You are here

  • Home
  • Archive
  • Volume 12, Issue 4
  • Clinical and imaging characteristics of patients with COVID-19 predicting hospital readmission after emergency department discharge: a single-centre cohort study in Italy

Article Text

Article menu
Emergency medicine
Original research
Clinical and imaging characteristics of patients with COVID-19 predicting hospital readmission after emergency department discharge: a single-centre cohort study in Italy
  1. Maria Giulia Galli1,
  2. Olivera Djuric2,3,
  3. Giulia Besutti4,5,
  4. Marta Ottone2,
  5. Lucia Amidei1,
  6. Lee Bitton1,
  7. Carlotta Bonilauri1,
  8. Luca Boracchia1,
  9. Sergio Campanale1,
  10. Vittoria Curcio1,
  11. Davide Maria Francesco Lucchesi1,
  12. Cesare Salvatore Mulas1,
  13. Francesca Santi1,
  14. Anna Maria Ferrari1,
  15. Paolo Giorgi Rossi2,
  16. Francesco Luppi1
  17. On behalf of the Reggio Emilia COVID-19 Working Group
    1. 1Emergency Department, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Emilia-Romagna, Italy
    2. 2Epidemiology Unit, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Emilia-Romagna, Italy
    3. 3Section of Public Health, Department of Biomedical, Metabolic and Neural Sciences, Center for Environmental, Nutritional and Genetic Epidemiology (CREAGEN), University of Modena and Reggio Emilia, Modena, Emilia-Romagna, Italy
    4. 4Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Modena, Emilia-Romagna, Italy
    5. 5Radiology Unit, Department of Diagnostic Imaging and Laboratory Medicine, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Emilia-Romagna, Italy
    1. Correspondence to Dr Marta Ottone; marta.ottone{at}ausl.re.it

    Abstract

    Objective We aimed at identifying baseline predictive factors for emergency department (ED) readmission, with hospitalisation/death, in patients with COVID-19 previously discharged from the ED. We also developed a disease progression velocity index.

    Design and setting Retrospective cohort study of prospectively collected data. The charts of consecutive patients with COVID-19 discharged from the Reggio Emilia (Italy) ED (2 March 2 to 31 March 2020) were retrospectively examined. Clinical, laboratory and CT findings at first ED admission were tested as predictive factors using multivariable logistic models. We divided CT extension by days from symptom onset to build a synthetic velocity index.

    Participants 450 patients discharged from the ED with diagnosis of COVID-19.

    Main outcome measure ED readmission within 14 days, followed by hospitalisation/death.

    Results Of the discharged patients, 84 (18.7%) were readmitted to the ED, 61 (13.6%) were hospitalised and 10 (2.2%) died. Age (OR=1.05; 95% CI 1.03 to 1.08), Charlson Comorbidity Index 3 versus 0 (OR=11.61; 95% CI 1.76 to 76.58), days from symptom onset (OR for 1-day increase=0.81; 95% CI 0.73 to 0.90) and CT extension (OR for 1% increase=1.03; 95% CI 1.01 to 1.06) were associated in a multivariable model for readmission with hospitalisation/death. A 2-day lag velocity index was a strong predictor (OR for unit increase=1.21, 95% CI 1.08 to 1.36); the model including this index resulted in less information loss.

    Conclusions A velocity index combining CT extension and days from symptom onset predicts disease progression in patients with COVID-19. For example, a 20% CT extension 3 days after symptom onset has the same risk as does 50% after 10 days.

    • COVID-19
    • computed tomography
    • accident & emergency medicine
    • epidemiology

    Data availability statement

    Data are available in a public, open access repository. All data relevant to the study are included in the article or uploaded as supplementary information. Aggregated data for the main model are available on an open source repository (https://doi.org/10.5281/zenodo.5675150).

    http://creativecommons.org/licenses/by-nc/4.0/

    This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

    https://doi.org/10.1136/bmjopen-2021-052665

    Statistics from Altmetric.com

      Request Permissions

      If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

      Strengths and limitations of this study

      • This study proposes a disease progression velocity index based on CT scan-derived parenchymal involvement and time from symptom onset.

      • The velocity of disease progression can orient patient management by correctly identifying those at higher risk of readmission to emergency department who may require hospitalisation.

      • The main limitation of this study is that we could not validate the performance of our model on a validation set.

