The role of prone positioning in patients with SARS-CoV-2-related respiratory failure in non-intensive care unit.

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. OBJECTIVES: We aimed at assessing the role of PP in death and ICU admission in non-intubated patients with acute respiratory failure related to COronaVIrus Disease-19 (COVID-19) pneumonia. DESIGN: This is a retrospective analysis of a collaborative multicenter database obtained by merging local non-interventional cohorts. METHODS: Consecutive adult patients with COVID-19-related respiratory failure were included in a collaborative cohort and classified based on the severity of respiratory failure according to 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. The 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 in-hospital death in 129 (24.1%). PP was not associated with death or ICU admission (HR 1.17, 95% CI 0.78-1.74) and not with death (HR 1.01, 95% CI 0.61-1.67) at multivariable analysis; PP was an independent predictor of ICU admission (HR 2.64, 95% CI 1.53-4.40). The lack of association between PP and death or ICU admission was confirmed at propensity score-matching analysis. CONCLUSION: PP is used in a non-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 merits further evaluation in randomized studies.

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.

Clinical prediction models in hospitalized patients with COVID-19: A multicenter cohort study.

BACKGROUND: Clinical spectrum of novel coronavirus disease (COVID-19) ranges from asymptomatic infection to severe respiratory failure that may result in death. We aimed at validating and potentially improve existing clinical models to predict prognosis in hospitalized patients with acute COVID-19. METHODS: Consecutive patients with acute confirmed COVID-19 pneumonia hospitalized at 5 Italian non-intensive care unit centers during the 2020 outbreak were included in the study. Twelve validated prognostic scores for pneumonia and/or sepsis and specific COVID-19 scores were calculated for each study patient and their accuracy was compared in predicting in-hospital death at 30 days and the composite of death and orotracheal intubation. RESULTS: During hospital stay, 302 of 1044 included patients presented critical illness (28.9%), and 226 died (21.6%). Nine out of 34 items included in different prognostic scores were independent predictors of all-cause-death. The discrimination was acceptable for the majority of scores (APACHE II, COVID-GRAM, REMS, CURB-65, NEWS II, ROX-index, 4C, SOFA) to predict in-hospital death at 30 days and poor for the rest. A high negative predictive value was observed for REMS (100.0%) and 4C (98.7%) scores; the positive predictive value was poor overall, ROX-index having the best value (75.0%). CONCLUSIONS: Despite the growing interest in prognostic models, their performance in patients with COVID-19 is modest. The 4C, REMS and ROX-index may have a role to select high and low risk patients at admission. However, simple predictors as age and PaO2/FiO2 ratio can also be useful as standalone predictors to inform decision making.

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%.

Estimated pulse wave velocity improves risk stratification for all-cause mortality in patients with COVID-19.

Accurate risk stratification in COVID-19 patients consists a major clinical need to guide therapeutic strategies. We sought to evaluate the prognostic role of estimated pulse wave velocity (ePWV), a marker of arterial stiffness which reflects overall arterial integrity and aging, in risk stratification of hospitalized patients with COVID-19. This retrospective, longitudinal cohort study, analyzed a total population of 1671 subjects consisting of 737 hospitalized COVID-19 patients consecutively recruited from two tertiary centers (Newcastle cohort: n = 471 and Pisa cohort: n = 266) and a non-COVID control cohort (n = 934). Arterial stiffness was calculated using validated formulae for ePWV. ePWV progressively increased across the control group, COVID-19 survivors and deceased patients (adjusted mean increase per group 1.89 m/s, P < 0.001). Using a machine learning approach, ePWV provided incremental prognostic value and improved reclassification for mortality over the core model including age, sex and comorbidities [AUC (core model + ePWV vs. core model) = 0.864 vs. 0.755]. ePWV provided similar prognostic value when pulse pressure or hs-Troponin were added to the core model or over its components including age and mean blood pressure (p < 0.05 for all). The optimal prognostic ePWV value was 13.0 m/s. ePWV conferred additive discrimination (AUC: 0.817 versus 0.779, P < 0.001) and reclassification value (NRI = 0.381, P < 0.001) over the 4C Mortality score, a validated score for predicting mortality in COVID-19 and the Charlson comorbidity index. We suggest that calculation of ePWV, a readily applicable estimation of arterial stiffness, may serve as an additional clinical tool to refine risk stratification of hospitalized patients with COVID-19 beyond established risk factors and scores.

