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.

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Rationale: Recent studies suggest that both hypo- and hyper-inflammatory ARDS phenotypes characterize severe COVID-19-related pneumonia. The role of lung SARS-CoV-2 viral load in contributing to these phenotypes remains unknown. Objectives: To redefine COVID-19 ARDS phenotypes when considering semi-quantitative SARS-CoV-2 RT-PCR in the bronchoalveolar lavage of intubated patients. To compare the relevance of deep respiratory samples vs plasma in linking the immune response and the semi-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 semi-quantitative SARS-CoV-2 RT-PCR in the bronchoalveolar lavage. Hierarchical clustering on principal components was applied separately on the two compartments datasets. Baseline characteristics were compared between clusters. Measurements and Results: 20 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 two cluster model of ARDS revealed a new cluster characterized by hypo-inflammatory 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 hypo-inflammatory responses but high viral loads in the lower airways. Our approach adds the infection dimension to ARDS phenotypes described in COVID-19 pneumonia

Background COVID-19 rapidly evolved into a global pandemic. Contact tracing with isolation and quarantine contribute to epidemic control but they are time consuming, costly and may be incomplete. We set out to assess the usability and performance characteristics of Bluetooth Low-Energy (BLE) wireless technology for indoor localization applied to contact tracing in healthcare settings. Methods Consented healthcare workers (HCW) from 2 designated COVID-19 wards (one intensive care unit (ICU) and one medical ward) were equipped with coin-sized BLE- emitting beacons. The signal was captured by small embedded computers (anchors) placed at designated locations, time-stamped and transmitted to an edge server via secure Wi-Fi where data were stored and real time contact algorithms were run (Fig.1). We developed experiments mimicking clinical scenarios and tested indoor localization during observed clinical activity for 6 months. We constructed our algorithms based on room structure (e.g. open spaces vs computer rooms) and activity characteristics (e.g. rounding in a large group vs 2 healthcare workers sitting together). We used 1) radio fingerprint localization where an initial virtual radio map was developed, 2) semantic localization which carries additional information such as proximity to a computer to define indirect transmission via fomites, and 3) clustering contact tracing to identify individuals rounding together. Close contact was defined as per the CDC guidelines. Fig. 1System configuration Results Consent rate was 43.3% with 187 HCW enrolled in the study. Consent rate was higher in the ICU and among attendings. All participants were compliant with wearing the beacons for the duration of the study. The performance characteristics for contact tracing using fingerprinting methods were AUROC 0.93, AUPRC 0.96, sensitivity 0.9, specificity 0.77 with F1 score of 0.89 and overall accuracy of 0.85. The clustering contact tracing registered a sensitivity of 0.86, specificity 0.89, F1 score 0.91 and accuracy 0.87. Computation time necessary to generate a list of close contacts as per specified criteria was less than 30 minutes. Conclusion We have developed and tested a reliable and accurate, low-cost and easily deployable system based on BLE technology to improve contact tracing among healthcare workers. Disclosures M Cristina Vazquez Guillamet, MD, AUPH: Stocks/Bonds|BNGO: Stocks/Bonds|OCGN: Stocks/Bonds|SESN: Stocks/Bonds.