Long-term lung ultrasound follow-up in patients after COVID-19 pneumonia hospitalization: A prospective comparative study with chest computed tomography.

During COVID-19 pandemic, lung ultrasound (LUS) proved to be of great value in the diagnosis and monitoring of patients with pneumonia. However, limited data exist regarding its use to assess aeration changes during follow-up (FU). Our study aims to prospectively evaluate 232 subjects who underwent a 3-month-FU program after hospitalization for COVID-19 at the University Hospital of Pisa. The goals were to assess the usefulness of standardized LUS compared with the gold standard chest computed tomography (CT) to evaluate aeration changes and to verify LUS and CT agreement at FU. Patients underwent in the same day a standardized 16-areas LUS and high-resolution chest CT reported by expert radiologists, assigning interpretative codes. Based on observations distribution, LUS score cut-offs of 3 and 7 were selected, corresponding to the 50th and 75th percentile, respectively. Patients with LUS scores above both these thresholds were older and with longer hospital stay. Patients with a LUS score ≥3 had more comorbidities. LUS and chest CT showed a high agreement in identifying residual pathological findings, using both cut-off scores of 3 (OR 14,7; CL 3,6-64,5, Sensitivity 91%, Specificity 49%) and 7 (OR 5,8; CL 2,3-14,3, Sensitivity 65%, Specificity 79%). Our data suggest that LUS is very sensitive in identifying pathological findings at FU after a hospitalization for COVID-19 pneumonia, compared to CT. Given its low cost and safety, LUS could replace CT in selected cases, such as in contexts with limited resources or it could be used as a gate-keeper examination before more advanced techniques.

Lung Ultrasound Findings in Healthy Children and in Those Who Had Recent, Not Severe COVID-19 Infection.

BACKGROUND: Lung ultrasound (LUS) is gaining consensus as a non-invasive diagnostic imaging method for the evaluation of pulmonary disease in children. AIM: To clarify what type of artifacts (e.g., B-lines, pleural irregularity) can be defined normal LUS findings in children and to evaluate the differences in children who did not experience COVID-19 and in those with recent, not severe, previous COVID-19. METHODS: LUS was performed according to standardized protocols. Different patterns of normality were defined: pattern 1: no plural irregularity and no B-lines; pattern 2: only mild basal posterior plural irregularity and no B-lines; pattern 3: mild posterior basal/para-spine/apical pleural irregularity and no B-lines; pattern 4: like pattern 3 plus rare B-lines; pattern 5: mild, diffuse short subpleural vertical artifacts and rare B-lines; pattern 6: mild, diffuse short subpleural vertical artifacts and limited B-lines; pattern 7: like pattern 6 plus minimal subpleural atelectasis. Coalescent B-lines, consolidations, or effusion were considered pathological. RESULTS: Overall, 459 healthy children were prospectively recruited (mean age 10.564 ± 3.839 years). Children were divided into two groups: group 1 (n = 336), those who had not had COVID-19 infection, and group 2 (n = 123), those who experienced COVID-19 infection. Children with previous COVID-19 had higher values of LUS score than those who had not (p = 0.0002). Children with asymptomatic COVID-19 had similar LUS score as those who did not have infections (p > 0.05), while those who had symptoms showed higher LUS score than those who had not shown symptoms (p = 0.0228). CONCLUSIONS: We report the pattern of normality for LUS examination in children. We also showed that otherwise healthy children who recovered from COVID-19 and even those who were mildly symptomatic had more "physiological" artifacts at LUS examinations.

A causal learning framework for the analysis and interpretation of COVID-19 clinical data.