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.

JAK inhibition reduces SARS-CoV-2 liver infectivity and modulates inflammatory responses to reduce morbidity and mortality.

Using AI, we identified baricitinib as having antiviral and anticytokine efficacy. We now show a 71% (95% CI 0.15 to 0.58) mortality benefit in 83 patients with moderate-severe SARS-CoV-2 pneumonia with few drug-induced adverse events, including a large elderly cohort (median age, 81 years). An additional 48 cases with mild-moderate pneumonia recovered uneventfully. Using organotypic 3D cultures of primary human liver cells, we demonstrate that interferon-α2 increases ACE2 expression and SARS-CoV-2 infectivity in parenchymal cells by greater than fivefold. RNA-seq reveals gene response signatures associated with platelet activation, fully inhibited by baricitinib. Using viral load quantifications and superresolution microscopy, we found that baricitinib exerts activity rapidly through the inhibition of host proteins (numb-associated kinases), uniquely among antivirals. This reveals mechanistic actions of a Janus kinase-1/2 inhibitor targeting viral entry, replication, and the cytokine storm and is associated with beneficial outcomes including in severely ill elderly patients, data that incentivize further randomized controlled trials.

C-C motive chemokine ligand 2 and thromboinflammation in COVID-19-associated pneumonia: A retrospective study.

PURPOSE: A derangement of the coagulation process and thromboinflammatory events has emerged as pathologic characteristics of severe COVID-19, characterized by severe respiratory failure. CC motive chemokine ligand 2 (CCL2), a chemokine originally described as a chemotactic agent for monocytes, is involved in inflammation, coagulation activation and neoangiogenesis. We investigated the association of CCL2 levels with coagulation derangement and respiratory impairment in patients with COVID-19. METHODS: We retrospectively evaluated 281 patients admitted to two hospitals in Italy with COVID-19. Among them, CCL2 values were compared in different groups (identified according to D-dimer levels and the lowest PaO2/FiO2 recorded during hospital stay, P/Fnadir) by Jonckheere-Terpstra tests; linear regression analysis was used to analyse the relationship between CCL2 and P/Fnadir. We performed Mann-Whitney test and Kaplan-Meier curves to investigate the role of CCL2 according to different clinical outcomes (survival and endotracheal intubation [ETI]). RESULTS: CCL2 levels were progressively higher in patients with increasing D-dimer levels and with worse gas exchange impairment; there was a statistically significant linear correlation between log CCL2 and log P/Fnadir. CCL2 levels were significantly higher in patients with unfavourable clinical outcomes; Kaplan-Meier curves for the composite outcome death and/or need for ETI showed a significantly worse prognosis for patients with higher (> median) CCL2 levels. CONCLUSIONS: CCL2 correlates with both indices of activation of the coagulation cascade and respiratory impairment severity, which are likely closely related in COVID-19 pathology, thus suggesting that CCL2 could be involved in the thromboinflammatory events characterizing this disease.

The relationship between cardiac injury, inflammation and coagulation in predicting COVID-19 outcome.

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. 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. 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. 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.

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.

Role of Low-Molecular-Weight Heparin in Hospitalized Patients With Severe Acute Respiratory Syndrome Coronavirus 2 Pneumonia: A Prospective Observational Study.

