Hrev_master [page 38] [Emergency Care Journal 2021; 17:9859] Emergency Care Journal 2021; volume 17:9859 Abstract COVID-19 patients require early treatment and admission to an appropriate care setting, considering possible rapid and unpre- dictable to Severe Acute Respiratory Syndrome. A flow-chart was developed by a multidisciplinary team of Emergency Department (ED) clinicians, intensivists and radiologists aiming to provide tools for disease severity stratification, appropriate ventilation strategy and hospitalization setting identification. We conducted a retrospective application of our model on 313 hospitalized patients at Pisa University Hospital including 222 patients admitted to ED for respiratory failure between March and April 2020. Risk strati- fication score was based on respiratory and chest imaging param- eters, while management strategy on comorbidities and age. Age, comorbidities, clinical respiratory and arterial blood gas parame- ters, semi-quantitative chest computed tomography score were sig- nificant predictors of mortality (p<0.05). Mortality rate was higher in patients treated in intensive care units (26.5%) and undergoing endo-tracheal intubation (32.7%), compared to medical area (21.3%). We verified a good concordance (81.7%) between the proposed model and actual evaluation in ED. Outcomes analysis of subgroups of patients homogeneous for baseline features allowed to verify safety of our model: in non-elderly and/or non-comorbid patients (15% mortality) our scheme overestimates the risk in 30% of cases, but it suggests non-intensive management in patients with reduced functional reserve, elderly and with comorbidities (50% mortality). Correct management of respiratory failure COVID-19 patients is crucial in this unexpected pandemic. Our flow-chart, despite retrospectively application in small sample, could repre- sents a valid and safe proposal for evaluation in ED. Introduction Background The rapid spread of CO-rona VI-rus D-isease 2019 (COVID- 19) cases resulted in overcrowd of Emergency Departments (ED).1 Even though most patients with COVID-19 infection has minor symptoms and good prognosis, a considerable group shows a Severe Acute Respiratory Syndrome Coronavirus 2 related (SARS-CoV-2)2, which requires early treatment and admission to an appropriate care setting, in order to avoid negative outcomes.1,3 The official guidelines of Italian Society of Anesthesia Analgesia Resuscitation and Intensive Care (Società Italiana di Anestesia Analgesia Rianimazione e Terapia Intensiva, SIAARTI), published on the 6th of March 2020, drew physician’s attention to an epidemic scenario of such magnitude that might cause an imbal- ance between the population real clinical needs and the effective availability of Intensive Care Unit (ICU) resources.4 Considering this possible derangement, the use of appropriate clinical tools is Correspondence: Greta Barbieri, Department of Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Via Savi 10, 56126 Pisa, Italy. Tel.: +39.3470104897 E-mail: greta.barbieri@phd.unipi.it Key words: COVID-19; ventilation; pneumonia; emergency depart- ment; management strategy. Conflict of interest: The authors declare no conflict of interests. GB and LG are members of ECJ editorial board. Contributions: GB was responsible for drafting, data analysis, and reviewing the manuscript. PM, LG, and AC designed the study and revised the manuscript. SC was responsible for data collection. SS, FC, FF, MF, FA, MS and MT revised the manuscript. Availability of data and material: The data are available from the authors upon reasonable request. Ethics approval and consent to participate: Not applicable. Informed consent: Not applicable. Conference presentation: The work was presented as oral communica- tion at Webinar “L’approccio del DEA alla luce delle più recenti Linee Guida” (7 November 2020, Pisa, Italy) and at Webinar “Conseguenze a breve e lungo termine del COVID” (11 Novembre 2021, Pisa, Italy). Received for publication: 16 May 2019. Revision received: 29 November 2021. Accepted for publication: 29 November 2021. This work is licensed under a Creative Commons Attribution 4.0 License (by-nc 4.0). ©Copyright: the Author(s), 2021 Licensee PAGEPress, Italy Emergency Care Journal 2021; 17:9859 doi:10.4081/ecj.2021.9859 SARS-CoV-2 management in emergency department: Risk stratification and care setting identification proposal based on first pandemic wave in Pisa University Hospital Greta Barbieri,1,2 Alessandro Cipriano,2 Stella Carrara,3 Stefano Spinelli,2 Francesco Cinotti,2 Francesca Foltran,4 Matteo Filippi,4 Ferruccio Aquilini,4 Michele Tonerini,5 Massimo Santini,2 Paolo Malacarne,3 Lorenzo Ghiadoni2,6 1Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa; 2Emergency Medicine Department; 3Department of Anaesthesia and Critical Care Medicine; 4Medical Directional Department; 5Department of Emergency Radiology, Pisa University Hospital, Pisa; 6Department of Clinical and Experimental Medicine, University