      Background

      Emergency admissions are a critical point in the management of the SARS-CoV-2 pandemic, especially during the peak of an epidemic wave. Many countries have experienced two waves in 2020, with a lower case fatality rate in the second wave compared with that in March 2020, probably due to better testing capabilities and diagnosis of less severe cases.1 2 Although the critical care bed occupancy crisis is expected to slow down, COVID-19 hospital admissions periodically increase, thus continuing to be a challenge both for healthcare workers and public health officials.3

      Emergency departments (EDs) play a crucial role in deciding which visits require hospital treatment and which do not, thus preventing unnecessary hospital admissions in a landscape of reduced resources and increasing demands for inpatient care during the COVID-19 pandemic. In fact, shortly after the beginning of the pandemic and the implementation of public health measures to reduce transmission of the virus, the use of healthcare services for elective and emergency conditions decreased, while hospital admissions from the ED increased.4 5

      During the first wave in Spain, it has been estimated that among all the people with COVID-19, 14.8% required hospitalisation,6 but actually in Italy, in the same phase, the vast majority of cases were diagnosed at ED and most of them were hospitalised.7 The unpredictable nature of the disease and its insufficiently known natural history make it difficult for emergency staff to tell which people will need to be admitted to the hospital. Although estimated median time from symptom onset to the time patients experience dyspnoea is 5–8 days, acute respiratory distress syndrome is 8–12 days and intensive care unit (ICU) admission is 9.5–12 days, some patients with COVID-19 rapidly deteriorate about 1 week after symptom onset8 or shortly after being discharged from the ED.9 Many studies aimed at building models to predict the prognosis and the need for critical care10 and some specifically starting from a set of information available at ED admission,11 12 little is known about the probability of readmission in patients initially discharged after the first ED admission.9 Some studies have shown that patients with a rapid worsening of symptoms and a short time between symptom onset and seeking care had the worst outcomes.13–15

      The aim of this study was to investigate the rate of COVID-19-related readmission to the ED and its determinants in a cohort of consecutive patients with COVID-19 presenting to the provincial ED hub of Reggio Emilia, Italy, and discharged during the first epidemic peak in the Reggio Emilia province. In particular, we wanted to estimate whether a measure of the velocity of disease progression at ED admission could orient patient management by correctly identifying those at higher risk of readmission to ED who would require hospitalisation.

      Methods

      Setting

      The province of Reggio Emilia, in northern Italy, has a population of 531 751 inhabitants. The first case of SARS-COv-2 in Reggio Emilia was diagnosed on 27 February 2020; the cumulative incidence of cases during the first wave of the pandemic (March to April 2020) was about 0.9%. In October, the province began experiencing a second wave of the pandemic, with cumulative incidence reaching 1.8%. The first wave was characterised by a very high fatality rate (up to 18% in the first month),13 and testing was limited only to symptomatic individuals, who often presented with severe symptoms.

      There are six hospitals in this province, with an ED service at the main hospital in the city of Reggio Emilia, as well as at four of the five smaller health district hospitals. There is no private ED service. The annual ED admission rate in 2019 was 354.2 per 1000 inhabitants.

      Management of COVID-19 at the ED

      With COVID-19, the organisation of the Reggio Emilia ED Service has changed; there are now two distinct physical areas so as to separate patients with fever and/or epidemiological criteria for SARS-CoV2 infection from all other patients. A pretriage facility outside the ED has been set up where patients’ history is taken, especially regarding epidemiological data and COVID-19-related symptoms.

      During the first wave of the pandemic, the local diagnostic protocol for patients with suspected COVID-19 pneumonia included nasopharyngeal and oropharyngeal swabs for reverse transcription PCR (RT-PCR), blood tests, chest ultrasound and radiological assessment (chest X-rays and a CT scan in cases of suggestive X-ray findings or negative X-ray but highly suggestive clinical features).

      Given the paucity of information on COVID-19 available in early March 2020, a multidisciplinary team (pulmonologists, infectious diseases specialists, intensive care specialists and emergency physicians) defined a six-class classification based on patient features, vital signs, medical history, symptoms, blood test results and instrumental findings to manage patients presenting to the ED (table 1).

      View this table:
      Table 1

      Phenotype classification adopted for patients presenting to ED in March and April 2020, in Reggio Emilia, Italy

      Study design and selection of participants

      A retrospective cohort study was conducted on prospectively collected data. We included all consecutive patients aged>18 years who presented to the provincial ED hub of Reggio Emilia between 2 March and 31 March 2020, who were positive for SARS-CoV-2 on RT-PCR before or during ED admission and who were discharged for home treatment after initial evaluation. No exclusion criteria were adopted.

