strongest differences observed in pain-medication prescribing were across racial categories and for patients with previous pain prescriptions. Further research is needed to understand these differences and to optimize prescribing.

BACKGROUND CONTEXT: Osteoporotic vertebral fractures (OVFs) can lead to severe pain and reduced function and quality-of-life, but the strength of evidence for treatments remains low, particularly in younger populations. PURPOSE: To determine whether patients with OVFs who received kyphoplasty had different patterns of healthcare utilization compared to propensity-matched patients who did not receive vertebral augmentation. DESIGN: Observational cohort study. PATIENT SAMPLE: We identified patients with OVFs from 2007-2018 in the IBM® MarketScan® Commercial Claims and Encounters Databases who received kyphoplasty and compared them to propensity-matched controls who did not receive vertebral augmentation (either kyphoplasty or vertebroplasty). OUTCOME MEASURES: Major medical complications within 30 days, fills of opioids from 1-week through 1-month post-augmentation, and spine-related gross covered payments from 3-days post-kyphoplasty through 1-year post-OVF. METHODS: We used logistic regression to obtain odds ratios (ORs) and 95% confidence intervals (95% CIs) to compare binary outcomes and median analysis to compare continuous outcomes. RESULTS: Of the 15,197 OVF patients identified, 1969 (13%) received kyphoplasty and 1928 (98%) of these were propensity matched to non-augmented controls. We did not observe differences in the odds of major medical complications within 30 days (adjusted OR (95% CI): 1.0 (0.6, 1.8)) but patients who received kyphoplasty were more likely to have filled opioid medications within 30 days (adjusted OR (95% CI): 1.3 (1.1, 1.5)) and had greater spine-related gross covered payments (kyphoplasty median (95% CI): $1340 ($240, $4850); non-augmented: $7870 ($7480, $8270); adjusted difference in medians (95% CI): $260 ($190, $2050)). CONCLUSIONS: In this cohort of patients

Infants born extremely preterm (28 weeks' gestation) are at high risk of neurodevelopmental impairment (NDI) with 50% of survivors showing moderate or severe NDI when at 2 years of age. We sought to develop novel models by which to predict neurodevelopmental outcomes, hypothesizing that combining baseline characteristics at birth with medical care and environmental exposures would produce the most accurate model.Using a prospective database of 692 infants from the Preterm Epo Neuroprotection (PENUT) Trial, which was carried out between December 2013 and September 2016, we developed three predictive algorithms of increasing complexity using a Bayesian Additive Regression Trees (BART) machine learning approach to predict both NDI and continuous Bayley Scales of Infant and Toddler Development 3rd ed subscales at 2 year follow-up using: 1) the 5 variables used in the National Institute of Child Health and Human Development (NICHD) Extremely Preterm Birth Outcomes Tool, 2) 21 variables associated with outcomes in extremely preterm (EP) infants, and 3) a hypothesis-free approach using 133 potential variables available for infants in the PENUT database.The NICHD 5-variable model predicted 3-4% of the variance in the Bayley subscale scores, and predicted NDI with an area under the receiver operator curve (AUROC, 95% CI) of 0.62 (0.56-0.69). Accuracy increased to 12-20% of variance explained and an AUROC of 0.77 (0.72-0.83) when using the 21 pre-selected clinical variables. Hypothesis-free variable selection using BART resulted in models that explained 20-31% of Bayley subscale scores and AUROC of 0.87 (0.83-0.91) for severe NDI, with good calibration across the range of outcome predictions. However, even with the most accurate models, the average prediction error for the Bayley subscale predictions was around 14-15 points, leading to wide prediction intervals. Higher total transfusion volume was the most important predictor of severe NDI and lower Bayley scores across all subscales.While the machine learning BART approach meaningfully improved predictive accuracy above a widely used prediction tool (NICHD) as well as a model utilizing NDI-associated clinical characteristics, the average error remained approximately 1 standard deviation on either side of the true value. Although dichotomous NDI prediction using BART was more accurate than has been previously reported, and certain clinical variables such as transfusion exposure were meaningfully predictive of outcomes, our results emphasize the fact that the field is still not able to accurately predict the results of complex long-term assessments such as Bayley subscales in infants born EP even when using rich datasets and advanced analytic methods. This highlights the ongoing need for long-term follow-up of all EP infants.Supported by the National Institute of Neurological Disorders and StrokeU01NS077953 and U01NS077955.

