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Research Article| Volume 102, P212-219, January 2021

Low mortality of hospitalised patients with COVID-19 in a tertiary Danish hospital setting

Open AccessPublished:October 12, 2020DOI:https://doi.org/10.1016/j.ijid.2020.10.018

      Highlights

      • COVID-19 in Danish hospitalised patients with equal and free access to health care.
      • Early lock down and preparedness of the health system.
      • Multidisciplinary team approach to COVID-19 patients.
      • Low overall mortality less than 5% and mortality of 0% for ICU patients.

      Abstract

      Objectives

      We aimed to describe clinical characteristics and outcomes of admitted COVID-19 patients in a Danish hospital setting where an early active government intervention was taken.

      Methods

      Prospective cohort study including all admitted patients to the COVID-19 unit at Odense University Hospital from March 10 to April 21, 2020. Patients were assessed by a multidisciplinary team at admission. Outcome parameters were development of acute respiratory distress syndrome (ARDS), intensive care unit (ICU) admission, death and admission time.

      Results

      We included 83 patients (median age 62 years, 62.7% male). At hospitalization, 31.3% needed oxygen supplementation and the median National Early Warning Score was four. Median admission time was 7 days (Interquartile ranges (IQR) 3-12). In total, ARDS was diagnosed in 33.7% (28/83) of the patients corresponding to an incidence rate of 7.1 per 100 person days (95% CI: 4.1-10.2). Overall 13 patients (15.7%) were transferred to the ICU of whom 11 (84.6%) received corticosteroids.. No patients died while admitted to the ICU. Four patients (4.8%) died during admission.

      Conclusion

      Despite similar patient characteristics compared to those reported by others, we found a low overall mortality of < 5%.