of Pisa, Italy Non commercial use only determinant to avoid decisions based only on medical resources availability (hospital beds or ventilators). Importance Considering these issues, ED management should provide prompt recognition of unstable or evolutive conditions, given pos- sibility of a rapid and unpredictable clinical deterioration, early identification of the most correct ventilation strategy and wise choice of the most appropriate care setting, balancing limited resources, and preventing ED over-crowding.5 From this point of view, the identification of anamnestic, clinical, or instrumental fea- tures predicting negative outcomes in COVID-19 patients could represent valuable tools for risk stratification.6-8 Despite prognosis of SARS-CoV-2 related disease are not yet completely understood and clinical presentation is variable ranging from asymptomatic carriers to lethal respiratory syndrome, many studies identified age, underlying comorbidities (i.e. chronic car- diovascular and respiratory diseases) as predictors of mortality.3,9 The development of respiratory failure is the most fearful com- plication.10 For this reasons, dyspnoea and respiratory parameters are fundamental to predict disease severity.11 Therefore, some clin- ical parameters could reveal respiratory deterioration, which requires ICU admission.6,7 Imaging can be helpful to stratify clini- cal impact of the respiratory syndrome. Chest X-ray has low sen- sitivity,12 while a visual semi-quantitative evaluation or software quantification of disease extent at chest Computed Tomography (CT) shows correlation with clinical severity,13-15 and predictors of ICU admission or death.16 Moreover, bedside lung ultrasound (LUS) may play a role in COVID-19 pneumonia diagnosis, consid- ering its logistical advantages, in determining the patient’s progno- sis and in the decision-making process.17-20 Goals of investigation The aim of our study is to propose a new model of patients’ management in ED, providing a risk stratification of COVID-19 patient with respiratory failure, which can suggest ventilation strat- egy and a care setting based on clinical history, symptoms, respi- ratory parameters, and imaging. Furthermore, has been verified the applicability, appropriateness, and safety of the proposed model through outcome analysis on population of the first pandemic wave in our hospital. Material and Methods Setting The Pisa University Hospital, Italy, is one of the three Regional tertiary-care hospitals, serving as local Primary Health Care center for Pisa (approximately 100.000 inhabitants) and hub center for the North-West part of Tuscany (approximately 1 million 200 thou- sand inhabitants). The first patient affected by SARS-CoV-2 relat- ed respiratory disease was admitted on the 5th of March 2020, 2 weeks later the beginning of SARS-CoV-2 outbreak in the Italian region of Lombardy.21 Study design: Proposed flow-chart description ED clinicians, intensivists and radiologists of Pisa University Hospital developed a management flow-chart for respiratory fail- ure COVID-19 patients. This model was designed on information collected from other EDs already facing up with the infection out- break for would trying to summarize poor evidences in literature available on March 2020. The multidisciplinary team “empirical- ly” identified elements to evaluate and stratify the disease severity, according to age, comorbidities, Arterial Blood Gases analysis (ABG), respiratory dynamics and chest imaging. A final score (PISA CoViD Triage Score – PiCoTS) consists of two consecutive sections: i) disease severity risk stratification, based on respiratory and chest imaging elements; ii) identification of the most appropriate management on ventilation strategy and care setting for the patients, considering their anamnestic charac- teristics and disease severity. Section relating to risk stratification (Table 1), provides for assignment of a positive numerical score on 4 items relating to AGB parameters. Two ABG values measured in ambient air and 2 measured after oxygen therapy administration were used for high- lighting the ability to correct respiratory failure (Table 1). Imaging was entered into our model as chest CT data convert- ed to quantitative value using Total Severity Score (TSS),14,15 which assigns an increasing numerical value with lung inflamma- tory involvement, divided by each lobe, allowing multiple levels of severity identification. Each of the five lung lobes was assessed for percentage of lobar involvement and classified as none (0%), min- imal (1–25%), mild (26–50%), moderate (51–75%), or severe (76– 100%), corresponding to score of 0, 1, 2, 3, 4. Final TSS score was reached by summing five lobe scores (from 0 to 20). Our risk strat- ification model assigned a negative score to the category with low lung commitment and an increasing score in the other classes. Finally, a positive score was assigned to respiratory clinical parameters, respiratory rate (RR) > 25 acts/minute and-or