University Hospitals of Cleveland

BACKGROUND:Corticosteroid injections (CSIs) are widely employed in facet and sacroiliac joint pain. Similar to CSIs at other sites (peripheral nerve blocks, joints, epidural), these injections are associated with potential adverse events. These multisociety consensus recommendations aim to develop evidence-based statements and recommendations on the safe use of CSIs in facet joint and sacroiliac joint injections. METHODS:Development of the consensus recommendations was approved by the American Society of Regional Anesthesia and Pain Medicine Board of Directors and several other societies that agreed to participate. The scope of statements and recommendations was agreed on to include safety of the injection technique (landmark-guided, ultrasound, or radiology-aided injections); effect of the addition of the corticosteroid on effectiveness (vs local anesthetic or saline); and adverse events related to the injection. Experts were assigned topics to perform a comprehensive literature review and draft statements and recommendations, which were refined and voted for consensus (>75% agreement) using a modified Delphi process. A modified version of the US Preventive Services Task Force grading of evidence and strength of recommendation was followed. RESULTS:All statements and recommendations were approved by all participants after four rounds of discussion. The Practice Guidelines Committees and Boards of Directors of the participating societies also approved all statements and recommendations. Injection of corticosteroid into the facet joint in patients with joint inflammation may relieve pain and improves function. Intra-articular, extra-articular (periarticular), and combined administration are effective for sacroiliac joint injections. No dose-response studies exist, but CSIs containing 10 mg of methylprednisolone or triamcinolone per facet joint and 40 mg per sacroiliac joint (SIJ) (or their respective pharmacologic equivalents) are reasonable. CONCLUSIONS:In this practice recommendation, we provide statements and recommendations on facet and sacroiliac joint CSIs, the optimal doses, and intervals and criteria for repeating the CSIs in patients with facet joint and sacroiliac joint pain.

The post-acute sequelae of SARS-CoV-2 (PASC), also known as long COVID, remain a significant health issue that is incompletely understood. Predicting which acutely infected individuals will develop long COVID is challenging due to the absence of established biomarkers, clear disease mechanisms, or well-defined sub-phenotypes. Machine learning (ML) models may address this gap by leveraging clinical data to enhance diagnostic precision. Clinical data, including antibody titers and viral load measurements collected at the time of hospital admission, are used to predict the likelihood of acute COVID-19 progressing to long COVID. Machine learning models are trained and evaluated for predictive performance. Feature importance analysis is performed to identify the most influential predictors. The machine learning models achieve median AUROC values ranging from 0.64 to 0.66 and AUPRC values between 0.51 and 0.54, demonstrating predictive capabilities. Low antibody titers and high viral loads at hospital admission emerge as the strongest predictors of long COVID outcomes. Comorbidities—such as chronic respiratory, cardiac, and neurologic diseases—and female sex are also identified as significant risk factors. Machine learning models identify patients at risk for developing long COVID based on baseline clinical characteristics. These models guide early interventions, improve patient outcomes, and mitigate the long-term public health impacts of SARS-CoV-2. Long COVID, or post-acute sequelae of SARS-CoV-2, is a prolonged health condition that can occur after acute COVID-19 infection. However, the ability to predict who will develop long COVID remains limited due to the absence of clear tests or biomarkers. We looked at patients’ medical information, including the amount of virus in their body at hospital admission, and how strong their immune response was. Using computer programs that can find hidden patterns in large sets of data, we discovered that people with a weaker immune response, higher amounts of virus, certain long term health problems and women are more likely to develop long COVID. This study highlights that computer-based tools could help doctors identify high-risk patients early and provide care that may prevent long-term complications. Jayavelu, Samaha et al., apply machine learning models on hospital admission data, including antibody titers and viral load, to identify patients at high risk for Long COVID. Low antibody levels, high viral loads, chronic diseases, and female sex are key predictors, supporting early, targeted interventions.

