Prolonged non-invasive respiratory supports in a COVID-19 patient with severe acute hypoxemic respiratory failure.

A pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 was declared in 2020. Severe cases were characterized by the development of acute hypoxemic respiratory failure (AHRF) requiring advanced respiratory support. However, intensive care units (ICU) were saturated, and many patients had to be treated out of ICU. This case describes a 75-year-old man affected by AHRF due to Coronavirus Disease 2019 (COVID-19), hospitalized in a high-dependency unit, with PaO2/FiO2 <100 for 28 consecutive days. An experienced team with respiratory physiotherapists was in charge of the noninvasive ventilatory support (NIVS). The patient required permanent NIVS with continuous positive airway pressure, non-invasive ventilation, high flow nasal oxygen and body positioning. He was weaned from NIVS after 37 days and started exercise training afterwards. The patient was discharged at home with low-flow oxygen therapy. This case represents an example of a successful treatment of AHRF with the still controversial noninvasive respiratory support in one patient with COVID-19.

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features.

Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann-Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, p < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, p < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.

Correlation between lung ultrasound and chest CT patterns with estimation of pulmonary burden in COVID-19 patients.

PURPOSE: The capability of lung ultrasound (LUS) to distinguish the different pulmonary patterns of COVID-19 and quantify the disease burden compared to chest CT is still unclear. METHODS: PCR-confirmed COVID-19 patients who underwent both LUS and chest CT at the Emergency Department were retrospectively analysed. In both modalities, twelve peripheral lung zones were identified and given a Severity Score basing on main lesion pattern. On CT scans the well-aerated lung volume (%WALV) was visually estimated. Per-patient and per-zone assessments of LUS classification performance taking CT findings as reference were performed, further revisioning the images in case of discordant results. Correlations between number of disease-positive lung zones, Severity Score and %WALV on both LUS and CT were assessed. The area under receiver operating characteristic curve (AUC) was calculated to determine LUS performance in detecting %WALV ≤ 70 %. RESULTS: The study included 219 COVID-19 patients with abnormal chest CT. LUS correctly identified as positive 217 (99 %) patients, but per-zone analysis showed sensitivity = 75 % and specificity = 66 %. The revision of the 121 (55 %) cases with positive LUS and negative CT revealed COVID-compatible lesions in 42 (38 %) CT scans. Number of disease-positive zones, Severity Score and %WALV between LUS and CT showed moderate correlations. The AUCs for LUS Severity Score and number of LUS-positive zones did not differ in detecting %WALV ≤ 70 %. CONCLUSION: LUS in COVID-19 is valuable for case identification but shows only moderate correlation with CT findings as for lesion patterns and severity quantification. The number of disease-positive lung zones in LUS alone was sufficient to discriminate relevant disease burden.

CT-derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19.

Background Lower muscle mass is a known predictor of unfavorable outcomes, but its prognostic impact on patients with COVID-19 is unknown. Purpose To investigate the contribution of CT-derived muscle status in predicting clinical outcomes in patients with COVID-19. Materials and Methods Clinical or laboratory data and outcomes (intensive care unit [ICU] admission and death) were retrospectively retrieved for patients with reverse transcriptase polymerase chain reaction-confirmed SARS-CoV-2 infection, who underwent chest CT on admission in four hospitals in Northern Italy from February 21 to April 30, 2020. The extent and type of pulmonary involvement, mediastinal lymphadenopathy, and pleural effusion were assessed. Cross-sectional areas and attenuation by paravertebral muscles were measured on axial CT images at the T5 and T12 vertebral level. Multivariable linear and binary logistic regression, including calculation of odds ratios (ORs) with 95% CIs, were used to build four models to predict ICU admission and death, which were tested and compared by using receiver operating characteristic curve analysis. Results A total of 552 patients (364 men and 188 women; median age, 65 years [interquartile range, 54-75 years]) were included. In a CT-based model, lower-than-median T5 paravertebral muscle areas showed the highest ORs for ICU admission (OR, 4.8; 95% CI: 2.7, 8.5; P < .001) and death (OR, 2.3; 95% CI: 1.0, 2.9; P = .03). When clinical variables were included in the model, lower-than-median T5 paravertebral muscle areas still showed the highest ORs for both ICU admission (OR, 4.3; 95%: CI: 2.5, 7.7; P < .001) and death (OR, 2.3; 95% CI: 1.3, 3.7; P = .001). At receiver operating characteristic analysis, the CT-based model and the model including clinical variables showed the same area under the receiver operating characteristic curve (AUC) for ICU admission prediction (AUC, 0.83; P = .38) and were not different in terms of predicting death (AUC, 0.86 vs AUC, 0.87, respectively; P = .28). Conclusion In hospitalized patients with COVID-19, lower muscle mass on CT images was independently associated with intensive care unit admission and in-hospital mortality. © RSNA, 2021 Online supplemental material is available for this article.

