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Background While point-of-care ultrasound (POCUS) has been used to track disease resolution, temporal trends in lung ultrasound (LUS) findings among hospitalized patients with COVID-19 is not well-characterized. Methods We studied 413 LUS scans in 244 participants ≥ 18 years of age hospitalized for COVID-19 pneumonia within 28 days of symptom onset from April, 2020 until September, 2021 at the Johns Hopkins Hospital, Baltimore Maryland. All patients were scanned using a 12-lung zone protocol and repeat scans were obtained in 3 days (N=114), 7 days (N=53), and weekly (N=9) from the initial scan. Participants were followed to determine clinical outcomes until hospital discharge and vital status at 28-days. Ultrasounds were independently reviewed for lung artifacts, and the composite mean LUS score (ranging from 0 to 3) across lung zones was determined. Trends of mean LUS scores and % lung fields with A-lines (indicating proportion of normal lung fields) were plotted by peak severity (mild, moderate, and severe defined by the World Health Organization Ordinal Scale) over time from symptom onset. Differences in mean LUS score or % A-lines changes over time between peak severity levels were evaluated using a Kruskal-Wallis test and linear mixed-effected models with an exchangeable correlation structure. Results Among 244 patients in our cohort (mean age of 58.2 (SD 15.0) years, and 55.7% female) (Table 1), there was no change in average mean LUS scores between the first two visits by severity groups (Figure 1; Kruskal-Wallis p=0.63). Mean LUS scores were elevated by 0.22 (p< 0.001) in a dose-response manner regardless of duration of illness, but there was no change over time associated with peak severity (p=0.73). Similarly, percentage of A-lines were in 13.9% less lung fields for each increase in peak severity (p< 0.001; Figure 2) regardless of duration of illness. However, a change in mean LUS score did not differ significantly among peak severity levels (p=0.36). Conclusion Mean LUS scores correlated with clinical severity among hospitalized adults when assessed cross-sectionally, however mean LUS score did not change or differ between peak severity levels over the time course of hospitalization. These results do not support serial LUS scans to monitor disease progression. Disclosures All Authors: No reported disclosures.

ObjectiveThe clinical utility of point-of-care lung ultrasound (LUS) for disease severity triage of hospitalized patients with COVID-19 is unclear.DesignProspective cohort studySettingA large tertiary care center in Maryland, USA between April 2020 to September 2021.PatientsHospitalized adults (≥18 years of age) with positive SARS-CoV-2 RT-PCR results.InterventionsNone.Measurements and Main ResultsAll patients were scanned using a standardized protocol including 12 lung zones and followed to determine clinical outcomes until hospital discharge and vital status at 28-days. Ultrasounds were independently reviewed for lung and pleural line artifacts and abnormalities, and the mean Lung Ultrasound Score (ranging from 0 to 3) across lung zones (mLUSS) was determined. The primary outcome was time to ICU-level care, defined as high flow oxygen, noninvasive, or mechanical ventilation, within 28-days of the initial ultrasound. Cox proportional hazards regression models adjusted for age and sex were fit for mLUSS and each ultrasound covariate. A total of 264 participants were enrolled in the study; the median age was 59 years and 114 (43.2) % of participants were female. The median mLUSS was 1 (interquartile range: 0.5 to 1.3). Following enrollment, 29 (11.0%) participants went on to require ICU-level care and 14 (5.3%) subsequently died by 28 days. Each increase in mLUSS at enrollment was associated with disease progression to ICU-level care (aHR = 3.63; 95% CI: 1.23 to 10.65) and 28-day mortality (aHR = 4.50; 95% CI: 1.52 to 13.31). Pleural line abnormalities were independently associated with disease progression to ICU-level care (aHR = 18.86; CI: 1.57 to 226.09).ConclusionsParticipants with a mLUSS ≥1 or pleural line changes on LUS had an increased likelihood of subsequent requirement of high flow oxygen or greater. LUS is a promising tool for assessing risk of COVID-19 progression at the bedside.

STUDY OBJECTIVES: The objectives were to develop and validate an algorithm for editing WatchPAT scoring and assess the accuracy in an unselected clinical population as well as age and sex substrata. METHODS: Two hundred sixty-two participants were enrolled to undergo WatchPAT simultaneously with in-lab polysomnography (PSG) recordings for developing (n = 30), optimizing (n = 62), and validating (n = 170) an algorithm to review and edit respiratory events and sleep architecture of WatchPAT recordings, which was based on visual inspection of WatchPAT signals. Apnea-hypopnea index (AHI) and sleep indices were compared with PSG-derived and automated WatchPAT indices. RESULTS: Although estimation of total sleep time (TST) was comparable between automated and manual algorithm, estimation of rapid eye movement (REM) sleep time was markedly improved with manual editing from 0.48, 23.0 min (−43.9 to 89.8) to 0.64, 18.3 min (−32.6 to 69.1) (correlation with PSG, mean difference [reference range] from PSG, respectively). Manual scoring also improved correlation and agreement with PSG AHI from 0.65, 2.5 events/h (−24.0 to 28.9) to 0.81, −4.5 events/h (−22.5 to 13.6) as well as concordance for categorical agreement of sleep-disordered breathing severity and concordance for detecting severe REM-related sleep-disordered breathing. Interscorer reliabilities were excellent for TST and AHI, while good for REM sleep time. The automated algorithm performed better in younger than in older patients, while performed similarly between men and women with respect to concordance statistics. The manual algorithm markedly improved concordances more in older patients and women than in their counterparts. CONCLUSIONS: Our manual editing algorithm improves correlation and agreement with PSG-derived sleep and breathing indices. Sex and age influence the accuracy of automated analysis and the performance of manual editing on AHI concordance. CITATION: Zhang Z, Sowho M, Otvos T, et al. A comparison of automated and manual sleep staging and respiratory event recognition in a portable sleep diagnostic device with in-lab sleep study. J Clin Sleep Med. 2020;16(4):563–573.

Solar thermal energy harvesting, storage and conversion are crucial parameters to have an effective renewable energy technology that can lead to sustainable and grid-independent technologies as well as lessening of global carbon footprints. Phase change materials (PCMs) are capable of storing and releasing great amounts of thermal energy through utilization of latent heat of fusion as result of material phase change. PCM suffers mainly from low rate of response to demand that is mainly due to low thermal conductivity of PCMs. This drawback could be improved by mixing the PCMs with matrix of high thermal conductive materials. The purpose of this paper is to test the efficacy of expanded graphite (EG) matrix combined with PCM at various percentages of 0–90% by volume in terms of response rate to demand and energy storage capacity PCM-EG composite samples were designed and prepared, and tests were performed under controlled solar radiation (using solar simulator) to study the heating cycle. Temperature data were recorded by means of thermocouples and infrared camera. Time-dependent results were analyzed under various testing conditions. The results show that the tested 80% EG-20% PCM composite showed 7 times faster response rate to charging when compared to 10% EG and 90% PCM mixture, while about 30% energy storage reduction was observed as the penalty. There must be a sweet spot where response rate improvement and energy storage reduction are at their optimum condition for a particular application.