signs of respiratory distress. The total score obtained stratifies disease in mild (if the score is <3) or severe (if the score is ≥3). The second step of the flow-chart identifies groups of patients based on their characteristics (age and comorbidities) for applica- tion of specific ventilation and hospitalization settings manage- ment (Figure 1). Comorbidities (cardiovascular disease, hyperten- sion, diabetes mellitus, respiratory disease, cerebrovascular pathol- ogy) were considered significant if present in number ≥3. To pro- vide additional indicators for ventilation strategy choice, we pro- posed a qualitative and functional assessment of the lung by tho- racic imaging (chest TC or LUS bedside) for obtaining information Article Table 1. Risk stratification in proposed model. The table identi- fies severity of disease based on the final score obtained from the sum of the items considered. A score lower than 3 defined a mild severity whereas a score greater than or equal to 3 a severe disease. PaO2: Arterial partial pressure of oxygen; PCO2: Partial pressure of carbon dioxide; P/F: Arterial partial pressure of oxygen / Fractional inspired oxygen ratio; TSS: Total Severity Score; RR: Respiratory rate. BLOOD GASES PaO2 < 60 mmHg ADMISSION + 1 PCO2 < 30 mmHg ADMISSION + 1 PCO2 < 35 mmHg AFTER O2 THERAPY + 1 P/F > 200 AFTER O2 THERAPY + 1 CT CHEST IMAGING → TTS score TTS < 5 - 1 TTS 5-9 +1 TTS 10-14 + 2 TTS > 15 + 3 RESPIRATORY PARAMETERS RR ≥ 25 and/or clinical signs of respiratory distress +1 [Emergency Care Journal 2021; 17:9859] [page 39] Non commercial use only about prevailing pattern (interstitial or consolidative), distribution of pneumonia (sub-pleural, ubiquitous) and diaphragmatic func- tion (motility, shortening). Accordingly, we identified four groups and five categories of patients with a specific indication for management: A. Severe disease, patients <70 years or 70-80 years WITHOUT comorbidities: i) Category 1: Patients not elderly or without pathologies, indication to endo-tracheal intubation (ETI) in ED and indication to intensive care setting; ii) Category 2: Patients not elderly or without pathologies, indication to Continuous Positive Airway Pressure (CPAP) in ED indication to intensive care setting.A choice between the 2 approaches based on clin- ical judgment and additional instrumental data is recommend- ed. B. Mild disease, patients <70 years or 70-80 years WITHOUT comorbidities: i) Category 3: Patients not elderly or without pathologies, indication to oxygen therapy in ED and possible intensive care setting; ii) Category 4: Patients not elderly or without pathologies, indication to oxygen therapy in ED and medical area hospitalization. A choice between the 2 approach- es based on clinical judgment after the first hours of treatment and additional instrumental data is recommended. C. Severe disease, patients > 80 years or 70-80 years with comor- bidities D. Mild disease, patients > 80 years or 70-80 years WITH comor- bidities. Both group C and D identify Category 5: Patients elderly or with pathologies, with indication to oxygen therapy as a “maxi- mum of care” provided in ED and admission to medical area. Application in study population Empirical, non-standardized, evaluation based on the above elements was used for management of COVID-19 patients in ED during first pandemic wave. Subsequently, a detailed retrospective statistical analysis of data relating to this group of patients was conducted, with the aim of verifying the applicability, appropriate- ness, safety, and outcomes of the proposed model. Clinical data were extracted by ED (FirstAid®) and hospital- ization (Pleiade®) electronic medical records. We analyzed ABG data only in the group of patients with respiratory failure, since in the other subjects there were frequently missing data. Chest CT was available in 269 out of 313 patients (86%) admitted to ED. Radiologists calculated retrospectively TSS score to allow ana- lyzes on quantitative data. Finally, in 206 patients with full data available, the proposed flow-chart was retrospectively calculated to verify the agreement with the evaluations actually performed in ED. Analysis Continuous variables are expressed, depending on the type of distribution, as mean ± Standard Deviation (SD) or median and Interquartile Ranges (IQRs). The normality distribution was assessed by the Kolmogorov–Smirnov test. The continuous vari- Article Figure 1. Ventilation and care setting management in proposed model. Heart symbol: Comorbidities. Pathologies are considered signif- icant if present greater than or equal to 3 among diabetes, respiratory pathology, cardio and cerebro-vascular disease. Lung symbol: Qualitative and functional evaluation of thoracic imaging, concerning pneumonia pattern (interstitial, consolidative), distribution and diaphragm, to be considered for the choice of the ventilation strategy. Square: ventilation strategy. Red: ETI or CPAP; green: oxygen therapy; yellow: ETI not indicated. Circle/triangle: setting. Open circle: useful/possible intensive care setting; tringle: medical area; crossed out circle: intensive care setting not indicated. Category 1: Patients not elderly or without pathologies, indication to ETI in ED, useful or possible intensive care setting. Category 2: Patients not elderly or without pathologies, indication to CPAP in ED, indication to intensive care setting. Category 3: Patients not elderly or without pathologies, indication to oxygen therapy in ED, possible intensive care setting. Category 4: Patients not elderly or without pathologies, indication to oxygen therapy in ED and medical area hospitaliza- tion. Category 5: Patients elderly or with pathologies, indication to oxygen therapy in ED, intensive care setting not indicated. [page 40] [Emergency Care Journal 2021; 17:9859] Non commercial use only ables were evaluated with the Mann – Whitney test. The categori- cal variables are expressed across frequency distributions and chi- square was used to establish the difference between groups. Statistical significance was set for p <0.05. All the results obtained were analyzed with statistical software (NCSS). Patient and public involvement As we conducted a retrospective study during a pandemic peri- od, patient and public involvement was not possible. Results Characteristics of study subjects In the period from March 5th to April 30th 2020, a total of 313 COVID-19 patients were hospitalized in University Hospital of Pisa. Of these, 222 were evaluated in ED with the aid of intensivist consulting, due to clinical presentation with respiratory distress. Gender and demographics characteristics showed a prevalence of males among patients hospitalized for COVID-19 (209 out of 313, 66.8%). Mean age was 67 years, with men younger than women (66 versus 70 years). Patients with respiratory failure at ED admission showed mean age of 68 years, 66 years in the 144 men (65%) and 70 years in the 78 women, respectively. Prediction of flow-chart items We analysed each item present in our risk stratification scheme dividing the population between survived and deceased patients, in order to verify their ability to predict the negative outcome. An alteration of clinical and ABG parameters were more frequently present (p value < 0.05) in patients with adverse outcome (Table 2). As regards chest imaging, we observed that TSS value, was sig- nificantly higher in deceased patients (average values 12.09±4.92) than in the survivors (average values 8.86±4.44) in the group of patients with respiratory failure. This difference was also observed in total population of hospitalized COVID-19 patients (average values in deceased patients was 11.6±5.6 and in discharged patients was 8.2±4.9), as shown in Figure 2. Comorbidities (cardiovascular and cerebrovascular disease, hypertension, diabetes mellitus, respiratory disease) were signifi- cantly associated to mortality in COVID-19 patients assessed in ED for respiratory failure and those hospitalized globally (Table 3). In addition, mean age is significantly higher in deceased patients, in both groups (Table 3). Dividing COVID-19 respiratory failure population according to our risk stratification score with cut-off of 3, we observed a pos- itive outcome in 63% of cases of patients with score <3, compared to the group with a score ≥3, in which survival was 37%. This dif- ference is statistically significant (p>0.05) Article Figure 3. COVID-19 patients’ mortality by care setting. Graphs show results on mortality in patients hospitalized in medical area, ICU and ICU undergoing ETI. ETI: Endo-tracheal Intubation. Figure 2. Average total severity score in COVID 19 patients. Respiratory failure patients (left panel) and total of hospitalized (right panel), population divided according to mortality (dis- charged or death). Table 2. Outcome Predictors in COVID-19 patients with Respiratory Failure. Blood gas analysis and respiratory parameters in the pop- ulation according to mortality. PaO2: Arterial partial pressure of oxygen; PCO2: Partial pressure of carbon dioxide; P/F: Arterial partial pressure of oxygen / Fractional inspired oxygen ratio; TSS: Total Severity Score; RR: Respiratory rate. Outcome Predictors in COVID-19 patients with Respiratory Failure Discharged Deaths p value N./Tot. (%) N./Tot. (%) PaO <60 mmHg at presentation 51/135 (37.8) 22/30 (73.3) <0.001 PCO <30 mmHg at presentation 33/133 (24.8) 14/29 (48.3) 0.012 PCO <35 mmHg after oxygen therapy 37/99 (37.4) 17/25 (68.0) 0.006 P/F < 200 mmHg after oxygen therapy 27/99 (27.3) 21/25 (84.0) <0.001 RR ≥ 25 breaths/min and/or respiratory distress 16/44 (36.4) 12/17 (70.6) 0.016 [Emergency Care Journal 2021; 17:9859] [page 41] Non commercial use only Proposed model and real evaluation concordance We were able to retrospectively apply our model in 142 patients (who had all the required items of PiCoTS flow-chart). The agreement of the proposed “theoretic” score with the evalua- tion about patients severity and management actually