      Putative determinants and definitions

      We included in initial analyses all the variables that were available as standard parameters routinely collected for all patients and some additional information specifically introduced in ED protocols for COVID-19 suspected cases as of March 2020. Evaluated covariates included patient characteristics and presence of comorbidities, calculated separately, as well as the Charlson Comorbidity Index, which provides an overall measure of an individual patient’s complexity.16 We categorised the index in four classes: 0 (no presence of relevant comorbidity), 1, 2 and ≥3. Variables which reflect clinical criteria relevant for ED discharge included presence of fever and duration of symptoms, lab test results (arterial blood gas tests, biochemical profile) and imaging (chest CT scan).

      Blood tests and RT-PCR

      C reactive protein (CRP) and lactate dehydrogenase levels, white cell count, lymphocyte, neutrophil and platelet counts as well as arterial blood gas analysis data, all measured at ED presentation, were collected. The tests were carried out in the Reggio Emilia Hospital Clinical Laboratories with routine automated methods.

      To diagnose SARS-CoV-2 infection, a commercial one-step RT-PCR (GeneFinder) COVID-19 Plus Real Amp Kit was used, and RT-PCR assay was performed on an Applied Biosystems 7500 Sequence Detection System.

      Data sources

      Data, including date of symptom onset, diagnosis, hospitalisation and death, were retrieved from the COVID-19 Surveillance Registry, coordinated by the National Institute of Health and implemented in each local health authority. The surveillance is fed by several sources: Reggio Emilia Department of Public Health’s epidemiological investigations, contact tracing and symptom surveillance for people in self-isolation, laboratory reports, ED and hospital electronic records and death certificates. The system was developed to manage each individual case. In the Reggio Emilia province, the surveillance system is linked to a dedicated software collecting data on patients tested for SARS-CoV-2 and for positive cases, data on disease outcome. Thus, the software permits the management of surveillance activities on all cases; it produces a report containing all the relevant information (test results, ED admission, hospitalisation, in-hospital death) available in the data warehouse; to this, the public health officials add information on the epidemiological investigation, contract tracing and symptom surveillance for those in self-isolation.

      Registry data were linked with hospital discharge databases to collect information on comorbidities from all hospital admissions occurring up to 31 January 2020, that is, before the start of the SARS-CoV-2 pandemic in Italy.

      The ED database contains information on all ED visits in the Reggio Emilia province, including the level of urgency and the modality of discharge.17

      The local health authority’s laboratory information system database contains the laboratory results of all tests carried out in the province’s public health network, coded using an internal classification.

      Radiological data

      CT scans were performed using one of the three scanners (128-slice Somatom Definition Edge, Siemens Healthineers; 64-slice Ingenuity, Philips Healthcare; 16-slice GE Brightspeed, GE Healthcare) without contrast media injection, with the patient in the supine position, during end-inspiration. Scanning parameters were tube voltage 120 KV, automatic tube current modulation, collimation width 0.625 or 1.25 mm, acquisition slice thickness 2.5 mm and interval 1.25 mm. Images were reconstructed with a high-resolution algorithm at slice thickness 1.0/1.25 mm. From the verbal and structured CT reports, the extension of pulmonary lesions estimated by using a visual scoring system resulted in a percentage of total lung parenchyma which had any pathological changes likely due to COVID-19.

      Outcome measures

      The main outcome was readmission to the ED within 14 days from the first admission, followed by hospitalisation (within 72 hours from ED admission) lasting at least 48 hours (to exclude brief observation stays in the ED) or by death. We also considered any second ED visit occurring within 14 days from the first admission. All patients were followed up to 45 days from symptom onset or two negative RT-PCRs, whichever occurred first.

      Data analyses

      Continuous variables are reported as median and IQR and categorical variables as proportions. Logistic regression models were used to estimate ORs with 95% CI for the second ED visit and hospitalisation/death, unadjusted and adjusted for age and sex. We built a synthetic indicator of the velocity of disease progression at ED admission, that is, the ratio of CT involvement and days from symptom onset to first ED admission. We used CT scan to measure disease extension, which is the most stable index of disease progression among the available data, and we can assume that, between symptom onset and ED presentation, it will not regress. We set a minimum value of 1 day from symptom onset, and we considered velocity indices with different lag times from 1 to 6 days, plus ln (1-day lag) approximating 1–1.1, to avoid losing observations at 1 day from symptom onset (ie, with denominator equal to 0).

      Sex and age were selected prior to analysis, while the other potential covariates were based on the association found in the age-adjusted and sex-adjusted regression analyses. Then, the candidate variables were included in a multivariable logistic model and those with p<0.1 were used for the final set of variables used to compare models with different velocity indices, that is, combinations of CT involvement and days from symptom onset. The comparison of model performances was done by log likelihood, Akaike information criterion (AIC), p value for Z-test for velocity index and receiver operating characteristic curves from logistic model. We presented the final models using logistic models with cross-validated area under the curve, to compare the performance of the models for readmission followed by hospitalisation or death. On the same original sample, we fitted a logit model and used tenfolds cross-validation to obtain a bias-corrected estimate of predictive accuracy. This technique averages the AUCs corresponding to each fold and applies the bootstrap procedure to the cross-validated AUC. We used Stata V.16.1 IC (Stata Corporation, College Station, Texas, USA) software package.

