Thoracic UltrasONOgraphy Reporting: The TUONO Consensus.

The widespread use of the lung ultrasound (LUS) has not been followed by the development of a comprehensive standardized tool for its reporting in the intensive care unit (ICU) which could be useful to promote consistency and reproducibility during clinical examination. This work aims to define the essential features to be included in a standardized reporting tool and provides a structured model form to fully express the diagnostic potential of LUS and facilitate intensivists in the use of a LUS in everyday clinical ICU examination. We conducted a modified Delphi process to build consensus on the items to be integrated in a standardized report form and on its structure. A committee of 19 critical care physicians from 19 participating ICUs in Italy was formed, including intensivists experienced in ultrasound from both teaching hospitals and referral hospitals, and internationally renowned experts on the LUS. The consensus for 31 statements out of 33 was reached at the third Delphi round. A structured model form was developed based on the approved statements. The development of a standardized model as a backbone to report a LUS may facilitate the guidelines' application in clinical practice and increase inter-operator agreement. Further studies are needed to evaluate the effects of standardized reports in critically ill patients.

COVID-19 Detection Using Photoplethysmography and Neural Networks.

The early identification of microvascular changes in patients with Coronavirus Disease 2019 (COVID-19) may offer an important clinical opportunity. This study aimed to define a method, based on deep learning approaches, for the identification of COVID-19 patients from the analysis of the raw PPG signal, acquired with a pulse oximeter. To develop the method, we acquired the PPG signal of 93 COVID-19 patients and 90 healthy control subjects using a finger pulse oximeter. To select the good quality portions of the signal, we developed a template-matching method that excludes samples corrupted by noise or motion artefacts. These samples were subsequently used to develop a custom convolutional neural network model. The model accepts PPG signal segments as input and performs a binary classification between COVID-19 and control samples. The proposed model showed good performance in identifying COVID-19 patients, achieving 83.86% accuracy and 84.30% sensitivity (hold-out validation) on test data. The obtained results indicate that photoplethysmography may be a useful tool for microcirculation assessment and early recognition of SARS-CoV-2-induced microvascular changes. In addition, such a noninvasive and low-cost method is well suited for the development of a user-friendly system, potentially applicable even in resource-limited healthcare settings.

Automatic COVID-19 severity assessment from HRV.

COVID-19 is known to be a cause of microvascular disease imputable to, for instance, the cytokine storm inflammatory response and the consequent blood coagulation. In this study, we propose a methodological approach for assessing the COVID-19 presence and severity based on Random Forest (RF) and Support Vector Machine (SVM) classifiers. Classifiers were applied to Heart Rate Variability (HRV) parameters extracted from photoplethysmographic (PPG) signals collected from healthy and COVID-19 affected subjects. The supervised classifiers were trained and tested on HRV parameters obtained from the PPG signals in a cohort of 50 healthy subjects and 93 COVID-19 affected subjects, divided into two groups, mild and moderate, based on the support of oxygen therapy and/or ventilation. The most informative feature set for every group's comparison was determined with the Least Absolute Shrinkage and Selection Operator (LASSO) technique. Both RF and SVM classifiers showed a high accuracy percentage during groups' comparisons. In particular, the RF classifier reached 94% of accuracy during the comparison between the healthy and minor severity COVID-19 group. Obtained results showed a strong capability of RF and SVM to discriminate between healthy subjects and COVID-19 patients and to differentiate the two different COVID-19 severity. The proposed method might be helpful for detecting, in a low-cost and fast fashion, the presence and severity of COVID-19 disease; moreover, these reasons make this method interesting as a starting point for future studies that aim to investigate its effectiveness as a possible screening method.

COVID-19 detection using a model of photoplethysmography (PPG) signals.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) targets several tissues of the human body; among these, a serious impact has been observed in the microvascular system. The aim of this study was to verify the presence of photoplethysmographic (PPG) signal modifications in patients affected by COVID-19 at different levels of severity. APPROACH: The photoplethysmographic signal was evaluated in 93 patients with COVID-19 of different severity (46: grade 1; 47: grade 2) and in 50 healthy control subjects. A pre-processing step removes the long-term trend and segments of each pulsation in the input signal. Each pulse is approximated with a model generated from a multi-exponential curve, and a Least Squares fitting algorithm determines the optimal model parameters. Using the parameters of the mathematical model, three different classifiers (Bayesian, SVM and KNN) were trained and tested to discriminate among healthy controls and patients with COVID, stratified according to the severity of the disease. Results are validated with the leave-one-subject-out validation method. MAIN RESULTS: Results indicate that the fitting procedure obtains a very high determination coefficient (above 99% in both controls and pathological subjects). The proposed Bayesian classifier obtains promising results, given the size of the dataset, and variable depending on the classification strategy. The optimal classification strategy corresponds to 79% of accuracy, with 90% of specificity and 67% of sensibility. SIGNIFICANCE: The proposed approach opens the possibility of introducing a low cost and non-invasive screening procedure for the fast detection of COVID-19 disease, as well as a promising monitoring tool for hospitalized patients, with the purpose of stratifying the severity of the disease.

