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Background: Automated analysis of lung computed tomography (CT) scans may help characterize subphenotypes of acute respiratory illness. We integrated lung CT features measured via deep learning with clinical and laboratory data in spontaneously breathing subjects to enhance the identification of COVID-19 subphenotypes. Methods: This is a multicenter observational cohort study in spontaneously breathing patients with COVID-19 respiratory failure exposed to early lung CT within 7 days of admission. We explored lung CT images using deep learning approaches to quantitative and qualitative analyses; latent class analysis (LCA) by using clinical, laboratory and lung CT variables; regional differences between subphenotypes following 3D spatial trajectories. Results: Complete datasets were available in 559 patients. LCA identified two subphenotypes (subphenotype 1 and 2). As compared with subphenotype 2 (n = 403), subphenotype 1 patients (n = 156) were older, had higher inflammatory biomarkers, and were more hypoxemic. Lungs in subphenotype 1 had a higher density gravitational gradient with a greater proportion of consolidated lungs as compared with subphenotype 2. In contrast, subphenotype 2 had a higher density submantellar–hilar gradient with a greater proportion of ground glass opacities as compared with subphenotype 1. Subphenotype 1 showed higher prevalence of comorbidities associated with endothelial dysfunction and higher 90-day mortality than subphenotype 2, even after adjustment for clinically meaningful variables. Conclusions: Integrating lung-CT data in a LCA allowed us to identify two subphenotypes of COVID-19, with different clinical trajectories. These exploratory findings suggest a role of automated imaging characterization guided by machine learning in subphenotyping patients with respiratory failure. Trial registration: ClinicalTrials.gov Identifier: NCT04395482. Registration date: 19/05/2020.

A 21-year-old patient has been treated in emergency with venovenous extracorporeal membrane oxygenation after severe thoracic trauma causing severe air leak and haemothorax. The extracorporeal assistance was managed without heparin for 10 days till the full recovery of the lung, and no side-effect was recorded.

Heart failure (HF) is a pandemic syndrome characterised by raised morbidity and mortality. An acute HF event requiring hospitalisation is associated with a poor prognosis, in both the short and the long term. Moreover, early rehospitalisation after discharge negatively affects HF management and survival rates. Cardiovascular and non-cardiovascular conditions combine to increase rates of HF hospital readmission at 30 days. A tailored approach for HF pharmacotherapy while the patient is in hospital and immediately after discharge could be useful in reducing early adverse events that cause rehospitalisation and, consequently, prevent worsening HF and readmission during the vulnerable phase after discharge.

Sacubitril with valsartan (sacubitril/valsartan) is a relatively novel compound that has become a milestone in the treatment of patients with chronic heart failure (HF) with reduced ejection fraction (HFrEF) in the last decade. Contemporary data suggest that sacubitril/valsartan is associated with improved outcomes compared with angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, and has a greater beneficial effect on myocardial reverse remodelling. Additionally, two recent trials have shown that sacubitril/valsartan is well-tolerated even in the acute HF setting, thus enabling a continuum of use in the patient’s journey with HFrEF. This article summarises available data on the effectiveness and tolerability of sacubitril/valsartan in patients with HFrEF, and provides the clinician with practical insights to facilitate the use of this drug in every setting, with an emphasis on acute HF, hypotension, electrolyte imbalance and renal insufficiency

The autonomic nervous system, the renin–angiotensin–aldosterone system, and the natriuretic peptide system represent critical regulatory pathways in heart failure and as such have been the major targets of pharmacological development. The introduction and approval of angiotensin receptor neprilysin inhibitors (ARNi) have broadened the available drug treatments of patients with chronic heart failure with reduced ejection fraction. Neprilysin catalyses the degradation of a number of vasodilator peptides, including the natriuretic peptides, bradykinin, substance P, and adrenomedullin, as well as vasoconstrictor peptides, including endothelin-1 and angiotensin I and II. We review the multiple, potentially competing, substrates for neprilysin inhibition, and the resultant composite clinical effects of ARNi therapy. A mechanistic understanding of this novel therapeutic class may provide important insights into the expected on-target and off-target effects when this agent is more widely prescribed.