Neurovascular and infectious disease phenotype of acute stroke patients with and without COVID-19.

BACKGROUND: The infectious disease phenotype of acute stroke associated with COVID-19 has been poorly characterized. OBJECTIVE: We investigated the neurovascular and infectious disease phenotype of stroke patients with and without COVID-19 infection, and their effect on in-hospital mortality. METHODS: This is a retrospective cohort study of consecutive patients with acute stroke, admitted to any ward of a hub hospital for stroke in Lombardy, Italy, during the first wave of COVID-19. Demographic, neurovascular, infectious disease, and respiratory characteristics were collected. The effect of clinical variables on survival was evaluated using logistic regression models. RESULTS: One hundred thirty-seven patients with acute stroke were recruited; 30 (21.9%) patients had COVID-19 and represented 2.5% of the 1218 COVID-19 patients hospitalized in the study period. Demographics, comorbidities, stroke type, stroke severity, and etiology did not differ between COVID + stroke patients and non-COVID stroke patients, except for an excess of multi-embolic ischemic stroke in the COVID + group. Most COVID + stroke patients had symptomatic infection (60%) and interstitial pneumonia (70%). COVID + stroke patients required more frequently respiratory support (77% versus 29%; p < 0.0001) and had higher in-hospital mortality (40% versus 12%; p = 0.0005) than non-COVID stroke patients. Mortality was independently associated with symptomatic interstitial pneumonia (aOR 6.7; 95% CI 2.0-22.5; p = 0.002) and, to a lesser extent, with NIHSS on admission (aOR 1.1; 95% CI 1.03-1.2; p = 0.007) and recanalization therapies (aOR 0.2; 95% CI 0.04-0.98; p = 0.046). CONCLUSION: Symptomatic interstitial pneumonia was the major driver of in-hospital mortality in COVID + stroke patients.

Early versus late intubation in COVID-19 patients failing helmet CPAP: A quantitative computed tomography study.

PURPOSE: To describe the effects of timing of intubation in COVID-19 patients that fail helmet continuous positive airway pressure (h-CPAP) on progression and severity of disease. METHODS: COVID-19 patients that failed h-CPAP, required intubation, and underwent chest computed tomography (CT) at two levels of positive end-expiratory pressure (PEEP, 8 and 16 cmH2O) were included in this retrospective study. Patients were divided in two groups (early versus late) based on the duration of h-CPAP before intubation. Endpoints included percentage of non-aerated lung tissue at PEEP of 8 cmH2O, respiratory system compliance and oxygenation. RESULTS: Fifty-two patients were included and classified in early (h-CPAP for ≤2 days, N = 26) and late groups (h-CPAP for >2 days, N = 26). Patients in the late compared to early intubation group presented: 1) lower respiratory system compliance (median difference, MD -7 mL/cmH2O, p = 0.044) and PaO2/FiO2 (MD -29 mmHg, p = 0.047), 2) higher percentage of non-aerated lung tissue (MD 7.2%, p = 0.023) and 3) similar lung recruitment increasing PEEP from 8 to 16 cmH2O (MD 0.1%, p = 0.964). CONCLUSIONS: In COVID-19 patients receiving h-CPAP, late intubation was associated with worse clinical presentation at ICU admission and more advanced disease. The possible detrimental effects of delaying intubation should be carefully considered in these patients.

Automatic Lung Segmentation and Quantification of Aeration in Computed Tomography of the Chest Using 3D Transfer Learning.