To evaluate alternative diagnoses in patients referred to neuroimmunology for evaluation of autoimmune encephalitis (AE) and/or positive neural antibodies. With increased awareness of AE, AE misdiagnosis has increased—often from improper suspicion of AE or misinterpretation of clinically irrelevant neural antibodies. We retrospectively evaluated all cases referred to our center for AE evaluation and/or a positive neural antibody. We evaluated the frequency and characteristics of patients eventually diagnosed with an alternative diagnosis. A total of 119 patients were referred between 2017 and 2024. Twenty-two were referred for a positive neural antibody, and seven for possible antibody-negative AE after testing negative before referral. Eighty-one patients1 were tested by our center after inpatient admission or outpatient referral. Our center deemed antibody testing unnecessary in 9 patients. Overall, 74 patients were antibody-positive (62

Physical activity is critical for older breast cancer survivors. We explored the experiences and recommendations of older breast cancer survivors from the IMPROVE trial, including a sizable number of older African American and socioeconomically disadvantaged survivors, to inform future implementation and dissemination of sustainable programs. Participants included women, ≥ 65 years, within five years of treatment completion for stage I-III breast cancer who were enrolled into a randomized controlled trial of supervised group moderate-intensity exercise for 20-weeks followed by 32 weeks of unsupervised exercise versus support group (SG) plus Fitbit intervention. Semi-structured exit interviews were conducted at study completion. Interviews were audio-recorded, transcribed verbatim, and analyzed using thematic analysis with constant comparison. Two researchers independently coded transcripts, discussing discrepancies to enrich interpretation. The Social Cognitive Theory and the Transtheoretical Model guided interpretation of results. Between 2016 and 2020, 213 older breast cancer survivors were randomized into the exercise arm, (n = 108) or a SG + Fitbit arm, (n = 105). At study completion, 145 (68

Introduction Heart Failure (HF) and Chronic Obstructive Pulmonary Disease (COPD) are leading causes of hospital readmissions and subsequent healthcare costs. Music therapy (MT), an integrative psychosocial intervention, has shown promise within various populations for reducing stress, anxiety, and depression - significant risk factors for readmission. Our initial single-arm pilot study supported the feasibility and acceptability of a hybrid MT approach. However, the impact of MT on readmission rates and health-related quality of life (HRQoL) within this population remains largely unexplored. This study seeks to address this gap by evaluating the feasibility, acceptability, and preliminary efficacy of a hybrid MT intervention among adult patients with HF or COPD. Hypothesis The hybrid MT intervention will be (1) feasible as evidenced by 70% retention, attendance, and measure completion rates; (2) acceptable based on feedback from qualitative interviews; and (3) superior to waitlist control (WLC) for improving patient-reported outcome measures (PROMs) from baseline to 30-days post-discharge. Methods Using a mixed-methods intervention approach, 60 inpatients aged 30-89 with COPD or HF and access to home videoconferencing technology and a mobile device with a data plan will be recruited during their hospitalizations. Patients currently receiving dialysis and those with significant hearing/visual impairments, severe psychological comorbidities, substance abuse, terminal medical conditions, or end-stage disease will be excluded. Patients will be randomized to receive either (1) two inpatient in-person MT sessions prior to discharge and two virtual MT sessions following discharge that focus on music-assisted relaxation, stress management, and disease-specific exercises (e.g., harmonica training for respiratory health in COPD); or (2) WLC. PROMs for HRQoL, perceived stress, and self-efficacy will be assessed at baseline, 15-, and 30-days post-discharge. MT participants will also be invited to complete a semi-structured interview to further investigate acceptability. Results Recruitment for this trial began in August 2024. Full results are expected in Spring 2025 and will be presented should this abstract be accepted. As of March 10, 2025, we have recruited 49 participants (mean age 63.9 years, 60.4% female, 69.4% with HF, and 24.9% of those approached) with 25 randomized to MT, 23 randomized to WLC, and 1 withdrawing before randomization. Average session attendance among MT participants who have completed study activities is currently 71.7%. Within the MT group, 80.0% have completed 15-day and 86.7% have completed 30-day post-discharge measures, while measure completion rates in the WLC group are currently 94.1% at both time points. Conclusions Preliminary results of this ongoing RCT support the feasibility of hybrid MT session delivery and data collection procedures. Successful conduct of this feasibility study will inform a future fully-powered trial to examine MT’s efficacy for addressing 30-day readmission rates.