Quantitative SARS-CoV-2 RT-PCR and Bronchoalveolar Cytokine Concentrations Redefine the COVID-19 Phenotypes in Critically Ill Patients.

RATIONALE: Recent studies suggest that both hypo- and hyperinflammatory acute respiratory distress syndrome (ARDS) phenotypes characterize severe COVID-19-related pneumonia. The role of lung Severe Acute Respiratory Syndrome - Coronavirus 2 (SARS-CoV-2) viral load in contributing to these phenotypes remains unknown. OBJECTIVES: To redefine COVID-19 ARDS phenotypes when considering quantitative SARS-CoV-2 RT-PCR in the bronchoalveolar lavage of intubated patients. To compare the relevance of deep respiratory samples versus plasma in linking the immune response and the quantitative viral loads. METHODS: Eligible subjects were adults diagnosed with COVID-19 ARDS who required mechanical ventilation and underwent bronchoscopy. We recorded the immune response in the bronchoalveolar lavage and plasma and the quantitative SARS-CoV-2 RT-PCR in the bronchoalveolar lavage. Hierarchical clustering on principal components was applied separately on the 2 compartments' datasets. Baseline characteristics were compared between clusters. MEASUREMENTS AND RESULTS: Twenty subjects were enrolled between August 2020 and March 2021. Subjects underwent bronchoscopy on average 3.6 days after intubation. All subjects were treated with dexamethasone prior to bronchoscopy, 11 of 20 (55.6%) received remdesivir and 1 of 20 (5%) received tocilizumab. Adding viral load information to the classic 2-cluster model of ARDS revealed a new cluster characterized by hypoinflammatory responses and high viral load in 23.1% of the cohort. Hyperinflammatory ARDS was noted in 15.4% of subjects. Bronchoalveolar lavage clusters were more stable compared to plasma. CONCLUSIONS: We identified a unique group of critically ill subjects with COVID-19 ARDS who exhibit hypoinflammatory responses but high viral loads in the lower airways. These clusters may warrant different treatment approaches to improve clinical outcomes.

Leveraging Bluetooth low-energy technology to improve contact tracing among healthcare personnel in hospital setting during the coronavirus disease 2019 (COVID-19) pandemic.

To improve contact tracing for healthcare workers, we built and configured a Bluetooth low-energy system. We predicted close contacts with great accuracy and provided an additional contact yield of 14.8%. This system would decrease the effective reproduction number by 56% and would unnecessarily quarantine 0.74% of employees weekly.

Potential Role of Computed Tomography Volumetry in Size Matching in Lung Transplantation.

BACKGROUND: Accumulated knowledge on the outcomes related to size mismatch in lung transplantation derives from predicted total lung capacity equations rather than individualized measurements of donors and recipients. The increasing availability of computed tomography (CT) makes it possible to measure the lung volumes of donors and recipients before transplantation. We hypothesize that CT-derived lung volumes predict a need for surgical graft reduction and primary graft dysfunction. METHODS: Donors from the local organ procurement organization and recipients from our hospital from 2012 to 2018 were included if their CT exams were available. The CT lung volumes and plethysmography total lung capacity were measured and compared with predicted total lung capacity using Bland Altman methods. We used logistic regression to predict the need for surgical graft reduction and ordinal logistic regression to stratify the risk for primary graft dysfunction. RESULTS: A total of 315 transplant candidates with 575 CT scans and 379 donors with 379 CT scans were included. The CT lung volumes closely approximated plethysmography lung volumes and differed from the predicted total lung capacity in transplant candidates. In donors, CT lung volumes systematically underestimated predicted total lung capacity. Ninety-four donors and recipients were matched and transplanted locally. Larger donor and smaller recipient lung volumes estimated by CT predicted a need for surgical graft reduction and were associated with higher primary graft dysfunction grade. CONCLUSION: The CT lung volumes predicted the need for surgical graft reduction and primary graft dysfunction grade. Adding CT-derived lung volumes to the donor-recipient matching process may improve recipients' outcomes.

Uncertainty analysis of chest X-ray lung height measurements and size matching for lung transplantation.

Background: Errors in measuring chest X-ray (CXR) lung heights could contribute to the occurrence of size-mismatched lung transplant procedures. Methods: We first used Bland-Altman analysis for repeated measures to evaluate contributors to measurement error of chest X-ray lung height. We then applied error propagation theory to assess the impact of measurement error on size matching for lung transplantation. Results: A total 387 chest X-rays from twenty-five donors and twenty-five recipients were measured by two raters. Individual standard deviation for lung height differences were independent of age, sex, donor vs. recipient, diagnostic group and race/ethnicity and all were pooled for analysis. Bias between raters was 0.27 cm (±0.03) and 0.22 cm (±0.06) for the right and left lung respectively. Within subject variability was the biggest contributor to error in measurement, 2.76 cm (±0.06) and 2.78 cm (±0.2) for the right and left lung height. A height difference of 4.4 cm or more (95% CI: ±4.2, ±4.6 cm) between the donor and the recipient right lung height has to be accepted to ensure matching for at least 95% of patients with the same true lung height. This difference decreases to ±1.1 cm (95% CI: ±0.9, ±1.3 cm) when the average from all available chest X-rays is used. The probability of matching a donor and a recipient decreases with increasing true lung height difference. Conclusions: Individual chest X-ray lung heights are imprecise for the purpose of size matching in lung transplantation. Averaging chest X-rays lung heights reduced uncertainty.