The synovial fluids (SF) of patients with rheumatoid arthritis (RA) were investigated for their effects on thymocytes of C3H/HeJ mice. Of the 20 SF tested, 17 (85%) showed an augmentation of the phytohaemagglutinin (PHA) induced thymocyte stimulation. Out of 16 SF of patients with osteoarthrosis, such an activity was detected in only one (6.25%). Further characterisation of the amplification factor revealed that (1) the SF of RA patients augmented both the PHA and the Concanavalin A response of the thymocytes (2) in the absence of mitogens, SF-treated thymocytes showed an increased uptake of 3H-thymidine, (3) the SF did not propagate the growth of an interleukin 2 dependent ovalbumin specific T cell clone, but (4) the SF were found to be required for optimal interleukin 2 release by spleen cells stimulated with suboptimal doses of lectin. Based on these biological effects the factor in the SF of RA patients is suggested to represent an interleukin 1 (IL-1). IL-1 produced in cultures by activated macrophages has been shown to stimulate T and B cell functions and to induce the production of collagenase and prostaglandins by cultured synovial cells. Both properties of IL-1 could be relevant in the pathogenesis of RA.

OBJECTIVES: To evaluate the impact of ICU surge on mortality and to explore clinical and sociodemographic predictors of mortality. DESIGN: Retrospective cohort analysis. SETTING: NYC Health + Hospitals ICUs. PATIENTS: Adult ICU patients with coronavirus disease 2019 admitted between March 24, and May 12, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Hospitals reported surge levels daily. Uni- and multivariable analyses were conducted to assess factors impacting in-hospital mortality. Mortality in Hispanic patients was higher for high/very high surge compared with low/medium surge (69.6% vs 56.4%; p = 0.0011). Patients 65 years old and older had similar mortality across surge levels. Mortality decreased from high/very high surge to low/medium surge in, patients 18–44 years old and 45–64 (18–44 yr: 46.4% vs 27.3%; p = 0.0017 and 45–64 yr: 64.9% vs 53.2%; p = 0.002), and for medium, high, and very high poverty neighborhoods (medium: 69.5% vs 60.7%; p = 0.019 and high: 71.2% vs 59.7%; p = 0.0078 and very high: 66.6% vs 50.7%; p = 0.0003). In the multivariable model high surge (high/very high vs low/medium odds ratio, 1.4; 95% CI, 1.2–1.8), race/ethnicity (Black vs White odds ratio, 1.5; 95% CI, 1.1–2.0 and Asian vs White odds ratio 1.5; 95% CI, 1.0–2.3; other vs White odds ratio 1.5, 95% CI, 1.0–2.3), age (45–64 vs 18–44 odds ratio, 2.0; 95% CI, 1.6–2.5 and 65–74 vs 18–44 odds ratio, 5.1; 95% CI, 3.3–8.0 and 75+ vs 18–44 odds ratio, 6.8; 95% CI, 4.7–10.1), payer type (uninsured vs commercial/other odds ratio, 1.7; 95% CI, 1.2–2.3; medicaid vs commercial/other odds ratio, 1.3; 95% CI, 1.1–1.5), neighborhood poverty (medium vs low odds ratio 1.6, 95% CI, 1.0–2.4 and high vs low odds ratio, 1.8; 95% CI, 1.3–2.5), comorbidities (diabetes odds ratio, 1.6; 95% CI, 1.2–2.0 and asthma odds ratio, 1.4; 95% CI, 1.1–1.8 and heart disease odds ratio, 2.5; 95% CI, 2.0–3.3), and interventions (mechanical ventilation odds ratio, 8.8; 95% CI, 6.1–12.9 and dialysis odds ratio, 3.0; 95% CI, 1.9–4.7) were significant predictors for mortality. CONCLUSIONS: Patients admitted to ICUs with higher surge scores were at greater risk of death. Impact of surge levels on mortality varied across sociodemographic groups.

Background Despite evidence of socio-demographic disparities in outcomes of COVID-19, little is known about characteristics and clinical outcomes of patients admitted to public hospitals during the COVID-19 outbreak. Objective To assess demographics, comorbid conditions, and clinical factors associated with critical illness and mortality among patients diagnosed with COVID-19 at a public hospital in New York City (NYC) during the first month of the COVID-19 outbreak. Design Retrospective chart review of patients diagnosed with COVID-19 admitted to NYC Health + Hospitals / Bellevue Hospital from March 9th to April 8th, 2020. Results A total of 337 patients were diagnosed with COVID-19 during the study period. Primary analyses were conducted among those requiring supplemental oxygen (n = 270); half of these patients (135) were admitted to the intensive care unit (ICU). A majority were male (67.4%) and the median age was 58 years. Approximately one-third (32.6%) of hypoxic patients managed outside the ICU required non-rebreather or non-invasive ventilation. Requirement of renal replacement therapy occurred in 42.3% of ICU patients without baseline end-stage renal disease. Overall, 30-day mortality among hypoxic patients was 28.9% (53.3% in the ICU, 4.4% outside the ICU). In adjusted analyses, risk factors associated with mortality included dementia (adjusted risk ratio (aRR) 2.11 95%CI 1.50–2.96), age 65 or older (aRR 1.97, 95%CI 1.31–2.95), obesity (aRR 1.37, 95%CI 1.07–1.74), and male sex (aRR 1.32, 95%CI 1.04–1.70). Conclusion COVID-19 demonstrated severe morbidity and mortality in critically ill patients. Modifications in care delivery outside the ICU allowed the hospital to effectively care for a surge of critically ill and severely hypoxic patients.

Setting We conducted a retrospective study among HIV-infected adult (≥18 years) pulmonary tuberculosis (TB) suspects who underwent Xpert MTB/RIF (Xpert) testing at McCord Hospital and its adjoining HIV clinic in Durban, South Africa. Objective To determine if Xpert testing performed at a centralized laboratory accelerated time to TB diagnosis. Design We obtained data on sputum smear microscopy (AFB), Xpert and the rationale for treatment initiation from medical records. The primary outcome was “total diagnostic time,” defined as time from sputum collection to clinicians’ receipt of results. A linear mixed-effects model compared the duration of steps in the diagnostic pathway across testing modalities. Results Among 403 participants, the median “total diagnostic time” for AFB and Xpert was 3.3 and 6.4 days, respectively (P <0.001). When compared to AFB, the median delay for Xpert “laboratory processing” was 1.4 days (P<0.001) and “result transfer to clinic” was 1.7 days (P<0.001). Among 86 Xpert-positive participants who initiated treatment, 49 (57%) started treatment based on clinical suspicion or AFB-positive results, while only 32 (37%) started treatment based on Xpert-positive results. Conclusion In our setting, Xpert results took twice as long as AFB results to reach clinicians. Replacing AFB with centralized Xpert may delay TB diagnoses in some settings.