      Keywords

      Introduction

      The coronavirus disease 2019 (COVID-19) pandemic has caused a major worldwide health crisis and it is essential to learn more about the disease and its management. Publications from the most severely affected regions have demonstrated substantial variation in patient characteristics, disease course, and treatment outcome (
      • Colaneri M.
      • Sacchi P.
      • Zuccaro V.
      • Biscarini S.
      • Sachs M.
      • Roda S.
      • et al.
      Clinical characteristics of coronavirus disease (COVID-19) early findings from a teaching hospital in Pavia, North Italy, 21 to 28 February 2020.
      ,
      • Guan W.J.
      • Ni Z.Y.
      • Hu Y.
      • Liang W.H.
      • Ou C.Q.
      • He J.X.
      • et al.
      Clinical Characteristics of Coronavirus Disease 2019 in China.
      ,
      • Lechien J.R.
      • Chiesa-Estomba C.M.
      • Place S.
      • Van Laethem Y.
      • Cabaraux P.
      • Mat Q.
      • et al.
      Clinical and Epidemiological Characteristics of 1,420 European Patients with mild-to-moderate Coronavirus Disease 2019.
      ,
      • Richardson S.
      • Hirsch J.S.
      • Narasimhan M.
      • Crawford J.M.
      • McGinn T.
      • Davidson K.W.
      • et al.
      Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area.
      ). The political engagement, socio-demography of affected population and the preparedness of health care facilities of the individual countries appear to affect the course of the pandemic (
      • Docherty A.B.
      • Harrison E.M.
      • Green C.A.
      • Hardwick H.E.
      • Pius R.
      • Norman L.
      • et al.
      Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study.
      ,
      • Grasselli G.
      • Zangrillo A.
      • Zanella A.
      • Antonelli M.
      • Cabrini L.
      • Castelli A.
      • et al.
      Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy.
      ,
      • Habib H.
      Has Sweden’s controversial covid-19 strategy been successful?.
      ,
      • Ihle-Hansen H.
      • Berge T.
      • Tveita A.
      • Rønning E.J.
      • Ernø P.E.
      • Andersen E.L.
      • et al.
      COVID-19: Symptoms, course of illness and use of clinical scoring systems for the first 42 patients admitted to a Norwegian local hospital.
      ,
      • Israelsen S.B.
      • Kristiansen K.T.
      • Hindsberger B.
      • Ulrik C.S.
      • Andersen O.
      • Jensen M.
      • et al.
      Characteristics of patients with COVID-19 pneumonia at Hvidovre Hospital, March-April 2020.
      ,
      • Stafford N
      Covid-19: Why Germany’s case fatality rate seems so low.
      ,
      • Steffens I.
      A hundred days into the coronavirus disease (COVID-19) pandemic.
      ). Organization of the health care system and its capacity seems to play a key role in the management of the patients and treatment outcomes (
      • ECDC
      European Centre for Disease Prevention and Control. Infection prevention and control and preparedness for COVID-19 in healthcare settings.
      ). The Danish social and health care system is tax funded and provides equal access to free universal healthcare and government sponsored compensation in the event of unemployment or illness.
      The first Danish case of COVID-19 was reported on February 26, 2020 (
      • SSI
      Statens Serium Institut (SSI). COVID-19 i Danmark.
      c). The initial spread of the infection was believed to be from returning Danish ski tourist from epicenters in Northern Italy and Austria but with-in a few weeks local transmission was evident. On March 11 the Danish government locked down large sections of the Danish society to reduce further spread of the infection. At that time, Denmark had 514 confirmed COVID-19 cases in a country with 5.8 million inhabitants which corresponded to 8.9 cases per 100,000 population (
      • Danmarks Statistik
      Population statistics D.
      . Population statistics, Office, 2020). The epidemic in Denmark peaked at the end of March - early April, with the highest number of admitted COVID-19 patients registered on April 1 2020 (9.2 patients per 100,000 population including 2.5 patients per 100,000 population in the intensive care units (ICU)) (
      • SSI
      Statens Serium Institut (SSI). COVID-19 i Danmark.
      a). After the lockdown, the number of new COVID-19 cases requiring admission has steadily declined leading to the first phase of the re-opening of the society, which was launched on April 15. On May 7, the number of total admitted patients to Danish hospitals was as low as 145 and the second phase of the re-opening of the society was announced (Police, 2020,
      • SSI
      Statens Serium Institut (SSI). COVID-19 i Danmark.
      b). So far, there is little information about COVID-19 from countries with a social and health structure comparable to Denmark.
      The overall aim of this prospective observational cohort study was to report the clinical characteristics and outcome of hospitalized patients with COVID-19 in a tertiary Danish hospital setting, with special focus on patients admitted to the ICU and patients with and without ARDS.
      In addition, we will report on the model of care for COVID-19 and the capacity with-in our health system.

      Methods

      Setting

      The study was performed at Odense University Hospital (OUH), which serves both as a community-hospital, for app. 500,000 persons and as a tertiary hospital for the Region of Southern Denmark (app. 1.2 million inhabitants)(Danmarks Statistik. Population statistics). In the beginning of the epidemic all COVID-19 patients, admitted in the Region of Southern Denmark, who were deemed clinically stable for transportation, were transferred to a dedicated corona unit at OUH for further therapy. A substantial number of healthcare personal was trained. The strategy was aimed to provide each COVID-19 unit all necessary basic skills in e.g. lung, heart or neurological disease. This ensured that the allocated staff cared for patients with COVID-19 with co-morbidities within their area of expertise, assisted by Infectious Disease (ID) specialist. Thereby building on existing capacity and forming multidisciplinary teams (MDT). The core members of the team of responsible physicians for the COVID-19 patients comprised ID physicians, pulmonologists, intensive care specialists and nephrologists.

      Study design

      A hospital-based prospective cohort study.

      COVID-19 management

      COVID-19 management was based on local and national guidelines and consisted primarily of supportive therapy (OUH). The indication for steroid treatment was moderate to severe acute respiratory distress syndrome (ARDS) and use of steroids was based on a MDT consensus where standard treatment was dexamethasone 20 mg iv for 5 days followed by 10 mg for a further 5 days. No other therapy was given unless patients were included in randomized clinical trials (RCTs). Upon admission, each patient was evaluated by a senior consultant and an intensive care specialist to determine whether the patient was a candidate for ICU treatment and/or resuscitation based on the patient`s wishes along with a combination of age, comorbidities or conditions that may limit the potential for future rehabilitation. Based on this evaluation, patients who developed severe respiratory failure or multi-organ failure during admission were either transferred to the ICU or kept on the COVID-19 unit, for further therapy.

