Five Level Triage vs. Four Level Triage in a Quaternary Emergency Department: National Analysis on Waiting Time, Validity, and Crowding-The CREONTE (Crowding and RE-Organization National TriagE) Study Group.

Background and Objectives: Triage systems help provide the right care at the right time for patients presenting to emergency departments (EDs). Triage systems are generally used to subdivide patients into three to five categories according to the system used, and their performance must be carefully monitored to ensure the best care for patients. Materials and Methods: We examined ED accesses in the context of 4-level (4LT) and 5-level triage systems (5LT), implemented from 1 January 2014 to 31 December 2020. This study assessed the effects of a 5LT on wait times and under-triage (UT) and over-triage (OT). We also examined how 5LT and 4LT systems reflected actual patient acuity by correlating triage codes with severity codes at discharge. Other outcomes included the impact of crowding indices and 5LT system function during the COVID-19 pandemic in the study populations. Results: We evaluated 423,257 ED presentations. Visits to the ED by more fragile and seriously ill individuals increased, with a progressive increase in crowding. The length of stay (LOS), exit block, boarding, and processing times increased, reflecting a net raise in throughput and output factors, with a consequent lengthening of wait times. The decreased UT trend was observed after implementing the 5LT system. Conversely, a slight rise in OT was reported, although this did not affect the medium-high-intensity care area. Conclusions: Introducing a 5LT improved ED performance and patient care.

An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study.

Introduction: Identifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow one to analyze large amounts of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning. Material and methods: We conducted a retrospective cohort study on hospitalized adult COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach based on vital parameters, laboratory values and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation. Results: 1,135 consecutive patients (median age 70 years, 64% male) were enrolled, 48 patients were excluded, and the cohort was randomly divided into training (760) and test (327) groups. During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88 ±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85 ±0.025), and three levels were defined that correlated well with in-hospital mortality. Conclusions: Machine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.

BNT162b2 vaccine induces antibody release in saliva: a possible role for mucosal viral protection?

Vaccination against an airborne pathogen is very effective if it induces also the development of mucosal antibodies that can protect against infection. The mRNA-based vaccine-encoding SARS-CoV-2 full-length spike protein (BNT162b2, Pfizer/BioNTech) protects also against infection despite being administered systemically. Here, we show that upon vaccination, cognate IgG molecules are also found in the saliva and are more abundant in SARS-CoV-2 previously exposed subjects, paralleling the development of plasma IgG. The antibodies titer declines at 3 months from vaccination. We identified a concentration of specific IgG in the plasma above which the relevant IgG can be detected in the saliva. Regarding IgA antibodies, we found only protease-susceptible IgA1 antibodies in plasma while they were present at very low levels in the saliva over the course of vaccination of SARS-CoV-2-naïve subjects. Thus, in response to BNT162b2 vaccine, plasma IgG can permeate into mucosal sites and participate in viral protection. It is not clear why IgA1 are detected in low amount, they may be proteolytically cleaved.

A 'Multiomic' Approach of Saliva Metabolomics, Microbiota, and Serum Biomarkers to Assess the Need of Hospitalization in Coronavirus Disease 2019.

BACKGROUND AND AIMS: The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the health care systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized (inpatients) or discharged (outpatients). Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration, and discharged patients often returned to the hospital for aggravation of their condition. Here, we have developed a new combined approach of omics to identify factors that could distinguish coronavirus disease 19 (COVID-19) inpatients from outpatients. METHODS: Saliva and blood samples were collected over the course of two observational cohort studies. By using machine learning approaches, we compared salivary metabolome of 50 COVID-19 patients with that of 270 healthy individuals having previously been exposed or not to SARS-CoV-2. We then correlated the salivary metabolites that allowed separating COVID-19 inpatients from outpatients with serum biomarkers and salivary microbiota taxa differentially represented in the two groups of patients. RESULTS: We identified nine salivary metabolites that allowed assessing the need of hospitalization. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrrolidineacetic acid) and one serum protein, chitinase 3-like-1 (CHI3L1), were sufficient to separate inpatients from outpatients completely and correlated with modulated microbiota taxa. In particular, we found Corynebacterium 1 to be overrepresented in inpatients, whereas Actinomycetaceae F0332, Candidatus Saccharimonas, and Haemophilus were all underrepresented in the hospitalized population. CONCLUSION: This is a proof of concept that a combined omic analysis can be used to stratify patients independently from COVID-19.

Short-Term Adverse Events and Antibody Response to the BNT162b2 SARS-CoV-2 Vaccine in 4156 Health Care Professionals.