performed in ED was to 81.7% (116 out of 142 patients). Mortality analysis by care setting In our population, 230 out of 313 patients (73.5%) were hospi- talized exclusively in medical area, while 83 were managed in intensive area. Of these 83 patients, 52 (62.7%) underwent ETI, whereas 31 (37.3%) received exclusively non-invasive ventilation. When mortality was analysed in various care settings, it was higher in patients managed in an intensive care setting in patients evaluated in the ED for respiratory failure, and in the total of COVID-19 hospitalized patients. The highest mortality was in patients undergoing ETI (Figure 3). Subcategories analysis Patients with low severity score, under the age of 70 or between 70 and 80 years of age and without comorbidities (n=87; indication to oxygen therapy as an initial strategy, but possibly eli- gible for an intensive care setting) showed the lowest mortality rate (n=6; 6.7%). Patients with severe disease, under the age of 70 or between 70 and 80 years of age but without comorbidity, (n=52; indication for CPAP or ETI in ED, management in intensive care setting) showed a mortality of 15% (n=8). Among these, a discrepancy between the indication of the proposed score and the evaluation actually per- formed was demonstrated in 16 out of 52 patients (30.8%). In each patient, the proposed model overestimated risk. Approximately 40% of patients (n=22) in this group received an indication to CPAP in ED, which was not performed in ED, but subsequently during hospitalisation, in intensive care setting. Finally, the analysis of COVID-19 patients over 80 or 70-80 years of age with comorbidity (n=67; indication of oxygen therapy as maximum intensity of care, not eligible for management in intensive care setting) shows a mortality of 50% (n=34). In 93% of cases, this group of patients was managed in a medical setting. However, even the small group of patients to whom intensive care was available, 4 out 5 (80%) had a negative outcome. Discussion During the COVID-19 pandemic, ED clinicians have to cope with the decision of offering the more appropriate intensity and thereafter setting of care, sometimes in situation of reduced health- care resource. The management of a potentially rapidly worsening condition demands even more an effective triage systems and care’s pathways. Identifying and treating appropriately patients with rapidly evolving disease is crucial in this unexpected pandem- ic.5,7 A large scientific literature has emerged in recent months for identification of negative prognostic factors in patients with COVID-19 pneumonia, based on comorbidities, clinical and respi- ratory parameters.3,6,7 The present analysis also identified as pre- dictors of death within our population, ABG parameters (sugges- tive for hypoxia and inadequate response to oxygen therapy), clinic Respiratory Parameters (RR), age and pre-existing pathologies (chronic respiratory, cardio/cerebrovascular disease, diabetes, hypertension), visual semi-quantitative evaluation of disease extent at chest CT, in accordance with previous reports from the literature.7,14-16,22-24 The proposed flow-chart, providing the use of these indicators for stratifying patient’s risk during first ED evaluations, aimed to identify non-arbitrary elements to be verified subsequently in our population. Our approach is in line with other studies that suggest stratifying the severity of disease based on comparable respiratory parameters.6,7,22 A multidisciplinary teamwork allowed to “outline” our model and retrospectively tested it on data of the first pandemic wave in University Hospital of Pisa. We verified the concordance between the evaluations actually applied during the first pandemic wave and those “theoretical” and standardized applied by the retrospec- tive application of the flow-chart. The overall mortality in our sample (22.7%) and the mortality of patients hospitalized in ICU (26.5%) were in line to the mortal- ity reported in the literature, with a comparable age and gender dis- tribution of other Italian hospitals. We observed a substantial dif- ference between the mortality rates of patients managed in a low intensity and those managed in an intensive care setting. This gap could be explained by a greater severity of disease presentation in the group underwent intensive care management. However, higher Article Table 3. Outcome Predictors in COVID-19 patients with Respiratory Failure and total hospitalized patients. Outcome Predictors Discharged Deaths p value COVID-19 patients with Respiratory Failure (N. 222) N. 168 (%) N. 54 () Cardiovascular disease 12 (7.1) 23 (42.6) <0.01 Hypertension 31 (18.5) 22 (40.7) <0.01 Diabetes mellitus 14 (8.3) 13 (24.1) <0.01 Chronic lung disease 11 (6.5) 9 (16.7) 0.024 Cerebrovascular disease 6 (3.6) 6 (11.1) 0.033 Age 64±16 80±11 <0.01 Total hospitalized COVID-19 patients (N. 313) N. 242 () N. 71 () Cardiovascular disease 68 (28.1) 37 (52,1) <0.01 Hypertension 99 (40,9) 44 (62) <0.01 Diabetes mellitus 