Azithromycin during Acute Chronic Obstructive Pulmonary Disease Exacerbations Requiring Hospitalization (BACE). A Multicenter, Randomized, Double-Blind, Placebo-controlled Trial.

Rationale: Azithromycin prevents acute exacerbations of chronic obstructive pulmonary disease (AECOPDs); however, its value in the treatment of an AECOPD requiring hospitalization remains to be defined.Objectives: We investigated whether a 3-month intervention with low-dose azithromycin could decrease treatment failure (TF) when initiated at hospital admission and added to standard care.Methods: In an investigator-initiated, multicenter, randomized, double-blind, placebo-controlled trial, patients who had been hospitalized for an AECOPD and had a smoking history of ≥10 pack-years and one or more exacerbations in the previous year were randomized (1:1) within 48 hours of hospital admission to azithromycin or placebo. The study drug (500 mg/d for 3 d) was administered on top of a standardized acute treatment of systemic corticosteroids and antibiotics, and subsequently continued for 3 months (250 mg/2 d). The patients were followed for 6 months thereafter. Time-to-first-event analyses evaluated the TF rate within 3 months as a novel primary endpoint in the intention-to-treat population, with TF defined as the composite of treatment intensification with systemic corticosteroids and/or antibiotics, a step-up in hospital care or readmission for respiratory reasons, or all-cause mortality.Measurements and Main Results: A total of 301 patients were randomized to azithromycin (n = 147) or placebo (n = 154). The TF rate within 3 months was 49% in the azithromycin group and 60% in the placebo group (hazard ratio, 0.73; 95% confidence interval, 0.53-1.01; P = 0.0526). Treatment intensification, step-up in hospital care, and mortality rates within 3 months were 47% versus 60% (P = 0.0272), 13% versus 28% (P = 0.0024), and 2% versus 4% (P = 0.5075) in the azithromycin and placebo groups, respectively. Clinical benefits were lost 6 months after withdrawal.Conclusions: Three months of azithromycin for an infectious AECOPD requiring hospitalization may significantly reduce TF during the highest-risk period. Prolonged treatment seems to be necessary to maintain clinical benefits.

Prognostic Value and Reproducibility of AI-assisted Analysis of Lung Involvement in COVID-19 on Low-Dose Submillisievert Chest CT: Sample Size Implications for Clinical Trials.

PURPOSE: To compare the prognostic value and reproducibility of visual versus AI-assisted analysis of lung involvement on submillisievert low-dose chest CT in COVID-19 patients. MATERIALS AND METHODS: This was a HIPAA-compliant, institutional review board-approved retrospective study. From March 15 to June 1, 2020, 250 RT-PCR confirmed COVID-19 patients were studied with low-dose chest CT at admission. Visual and AI-assisted analysis of lung involvement was performed by using a semi-quantitative CT score and a quantitative percentage of lung involvement. Adverse outcome was defined as intensive care unit (ICU) admission or death. Cox regression analysis, Kaplan-Meier curves, and cross-validated receiver operating characteristic curve with area under the curve (AUROC) analysis was performed to compare model performance. Intraclass correlation coefficients (ICCs) and Bland- Altman analysis was used to assess intra- and interreader reproducibility. RESULTS: Adverse outcome occurred in 39 patients (11 deaths, 28 ICU admissions). AUC values from AI-assisted analysis were significantly higher than those from visual analysis for both semi-quantitative CT scores and percentages of lung involvement (all P<0.001). Intrareader and interreader agreement rates were significantly higher for AI-assisted analysis than visual analysis (all ICC ≥0.960 versus ≥0.885). AI-assisted variability for quantitative percentage of lung involvement was 17.2% (coefficient of variation) versus 34.7% for visual analysis. The sample size to detect a 5% change in lung involvement with 90% power and an α error of 0.05 was 250 patients with AI-assisted analysis and 1014 patients with visual analysis. CONCLUSION: AI-assisted analysis of lung involvement on submillisievert low-dose chest CT outperformed conventional visual analysis in predicting outcome in COVID-19 patients while reducing CT variability. Lung involvement on chest CT could be used as a reliable metric in future clinical trials.