BACKGROUND: Identification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. This is especially true in pathological conditions, hindering the clinical application of aeration compartment (AC) analysis. Deep learning based algorithms have lately been shown to be reliable and time-efficient in segmenting pathologic lungs. In this contribution, we thus propose a novel 3D transfer learning based approach to quantify lung volumes, aeration compartments and lung recruitability. METHODS: Two convolutional neural networks developed for biomedical image segmentation (uNet), with different resolutions and fields of view, were implemented using Matlab. Training and evaluation was done on 180 scans of 18 pigs in experimental ARDS (u2Net Pig ) and on a clinical data set of 150 scans from 58 ICU patients with lung conditions varying from healthy, to COPD, to ARDS and COVID-19 (u2Net Human ). One manual segmentations (MS) was available for each scan, being a consensus by two experts. Transfer learning was then applied to train u2Net Pig on the clinical data set generating u2Net Transfer . General segmentation quality was quantified using the Jaccard index (JI) and the Boundary Function score (BF). The slope between JI or BF and relative volume of non-aerated compartment (S JI and S BF , respectively) was calculated over data sets to assess robustness toward non-aerated lung regions. Additionally, the relative volume of ACs and lung volumes (LV) were compared between automatic and MS. RESULTS: On the experimental data set, u2Net Pig resulted in JI = 0.892 [0.88 : 091] (median [inter-quartile range]), BF = 0.995 [0.98 : 1.0] and slopes S JI = -0.2 {95% conf. int. -0.23 : -0.16} and S BF = -0.1 {-0.5 : -0.06}. u2Net Human showed similar performance compared to u2Net Pig in JI, BF but with reduced robustness S JI = -0.29 {-0.36 : -0.22} and S BF = -0.43 {-0.54 : -0.31}. Transfer learning improved overall JI = 0.92 [0.88 : 0.94], P < 0.001, but reduced robustness S JI = -0.46 {-0.52 : -0.40}, and affected neither BF = 0.96 [0.91 : 0.98] nor S BF = -0.48 {-0.59 : -0.36}. u2Net Transfer improved JI compared to u2Net Human in segmenting healthy (P = 0.008), ARDS (P < 0.001) and COPD (P = 0.004) patients but not in COVID-19 patients (P = 0.298). ACs and LV determined using u2Net Transfer segmentations exhibited < 5% volume difference compared to MS. CONCLUSION: Compared to manual segmentations, automatic uNet based 3D lung segmentation provides acceptable quality for both clinical and scientific purposes in the quantification of lung volumes, aeration compartments, and recruitability.

Tracheostomy Timing and Outcome in Severe COVID-19: The WeanTrach Multicenter Study.

BACKGROUND: Tracheostomy can be performed safely in patients with coronavirus disease 2019 (COVID-19). However, little is known about the optimal timing, effects on outcome, and complications. METHODS: A multicenter, retrospective, observational study. This study included 153 tracheostomized COVID-19 patients from 11 intensive care units (ICUs). The primary endpoint was the median time to tracheostomy in critically ill COVID-19 patients. Secondary endpoints were survival rate, length of ICU stay, and post-tracheostomy complications, stratified by tracheostomy timing (early versus late) and technique (surgical versus percutaneous). RESULTS: The median time to tracheostomy was 15 (1-64) days. There was no significant difference in survival between critically ill COVID-19 patients who received tracheostomy before versus after day 15, nor between surgical and percutaneous techniques. ICU length of stay was shorter with early compared to late tracheostomy (p < 0.001) and percutaneous compared to surgical tracheostomy (p = 0.050). The rate of lower respiratory tract infections was higher with surgical versus percutaneous technique (p = 0.007). CONCLUSIONS: Among critically ill patients with COVID-19, neither early nor percutaneous tracheostomy improved outcomes, but did shorten ICU stay. Infectious complications were less frequent with percutaneous than surgical tracheostomy.

Neurological Complications and Noninvasive Multimodal Neuromonitoring in Critically Ill Mechanically Ventilated COVID-19 Patients.

Purpose: The incidence and the clinical presentation of neurological manifestations of coronavirus disease-2019 (COVID-19) remain unclear. No data regarding the use of neuromonitoring tools in this group of patients are available. Methods: This is a retrospective study of prospectively collected data. The primary aim was to assess the incidence and the type of neurological complications in critically ill COVID-19 patients and their effect on survival as well as on hospital and intensive care unit (ICU) length of stay. The secondary aim was to describe cerebral hemodynamic changes detected by noninvasive neuromonitoring modalities such as transcranial Doppler, optic nerve sheath diameter (ONSD), and automated pupillometry. Results: Ninety-four patients with COVID-19 admitted to an ICU from February 28 to June 30, 2020, were included in this study. Fifty-three patients underwent noninvasive neuromonitoring. Neurological complications were detected in 50% of patients, with delirium as the most common manifestation. Patients with neurological complications, compared to those without, had longer hospital (36.8 ± 25.1 vs. 19.4 ± 16.9 days, p < 0.001) and ICU (31.5 ± 22.6 vs. 11.5±10.1 days, p < 0.001) stay. The duration of mechanical ventilation was independently associated with the risk of developing neurological complications (odds ratio 1.100, 95% CI 1.046-1.175, p = 0.001). Patients with increased intracranial pressure measured by ONSD (19% of the overall population) had longer ICU stay. Conclusions: Neurological complications are common in critically ill patients with COVID-19 receiving invasive mechanical ventilation and are associated with prolonged ICU length of stay. Multimodal noninvasive neuromonitoring systems are useful tools for the early detection of variations in cerebrovascular parameters in COVID-19.