44 (18.2) 60 (19.2) <0.01 Chronic lung disease 17 (7) 20 (28,2) <0.01 Cerebrovascular disease 22 (9) 17 (23.9) <0.01 Age 64.3±15.5 78.7±11 <0.01 [page 42] [Emergency Care Journal 2021; 17:9859] Non commercial use only mortality of subjects subjected to ETI raises the question about appropriateness of choosing this ventilation strategy in the face of non-invasive approaches. Indeed, ventilation management of COVID-19 patients with respiratory failure is still controversial and no clear scientific evidence is currently available. Gattinoni et al. described lung damage in the affected patient from COVID-19 pneumonia similar in some respects to Acute Respiratory Distress Syndrome (ARDS).25 They were recognized in this context two different pulmonary pictures on the basis of respiratory compli- ance, which suggest different management in terms of ventilatory strategy.25,26 Furthermore, our model, based on anamnestic, demo- graphic baseline characteristics with additional lung and diaphrag- matic imaging tools18 useful for focusing the disease phase, sug- gests a possible appropriate ventilatory strategy and hospitalization setting, in line with literature17,27 and resuscitation recommenda- tions.4 The analysis of patients with low severity score, under the age of 70 or between 70 and 80 years of age without comorbidity, shows the lowest mortality rate (6.7%), despite a proportion of these patients needed intensive treatments. Auxiliary evaluation in the view of a possible increase in intensity of care in this group can be represented by LUS, by identifying of specific patterns (consol- idative or interstitial)18 and evaluating of diaphragmatic function.27 The analysis of patients with high severity score, under the age of 70 or between 70 and 80 years of age without comorbidity shows a high mortality rate (15%), related to the severity of disease in the younger group and/or the relatively good clinical baseline conditions. It is to be highlighted that a discrepancy between the indication of proposed score and the evaluation performed during first pandemic wave was observed in some patients. Indeed, the proposed model overestimates patient’s risk, suggesting that an intensive management could be implement in this subgroup of healthy and non-elderly patients and also a good “safety” of the model. Regarding ventilation treatments, approximately 40% of patients in this group had received an indication to CPAP in ED, which has not applied for organizational problems in our setting.21 However, it was applied later on, during hospitalization. These considerations provide useful tips for better management during the new pandemic wave, including LUS, which can be used not only for diagnostic purposes, but also to guide ventilation choice (through evaluation of prevailing patterns and diaphragmatic func- tion).17,18,27 Finally, the analysis of COVID-19 patients over 80 or 70-80 years of age with comorbidity, shows a high mortality rate (approximately 50%), which is not different when patients undergo ICU. This suggests that a low care is more appropriate for this group, in accordance with ethical considerations and available rec- ommendations.4 A strong functional reserve is necessary to over- came long and aggressive ICU treatment, which is why it is useful to delineate a “limit” to care in these subjects, as our model sug- gests. Indeed, patients of this group should be managed in the med- ical area, since intensive care treatments did not improve outcomes in our populations. Therefore, a non-intensive setting could repre- sent a valid alternative, in which CPAP can be applied in selected cases as a possible therapeutic attempt. Limitations and perspectives Among the limitations of this study, the small sample size should be considered. The proposed methodology was outlined empirically by experts in the sector (due to the lack of scientific references) and only subsequently verified through statistical ana- lyzes. In addition, the proposed methodology was verified on pop- ulation of a single center and retrospectively. This may have result- ed in observation of negative effects related to organizational prob- lems of our hospital, not necessarily related to ED evaluation phase. A possible application of our flow-chart could be the prospec- tive use in patients of second pandemic wave in our ED and in other geographical areas, with the aim of comparing the outcomes between the “empirical” and the “standardized” approach. Conclusions A tool which integrates the physiological patterns of disease, by clinical and ABG parameters, imaging findings, together with patient’s underlying conditions, may prove useful to ED physi- cians, to stratify COVID-19 patient’s risk early and to choose the appropriate level of care and ventilation strategy. Our model, despite the limitations of low number and retrospective applica- tion, meets to these needs through a systematic and multidiscipli- nary evaluation. 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