      Aggregated data for the main model are available on an open source repository.18

      Patient and public involvement

      Patients and the public were not directly involved in the study design or implementation.

      Results

      Characteristics of study subjects

      Of the 959 patients admitted to the ED with a diagnosis of COVID-19 in the period 2–31 March 2020, 450 (47%) patients discharged to self-isolation and treatment were included in the analysis (figure 1). Of the discharged patients, 84 returned to the ED because their COVID-19 symptoms had worsened, resulting in an ED readmission rate of 18.7% (median time for the ED readmission was 4 days). Of these 84 patients, 61 (13.6%) were hospitalised and 10 (2.2%) died.

      Figure 1
      Figure 1

      Flowchart representing patient enrolment and main outcomes (readmission and hospitalisation). ED, emergency department.

      Both those readmitted to the ED and those hospitalised or who died after readmission to the ED were older and presented to the ED earlier after symptom onset. They had lower values of pCO2 and higher values of PCR, procalcitonin (PCT) and creatine than did non-readmitted patients. Readmitted patients and those who were hospitalised/died more frequently had three or more comorbidities and higher CT disease extension (table 2). Online supplemental table 1 shows the comorbidities in patients with COVID-19 presenting to the ED. Finally, readmitted and hospitalised/deceased patients had a shorter median time between symptom onset and ED presentation.

      View this table:
      Table 2

      Baseline characteristics of patients with COVID-19 who presented to the ED

      Characteristics associated with ED readmission and hospitalisation

      Age-adjusted and sex-adjusted analyses showed that the probability of an ED readmission requiring hospitalisation was associated with a shorter time between symptom onset and ED admission, the presence of three or more comorbidities and creatine (table 3). Both univariable and multivariable regression analyses were used to examine the association between covariates and the risk of readmission to the ED (online supplemental table 2).

      View this table:
      Table 3

      Logistic regression models for readmission with hospitalisation/death

      We also tested the association between different indices that could indicate the velocity of disease progression before the first ED admission by combining the CT scan-derived proportion of lung parenchymal involvement and the time elapsed between disease onset and CT at the ED admission. The easiest way to combine these two measures to obtain a velocity was to divide the CT involvement observed by the time needed to reach it. To avoid values that were too high or infinite in patients reporting symptom onset as the same day as or the day before ED admission, we added a lag time representing the amount of time during which the disease had progressed before symptom onset. We set this lag time as 1–6 days (see Methods); we also tested a logarithmic transformation of 1-day lag.

      Finally, we built multivariable models to predict readmissions requiring hospitalisation. Age, Charlson Comorbidity Index, CT involvement and time from symptom onset were entered in the model according to the selection criteria adopted, and we maintained sex in all models as an a priori choice (table 4). We compared the model built using the two components independently (CT involvement and time from symptom onset) and a model built using the composite variable ‘velocity index’ with different lags (online supplemental table 3). The indices from 2-day to 4-day lag showed similar and better values in the parameters used for the performance of the models. AUC had little influence on the lag choice; we gave more importance to AIC and we chose the index with a 2-day lag. It showed strong association and the best parameters in terms of model fitting compared to other lags, and it also proved to be more parsimonious than the model with the number of days from symptom onset and CT scan results included as independent variables.

      View this table:
      Table 4

      Multivariable logistic models for readmission followed by hospitalisation or death

      Figure 2 shows the increased risk in patients with rapid disease progression, that is, in those showing lung damage a few days after symptom onset. For example, based on the multivariable model of readmissions followed by hospitalisation or death, we show the average predicted probabilities with the 2-day lag velocity index fixed at values 2 and 5. Here, the three areas of the graph defined by the two iso-risk curves identify patients with <11%, 11%–16% and >16% risk of readmission to the ED followed by death or hospitalisation. On average, a patient with 20% CT parenchymal involvement 3 days after symptom onset and a patient with 50% involvement after 10 days had the same risk of being readmitted and hospitalised or of dying.

      Figure 2
      Figure 2

      Distribution of events (readmissions followed by hospitalisation or death) over time since symptom onset and CT disease extension.