Short-term adverse events are common following the BNT162b2 vaccine for SARS-Cov-2 and have been possibly associated with IgG response. We aimed to determine the incidence of adverse reactions to the vaccine and the impact on IgG response. Our study included 4156 health-care professionals who received two doses of the BNT162b2 vaccine 21 days apart and obtained 6113 online questionnaires inquiring about adverse events. The serum response was tested in 2765 subjects 10 days after the second dose. Adverse events, most frequently a local reaction at the site of injection, were reported by 39% of subjects. Multivariate analysis showed that female sex (odds ratio-OR-1.95; 95% confidence interval-CI-1.74-2.19; p < 0.001), younger age (OR 0.98 per year, p < 0.001), second dose of vaccine (OR 1.36, p < 0.001), and previous COVID-19 infection (OR 1.41, p < 0.001) were independently associated with adverse events. IgG response was significantly higher in subjects with adverse events (1110 AU/mL-IQR 345-1630 vs. 386 AU/mL, IQR 261-1350, p < 0.0001), and the association was more pronounced in subjects experiencing myalgia, fever, and lymphadenopathy. We demonstrate that a more pronounced IgG response is associated with specific adverse events, and these are commonly reported by health care professionals after the BNT162b2 vaccine for SARS-Cov-2.

Clinical Outcomes in the Second versus First Pandemic Wave in Italy: Impact of Hospital Changes and Reorganization

The region of Lombardy was the epicenter of the COVID-19 outbreak in Italy. Emergency Hospital 19 (EH19) was built in the Milan metropolitan area during the pandemic’s second wave as a facility of Humanitas Clinical and Research Center (HCRC). The present study aimed to assess whether the implementation of EH19 was effective in improving the quality of care of COVID-19 patients during the second wave compared with the first one. The demographics, mortality rate, and in-hospital length of stay (LOS) of two groups of patients were compared: the study group involved patients admitted at HCRC and managed in EH19 during the second pandemic wave, while the control group included patients managed exclusively at HCRC throughout the first wave. The study and control group included 903 (56.7%) and 690 (43.3%) patients, respectively. The study group was six years older on average and had more pre-existing comorbidities. EH19 was associated with a decrease in the intensive care unit admission rate (16.9% vs. 8.75%, p <0.001), and an equal decrease in invasive oxygen therapy (3.8% vs. 0.23%, p <0.001). Crude mortality was similar but overlap propensity score weighting revealed a trend toward a potential small decrease. The adjusted difference in LOS was not significant. The implementation of an additional COVID-19 hospital facility was effective in improving the overall quality of care of COVID-19 patients during the first wave of the pandemic when compared with the second. Further studies are necessary to validate the suggested approach.

Lean-ing Method in an Emergency Department of the Italian Epicenter of the COVID-19 Outbreak: When the Algorithm Makes Difference

The Lean method entails a set of standardized processes intending to optimize resources, reduce waste, and improve results. Lean has been proposed as an operative model for the COVID-19 outbreak. Herein, we summarized data resulted from the Lean model adoption in an Emergency Department of the Lombardy region, the Italian epicenter of the pandemic, to critically appraise its effectiveness and feasibility. The Lean algorithm was applied in the Humanitas Clinical and Research Hospital, Milan, north of Italy. At admission, patients underwent outdoor pre-triage for fever, respiratory, and gastrointestinal symptoms, with a focus on SpO2. Based on these data, they were directed to the most appropriate area for the COVID-19 first-level screening. High-risk patients were assisted by trained staff for second-level screening and planning of treatment. Out of 7.778 patients, 21.9% were suspected of SARS-CoV-2 infection. Mortality was 21.9% and the infection rate in health workers was 4.8%. The lean model has proved to be effective in optimizing the overall management of COVID-19 patients in an emergency setting. It allowed for screening of a large volume of patients, while also limiting the health workers’ infection rate. Further studies are necessary to validate the suggested approach.

Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment.

Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.

The Use of Antiviral Agents against SARS-CoV-2: Ineffective or Time and Age Dependent Result? A Retrospective, Observational Study among COVID-19 Older Adults.

BACKGROUND: Our aim was to investigate the impact of therapeutics with antiviral activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on mortality of older adults affected by coronavirus disease 2019 (COVID-19), taking into consideration the time interval from symptoms onset to drugs administration. METHODS: Data from 143 COVID-19 patients over 65 years of age admitted to the Humanitas Clinical and Research Center Emergency Department (Milan, Italy) and treated with Lopinavir/ritonavir (LPV/r) or Darunavir/cobicistat (DVR/c) associated to Hydroxychloroquine (HCQ) were retrospectively analyzed. Statistical analysis was performed by using a logistic regression model and survival analysis to assess the role of different predictors of in-hospital mortality, including an early (<6 days from symptoms onset) vs. late treatment onset, signs and symptoms at COVID-19 presentation, type of antiviral treatment (LPV/r or DVR/c) and patients' age (65-80 vs. >80 years old). RESULTS: Multivariate analysis showed that an older age (OR: 2.54) and dyspnea as presenting symptom (OR: 2.01) were associated with higher mortality rate, whereas cough as presenting symptom (OR: 0.53) and a timely drug administration (OR: 0.44) were associated with lower mortality. Survival analysis demonstrated that the timing of drug administration had an impact on mortality in 65-80 years-old patients (p = 0.02), whereas no difference was seen in those >80 years-old. This impact was more evident in patients with dyspnea as primary symptom of COVID-19, in whom mortality decreased from 57.1% to 38.3% due to timely drug administration (OR: 0.5; p = 0.04). CONCLUSIONS: There was a significant association between the use of a combined antiviral regimen and HCQ and lower mortality, when timely-administered, in COVID-19 patients aged 65-80 years. Our findings support timely treatment onset as a key component in the treatment of COVID-19.