Cardiovascular Dynamics in COVID-19: A Heart Rate Variability Investigation.

COVID-19 is known to be a cause of microvascular disease due, for example, to the cytokine storm inflammatory response and the result of blood coagulation. This study reports an investigation on Heart Rate Variability (HRV) extracted from photoplethysmography (PPG) signals measured from healthy subjects and COVID-19 affected patients. We aimed to determine a statistical difference between HRV parameters among subjects' groups. Specifically, statistical analysis through Mann-Whitney U Test (MWUT) was applied to compare 42 dif-ferent parameters extracted from PPG signals of 143 subjects: 50 healthy subjects (i.e. group 0) and 93 affected from COVID-19 patients stratified through increasing COVID severity index (i.e. groups 1 and 2). Results showed significant statistical differences between groups in several HRV parameters. In particular, Multiscale Entropy (MSE) analysis provided the master key in patient stratification assessment. In fact, MSE11, MSE12, MSE15, MSE16, MSE17, MSE18, MSE19 and MSE20 keep statistical significant difference during all the comparisons between healthy subjects and patients from all the pathological groups. Our preliminary results suggest that it could be possible to distinguish between healthy and COVID-19 affected subjects based on cardiovascular dynamics. This study opens to future evaluations in using machine learning models for automatic decision-makers to distinguish between healthy and COVID-19 subjects, as well as within COVID-19 severity levels. Clinical Relevance - This establishes the possibility to distin-guish healthy subjects from COVID-19 affected patients basing on HRV parameters monitored non invasively by PPG.

Prognostic role of Interleukin-6/lymphocytes ratio in SARS-CoV2 related pneumonia.

INTRODUCTION AND AIM: Interleukin-6 to lymphocyte (IL-6/Lym) ratio has been identified as a potential prognostic tool in patients with SARS-CoV2 related pneumonia. The aim of our study was to compare the prognostic power of IL-6/Lym ratio with other biomarkers in patients initially admitted in a non intensive unit and suffering for respiratory failure associated with SARS-CoV2 related pneumonia. MATERIALS AND METHODS: IL-6/Lym ratio, IL-6, D-Dimer, D-Dimer/fibrinogen ratio, fibrinogen, C-reactive protein (CRP), lymphocytes count and neutrophil/lymphocyte (N/L) ratio collected at hospital admission were tested as prognosticators of negative outcome, defined as combined endpoint in-hospital mortality and/or Intensive Care Unit (ICU) admission requiring oro-tracheal intubation (OTI). RESULTS: Study population encompassed two hundreds and twenty-three patients (46% females) with mean age ± DS 69.4 ± 13.3 years. Eighty-nine patients (39.9%) suffered for severe respiratory failure and required non invasive ventilation, helmets and/or high flow nasal cannula. Fourty-one patients (18.3%) died during hospital stay and/or required OTI. In these patients mean values of IL-6/Lym ratio, IL-6, CRP and N/L were significantly higher and lymphocytes count was significantly lower compared with patients discharged alive and/or not requiring OTI, while no difference was found in mean values of D-Dimer, D-Dimer/Fibrinogen ratio and fibrinogen. AUC (0.797, 95% CI: 0.738-0.848) of IL-6/Lym ratio was the highest compared with those of all the other analyzed biomarkers and the difference was significant with the exception of IL-6. At multivariate logistic regression IL-6/Lym ratio > 66.5 resulted the only independent biomarker associated with mortality and/or OTI (OR 5.65; 95% 1.63-19.54). CONCLUSION: IL-6/Lym ratio seems to be an optimal prognosticator in SARS-CoV2 related pneumonia. Its routinary use in COVID-19 patients could be warranted.