Understanding the Link Between Obesity and Severe COVID-19 Outcomes: Causal Mediation by Systemic Inflammatory Response.

BACKGROUND: Obesity is an established risk factor for severe COVID-19 outcomes. The mechanistic underpinnings of this association are not well-understood. OBJECTIVE: To evaluate the mediating role of systemic inflammation in obesity-associated COVID-19 outcomes. METHODS: This hospital-based, observational study included 3828 SARS-CoV-2-infected patients who were hospitalized February to May 2020 at Massachusetts General Hospital (MGH) or Columbia University Irving Medical Center/New York Presbyterian Hospital (CUIMC/NYP). We use mediation analysis to evaluate whether peak inflammatory biomarkers (C-reactive protein [CRP], erythrocyte sedimentation rate [ESR], D-dimer, ferritin, white blood cell count and interleukin-6) are in the causal pathway between obesity (BMI ≥ 30) and mechanical ventilation or death within 28 days of presentation to care. RESULTS: In the MGH cohort (n = 1202), obesity was associated with greater likelihood of ventilation or death (OR = 1.73; 95% CI = [1.25, 2.41]; P = 0.001) and higher peak CRP (P < 0.001) compared with nonobese patients. The estimated proportion of the association between obesity and ventilation or death mediated by CRP was 0.49 (P < 0.001). Evidence of mediation was more pronounced in patients < 65 years (proportion mediated = 0.52 [P < 0.001] vs 0.44 [P = 0.180]). Findings were more moderate but consistent for peak ESR. Mediation by other inflammatory markers was not supported. Results were replicated in CUIMC/NYP cohort (n = 2626). CONCLUSION: Findings support systemic inflammatory pathways in obesity-associated severe COVID-19 disease, particularly in patients < 65 years, captured by CRP and ESR. Contextualized in clinical trial findings, these results reveal therapeutic opportunity to target systemic inflammatory pathways and monitor interventions in high-risk subgroups and particularly obese patients.

Artificial intelligence-based screening for diabetic retinopathy at community hospital.

OBJECTIVES: The purpose of this study is to assess the accuracy of artificial intelligence (AI)-based screening for diabetic retinopathy (DR) and to explore the feasibility of applying AI-based technique to community hospital for DR screening. METHODS: Nonmydriatic fundus photos were taken for 889 diabetic patients who were screened in community hospital clinic. According to DR international classification standards, ophthalmologists and AI identified and classified these fundus photos. The sensitivity and specificity of AI automatic grading were evaluated according to ophthalmologists' grading. RESULTS: DR was detected by ophthalmologists in 143 (16.1%) participants and by AI in 145 (16.3%) participants. Among them, there were 101 (11.4%) participants diagnosed with referable diabetic retinopathy (RDR) by ophthalmologists and 103 (11.6%) by AI. The sensitivity, specificity and area under the curve (AUC) of AI for detecting DR were 90.79% (95% CI 86.4-94.1), 98.5% (95% CI 97.8-99.0) and 0.946 (95% CI 0.935-0.956), respectively. For detecting RDR, the sensitivity, specificity and AUC of AI were 91.18% (95% CI 86.4-94.7), 98.79% (95% CI 98.1-99.3) and 0.950 (95% CI 0.939-0.960), respectively. CONCLUSION: AI has high sensitivity and specificity in detecting DR and RDR, so it is feasible to carry out AI-based DR screening in outpatient clinic of community hospital.