      Discussion

      This study was conducted during the first wave of the SARS-CoV-2 pandemic, when the overall case fatality rate was about 18%13 and the in-hospital mortality rate was about 27%.7 About one-fourth of the RT-PCR-confirmed COVID-19 cases were discharged home after the initial ED evaluation; of these, only 14% were hospitalised or died after re-presenting to the ED.

      The main determinants of readmission in our study were time between symptom onset and ED presentation, proportion of lung parenchyma involved as assessed on CT and the presence of comorbidities. Combining CT scan-derived parenchymal involvement and time from symptom onset, we observed that the velocity of disease progression may have been the real underlying clinical concept, explaining most of the predictive information contained in the two variables.

      CT percentage of lung involvement has been extensively described as a prognostic factor for short-term prognosis in patients with COVID-19,19–22 but it has not yet been explored as a predictor of readmission after discharge from the ED. Different scoring systems proposed in the literature are mostly based on the combined effects of the extent of pulmonary involvement and specific attenuation patterns (ie, normal, ground-glass and consolidation)21 22 but do not consider the time from symptom onset as an important factor in outcome prediction.13–15 Using CT scan-derived parenchymal involvement and time from symptom onset in a composite index to describe disease progression before ED admission in our study is consistent with previous evidence that CT lung changes appear early in the course of the disease, possibly in the asymptomatic phase as well,23 and that in studies with serial CT scans, CT scores increased faster in severe patients than in non-severe ones.24

      The results of our study are also in line with a single study that evaluated the outcome of hospital readmission in patients with COVID-19 following ED discharge.9 The authors observed a slightly lower rate (8.2%) of readmission in their study, but after a shorter follow-up, that is, within 7 days after ED discharge. The factors associated with readmission were the same as we observed: abnormal chest radiograph and comorbidities (hypertension and obesity, fever and hypoxia).

      Limitations

      The main limitation of this study is that we used the CT scan results to predict the outcome and also to define the velocity of disease progression, but now CT scan is not a recommended test for patients presenting with COVID-19-related symptoms at the ED.25 26

      We could not validate the performance of our model on a validation set: we built the model, including the choice of the best lag time for the velocity index, using the same patients for whom we estimated the ORs. Therefore, overfitting is certainly an issue in our model, which deals with methods of internal validation (cross-validation) for prediction models to generate a more realistic estimate of predictive performance when the number of observations is not very large.27 Furthermore, the sample size was limited by the period of observation and we could not increase this period because of protocol changes. Nevertheless, the number of ED visits, the events and the strength of association observed in the models, in terms of pseudo R2, were sufficient to estimate the number of parameters included.28

      In our subset of patients identified as having a good prognosis, some of the most important prognostic factors of COVID-19 prognosis, such as sex,29 oxygen saturation,9 29 CRP29 30 and even age,29 31 had, instead, a surprisingly limited impact on outcome. This is most likely the effect of the criteria used to decide between hospitalisation and discharge. In fact, all patients with low oxygen saturation were referred to hospitalisation (the threshold was below 90%, but an evaluation of all other clinical characteristics was always taken into account), as were patients with >60% parenchymal involvement at CT scan and patients aged>80 years. Other clinical characteristics also influenced the decision of whether to hospitalise immediately, particularly the presence of COPD and diabetes. The consequence of this selection is that for some variables (eg, oxygen saturation), we had very small range of variation. We can therefore suppose that the threshold used was not close enough to critical values, as a certain association with the outcome should have otherwise been observed.

      Moreover, our sole purpose in investigating discharged patients was to see how often an ED discharge led to a readmission just a few days later. We have no idea, instead, how many of the people who were hospitalised at the first ED presentation could in fact have been safely discharged. Furthermore, we cannot consider all the readmissions within 2 weeks from the first ED visit a failure in decision-making: some readmissions occurred more than 7 days after the first ED visit, and self-isolation might have been appropriate for that initial phase of the disease.

      Implications for practice

      We show that time from symptom onset and lung parenchyma involvement were the main determinants of ED readmission. This suggests that, compared with the other known prognostic factors used to decide on hospitalisation, these two were probably not sufficiently considered: the threshold of 60% of parenchymal involvement may have been too high, compared with the 90% threshold used for O2 saturation.

      Furthermore, we show that these two variables together provide relevant information about the velocity of disease progression prior to ED admission and that this velocity is a strong predictor of readmission. This means that, on average, a middle-aged patient with good oxygen saturation and no important comorbidities, with 20% parenchymal involvement on the second day after symptom onset, has the same probability of symptoms worsening, leading to hospitalisation, as does a patient with 50% parenchymal involvement 7 days after symptom onset.

      It is important to confirm whether an indicator of the velocity of disease progression can be defined using other biomarkers of lung damage, detectable on more commonly used imaging examinations such as chest X-ray or ultrasound.