The role of anti-hypertensive treatment, comorbidities and early introduction of LMWH in the setting of COVID-19: A retrospective, observational study in Northern Italy.

BACKGROUND: There is a great deal of debate about the role of cardiovascular comorbidities and the chronic use of antihypertensive agents (such as ACE-I and ARBs) on mortality on COVID-19 patients. Of note, ACE2 is responsible for the host cell entry of the virus. METHODS: We extracted data on 575 consecutive patients with laboratory-confirmed SARS-CoV-2 infection admitted to the Emergency Department (ED) of Humanitas Center, between February 21 and April 14, 2020. The aim of the study was to evaluate the role of chronic treatment with ACE-I or ARBs and other clinical predictors on in-hospital mortality in a cohort of COVID-19 patients. RESULTS: Multivariate analysis showed that a chronic intake of ACE-I was associated with a trend in reduction of mortality (OR: 0.53; 95% CI: 0.27-1.03; p = 0.06), differently from a chronic intake of ARB (OR: 1.1; 95% CI: 0.5-2.8; p=0.8). Increased age (ORs ranging from 3.4 to 25.2 and to 39.5 for 60-70, 70-80 and >80 years vs <60) and cardiovascular comorbidities (OR: 1.90; 95% CI: 1.1-3.3; p = 0.02) were confirmed as important risk factors for COVID-19 mortality. Timely treatment with low-molecular-weight heparin (LMWH) in ED was found to be protective (OR: 0.36; 95% CI: 0.21-0.62; p < 0.0001). CONCLUSIONS: This study can contribute to understand the reasons behind the high mortality rate of patients in Lombardy, a region which accounts for >50% of total Italian deaths. Based on our findings, we support that daily intake of antihypertensive medications in the setting of COVID-19 should not be discontinued and that a timely LMWH administration in ED has shown to decrease in-hospital mortality.

Effectiveness of Streptococcus Pneumoniae Urinary Antigen Testing in Decreasing Mortality of COVID-19 Co-Infected Patients: A Clinical Investigation.

BACKGROUND AND OBJECTIVES: Streptococcus pneumoniae urinary antigen (u-Ag) testing has recently gained attention in the early diagnosis of severe and critical acute respiratory syndrome coronavirus-2/pneumococcal co-infection. The aim of this study is to assess the effectiveness of Streptococcus pneumoniae u-Ag testing in coronavirus disease 2019 (COVID-19) patients, in order to assess whether pneumococcal co-infection is associated with different mortality rate and hospital stay in these patients. MATERIALS AND METHODS: Charts, protocols, mortality, and hospitalization data of a consecutive series of COVID-19 patients admitted to a tertiary hospital in northern Italy during COVID-19 outbreak were retrospectively reviewed. All patients underwent Streptococcus pneumoniae u-Ag testing to detect an underlying pneumococcal co-infection. Covid19+/u-Ag+ and Covid19+/u-Ag- patients were compared in terms of overall survival and length of hospital stay using chi-square test and survival analysis. RESULTS: Out of 575 patients with documented pneumonia, 13% screened positive for the u-Ag test. All u-Ag+ patients underwent treatment with Ceftriaxone and Azithromycin or Levofloxacin. Lopinavir/Ritonavir or Darunavir/Cobicistat were added in 44 patients, and hydroxychloroquine and low-molecular-weight heparin (LMWH) in 47 and 33 patients, respectively. All u-Ag+ patients were hospitalized. Mortality was 15.4% and 25.9% in u-Ag+ and u-Ag- patients, respectively (p = 0.09). Survival analysis showed a better prognosis, albeit not significant, in u-Ag+ patients. Median hospital stay did not differ among groups (10 vs. 9 days, p = 0.71). CONCLUSIONS: The routine use of Streptococcus pneumoniae u-Ag testing helped to better target antibiotic therapy with a final trend of reduction in mortality of u-Ag+ COVID-19 patients having a concomitant pneumococcal infection. Randomized trials on larger cohorts are necessary in order to draw definitive conclusion.