Patients with severe COVID-19 disease have been characterized as having the acute respiratory distress syndrome (ARDS). Critically ill COVID-19 patients have relatively well-preserved lung mechanics despite severe gas exchange abnormalities, a feature not consistent with classical ARDS but more consistent with pulmonary vascular disease. Many patients with severe COVID-19 also demonstrate markedly abnormal coagulation, with elevated D-dimers and higher rates of venous thromboembolism. We present four cases of patients with severe COVID-19 pneumonia with severe respiratory failure and shock, with evidence of markedly elevated dead-space ventilation who received tPA. All showed post treatment immediate improvements in gas exchange and/or hemodynamics. We suspect that severe COVID-19 pneumonia causes respiratory failure via pulmonary microthrombi and endothelial dysfunction. Treatment for COVID-19 pneumonia may warrant anticoagulation for milder cases and thrombolysis for more severe disease. K E Y W O R D SCOVID-19, thrombolysis, tissue plasminogen activator, tPA INTRODUCTIONPatients with severe COVID-19-induced respiratory failure demonstrate gas exchange abnormalities including shunt andThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

SUMMARY CAP due to Legionella, Chlamydophyla, or Mycoplasma continues to be a diagnostic challenge due to the nonspecific clinical and radiographic presentations. The vague clinical presentations of atypical CAP contribute to its underdiagnosis and under-reporting. Advancements in diagnostic techniques bring hope to rapid and accurate diagnosis of atypical CAP. Macrolides and respiratory fluoroquinolones are currently the antibiotics of choice, but this may change in the near future as more antibiotics resistance patterns emerge for atypical CAP. Several controversies still exist in atypical CAP, underscoring the need for continued investigation of preventing atypical CAP and determine its association with chronic lung diseases.

Critically ill COVID-19 patients have relatively well-preserved lung mechanics despite severe gas exchange abnormalities, a feature not consistent with classical ARDS but more consistent with pulmonary vascular disease. Patients with severe COVID-19 also demonstrate markedly abnormal coagulation, with elevated D-dimers and higher rates of venous thromboembolism. We present four cases of patients with severe COVID-19 pneumonia with severe respiratory failure and shock who demonstrated immediate improvements in gas exchange and/or hemodynamics with systemic tPA.

At a glance Commentary (200 words): Scientific Knowledge on the Subject: Differentiating central form obstructive sleep apnea is critical in guiding treatment. This differentiation is largely dependent on classifying apneas and hypopneas using an assessment of inspiratory effort. Together with flow, effort determines upper airway resistance. Non-invasive signals that are surrogates of inspiratory effort are sufficient to classify apneas. However, for hypopneas, upper airway resistance quantified using invasive esophageal manometry is the gold-standard, which is not well-tolerated and results in sleep disruption. As such, non-invasive surrogates of upper airway resistance are imperative to classify hypopneas, and thus, separate central from obstructive sleep apnea. What This Study Adds to the Field:Our study shows that a probability of obstruction derived using a feature-engineered machine learning approach is a reliable and noninvasive surrogate of upper airway resistance and can successfully distinguish central from obstructive sleep apnea both on a breath-by-breath level and on a subject-level.Our probability of obstruction, which is derived within a matter of minutes, can determine the primary type of a subject's sleep apnea and aid in determining risks associated with untreated disorder and informing treatment approaches.

Study Objectives Phenotyping using polysomnography (PUP) is an algorithmic method to quantify physiologic mechanisms underlying obstructive sleep apnea (OSA): loop gain (LG1), arousal threshold (ArTH), and upper airway collapsibility (Vpassive) and muscular compensation (Vcomp). The consecutive-night test-retest reliability and agreement of PUP-derived estimates is unknown. From a cohort of elderly (age ≥55 years), largely non-sleepy, community-dwelling volunteers who underwent in-lab polysomnography (PSG) on 2 consecutive nights, we determined the test-retest reliability and agreement of PUP-estimated physiologic factors. Methods Subjects who had an apnea-hypopnea index (AHI3A) of at least 15 events per hour on the first night were included. PUP analyses were performed on each of 2 PSGs from each subject. Physiologic factor estimates were derived from NREM sleep and compared across nights using intraclass correlation coefficients (ICC) for reliability and smallest real differences (SRD) for agreement. Results Two PSGs from each of 43 subjects (86 total) were analyzed. A first-night effect was evident with increased sleep time and stability and decreased OSA severity on the second night. LG1, ArTH, and Vpassive demonstrated good reliability (ICC >0.80). Vcomp had modest reliability (ICC = 0.67). For all physiologic factors, SRD values were approximately 20% or more of the observed ranges, suggesting limited agreement of longitudinal measurements for a given individual. Conclusion For NREM sleep in cognitively normal elderly individuals with OSA, PUP-estimated LG1, ArTH, and Vpassive demonstrated consistent relative ranking of individuals (good reliability) on short-term repeat measurement. For all physiologic factors, longitudinal measurements demonstrated substantial intraindividual variability across nights (limited agreement).