Respiratory and peripheral muscular ultrasound characteristics in ICU COVID 19 ARDS patients.

PURPOSE: Severe cases of coronavirus disease 2019 develop ARDS requiring admission to the ICU. This study aimed to investigate the ultrasound characteristics of respiratory and peripheral muscles of patients affected by COVID19 who require mechanical ventilation. MATERIALS AND METHODS: This is a prospective observational study. We performed muscle ultrasound at the admission of ICU in 32 intubated patients with ARDS COVID19. The ultrasound was comprehensive of thickness and echogenicity of both parasternal intercostal and diaphragm muscles, and cross-sectional area and echogenicity of the rectus femoris. RESULTS: Patients who survived showed a significantly lower echogenicity score as compared with those who did not survive for both parasternal intercostal muscles. Similarly, the diaphragmatic echogenicity was significantly different between alive or dead patients. There was a significant correlation between right parasternal intercostal or diaphragm echogenicity and the cumulative fluid balance and urine protein output. Similar results were detected for rectus femoris echogenicity. CONCLUSIONS: The early changes detected by echogenicity ultrasound suggest a potential benefit of proactive early therapies designed to preserve respiratory and peripheral muscle architecture to reduce days on MV, although what constitutes a clinically significant change in muscle echogenicity remains unknown.

Mitigating bias in estimating epidemic severity due to heterogeneity of epidemic onset and data aggregation.

Outbreaks of infectious diseases, such as influenza, are a major societal burden. Mitigation policies during an outbreak or pandemic are guided by the analysis of data of ongoing or preceding epidemics. The reproduction number, R0, defined as the expected number of secondary infections arising from a single individual in a population of susceptibles is critical to epidemiology. For typical compartmental models such as the Susceptible-Infected-Recovered (SIR) R0 represents the severity of an epidemic. It is an estimate of the early-stage growth rate of an epidemic and is an important threshold parameter used to gain insights into the spread or decay of an outbreak. Models typically use incidence counts as indicators of cases within a single large population; however, epidemic data are the result of a hierarchical aggregation, where incidence counts from spatially separated monitoring sites (or sub-regions) are pooled and used to infer R0. Is this aggregation approach valid when the epidemic has different dynamics across the regions monitored? We characterize bias in the estimation of R0 from a merged data set when the epidemics of the sub-regions, used in the merger, exhibit delays in onset. We propose a method to mitigate this bias, and study its efficacy on synthetic data as well as real-world influenza and COVID-19 data.