Prone positioning for patients with SARS-CoV-2-related respiratory failure in non-intensive care unit (preprint)

Background Prone positioning (PP) is an established and commonly used lung recruitment method for intubated patients with severe acute respiratory distress syndrome, with potential benefits in clinical outcome. The role of PP outside the intensive care unit (ICU) setting is debated. We aimed at describing the use and potential benefits of PP in non-intubated patients with acute respiratory failure related to COronaVIrus Disease-19 (COVID-19)-pneumonia. Methods Consecutive adult patients with COVID-19-related respiratory failure were included in a prospective collaborative cohort and classified based on the severity of respiratory failure by the partial arterial oxygen pressure to fraction of inspired oxygen ratio (PaO2/FiO2) and on clinical severity by the quick Sequential Organ Failure Assessment (qSOFA) score. Primary study outcome was the composite of in-hospital death or ICU admission within 30 days from hospitalization. Results PP was used in 114 of 536 study patients (21.8%), more commonly in patients with lower PaO2/FiO2 or receiving non-invasive ventilation and less commonly in patients with known comorbidities. A primary study outcome event occurred in 163 patients (30.4%) and was in-hospital death in 129 (24.1%). PP was not associated with death or ICU admission (HR 1.15, CI 95% 0.78-1.72) and not with death (HR 1.03, CI 95% 0.62-1.69); PP was an independent predictor of ICU admission (HR 2.55, 95%CI 1.50-4.32). The severity of respiratory failure and non-invasive ventilation were independent predictors of death or ICU admission at 30 days. The lack of association between PP and death or ICU admission was confirmed at propensity score matching analysis.Conclusion PP is used in a not negligible proportion of non-intubated patients with COVID-19-related severe respiratory failure and is not associated with death but with ICU admission. The role of PP in this setting requires evaluation in randomized studies. 

A causal learning framework for the analysis and interpretation of COVID-19 clinical data (preprint)

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 COVID-19 dataset, showing that the proposed framework provides a schematic overview of the multi-factorial processes that jointly contribute to the outcome. We discuss how these computational findings are confirmed by current understanding of the COVID-19 pathogenesis. 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%.

Lung Ultrasound to Predict COVID-19 Pneumonia: A Multicenter Descriptive Study (preprint)

Background: The pandemic surge of Coronavirus disease 2019 (COVID-19) is posing the unprecedent challenge of rapidly identifying and isolating probable cases and diagnosing the main respiratory complications. We aimed to describe the application of a lung ultrasound (LUS)-based diagnostic approach, combining the LUS likelihood of COVID-19 pneumonia with patient’s symptoms and clinical history.Methods: This is an international multicenter prospective observational study on patients suspected for COVID-19, presenting to 22 different US and European hospitals. Patients underwent LUS and reverse transcription-polymerase chain reaction (RT-PCR) swab test. We identified 3 different clinical phenotypes based on pre-existing chronic cardiac or respiratory 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 4 different patterns, characterized by the presence and distribution of typical and atypical LUS signs: high (HPLUS), intermediate (IPLUS), alternative (APLUS) and low (LPLUS) probability patterns. The association between the combination of patterns and phenotypes with RT-PCR results was described and analyzed.Findings: We studied 1462 patients, classified in mild (n=400), severe (n=727) and mixed (n=335) phenotypes. In the overall population, the HPLUS corresponded to a positive RT-PCR in 92.6% of cases, with similarly high percentages in all clinical phenotypes ranging from 87.5% (mild) to 90.3% (mixed) and 96.5% (severe). The IPLUS yielded a lower match with positive RT-PCR (65.7%). In patients with respiratory failure, the LPLUS predicted a negative RT-PCR in 100% of cases. In the overall population, the APLUS indicated an alternative pulmonary condition in 81.1% of patients. At multivariate analysis the HPLUS strongly predicted RT-PCR positivity (odds ratio 4.173, interquartile range 2.595-6.712, p<0.0001), independently from age, low oxygen saturation and dyspnea.Interpretation: Combining LUS patterns of probability for interstitial pneumonia with clinical phenotypes at presentation could facilitate the early diagnosis of COVID-19 or suggest an alternative pulmonary condition. This approach may be useful to rapidly guide and support patient’s allocation for a wiser use of hospital resources during a pandemic surge.Funding: None.Conflict of Interest: The authors declare no conflicts of interest. Ethical Approval: The local Ethical Committee Boards of each center approved the study, and the study was conducted following the ethical standards of the 1964 Helsinki declaration and its later amendments and with local guidelines for good clinical practice.

The relationship between cardiac injury, inflammation and coagulation in predicting COVID-19 outcome (preprint)

Introduction: High sensitivity troponin T (hsTnT) is a strong predictor of adverse outcome during SARS-CoV-2 infection. However, its determinants remain partially unknown. We aimed to assess the relationship between severity of inflammatory response/coagulation abnormalities and hsTnT in Coronavirus Disease 2019 (COVID-19). We then explored the relevance of these pathways in defining mortality and complications risk and the potential effects of the treatments to attenuate such risk.Methods: In this single-center, prospective, observational study we enrolled 266 consecutive patients hospitalized for SARS-CoV-2 pneumonia. Primary endpoint was in-hospital COVID-19 mortality. Results: hsTnT, even after adjustment for confounders, was associated with mortality. D-dimer and CRP presented stronger associations with hsTnT than PaO2. Changes of hsTnT, D-dimer and CRP were related but only D-dimer was associated with mortality. Moreover, low molecular weight heparin showed attenuation of the mortality in the whole population, particularly in subjects with higher hsTnT.Conclusions: D-dimer possessed a strong relationship with hsTnT and mortality. Anticoagulation treatment showed greater benefits with regard to mortality. These findings suggest a major role of SARS-CoV-2 coagulopathy in hsTnT elevation and its related mortality in COVID-19. A better understanding of the mechanisms related to COVID-19 might pave the way to therapy tailoring in these high-risk individuals.

Low Diaphragm Muscle Mass Predicts Adverse Outcome in Patients Hospitalized for Covid-19 Pneumonia (preprint)

Purpose: The aim of this study was to evaluate whether measurement of diaphragm thickness by ultrasonography may be a clinically useful noninvasive method for identifying patients at risk of adverse outcomes defined as need of invasive mechanical ventilation or death. Methods: We retrospectively reviewed the records of consecutive of 77 patients with laboratory-confirmed Covid-19 infection admitted to our intermediate care unit in Pisa between March 5 and March 30, 2020, with follow up until hospital discharge or death. Logistic regression was used identify variables potentially associated with adverse outcomes and those P<0.10 were entered into a multivariate logistic regression model. Cumulative probability for lack of adverse outcomes in patients with or without low baseline diaphragm muscle mass was calculated with the Kaplan–Meier product-limit estimator.Results: The main findings of this study are that 1) patients who developed adverse outcomes had thinner diaphragm than those who did not (2.0 vs 2.2 mm, p:0.001), 2) DT and lymphocyte count were independent significant predictors of adverse outcomes, with end-expiratory DT being the strongest (-708, OR: 0.492, p: 0.018).Conclusion: Diaphragmatic ultrasound may be a valid tool to evaluate the risk of respiratory failure. Evaluating the need of mechanical ventilation treatment should be based not only on PaO2/FiO2, but on a more comprehensive assessment including DT because if the lungs become less compliant a thinner diaphragm, albeit free of intrinsic abnormality, may become exhausted, thus contributing to severe respiratory failure.