Risk Stratification in COVID-19 Pneumonia - Determining the Role of Lung Ultrasound.

LUS patterns of COVID-19 pneumonia have been described and shown to be characteristic. The aim of the study was to predict the prognosis of patients with COVID-19 pneumonia, using a score based on LUS findings. MATERIALS AND METHODS: An observational, retrospective study was conducted on patients admitted to Niguarda hospital with a diagnosis of COVID-19 pneumonia during the period of a month, from March 2nd to April 3rd 2020. Demographics, clinical, laboratory, and radiological findings were collected. LUS was performed in all patients. The chest was divided into 12 areas. The LUS report was drafted using a score from 0 to 3 with 0 corresponding to A pattern, 1 corresponding to well separated vertical artifacts (B lines), 2 corresponding to white lung and small consolidations, 3 corresponding to wide consolidations. The total score results from the sum of the scores for each area. The primary outcome was endotracheal intubation, no active further management, or death. The secondary outcome was discharge from the emergency room (ER). RESULTS: 255 patients were enrolled. 93.7 % had a positive LUS. ETI was performed in 43 patients, and 24 received a DNI order. The general mortality rate was 15.7 %. Male sex (OR 3.04, p = 0.014), cardiovascular disease and hypertension (OR 2.75, p = 0.006), P/F (OR 0.99, p < 0.001) and an LUS score > 20 (OR 2.52, p = 0.046) were independent risk factors associated with the primary outcome. Receiver operating characteristic (ROC) curve analysis for an LUS score > 20 was performed with an AUC of 0.837. Independent risk factors associated with the secondary outcome were age (OR 0.96, p = 0.073), BMI (OR 0.87, p = 0,13), P/F (OR 1.03, p < 0.001), and LUS score < 10 (OR 20.9, p = 0.006). ROC curve analysis was performed using an LUS score < 10 with an AUC 0.967. CONCLUSION: The extent of lung abnormalities evaluated by LUS score is a predictor of a worse outcome, ETI, or death. Moreover, the LUS score could be an additional tool for the safe discharge of patient from the ER.

Clinical characteristics and respiratory support of 310 COVID-19 patients, diagnosed at the emergency room: a single-center retrospective study.

An ongoing outbreak of pneumonia associated with severe acute respiratory coronavirus 2 (SARS-CoV-2) occurred at the end of February 2020 in Lombardy, Italy. We analyzed data from a retrospective, single-center case series of 310 consecutive patients, with confirmed SARS-CoV-2 infection, admitted to the emergency room. We aimed to describe the clinical course, treatment and outcome of a cohort of patients with COVID-19 pneumonia, with special attention to oxygen delivery and ventilator support. Throughout the study period, 310 consecutive patients, with confirmed SARS-CoV-2 infection, attended the Emergency Room (ER), of these, 34 were discharged home directly from the ER. Of the remaining 276 patients, the overall mortality was 30.4%: 7 patients died in the ER and 77 during hospitalization. With respect to oxygen delivery: 22 patients did not need any oxygen support (8.0%), 151 patients were treated with oxygen only (54.7%), and 49 (17.8%) were intubated. 90 patients (32.6%) were treated with CPAP (Continuous Positive Airway Pressure) or NIV (Non Invasive Ventilation); in this group, 27 patients had a Do Not Intubate (DNI) order and were treated with CPAP/NIV as an upper threshold therapy, showing high mortality rate (88.9%). Among the 63 patients treated with CPAP/NIV without DNI, NIV failure occurred in 36 patients (57.1%), with mortality rate of 47.2%. Twenty-seven (27) patients were treated with CPAP/NIV without needing mechanical ventilation and 26 were discharged alive (96.3%). The study documents the poor prognosis of patients with severe respiratory failure, although a considerable minority of patients treated with CPAP/NIV had a positive outcome.