We present a workflow for clinical data analysis that relies on Bayesian Structure Learning (BSL), an unsupervised learning approach, robust to noise and biases, that allows to incorporate prior medical knowledge into the learning process and that provides explainable results in the form of a graph showing the causal connections among the analyzed features. The workflow consists in a multi-step approach that goes from identifying the main causes of patient's outcome through BSL, to the realization of a tool suitable for clinical practice, based on a Binary Decision Tree (BDT), to recognize patients at high-risk with information available already at hospital admission time. We evaluate our approach on a feature-rich dataset of Coronavirus disease (COVID-19), showing that the proposed framework provides a schematic overview of the multi-factorial processes that jointly contribute to the outcome. We compare our findings with current literature on COVID-19, showing that this approach allows to re-discover established cause-effect relationships about the disease. Further, our approach yields to a highly interpretable tool correctly predicting the outcome of 85% of subjects based exclusively on 3 features: age, a previous history of chronic obstructive pulmonary disease and the PaO2/FiO2 ratio at the time of arrival to the hospital. The inclusion of additional information from 4 routine blood tests (Creatinine, Glucose, pO2 and Sodium) increases predictive accuracy to 94.5%.

Early outcome detection for COVID-19 patients.

With the outbreak of COVID-19 exerting a strong pressure on hospitals and health facilities, clinical decision support systems based on predictive models can help to effectively improve the management of the pandemic. We present a method for predicting mortality for COVID-19 patients. Starting from a large number of clinical variables, we select six of them with largest predictive power, using a feature selection method based on genetic algorithms and starting from a set of COVID-19 patients from the first wave. The algorithm is designed to reduce the impact of missing values in the set of variables measured, and consider only variables that show good accuracy on validation data. The final predictive model provides accuracy larger than 85% on test data, including a new patient cohort from the second COVID-19 wave, and on patients with imputed missing values. The selected clinical variables are confirmed to be relevant by recent literature on COVID-19.

Lung ultrasound for the early diagnosis of COVID-19 pneumonia: an international multicenter study.

PURPOSE: To analyze the application of a lung ultrasound (LUS)-based diagnostic approach to patients suspected of COVID-19, combining the LUS likelihood of COVID-19 pneumonia with patient's symptoms and clinical history. METHODS: This is an international multicenter observational study in 20 US and European hospitals. Patients suspected of COVID-19 were tested with reverse transcription-polymerase chain reaction (RT-PCR) swab test and had an LUS examination. We identified three clinical phenotypes based on pre-existing chronic diseases (mixed phenotype), and on the presence (severe phenotype) or absence (mild phenotype) of signs and/or symptoms of respiratory failure at presentation. We defined the LUS likelihood of COVID-19 pneumonia according to four different patterns: high (HighLUS), intermediate (IntLUS), alternative (AltLUS), and low (LowLUS) probability. The combination of patterns and phenotypes with RT-PCR results was described and analyzed. RESULTS: We studied 1462 patients, classified in mild (n = 400), severe (n = 727), and mixed (n = 335) phenotypes. HighLUS and IntLUS showed an overall sensitivity of 90.2% (95% CI 88.23-91.97%) in identifying patients with positive RT-PCR, with higher values in the mixed (94.7%) and severe phenotype (97.1%), and even higher in those patients with objective respiratory failure (99.3%). The HighLUS showed a specificity of 88.8% (CI 85.55-91.65%) that was higher in the mild phenotype (94.4%; CI 90.0-97.0%). At multivariate analysis, the HighLUS was a strong independent predictor of RT-PCR positivity (odds ratio 4.2, confidence interval 2.6-6.7, p < 0.0001). CONCLUSION: Combining LUS patterns of probability with clinical phenotypes at presentation can rapidly identify those patients with or without COVID-19 pneumonia at bedside. This approach could support and expedite patients' management during a pandemic surge.

Sonographic signs and patterns of COVID-19 pneumonia.

The pandemic of COVID-19 is seriously challenging the medical organization in many parts of the world. This novel corona virus SARS-CoV-2 has a specific tropism for the low respiratory airways, but causes severe pneumonia in a low percentage of patients. However, the rapid spread of the infection during this pandemic is causing the need to hospitalize a high number of patients. Pneumonia in COVID-19 has peculiar features and can be studied by lung ultrasound in the early approach to suspected patients. The sonographic signs are non-specific when considered alone, but observation of some aspects of vertical artifacts can enhance the diagnostic power of the ultrasound examination. Also, the combination of sonographic signs in patterns and their correlation with blood exams in different phenotypes of the disease may allow for a reliable characterization and be of help in triaging and admitting patients.