      In conclusion, CT lung involvement and time from symptom onset are the main determinants of hospital readmission in patients discharged after an ED admission. Combining the two variables to obtain a velocity of disease progression produces a predictor that is stronger than is each of the two variables singly.

      Data availability statement

      Data are available in a public, open access repository. All data relevant to the study are included in the article or uploaded as supplementary information. Aggregated data for the main model are available on an open source repository (https://doi.org/10.5281/zenodo.5675150).

      Ethics statements

      Patient consent for publication

      Ethics approval

      This study involves human participants. The study was approved by the Ethics Committee of the Area Vasta Emilia Nord (n.2020/0045199). Given the retrospective nature of the study, the ethics committee authorised the use of a patient’s data without his/her informed consent if all reasonable efforts have been made to contact that patient.

      Acknowledgments

      This study is part of a larger project supported by Ministry of Health, Italy (Grant Number COVID-2020-12371808). The authors would like to thank Jacqueline M. Costa for the English language editing.

      References

      1. ↵
        1. Fan G,
        2. Yang Z,
        3. Lin Q
        . Decreased case fatality rate of COVID-19 in the second wave: a study in 53 countries or regions. Transbound Emerg Dis 2020:1–3.
      2. ↵
        1. Griffin S
        . Covid-19: second wave death rate is doubling fortnightly but is lower and slower than in March. BMJ 2020;371:m4092.doi:10.1136/bmj.m4092pmid:http://www.ncbi.nlm.nih.gov/pubmed/33087318
        OpenUrlFREE Full Text
      3. ↵
        1. Heneghan C,
        2. Oke J
        . COVID-19: admissions to hospital – update, 2020. Available: https://www.cebm.net/covid-19/covid-19-uk-hospital-admissions/
      4. ↵
        1. Jeffery MM,
        2. D'Onofrio G,
        3. Paek H, et al
        . Trends in emergency department visits and hospital admissions in health care systems in 5 states in the first months of the COVID-19 pandemic in the US. JAMA Intern Med 2020;180:1328–33.doi:10.1001/jamainternmed.2020.3288pmid:http://www.ncbi.nlm.nih.gov/pubmed/32744612
        OpenUrlPubMed
      5. ↵
      6. ↵
        1. Prieto-Alhambra D,
        2. Balló E,
        3. Coma E, et al
        . Filling the gaps in the characterization of the clinical management of COVID-19: 30-day hospital admission and fatality rates in a cohort of 118 150 cases diagnosed in outpatient settings in Spain. Int J Epidemiol 2021;49:1930–9.doi:10.1093/ije/dyaa190pmid:http://www.ncbi.nlm.nih.gov/pubmed/33118037
        OpenUrlPubMed
      7. ↵
        1. Ferroni E,
        2. Giorgi Rossi P,
        3. Spila Alegiani S, et al
        . Survival of hospitalized COVID-19 patients in northern Italy: a population-based cohort study by the ITA-COVID-19 network. Clin Epidemiol 2020;12:1337–46.doi:10.2147/CLEP.S271763pmid:http://www.ncbi.nlm.nih.gov/pubmed/33335428
        OpenUrlCrossRefPubMed
      8. ↵
        1. CDC
        . Interim clinical guidance for management of patients with confirmed coronavirus disease (COVID-19), 2021. Available: https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-guidance-management-patients.html
      9. ↵
        1. Kilaru AS,
        2. Lee K,
        3. Snider CK, et al
        . Return hospital admissions among 1419 COVID-19 patients discharged from five U.S. emergency departments. Acad Emerg Med 2020;27:1039–42.doi:10.1111/acem.14117pmid:http://www.ncbi.nlm.nih.gov/pubmed/32853423
        OpenUrlPubMed
      10. ↵
        1. Wynants L,
        2. Van Calster B,
        3. Collins GS
        . Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2021;372:n236.
        OpenUrlFREE Full Text
      11. ↵
        1. Petrilli CM,
        2. Jones SA,
        3. Yang J, et al
        . Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ 2020;369:m1966.doi:10.1136/bmj.m1966pmid:http://www.ncbi.nlm.nih.gov/pubmed/32444366
        OpenUrlAbstract/FREE Full Text
      12. ↵
        1. Liang W,
        2. Liang H,
        3. Ou L, et al
        . Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Intern Med 2020;180:1081–9.doi:10.1001/jamainternmed.2020.2033pmid:http://www.ncbi.nlm.nih.gov/pubmed/32396163
        OpenUrlPubMed
      13. ↵
        1. Giorgi Rossi P,
        2. Broccoli S, et al., Emilia-Romagna COVID-19 working group
        . Case fatality rate in patients with COVID-19 infection and its relationship with length of follow up. J Clin Virol 2020;128:104415.doi:10.1016/j.jcv.2020.104415pmid:http://www.ncbi.nlm.nih.gov/pubmed/32403011
        OpenUrlCrossRefPubMed
      14. ↵
        1. EUROPEAN SOCIETY OF CLINICAL MICROBIOLOGY AND INFECTIOUS DISEASES
        . Shorter time from symptom onset to hospitalization is associated with worse outcome in patients with COVID-19, 2020. Available: https://eurekalert.org/pub_releases/2020-09/esoc-stf092520.php
      15. ↵
        1. Rodríguez-Molinero A,
        2. Gálvez-Barrón C,
        3. Miñarro A, et al