An ancillary study of the High-Dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) trial for neonates with hypoxic-ischemic encephalopathy (HIE) and treated with therapeutic hypothermia examined the hypothesis that neonates randomized to receive erythropoietin (Epo) would have a lower seizure risk and burden compared with neonates who received placebo.Electroencephalograms (EEGs) from 7/17 HEAL trial centers were reviewed. Seizure presence was compared across treatment groups using a logistic regression model adjusting for treatment, HIE severity, center, and seizure burden prior to the first dose. Among neonates with seizures, differences across treatment groups in median maximal hourly seizure burden were assessed using adjusted quantile regression models.Forty-six of 150 (31%) neonates had EEG seizures (31% in Epo vs 30% in placebo, p = 0.96). Maximal hourly seizure burden after the study drug was not significantly different between groups (median 11.4 for Epo, IQR: 5.6, 18.1 vs median 9.7, IQR: 4.9, 21.0 min/h for placebo).In neonates with HIE treated with hypothermia who were randomized to Epo or placebo, we found no meaningful between-group difference in seizure risk or burden. These findings are consistent with overall trial results, which do not support Epo use for neonates with HIE undergoing therapeutic hypothermia.In the HEAL trial of erythropoietin (Epo) vs placebo for neonates with encephalopathy presumed due to hypoxic-ischemic encephalopathy (HIE) who were also treated with therapeutic hypothermia, electrographic seizures were detected in 31%, which is lower than most prior studies. Epo did not reduce the proportion of neonates with acute provoked seizures (31% in Epo vs 30% in placebo) or maximal hourly seizure burden after the study drug (median 11.4, IQR 5.6, 18.1 for Epo vs median 9.7, IQR 4.9, 21.0 min/h for placebo). There was no anti- or pro-convulsant effect of Epo when combined with therapeutic hypothermia for HIE.

Neonatal hypoxic-ischemic encephalopathy is an important cause of death as well as long-term disability in survivors. Erythropoietin has been hypothesized to have neuroprotective effects in infants with hypoxic-ischemic encephalopathy, but its effects on neurodevelopmental outcomes when given in conjunction with therapeutic hypothermia are unknown.In a multicenter, double-blind, randomized, placebo-controlled trial, we assigned 501 infants born at 36 weeks or more of gestation with moderate or severe hypoxic-ischemic encephalopathy to receive erythropoietin or placebo, in conjunction with standard therapeutic hypothermia. Erythropoietin (1000 U per kilogram of body weight) or saline placebo was administered intravenously within 26 hours after birth, as well as at 2, 3, 4, and 7 days of age. The primary outcome was death or neurodevelopmental impairment at 22 to 36 months of age. Neurodevelopmental impairment was defined as cerebral palsy, a Gross Motor Function Classification System level of at least 1 (on a scale of 0 [normal] to 5 [most impaired]), or a cognitive score of less than 90 (which corresponds to 0.67 SD below the mean, with higher scores indicating better performance) on the Bayley Scales of Infant and Toddler Development, third edition.Of 500 infants in the modified intention-to-treat analysis, 257 received erythropoietin and 243 received placebo. The incidence of death or neurodevelopmental impairment was 52.5% in the erythropoietin group and 49.5% in the placebo group (relative risk, 1.03; 95% confidence interval [CI], 0.86 to 1.24; P = 0.74). The mean number of serious adverse events per child was higher in the erythropoietin group than in the placebo group (0.86 vs. 0.67; relative risk, 1.26; 95% CI, 1.01 to 1.57).The administration of erythropoietin to newborns undergoing therapeutic hypothermia for hypoxic-ischemic encephalopathy did not result in a lower risk of death or neurodevelopmental impairment than placebo and was associated with a higher rate of serious adverse events. (Funded by the National Institute of Neurological Disorders and Stroke; ClinicalTrials.gov number, NCT02811263.).

