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We hypothesise that EIT-guided PEEP, OD, CL, and Cdyn change over the course of mechanical ventilation during SARS-CoV-2 infection. Moreover, EIT may identify heterogeneity of pulmonary pathophysiology that differs between survivors and non-survivors over time. Therefore, we conducted an observational study primarily focusing on serial measurements and report on EIT-derived pulmonary parameters over the course of mechanical ventilation in patients with SARS-CoV-227,28.
For the present study, any EIT data measured during Pressure support (PS) and Continuous positive airway pressure (CPAP) were excluded. The support ventilation modes increase the chance of informative missing data as serial EIT data on PS and CPAP are likely related to survival since these modes are indicative for weaning from mechanical ventilation. In addition, the EIT algorithm for calculating the level of OD and CL is not validated for PS and CPAP.
All participants eligible for the study in the cohort during the first pandemic wave were included. The sample characteristics were described as mean and standard deviation (SD), median and interquartile range (IQR), or percentage, as appropriate. The statistical analysis was analysed with R version 3.6.1 using the R-studio interface.
Our prospective cohort study with serial EIT measurements in mechanically ventilated patients with SARS-CoV-2 infection is comprehensive and adds to the previous reports17,19,20,21,22,23,24,33,36,37,38,39,40,41,42. Additional to previous studies, we included much more patients with SARS-CoV-2 infection17,19,20,21,22,23,24,42,43,44,45 or acute respiratory distress syndrome33,36,37,38,39,40, most innovative of this study is that we performed a unique amount of serial EIT measurements (334 EIT observations)17,24,33,36,37,38,39,40,42,43 and mainly included patients with more than 1 PEEP trial per patient (supplementary Table S3)19,20,21,22,24. Moreover, OD, CL, and Cdyn were analysed on the population level using serial measurements to provide more in-depth information on lung pathophysiology over time.
The strengths of the study include its prospective design and the predefined protocol for serial EIT measurements. Furthermore, to study the course of OD, CL, and Cdyn over time, we used the date of intubation as a starting point. This minimises information bias due to the timing of EIT measurements. The large amount of data increases the precision of the population EIT curves, illustrated by the narrow 95% confidence limits.
Limitations of the study are the inclusion of SARS-CoV-2 infected patients only, which may limit the generalisability of the results to other pulmonary pathology33,36,37,38,39,40. Furthermore, we cannot fully exclude that selection has biased the results as patients were transported between hospitals during the pandemic. However, both the most severe critically ill patients were transported to our institution, for example, when advanced pulmonary support by extracorporeal membrane oxygenation techniques was considered, and the less critically ill patients, because transport was considered most feasible in those patients. Most likely, this averages the severity of pulmonary pathology and critical illness within our population. Patients transported from other centres are likely to be intubated for some time, and thus they may differ in the disease course. Nevertheless, the serial EIT data were aligned from intubation to match the disease courses between patients. Furthermore, although sex-differences likely play a role in COVID-19 pathophysiology28,48, the stratified results for women should be interpreted with caution due to lack of precision. In fact, this result illustrates the importance of a major amount of data when investigating EIT curves on a population level. Finally, we have described observational results, which limit conclusions with regards to causality.
SARS-CoV-2, the causative agent of COVID-19, spreads efficiently, with a basic reproductive number of 2.2 to 2.5 determined in Wuhan1,2. The effectiveness of control measures depends on several key epidemiological parameters (Fig. 1a), including the serial interval (duration between symptom onsets of successive cases in a transmission chain) and the incubation period (time between infection and onset of symptoms). Variation between individuals and transmission chains is summarized by the incubation period distribution and the serial interval distribution, respectively. If the observed mean serial interval is shorter than the observed mean incubation period, this indicates that a significant portion of transmission may have occurred before infected persons have developed symptoms. Significant presymptomatic transmission would probably reduce the effectiveness of control measures that are initiated by symptom onset, such as isolation, contact tracing and enhanced hygiene or use of face masks for symptomatic persons.
In this study, we compared clinical data on virus shedding with separate epidemiologic data on incubation periods and serial intervals between cases in transmission chains, to draw inferences on infectiousness profiles.
Simulated serial intervals assuming infectiousness started on the same day of symptom onset (top panel) and from 2 days before symptom onset (bottom panel) to about 10 days after symptom onset. Both scenarios assumed that infectiousness peaked on the first day of symptom onset.
Simulated proportions of negative serial intervals assuming start of infectiousness and peak infectiousness from 7 days before symptom onset to 3 days after symptom onset. From the estimated serial interval distribution based on infector-infectee pairs, 7.6% of the serial intervals were negative. Gray area represents the implausible range where the peak infectious is earlier than the start of infectiousness.
Forty-three years ago, Penn State University played for its first national championship in a football season that began against Temple on Sept. 1, 1978, and ended against second-ranked Alabama, on Jan. 1, 1979. It was the season in which Penn State football became Penn State Football, a season that saw head coach Joe Paterno become an American icon. It was also a season that saw a serial sexual predator attack multiple Penn State students.
His name was Todd Hodne, and he was perhaps the most dangerous predator ever to play college football. \"I have been a prosecutor for nearly 30 years,\" wrote John B. Collins, who prosecuted one of Hodne's crimes, in a letter to a parole board. \"I have prosecuted serial killers and capital cases. Todd Hodne, to this day, remains among the three most dangerous, physically imposing and ruthless excuses for a human being I have ever faced in court.\"
We included eight mathematical modelling studies (Fig 4) [19,20,33,51,63,69,78,91]. The models in five studies were informed by analysis of data from contact investigations in China, South Korea, Singapore, and the Diamond Princess cruise ship, using data to estimate the serial interval or generation time [19,20,33,69,78], and in three studies the authors used previously published estimates [51,63,91].
For studies that report outcomes in multiple settings, these are annotated in brackets. CI, confidence interval; GI, generation interval; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; SI, serial interval.
Models of the contribution of presymptomatic transmission used different assumptions about the durations and distributions of infection parameters such as incubation period, generation time, and serial interval [19,20,33,51,63,78,91]. In models that accounted for uncertainty appropriately, most estimates of the proportion of transmission resulting from people with SARS-CoV-2 who are presymptomatic ranged from 20% to 70%. In one study that estimated a contribution of
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