        . Association between COVID-19 prognosis and disease presentation, comorbidities and chronic treatment of hospitalized patients. PLoS One 2020;15:e0239571.doi:10.1371/journal.pone.0239571pmid:http://www.ncbi.nlm.nih.gov/pubmed/33057443
        OpenUrlCrossRefPubMed
      16. ↵
        1. Charlson ME,
        2. Pompei P,
        3. Ales KL, et al
        . A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373–83.doi:10.1016/0021-9681(87)90171-8pmid:http://www.ncbi.nlm.nih.gov/pubmed/3558716
        OpenUrlCrossRefPubMedWeb of Science
      17. ↵
        1. Ballotari P,
        2. D'Angelo S,
        3. Bonvicini L, et al
        . Effects of immigrant status on emergency room (ER) utilisation by children under age one: a population-based study in the province of Reggio Emilia (Italy). BMC Health Serv Res 2013;13:458.doi:10.1186/1472-6963-13-458pmid:http://www.ncbi.nlm.nih.gov/pubmed/24176109
        OpenUrlPubMed
      18. ↵
        1. Ottone M
        . Characteristics of COVID-19 patients predicting Hospital readmission after ED discharge, 2022. Zenodo. Available: https://doi.org/10.5281/zenodo.5675150doi:10.5061/dryad.mgqnk9918
      19. ↵
        1. Ruch Y,
        2. Kaeuffer C,
        3. Ohana M, et al
        . CT lung lesions as predictors of early death or ICU admission in COVID-19 patients. Clin Microbiol Infect 2020;26:1417.e5–1417.e8.doi:10.1016/j.cmi.2020.07.030
        OpenUrl
      20. ↵
        1. Feng Z,
        2. Yu Q,
        3. Yao S, et al
        . Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics. Nat Commun 2020;11:4968.doi:10.1038/s41467-020-18786-xpmid:http://www.ncbi.nlm.nih.gov/pubmed/33009413
        OpenUrlCrossRefPubMed
      21. ↵
        1. Francone M,
        2. Iafrate F,
        3. Masci GM, et al
        . Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis. Eur Radiol 2020;30:6808–17.doi:10.1007/s00330-020-07033-ypmid:http://www.ncbi.nlm.nih.gov/pubmed/32623505
        OpenUrlPubMed
      22. ↵
        1. Colombi D,
        2. Bodini FC,
        3. Petrini M, et al
        . Well-aerated lung on admitting chest CT to predict adverse outcome in COVID-19 pneumonia. Radiology 2020;296:E86–96.doi:10.1148/radiol.2020201433pmid:http://www.ncbi.nlm.nih.gov/pubmed/32301647
        OpenUrlPubMed
      23. ↵
        1. Parry AH,
        2. Wani AH,
        3. Yaseen M, et al
        . Clinicoradiological course in coronavirus disease-19 (COVID-19) patients who are asymptomatic at admission. BJR Open 2020;2:20200033.doi:10.1259/bjro.20200033pmid:http://www.ncbi.nlm.nih.gov/pubmed/33178987
        OpenUrlPubMed
      24. ↵
        1. Dai M,
        2. Liu X,
        3. Zhu X, et al
        . Temporal changes of CT findings between non-severe and severe cases of COVID-19 pneumonia: a multi-center, retrospective, longitudinal study. Int J Med Sci 2020;17:2653–62.doi:10.7150/ijms.51159pmid:http://www.ncbi.nlm.nih.gov/pubmed/33162793
        OpenUrlPubMed
      25. ↵
        1. Rubin GD,
        2. Ryerson CJ,
        3. Haramati LB, et al
        . The role of chest imaging in patient management during the COVID-19 pandemic: a multinational consensus statement from the Fleischner Society. Chest 2020;158:106–16.doi:10.1016/j.chest.2020.04.003pmid:http://www.ncbi.nlm.nih.gov/pubmed/32275978
        OpenUrlCrossRefPubMed
      26. ↵
        1. Revel M-P,
        2. Parkar AP,
        3. Prosch H, et al
        . COVID-19 patients and the radiology department - advice from the European Society of Radiology (ESR) and the European Society of Thoracic Imaging (ESTI). Eur Radiol 2020;30:4903–9.doi:10.1007/s00330-020-06865-ypmid:http://www.ncbi.nlm.nih.gov/pubmed/32314058
        OpenUrlPubMed
      27. ↵
        1. LeDell E,
        2. Petersen M,
        3. van der Laan M
        . Computationally efficient confidence intervals for cross-validated area under the ROC curve estimates. Electron J Stat 2015;9:1583–607.doi:10.1214/15-EJS1035pmid:http://www.ncbi.nlm.nih.gov/pubmed/26279737
        OpenUrlCrossRefPubMed
      28. ↵
        1. Riley RD,
        2. Ensor J,
        3. Snell KIE, et al
        . Calculating the sample size required for developing a clinical prediction model. BMJ 2020;368:m441.doi:10.1136/bmj.m441pmid:http://www.ncbi.nlm.nih.gov/pubmed/32188600
        OpenUrlFREE Full Text
      29. ↵
        1. Mesas AE,
        2. Cavero-Redondo I,
        3. Álvarez-Bueno C, et al
        . Predictors of in-hospital COVID-19 mortality: a comprehensive systematic review and meta-analysis exploring differences by age, sex and health conditions. PLoS One 2020;15:e0241742.doi:10.1371/journal.pone.0241742pmid:http://www.ncbi.nlm.nih.gov/pubmed/33141836
        OpenUrlCrossRefPubMed
      30. ↵
        1. Bastug A,
        2. Bodur H,
        3. Erdogan S, et al
        . Clinical and laboratory features of COVID-19: predictors of severe prognosis. Int Immunopharmacol 2020;88:106950.doi:10.1016/j.intimp.2020.106950pmid:http://www.ncbi.nlm.nih.gov/pubmed/32919217
        OpenUrlCrossRefPubMed
      31. ↵
        1. Figliozzi S,
        2. Masci PG,
        3. Ahmadi N, et al
        . Predictors of adverse prognosis in COVID-19: a systematic review and meta-analysis. Eur J Clin Invest 2020;50:e13362.doi:10.1111/eci.13362pmid:http://www.ncbi.nlm.nih.gov/pubmed/32726868
        OpenUrlCrossRefPubMed