To compare secondary patient reported outcomes of perceptions of treatment success and function for patients treated for appendicitis with appendectomy vs. antibiotics at 30 days.The Comparison of Outcomes of antibiotic Drugs and Appendectomy trial found antibiotics noninferior to appendectomy based on 30-day health status. To address questions about outcomes among participants with lower socioeconomic status, we explored the relationship of sociodemographic and clinical factors and outcomes.We focused on 4 patient reported outcomes at 30 days: high decisional regret, dissatisfaction with treatment, problems performing usual activities, and missing10 days of work. The randomized (RCT) and observational cohorts were pooled for exploration of baseline factors. The RCT cohort alone was used for comparison of treatments. Logistic regression was used to assess associations.The pooled cohort contained 2062 participants; 1552 from the RCT. Overall, regret and dissatisfaction were low whereas problems with usual activities and prolonged missed work occurred more frequently. In the RCT, those assigned to antibiotics had more regret (Odd ratios (OR) 2.97, 95% Confidence intervals (CI) 2.05-4.31) and dissatisfaction (OR 1.98, 95%CI 1.25-3.12), and reported less missed work (OR 0.39, 95%CI 0.27-0.56). Factors associated with function outcomes included sociodemographic and clinical variables for both treatment arms. Fewer factors were associated with dissatisfaction and regret.Overall, participants reported high satisfaction, low regret, and were frequently able to resume usual activities and return to work. When comparing treatments for appendicitis, no single measure defines success or failure for all people. The reported data may inform discussions regarding the most appropriate treatment for individuals.Clinicaltrials.gov Identifier: NCT02800785.

In the Comparison of Outcomes of Antibiotic Drugs and Appendectomy (CODA) trial, which found antibiotics to be noninferior, approximately half of participants randomized to receive antibiotics had outpatient management with hospital discharge within 24 hours. If outpatient management is safe, it could increase convenience and decrease health care use and costs.To assess the use and safety of outpatient management of acute appendicitis.This cohort study, which is a secondary analysis of the CODA trial, included 776 adults with imaging-confirmed appendicitis who received antibiotics at 25 US hospitals from May 1, 2016, to February 28, 2020.Participants randomized to antibiotics (intravenous then oral) could be discharged from the emergency department based on clinician judgment and prespecified criteria (hemodynamically stable, afebrile, oral intake tolerated, pain controlled, and follow-up confirmed). Outpatient management and hospitalization were defined as discharge within or after 24 hours, respectively.Outcomes compared among patients receiving outpatient vs inpatient care included serious adverse events (SAEs), appendectomies, health care encounters, satisfaction, missed workdays at 7 days, and EuroQol 5-dimension (EQ-5D) score at 30 days. In addition, appendectomy incidence among outpatients and inpatients, unadjusted and adjusted for illness severity, was compared.Among 776 antibiotic-randomized participants, 42 (5.4%) underwent appendectomy within 24 hours and 8 (1.0%) did not receive their first antibiotic dose within 24 hours, leaving 726 (93.6%) comprising the study population (median age, 36 years; range, 18-86 years; 462 [63.6%] male; 437 [60.2%] White). Of these participants, 335 (46.1%; site range, 0-89.2%) were discharged within 24 hours, and 391 (53.9%) were discharged after 24 hours. Over 7 days, SAEs occurred in 0.9 (95% CI, 0.2-2.6) per 100 outpatients and 1.3 (95% CI, 0.4-2.9) per 100 inpatients; in the appendicolith subgroup, SAEs occurred in 2.3 (95% CI, 0.3-8.2) per 100 outpatients vs 2.8 (95% CI, 0.6-7.9) per 100 inpatients. During this period, appendectomy occurred in 9.9% (95% CI, 6.9%-13.7%) of outpatients and 14.1% (95% CI, 10.8%-18.0%) of inpatients; adjusted analysis demonstrated a similar difference in incidence (-4.0 percentage points; 95% CI, -8.7 to 0.6). At 30 days, appendectomies occurred in 12.6% (95% CI, 9.1%-16.7%) of outpatients and 19.0% (95% CI, 15.1%-23.4%) of inpatients. Outpatients missed fewer workdays (2.6 days; 95% CI, 2.3-2.9 days) than did inpatients (3.8 days; 95% CI, 3.4-4.3 days) and had similar frequency of return health care visits and high satisfaction and EQ-5D scores.These findings support that outpatient antibiotic management is safe for selected adults with acute appendicitis, with no greater risk of complications or appendectomy than hospital care, and should be included in shared decision-making discussions of patient preferences for outcomes associated with nonoperative and operative care.ClinicalTrials.gov Identifier: NCT02800785.

