Treatment of Polymyalgia Rheumatica by Rheumatology Providers: Analysis from the American College of Rheumatology RISE Registry.

OBJECTIVE: This study describes the demographics, comorbidities, and treatment patterns in a national cohort of patients with polymyalgia rheumatica (PMR) who received care from rheumatology providers. METHODS: Patients with PMR were identified in the American College of Rheumatology Rheumatology Informatics System for Effectiveness registry from 2016 to 2022. Use of glucocorticoids and immunomodulatory antirheumatic medications used as steroid-sparing agents were examined overall and in a subgroup of patients new to rheumatology practices, the majority with presumed new-onset PMR. In these new patients, multivariate logistic regressions were performed to identify factors associated with persistent glucocorticoid and steroid-sparing agent use at 12 to 24 months. RESULTS: A total of 26,102 patients with PMR were identified, of which 16,703 new patients were included in the main analysis. Patients were predominantly female (55.8%) and White (46.7%), with a mean age of 72.0 years. Hypertension (81.2%), congestive heart failure (52.4%), hyperlipidemia (41.3%), and ischemic heart disease (36.0%) were the most prevalent comorbidities. At baseline, 92.3% of patients were on glucocorticoids, and only 13.1% were on a steroid-sparing agent. At 12 to 24 months, most patients remained on glucocorticoids (63.8%). Although there was an increase in use through follow-up, antirheumatic medications were prescribed only to a minority (39.0%) of patients with PMR. CONCLUSION: In this large US-based study of patients with PMR receiving rheumatology care, only a minority of patients were prescribed steroid-sparing agents during the first 24 months of follow-up; most patients remained on glucocorticoids past one year. Further identification of patients who would benefit from steroid-sparing agents and the timing of steroid-sparing agent initiation is needed.

Efficacy of probiotic treatment as post-exposure prophylaxis for COVID-19: A double-blind, Placebo-Controlled Randomized trial.

BACKGROUND & AIMS: The COVID-19 pandemic continues to pose unprecedented challenges to worldwide health. While vaccines are effective, additional strategies to mitigate the spread/severity of COVID-19 continue to be needed. Emerging evidence suggests susceptibility to respiratory tract infections in healthy subjects can be reduced by probiotic interventions; thus, probiotics may be a low-risk, low-cost, and easily implementable modality to reduce risk of COVID-19. METHODS: In this initial study, we conducted a randomized, double-blind, placebo-controlled trial across the United States testing probiotic Lacticaseibacillus rhamnosus GG (LGG) as postexposure prophylaxis for COVID-19 in 182 participants who had household exposure to someone with confirmed COVID-19 diagnosed within ≤7 days. Participants were randomized to receive oral LGG or placebo for 28 days. The primary outcome was development of illness symptoms within 28 days of COVID-19 exposure. Stool was collected to evaluate microbiome changes. RESULTS: Intention-to-treat analysis showed LGG treatment led to a lower likelihood of developing illness symptoms versus placebo (26.4 % vs. 42.9 %, p = 0.02). Further, LGG was associated with a statistically significant reduction in COVID-19 diagnosis (log rank, p = 0.049) via time-to-event analysis. Overall incidence of COVID-19 diagnosis did not significantly differ between LGG and placebo groups (8.8 % vs. 15.4 %, p = 0.17). CONCLUSIONS: This data suggests LGG is associated with prolonged time to COVID-19 infection, reduced incidence of illness symptoms, and gut microbiome changes when used as prophylaxis ≤7 days post-COVID-19 exposure, but not overall incidence. This initial work may inform future COVID-19 prevention studies worldwide, particularly in developing nations where Lacticaseibacillus probiotics have previously been utilized to reduce other non-COVID infectious-morbidity. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04399252, Date: 22/05/2020. https://clinicaltrials.gov/ct2/show/NCT04399252.

Long-Term Intracranial Outcomes With Combination Dual Immune-Checkpoint Blockade and Stereotactic Radiosurgery in Patients With Melanoma and Non-Small Cell Lung Cancer Brain Metastases.