      Supplementary materials

      • Supplementary Data

        This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

      Footnotes

      • Collaborators Reggio Emilia COVID-19 Working Group. Massimo Costantini, Roberto Grilli, Massimiliano Marino, Giulio Formoso, Debora Formisano, Paolo Giorgi Rossi, Emanuela Bedeschi, Cinzia Perilli, Elisabetta La Rosa, Eufemia Bisaccia, Ivano Venturi, Massimo Vicentini, Cinzia Campari, Francesco Gioia, Serena Broccoli, Marta Ottone, Pierpaolo Pattacini, Giulia Besutti, Valentina Iotti, Lucia Spaggiari, Pamela Mancuso, Andrea Nitrosi, Marco Foracchia, Rossana Colla, Alessandro Zerbini, Marco Massari, Anna Maria Ferrari, Mirco Pinotti, Nicola Facciolongo, Ivana Lattuada, Laura Trabucco, Stefano De Pietri, Giorgio Francesco Danelli, Laura Albertazzi, Enrica Bellesia, Simone Canovi, Mattia Corradini, Tommaso Fasano, Elena Magnani, Annalisa Pilia, Alessandra Polese, Silvia Storchi Incerti, Piera Zaldini, Efrem Bonelli, Bonanno Orsola, Matteo Revelli, Carlo Salvarani, Carmine Pinto and Francesco Venturelli.

      • Contributors MGG, AMF, PGR and FL conceived and designed the study. MGG, LA, LBitton, CB, LBoracchia, SC, VC, DMLF, CSM, FS, AMF and FL made substantial contributions to acquisition of data. OD, MO and PGR analysed data. GB, MGG, OD, AMF, MO, PGR and FL interpreted the data. GB, MGG, OD, MO, PGR and FL drafted the manuscript. MGG and OD contributed equally to this paper. MO accepts full responsibility for the work and the conduct of the study, had access to the data, and controlled the decision to publish. All authors read and critically reviewed the manuscript.

      • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

      • Competing interests None declared.

      • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

      • Provenance and peer review Not commissioned; externally peer reviewed.

      • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.