Stepped wedge cluster randomized controlled trials are typically analyzed using models that assume the full effect of the treatment is achieved instantaneously. We provide an analytical framework for scenarios in which the treatment effect varies as a function of exposure time (time since the start of treatment) and define the "effect curve" as the magnitude of the treatment effect on the linear predictor scale as a function of exposure time. The "time-averaged treatment effect", (TATE) and "long-term treatment effect" (LTE) are summaries of this curve. We analytically derive the expectation of the estimator resulting from a model that assumes an immediate treatment effect and show that it can be expressed as a weighted sum of the time-specific treatment effects corresponding to the observed exposure times. Surprisingly, although the weights sum to one, some of the weights can be negative. This implies that the estimator may be severely misleading and can even converge to a value of the opposite sign of the true TATE or LTE. We describe several models that can be used to simultaneously estimate the entire effect curve, the TATE, and the LTE, some of which make assumptions about the shape of the effect curve. We evaluate these models in a simulation study to examine the operating characteristics of the resulting estimators and apply them to two real datasets.

Objective To evaluate the effect of inserting epidemiological information into lumbar spine imaging reports on subsequent nonsurgical and surgical procedures involving the thoracolumbosacral spine and sacroiliac joints. Design Analysis of secondary outcomes from the Lumbar Imaging with Reporting of Epidemiology (LIRE) pragmatic stepped-wedge randomized trial. Setting Primary care clinics within four integrated health care systems in the United States. Subjects 238,886 patients ≥18 years of age who received lumbar diagnostic imaging between 2013 and 2016. Methods Clinics were randomized to receive text containing age- and modality-specific epidemiological benchmarks indicating the prevalence of common spine imaging findings in people without low back pain, inserted into lumbar spine imaging reports (the “LIRE intervention”). The study outcomes were receiving 1) any nonsurgical lumbosacral or sacroiliac spine procedure (lumbosacral epidural steroid injection, facet joint injection, or facet joint radiofrequency ablation; or sacroiliac joint injection) or 2) any surgical procedure involving the lumbar, sacral, or thoracic spine (decompression surgery or spinal fusion or other spine surgery). Results The LIRE intervention was not significantly associated with subsequent utilization of nonsurgical lumbosacral or sacroiliac spine procedures (odds ratio [OR] = 1.01, 95% confidence interval [CI] 0.93–1.09; P = 0.79) or any surgical procedure (OR = 0.99, 95 CI 0.91–1.07; P = 0.74) involving the lumbar, sacral, or thoracic spine. The intervention was also not significantly associated with any individual spine procedure. Conclusions Inserting epidemiological text into spine imaging reports had no effect on nonsurgical or surgical procedure utilization among patients receiving lumbar diagnostic imaging.

Background The COVID-19 pandemic has considerably disrupted nearly all aspects of daily life, including healthcare delivery and clinical research. Because pragmatic clinical trials are often embedded within healthcare delivery systems, they may be at high risk of disruption due to the dual impacts on the conduct of both care and research. Methods We collected qualitative data using multiple methods to characterize the impact of COVID-19 on the research activities of 14 active pragmatic clinical trials in the National Institutes of Health (NIH) Health Care Systems Research Collaboratory. A COVID-19 impact questionnaire was administered electronically to principal investigators in June 2020. Text responses were analyzed thematically, and qualitative summaries were subsequently reviewed by five independent reviewers, who made iterative revisions. Additional COVID-19-related impacts were identified during virtual meetings with trial teams during April–July 2020 and combined with questionnaire responses for analysis. Results Impacts of the pandemic were broadly classified into two main types: healthcare operations and social distancing. In some instances, trial delays created statistical challenges, particularly with trials using stepped-wedge designs, and necessitated changing data collection strategies or modifying interventions. The majority of projects used existing stakeholder-driven approaches to adapt interventions. Several benefits of these adaptions were identified, including expanded outreach capabilities and ability to study virtual intervention delivery. All trial teams were able to adapt to pandemic-related modifications. Conclusion In a group of 14 ongoing pragmatic clinical trials, there was significant impact of COVID-19 on trial activities. Engaging appropriate stakeholders was critical to designing and implementing trial modifications and making continued safe progress toward meeting research objectives.