BACKGROUND: This study was conducted to evaluate the impact of low-molecular-weight heparin (LMWH) on the outcome of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia. METHODS: This is a prospective observational study including consecutive patients with laboratory-confirmed SARS-CoV-2 pneumonia admitted to the University Hospital of Pisa (March 4-April 30, 2020). Demographic, clinical, and outcome data were collected. The primary endpoint was 30-day mortality. The secondary endpoint was a composite of death or severe acute respiratory distress syndrome (ARDS). Low-molecular-weight heparin, hydroxychloroquine, doxycycline, macrolides, antiretrovirals, remdesivir, baricitinib, tocilizumab, and steroids were evaluated as treatment exposures of interest. First, a Cox regression analysis, in which treatments were introduced as time-dependent variables, was performed to evaluate the association of exposures and outcomes. Then, a time-dependent propensity score (PS) was calculated and a PS matching was performed for each treatment variable. RESULTS: Among 315 patients with SARS-CoV-2 pneumonia, 70 (22.2%) died during hospital stay. The composite endpoint was achieved by 114 (36.2%) patients. Overall, 244 (77.5%) patients received LMWH, 238 (75.5%) received hydroxychloroquine, 201 (63.8%) received proteases inhibitors, 150 (47.6%) received doxycycline, 141 (44.8%) received steroids, 42 (13.3%) received macrolides, 40 (12.7%) received baricitinib, 13 (4.1%) received tocilizumab, and 13 (4.1%) received remdesivir. At multivariate analysis, LMWH was associated with a reduced risk of 30-day mortality (hazard ratio [HR], 0.36; 95% confidence interval [CI], 0.21-0.6; P < .001) and composite endpoint (HR, 0.61; 95% CI, 0.39-0.95; P = .029). The PS-matched cohort of 55 couples confirmed the same results for both primary and secondary endpoint. CONCLUSIONS: This study suggests that LMWH might reduce the risk of in-hospital mortality and severe ARDS in coronavirus disease 2019. Randomized controlled trials are warranted to confirm these preliminary findings.

Predictors of hospital-acquired bacterial and fungal superinfections in COVID-19: a prospective observational study.

BACKGROUND: Bacterial and fungal superinfections may complicate the course of hospitalized patients with COVID-19. OBJECTIVES: To identify predictors of superinfections in COVID-19. METHODS: Prospective, observational study including patients with COVID-19 consecutively admitted to the University Hospital of Pisa, Italy, between 4 March and 30 April 2020. Clinical data and outcomes were registered. Superinfection was defined as a bacterial or fungal infection that occurred ≥48 h after hospital admission. A multivariate analysis was performed to identify factors independently associated with superinfections. RESULTS: Overall, 315 patients with COVID-19 were hospitalized and 109 episodes of superinfections were documented in 69 (21.9%) patients. The median time from admission to superinfection was 19 days (range 11-29.75). Superinfections were caused by Enterobacterales (44.9%), non-fermenting Gram-negative bacilli (15.6%), Gram-positive bacteria (15.6%) and fungi (5.5%). Polymicrobial infections accounted for 18.3%. Predictors of superinfections were: intestinal colonization by carbapenem-resistant Enterobacterales (OR 16.03, 95% CI 6.5-39.5, P < 0.001); invasive mechanical ventilation (OR 5.6, 95% CI 2.4-13.1, P < 0.001); immunomodulatory agents (tocilizumab/baricitinib) (OR 5.09, 95% CI 2.2-11.8, P < 0.001); C-reactive protein on admission >7 mg/dl (OR 3.59, 95% CI 1.7-7.7, P = 0.001); and previous treatment with piperacillin/tazobactam (OR 2.85, 95% CI 1.1-7.2, P = 0.028). Length of hospital stay was longer in patients who developed superinfections ompared with those who did not (30 versus 11 days, P < 0.001), while mortality rates were similar (18.8% versus 23.2%, P = 0.445). CONCLUSIONS: The risk of bacterial and fungal superinfections in COVID-19 is consistent. Patients who need empiric broad-spectrum antibiotics and immunomodulant drugs should be carefully selected. Infection control rules must be reinforced.

