The association of clinically relevant variables with chest radiograph lung disease burden quantified in real-time by radiologists upon initial presentation in individuals hospitalized with COVID-19.

OBJECTIVES: We aimed to correlate lung disease burden on presentation chest radiographs (CXR), quantified at the time of study interpretation, with clinical presentation in patients hospitalized with coronavirus disease 2019 (COVID-19). MATERIAL AND METHODS: This retrospective cross-sectional study included 5833 consecutive adult patients, aged 18 and older, hospitalized with a diagnosis of COVID-19 with a CXR quantified in real-time while hospitalized in 1 of 12 acute care hospitals across a multihospital integrated healthcare network between March 24, 2020, and May 22, 2020. Lung disease burden was quantified in real-time by 118 radiologists on 5833 CXR at the time of exam interpretation with each lung annotated by the degree of lung opacity as clear (0%), mild (1-33%), moderate (34-66%), or severe (67-100%). CXR findings were classified as (1) clear versus disease, (2) unilateral versus bilateral, (3) symmetric versus asymmetric, or (4) not severe versus severe. Lung disease burden was characterized on initial presentation by patient demographics, co-morbidities, vital signs, and lab results with chi-square used for univariate analysis and logistic regression for multivariable analysis. RESULTS: Patients with severe lung disease were more likely to have oxygen impairment, an elevated respiratory rate, low albumin, high lactate dehydrogenase, and high ferritin compared to non-severe lung disease. A lack of opacities in COVID-19 was associated with a low estimated glomerular filtration rate, hypernatremia, and hypoglycemia. CONCLUSIONS: COVID-19 lung disease burden quantified in real-time on presentation CXR was characterized by demographics, comorbidities, emergency severity index, Charlson Comorbidity Index, vital signs, and lab results on 5833 patients. This novel approach to real-time quantified chest radiograph lung disease burden by radiologists needs further research to understand how this information can be incorporated to improve clinical care for pulmonary-related diseases.. An absence of opacities in COVID-19 may be associated with poor oral intake and a prerenal state as evidenced by the association of clear CXRs with a low eGFR, hypernatremia, and hypoglycemia.

Application of a Machine Learning Algorithm to Develop and Validate a Prediction Model for Ambulatory Non-Arrivals.

BACKGROUND: Non-arrivals to scheduled ambulatory visits are common and lead to a discontinuity of care, poor health outcomes, and increased subsequent healthcare utilization. Reducing non-arrivals is important given their association with poorer health outcomes and cost to health systems. OBJECTIVE: To develop and validate a prediction model for ambulatory non-arrivals. DESIGN: Retrospective cohort study. PATIENTS OR SUBJECTS: Patients at an integrated health system who had an outpatient visit scheduled from January 1, 2020, to February 28, 2022. MAIN MEASURES: Non-arrivals to scheduled appointments. KEY RESULTS: There were over 4.3 million ambulatory appointments from 1.2 million adult patients. Patients with appointment non-arrivals were more likely to be single, racial/ethnic minorities, and not having an established primary care provider compared to those who arrived at their appointments. A prediction model using the XGBoost machine learning algorithm had the highest AUC value (0.768 [0.767-0.770]). Using SHAP values, the most impactful features in the model include rescheduled appointments, lead time (number of days from scheduled to appointment date), appointment provider, number of days since last appointment with the same department, and a patient's prior appointment status within the same department. Scheduling visits close to an appointment date is predicted to be less likely to result in a non-arrival. Overall, the prediction model calibrated well for each department, especially over the operationally relevant probability range of 0 to 40%. Departments with fewer observations and lower non-arrival rates generally had a worse calibration. CONCLUSIONS: Using a machine learning algorithm, we developed a prediction model for non-arrivals to scheduled ambulatory appointments usable for all medical specialties. The proposed prediction model can be deployed within an electronic health system or integrated into other dashboards to reduce non-arrivals. Future work will focus on the implementation and application of the model to reduce non-arrivals.

Understanding Mental Health Needs and Gathering Feedback on Transcutaneous Auricular Vagus Nerve Stimulation as a Potential PTSD Treatment among 9/11 Responders Living with PTSD Symptoms 20 Years Later: A Qualitative Approach.

Posttraumatic stress disorder (PTSD) remains one of the most prevalent diagnoses of World Trade Center (WTC) 9/11 responders. Transcutaneous auricular vagus nerve stimulation (taVNS) is a potential treatment for PTSD, as it can downregulate activity in the brain, which is known to be related to stress responses and hyperarousal. To understand barriers and facilitators to engagement in mental health care and the feasibility and acceptability of using the taVNS device as a treatment for PTSD symptoms, a focus group was conducted among patients from the Queens WTC Health Program who had elevated symptoms of PTSD. The focus group discussion was recorded, transcribed, and analyzed. Three themes and subthemes emerged: (1) the continued prevalence of mental health difficulties and systematic challenges to accessing care; (2) positive reception toward the taVNS device as a potential treatment option, including a discussion of how to increase usability; and (3) feedback on increasing the feasibility and acceptance of the research methodology associated with testing the device in a pilot clinical trial. The findings highlight the need for additional treatment options to reduce PTSD symptoms in this population and provide key formative phase input for the pilot clinical trial of taVNS.

Transcutaneous auricular vagus nerve stimulation reduces pain and fatigue in patients with systemic lupus erythematosus: a randomised, double-blind, sham-controlled pilot trial.

OBJECTIVES: Musculoskeletal pain and fatigue are common features in systemic lupus erythematosus (SLE). The cholinergic anti-inflammatory pathway is a physiological mechanism diminishing inflammation, engaged by stimulating the vagus nerve. We evaluated the effects of non-invasive vagus nerve stimulation in patients with SLE and with musculoskeletal pain. METHODS: 18 patients with SLE and with musculoskeletal pain ≥4 on a 10 cm Visual Analogue Scale were randomised (2:1) in this double-blind study to receive transcutaneous auricular vagus nerve stimulation (taVNS) or sham stimulation (SS) for 4 consecutive days. Evaluations at baseline, day 5 and day 12 included patient assessments of pain, disease activity (PtGA) and fatigue. Tender and swollen joint counts and the Physician Global Assessment (PGA) were completed by a physician blinded to the patient's therapy. Potential biomarkers were evaluated. RESULTS: taVNS and SS were well tolerated. Subjects receiving taVNS had a significant decrease in pain and fatigue compared with SS and were more likely (OR=25, p=0.02) to experience a clinically significant reduction in pain. PtGA, joint counts and PGA also improved. Pain reduction and improvement of fatigue correlated with the cumulative current received. In general, responses were maintained through day 12. Plasma levels of substance P were significantly reduced at day 5 compared with baseline following taVNS but other neuropeptides, serum and whole blood-stimulated inflammatory mediators, and kynurenine metabolites showed no significant change at days 5 or 12 compared with baseline. CONCLUSION: taVNS resulted in significantly reduced pain, fatigue and joint scores in SLE. Additional studies evaluating this intervention and its mechanisms are warranted.