Faculty Bibliography

BACKGROUND:We previously demonstrated a high prevalence of transpulmonary bubble transit (TPBT) using transcranial Doppler (TCD) in patients with COVID-19 pneumonia, but these observations require confirmation. METHODS:Patients at two academic medical centres, hospitalised with COVID-19 pneumonia and requiring any form of respiratory support, were studied. The first TCD study was performed at the time of enrolment and repeated approximately 7 and 14 days later if participants remained hospitalised. RESULTS:91 participants were enrolled. At the first TCD, 14 participants (15%) were receiving oxygen by nasal cannula, 41 participants (45%) were receiving oxygen by high flow nasal cannula, 8 participants (9%) were receiving non-invasive positive pressure, 28 participants (31%) were receiving mechanical ventilation and 2 participants (2%) were receiving extracorporeal membrane oxygenation. 33 participants (36%) demonstrated TPBT at the first TCD. There was evidence that the presence of TPBT and increased heart rate together was associated with in-hospital death (p=0.02). For every one-unit increase in heart rate, the odds of death increased 11% (OR 1.11, 95% CI 1.02 to 1.20, p=0.01) for those with TPBT; however, there was no evidence of this increase for those without TPBT (OR 1.01, 95% CI 0.97 to 1.05, p=0.76). For participants with subsequent TCD assessments, 55% demonstrated TPBT during the second TCD assessment, and 85% demonstrated TPBT at the third TCD assessment. CONCLUSIONS:The prevalence of TPBT in hospitalised patients with COVID-19 pneumonia is higher than expected and the presence of TPBT increases over time in those that remained alive and hospitalised. In patients with TPBT, increased heart rate, a marker of hyperdynamic circulation, is associated with increased mortality.

BACKGROUND:Asthma is one of the most common chronic health conditions, and to leverage the wealth of data in the electronic medical record (EMR), it is important to be able to accurately identify asthma diagnosis. OBJECTIVE:We aimed to determine the rule-based algorithm with the most balanced performance for sensitivity and positive predictive value of asthma diagnosis. METHODS:We performed a diagnostic accuracy study of multiple rule-based algorithms intended to identify asthma diagnosis in the EMR. Algorithm performance was validated by manual chart review of 795 charts of patients seen in a multisite, tertiary-level, pulmonary specialty clinic using explicit diagnostic criteria to distinguish asthma cases from controls. RESULTS:An asthma diagnosis anywhere in the medical record had a 97% sensitivity and a 77% specificity for asthma (F-score 80) when tested on a validation set of asthma cases and nonasthma respiratory disease from a pulmonary specialty clinic. The most balanced performance was seen with asthma diagnosis restricted to an encounter, hospital problem, or problem list diagnosis with a sensitivity of 94% and specificity of 85% (F-score 84). High sensitivity was achieved with the modified Health Plan Employer Data and Information Set criteria and high specificity was achieved with the NUgene algorithm, an algorithm developed for identifying asthma cases by EMR for genome-wide association studies. CONCLUSION/CONCLUSIONS:Asthma diagnosis can be accurately identified for research purposes by restricting to encounter, hospital problem, or problem list diagnosis in a pulmonary specialty clinic. Additional rules lead to steep drop-offs in algorithm sensitivity in our population.