PURPOSE: The intracranial benefit of offering dual immune-checkpoint inhibition (D-ICPI) with ipilimumab and nivolumab to patients with melanoma or non-small cell lung cancer (NSCLC) receiving stereotactic radiosurgery (SRS) for brain metastases (BMs) is unknown. We hypothesized that D-ICPI improves local control compared with SRS alone. METHODS AND MATERIALS: Patients with melanoma or NSCLC treated with SRS from 2014 to 2022 were evaluated. Patients were stratified by treatment with D-ICPI, single ICPI (S-ICPI), or SRS alone. Local recurrence, intracranial progression (IP), and overall survival were estimated using competing risk and Kaplan-Meier analyses. IP included both local and distant intracranial recurrence. RESULTS: Two hundred eighty-eight patients (44% melanoma, 56% NSCLC) with 1,704 BMs were included. Fifty-three percent of patients had symptomatic BMs. The median follow-up was 58.8 months. Twelve-month local control rates with D-ICPI, S-ICPI, and SRS alone were 94.73% (95% CI, 91.11%-96.90%), 91.74% (95% CI, 89.30%-93.64%), and 88.26% (95% CI, 84.07%-91.41%). On Kaplan-Meier analysis, only D-ICPI was significantly associated with reduced local recurrence (P = .0032). On multivariate Cox regression, D-ICPI (hazard ratio [HR], 0.4003; 95% CI, 0.1781-0.8728; P = .0239) and planning target volume (HR, 1.022; 95% CI, 1.004-1.035; P = .0059) correlated with local control. One hundred seventy-three (60%) patients developed IP. The 12-month cumulative incidence of IP was 41.27% (95% CI, 30.27%-51.92%), 51.86% (95% CI, 42.78%-60.19%), and 57.15% (95% CI, 44.98%-67.59%) after D-ICPI, S-ICPI, and SRS alone. On competing risk analysis, only D-ICPI was significantly associated with reduced IP (P = .0408). On multivariate Cox regression, D-ICPI (HR, 0.595; 95% CI, 0.373-0.951; P = .0300) and presentation with >10 BMs (HR, 2.492; 95% CI, 1.668-3.725; P < .0001) remained significantly correlated with IP. The median overall survival after D-ICPI, S-ICPI, and SRS alone was 26.1 (95% CI, 15.5-40.7), 21.5 (16.5-29.6), and 17.5 (11.3-23.8) months. S-ICPI, fractionation, and histology were not associated with clinical outcomes. There was no difference in hospitalizations or neurologic adverse events between cohorts. CONCLUSIONS: The addition of D-ICPI for patients with melanoma and NSCLC undergoing SRS is associated with improved local and intracranial control. This appears to be an effective strategy, including for patients with symptomatic or multiple BMs.

Detecting schizophrenia with 3D structural brain MRI using deep learning.

Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods. A deep learning model was then developed, optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia. Our proposed model outperformed the benchmark model, which was also trained with structural MR images using a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987) distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regional analysis localized subcortical regions and ventricles as the most predictive brain regions. Subcortical structures serve a pivotal role in cognitive, affective, and social functions in humans, and structural abnormalities of these regions have been associated with schizophrenia. Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification. Together, these results further demonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structural neuroimaging signatures from a single, standard T1-weighted brain MRI.

Risk factors for deep surgical site infection following surgically treated peri-ankle fractures: a case-control study based on propensity score matching.

AIMS: This study aims to identify the risk factors for deep surgical site infection (DSSI) following surgically treated peri-ankle fractures. METHODS: We performed a retrospective case-control study using the propensity score matching (PSM) method in 1:2 ratio, based on the 6 baseline variables, including age, gender, living area, insurance type, fracture location and surgical date. Data on patients who underwent surgical treatment of peri-ankle fractures were collected by inquiring their hospitalization medical records and operative records, as well as the laboratory reports. Conditional logistic regression analysis was performed to identify the risk factors for DSSI. RESULTS: A total of 2147 patients were eligibly included and 74 had a DSSI, indicating an incidence rate of 3.4%. After PSM, 70 cases of DSSI and 140 controls without DSSI were matched, constituting the study cohort. The univariate analyses showed significant differences between groups in terms of history of any surgery, time to operation, surgical wound classification, smoking, alcohol drinking, RBC count, hemoglobin concentration and hematocrit (%). The conditional logistic regression analysis showed time to operation of < 4 or > 9 (vs 4-9 days); unclean wound, current smoking, high-energy injury mechanism and lower hematocrit were independent risk factors for DSSI. CONCLUSIONS: Timely modification of smoking and hematocrit (%), and limiting operation within a rational time frame for an optimized soft tissue condition, may provide potential clinical benefits for SSI prevention.

Improving across-dataset brain tissue segmentation for MRI imaging using transformer.

Brain tissue segmentation has demonstrated great utility in quantifying MRI data by serving as a precursor to further post-processing analysis. However, manual segmentation is highly labor-intensive, and automated approaches, including convolutional neural networks (CNNs), have struggled to generalize well due to properties inherent to MRI acquisition, leaving a great need for an effective segmentation tool. This study introduces a novel CNN-Transformer hybrid architecture designed to improve brain tissue segmentation by taking advantage of the increased performance and generality conferred by Transformers for 3D medical image segmentation tasks. We first demonstrate the superior performance of our model on various T1w MRI datasets. Then, we rigorously validate our model's generality applied across four multi-site T1w MRI datasets, covering different vendors, field strengths, scan parameters, and neuropsychiatric conditions. Finally, we highlight the reliability of our model on test-retest scans taken in different time points. In all situations, our model achieved the greatest generality and reliability compared to the benchmarks. As such, our method is inherently robust and can serve as a valuable tool for brain related T1w MRI studies. The code for the TABS network is available at: https://github.com/raovish6/TABS.