Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan.

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.

Transthoracic impedance variability to assess quality of chest compression in out-of-hospital cardiac arrest.

BACKGROUND: Chest compression is a lifesaving intervention in out-of-hospital cardiac arrest (OHCA), but the optimal metrics to assess its quality have yet to be identified. The objective of this study was to investigate whether a new parameter, that is, the variability of the chest compression-generated transthoracic impedance (TTI), namely ImpCC , which measures the consistency of the chest compression maneuver, relates to resuscitation outcome. METHODS: This multicenter observational, retrospective study included OHCAs with shockable rhythm. ImpCC variability was evaluated with the power spectral density analysis of the TTI. Multivariate regression model was used to examine the impact of ImpCC variability on defibrillation success. Secondary outcome measures were return of spontaneous circulation and survival. RESULTS: Among 835 treated OHCAs, 680 met inclusion criteria and 565 matched long-term outcomes. ImpCC was significantly higher in patients with unsuccessful defibrillation compared to those with successful defibrillation (p = .0002). Lower ImpCC variability was associated with successful defibrillation with an odds ratio (OR) of 0.993 (95% confidence interval [95% CI], 0.989-0.998, p = .003), while the standard chest compression fraction (CCF) was not associated (OR 1.008 [95 % CI, 0.992-1.026, p = .33]). Neither ImpCC nor CCF was associated with long-term outcomes. CONCLUSIONS: In this population, consistency of chest compression maneuver, measured by variability in TTI, was an independent predictor of defibrillation outcome. ImpCC may be a useful novel metrics for improving quality of care in OHCA.

Acute Lung Injury after Cardiopulmonary Resuscitation: A Narrative Review.

Although cardiopulmonary resuscitation (CPR) includes lifesaving maneuvers, it might be associated with a wide spectrum of iatrogenic injuries. Among these, acute lung injury (ALI) is frequent and yields significant challenges to post-cardiac arrest recovery. Understanding the relationship between CPR and ALI is determinant for refining resuscitation techniques and improving patient outcomes. This review aims to analyze the existing literature on ALI following CPR, emphasizing prevalence, clinical implications, and contributing factors. The review seeks to elucidate the pathogenesis of ALI in the context of CPR, assess the efficacy of CPR techniques and ventilation strategies, and explore their impact on post-cardiac arrest outcomes. CPR-related injuries, ranging from skeletal fractures to severe internal organ damage, underscore the complexity of managing post-cardiac arrest patients. Chest compression, particularly when prolonged and vigorous, i.e., mechanical compression, appears to be a crucial factor contributing to ALI, with the concept of cardiopulmonary resuscitation-associated lung edema (CRALE) gaining prominence. Ventilation strategies during CPR and post-cardiac arrest syndrome also play pivotal roles in ALI development. The recognition of CPR-related lung injuries, especially CRALE and ALI, highlights the need for research on optimizing CPR techniques and tailoring ventilation strategies during and after resuscitation.

Perfusion deficits may underlie lung and kidney injury in severe COVID-19 disease: insights from a multicenter international cohort study.

BACKGROUND: Lung perfusion defects, mainly due to endothelial and coagulation activation, are a key contributor to COVID-19 respiratory failure. COVID-19 patients may also develop acute kidney injury (AKI) because of renal perfusion deficit. We aimed to explore AKI-associated factors and the independent prediction of standardized minute ventilation (MV)-a proxy of alveolar dead space-on AKI onset and persistence in COVID-19 mechanically ventilated patients. METHODS: This is a multicenter observational cohort study. We enrolled 157 COVID-19 patients requiring mechanical ventilation and intensive care unit (ICU) admission. We collected clinical information, ventilation, and laboratory data. AKI was defined by the 2012 KDIGO guidelines and classified as transient or persistent according to serum creatinine criteria persistence within 48 h. Ordered univariate and multivariate logistic regression analyses were employed to identify variables associated with AKI onset and persistence. RESULTS: Among 157 COVID-19 patients on mechanical ventilation, 47% developed AKI: 10% had transient AKI, and 37% had persistent AKI. The degree of hypoxia was not associated with differences in AKI severity. Across increasing severity of AKI groups, despite similar levels of paCO2, we observed an increased MV and standardized MV, a robust proxy of alveolar dead space. After adjusting for other clinical and laboratory covariates, standardized MV remained an independent predictor of AKI development and persistence. D-dimer levels were higher in patients with persistent AKI. CONCLUSIONS: In critically ill COVID-19 patients with respiratory failure, increased wasted ventilation is independently associated with a greater risk of persistent AKI. These hypothesis-generating findings may suggest that perfusion derangements may link the pathophysiology of both wasted ventilation and acute kidney injury in our population.

Treatment with inhaled Argon: a systematic review of pre-clinical and clinical studies with meta-analysis on neuroprotective effect.

BACKGROUND: Argon (Ar) has been proposed as a potential therapeutic agent in multiple clinical conditions, specifically in organ protection. However, conflicting data on pre-clinical models, together with a great variability in Ar administration protocols and outcome assessments, have been reported. The aim of this study was to review evidence on treatment with Ar, with an extensive investigation on its neuroprotective effect, and to summarise all tested administration protocols. METHODS: Using the PubMed database, all existing pre-clinical and clinical studies on the treatment with Ar were systematically reviewed (registration: https://doi.org/10.17605/OSF.IO/7983D). Study titles and abstracts were screened, extracting data from relevant studies post full-text review. Exclusion criteria included absence of full text and non-English language. Furthermore, meta-analysis was also performed to assess Ar potential as neuroprotectant agent in different clinical conditions: cardiac arrest, traumatic brain injury, ischemic stroke, perinatal hypoxic-ischemic encephalopathy, subarachnoid haemorrhage. Standardised mean differences for neurological, cognitive and locomotor, histological, and physiological measures were evaluated, through appropriate tests, clinical, and laboratory variables. In vivo studies were evaluated for risk of bias using the Systematic Review Center for Laboratory Animal Experimentation tool, while in vitro studies underwent assessment with a tool developed by the Office of Health Assessment and Translation. FINDINGS: The systematic review detected 60 experimental studies (16 in vitro, 7 ex vivo, 31 in vivo, 6 with both in vitro and in vivo) investigating the role of Ar. Only one clinical study was found. Data from six in vitro and nineteen in vivo studies were included in the meta-analyses. In pre-clinical models, Ar administration resulted in improved neurological, cognitive and locomotor, and histological outcomes without any change in physiological parameters (i.e., absence of adverse events). INTERPRETATION: This systematic review and meta-analysis based on experimental studies supports the neuroprotective effect of Ar, thus providing a rationale for potential translation of Ar treatment in humans. Despite adherence to established guidelines and methodologies, limitations in data availability prevented further analyses to investigate potential sources of heterogeneity due to study design. FUNDING: This study was funded in part by Italian Ministry of Health-Current researchIRCCS and by Ministero della Salute Italiano, Ricerca Finalizzata, project no. RF 2019-12371416.