BACKGROUND: One in three cardiovascular disease (CVD) patients experience significant anxiety and depression. Current psychological interventions have limited efficacy in reducing such symptoms and are offered as a face-to-face intervention that may be a barrier to accessing treatment. We evaluated the feasibility and acceptability of delivering assisted home-based self-help metacognitive therapy (home-MCT) to cardiac rehabilitation (CR) patients experiencing anxiety and depression.METHOD: One hundred eight CR patients with elevated anxiety and/or depression were recruited to a single-blind randomized feasibility trial across two United Kingdom National Health Service Trusts and were randomized to usual CR or usual CR plus home-MCT. The feasibility and acceptability of adding home-MCT to CR was based on credibility or expectancy ratings, recruitment rate, drop-outs, number of CR and home-MCT modules completed, and ability of outcome measures to discriminate between patients. The study was used to refine the sample size estimate for a full-scale trial. The quality of telephone support calls delivered by CR staff trained in MCT was assessed.RESULTS: Home-MCT was found to be feasible and acceptable for the current CR patients with anxiety and depression. Recruitment and retention of participants was high, and attendance at CR was similar for both groups. Completion of home-MCT was high, but the quality of telephone support calls delivered was lower than expected.CONCLUSIONS: Home-MCT was acceptable and feasible to deliver to CR patients experiencing anxiety and depression, and the feasibility of conducting a full-scale trial of the intervention was established. Home-MCT may provide additional treatment options for cardiac patients experiencing psychological distress. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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.

Background Although it is generally accepted that physical activity and flares of low back pain (LBP) are related, evidence for the directionality of this association is mixed. The Flares of Low back pain with Activity Research Study (FLAReS) takes a novel approach to distinguish the short-term effects of specific physical activities on LBP flares from the cumulative effects of such activities, by conducting a longitudinal case-crossover study nested within a cohort study. The first aim is to estimate the short-term effects (≤ 24 h) of specific physical activities on LBP flares among Veterans in primary care in the Veterans Affairs healthcare system. The second aim is to estimate the cumulative effects of specific activities on LBP-related functional limitations at 1-year follow-up. Methods Up to 550 adults of working age (18—65 years) seen for LBP in primary care complete up to 36 “Scheduled” surveys over 1-year follow-up, and also complete unscheduled “Flare Window” surveys after the onset of new flares. Each survey asks about current flares and other factors associated with LBP. Surveys also inquire about activity exposures over the 24 h, and 2 h, prior to the time of survey completion (during non-flare periods) or prior to the time of flare onset (during flares). Other questions evaluate the number, intensity, duration, and/or other characteristics of activity exposures. Other exposures include factors related to mood, lifestyle, exercise, concurrent treatments, and injuries. Some participants wear actigraphy devices for weeks 1–4 of the study. The first aim will examine associations between 10 specific activity categories and participant-reported flares over 1-year follow-up. The second aim will examine associations between the frequency of exposure to 10 activity categories over weeks 1–4 of follow-up and long-term functional limitations at 12 months. All analyses will use a biopsychosocial framework accounting for potential confounders and effect modifiers. Discussion FLAReS will provide empirically derived estimates of both the short-term and cumulative effects of specific physical activities for Veterans with LBP, helping to better understand the role of physical activities in those with LBP. Trial Registration ClinicalTrials.gov NCT04828330, registered April 2, 2021.