Role of low-molecular weight heparin in hospitalized patients with SARS-CoV-2 pneumonia: a prospective observational study

Objectives To evaluate the impact of low molecular weight heparin (LMWH) on the outcome of patients with SARS-CoV-2 pneumonia. Methods Prospective observational study including consecutive patients with laboratory confirmed SARS-CoV-2 pneumonia admitted to the University Hospital of Pisa (4th March-30th April 2020). Demographic, clinical, and outcome data were collected. The primary endpoint was 30-day mortality. The secondary endpoint was a composite of death or severe ARDS. LMWH, hydroxychloroquine, doxycycline, macrolides, antiretrovirals, remdesivir, baricitinib, tocilizumab, and steroids were evaluated as treatment exposures of interest. First, a Cox-regression analysis, in which treatments were introduced as time-dependent variables, was performed to evaluate the association of exposures and outcomes. Then, a time-dependent Propensity-score (PS) was calculated and a PS-matching performed for each treatment variable. Results Among 315 patients with SARS-CoV-2 pneumonia, 70 (22.2%) died during hospital stay. The composite endpoint was achieved by 114 (36.2%) patients. Overall, 244 (77.5%) patients received LMWH, 238 (75.5%) hydroxychloroquine, 201 (63.8%) proteases inhibitors, 150 (47.6%) doxycycline, 141 (44.8%) steroids, 42 (13.3%) macrolides, 40 (12.7%) baricitinib, 13 (4.1%) tocilizumab, and 13 (4.1%) remdesivir. At multivariate analysis, LMWH was associated with a reduced risk of 30-day mortality (HR 0.36 [95% CI 0.21-0.6], p<0.001) and composite endpoint (HR 0.61 [95% CI 0.39-0.95], p=0.029). The PS-matched cohort of 55 couples confirmed the same results for both primary and secondary endpoint. Conclusions This study suggests that LMWH might reduce the risk of in-hospital mortality and severe ARDS in Covid-19. Randomized controlled trials are warranted to confirm these preliminary findings.

Hyperglycemia at Hospital Admission Is Associated With Severity of the Prognosis in Patients Hospitalized for COVID-19: The Pisa COVID-19 Study.

OBJECTIVE: To explore whether at-admission hyperglycemia is associated with worse outcomes in patients hospitalized for coronavirus disease 2019 (COVID-19). RESEARCH DESIGN AND METHODS: Hospitalized COVID-19 patients (N = 271) were subdivided based on at-admission glycemic status: 1) glucose levels <7.78 mmol/L (NG) (N = 149 [55.0%]; median glucose 5.99 mmol/L [range 5.38-6.72]), 2) known diabetes mellitus (DM) (N = 56 [20.7%]; 9.18 mmol/L [7.67-12.71]), and 3) no diabetes and glucose levels ≥7.78 mmol/L (HG) (N = 66 [24.3%]; 8.57 mmol/L [8.18-10.47]). RESULTS: Neutrophils were higher and lymphocytes and PaO2/FiO2 lower in HG than in DM and NG patients. DM and HG patients had higher D-dimer and worse inflammatory profile. Mortality was greater in HG (39.4% vs. 16.8%; unadjusted hazard ratio [HR] 2.20, 95% CI 1.27-3.81, P = 0.005) than in NG (16.8%) and marginally so in DM (28.6%; 1.73, 0.92-3.25, P = 0.086) patients. Upon multiple adjustments, only HG remained an independent predictor (HR 1.80, 95% CI 1.03-3.15, P = 0.04). After stratification by quintile of glucose levels, mortality was higher in quintile 4 (Q4) (3.57, 1.46-8.76, P = 0.005) and marginally in Q5 (29.6%) (2.32, 0.91-5.96, P = 0.079) vs. Q1. CONCLUSIONS: Hyperglycemia is an independent factor associated with severe prognosis in people hospitalized for COVID-19.