      Population

      We included all patients who were 1) 18 years or older, 2) had a positive real time polymerase chain reaction (RT-PCR) analysis for SARS- CoV-2 performed on material from a pharyngeal swab or tracheal secretion indicating COVID-19 disease, and 3) were admitted or transferred to OUH between March 10 - April 21. At end of follow-up for this study (May 1), all patients except one were either dead or discharged.
      The COVID-19 baseline date was defined as the day of admission for patients presenting with COVID-19 symptoms, or the first date of a positive RT- PCR analysis of SARS-CoV-2 for already hospitalised patients who developed symptoms of COVID-19 during admission.

      Data Collection

      A review of the patients’ electronic medical record (EMR) was performed in order to obtain demography-, clinical-, laboratory-, management- and outcome data. Comorbidity was defined as both current and past diseases; for further details see supplemental 1. All triage values (temperature, oxygen saturation with and without supplemental oxygen, blood pressure and heart rate) were registered and based on these values National Early Warning Scores version 2 (NEWS2) were calculated for each patient (Physicians, 2017). Based on the EMR the Eastern Cooperative Oncology Group (ECOG) performance status was derived for each patient (
      • Oken M.M.
      • Creech R.H.
      • Tormey D.C.
      • Horton J.
      • Davis T.E.
      • McFadden E.T.
      • et al.
      Toxicity and response criteria of the Eastern Cooperative Oncology Group.
      ).
      All relevant data were registered in a database by one investigator and subsequent checked by a second member of the study team. In case of discrepancy, a consensus was reached through discussion among team members. A complete codebook was predefined for all variables (supplemental 1). All blood samples were registered from the laboratory system to the database.

      Outcome definitions

      The following clinical defined outcomes were considered:
      1) ARDS: The criteria for ARDS and grading of severity of ARDS was based on the fraction of the partial pressure of oxygen (PaO2) to the fraction of inspired oxygen (FiO2) (PaO2/FIO2-ratio) and results of chest imaging as described in current international recommendations (
      • Ferguson N.D.
      • Fan E.
      • Camporota L.
      • Antonelli M.
      • Anzueto A.
      • Beale R.
      • et al.
      The Berlin definition of ARDS: an expanded rationale, justification, and supplementary material.
      ,
      • Griffiths M.J.D.
      • McAuley D.F.
      • Perkins G.D.
      • Barrett N.
      • Blackwood B.
      • Boyle A.
      • et al.
      Guidelines on the management of acute respiratory distress syndrome.
      ).
      2) ICU admission: Date of transfer to and discharge from the ICU as well as any supportive care given during ICU admission
      3) Length of admission to ICU: Total time from transfer to discharge from ICU
      4) Length of admission: Discharge from a COVID-19 unit ≤/> 7days
      5) Death: Date and cause of death

      Statistics

      Descriptive statistics were reported as proportions for categorical variables and medians with interquartile ranges (IQR) for continuous variables depending on the data distribution.
      Time was computed from COVID-19 baseline (as previously defined) until date of the outcome of interest or May 1 whichever came first. For all outcomes, the number, proportion and time to the outcome as well as median time was computed. We used Cox regression analyses to compute incidence rates (IR) and 95% confidence intervals (CI). We used cumulative incidence function to illustrate time to first occurrence of ARDS stratified on gender, body mass index ((BMI) </≥25) and age </≥ 70, respectively. To identify predictors for ARDS we used Cox regression to compute incidence rate ratios (IRR) as a measure of the relative risk. In the univariate model we examined potential risk factors for ARDS (age </≥70, sex, BMI </≥25, comorbidity and ECOG performance score), In the multivariate model we restricted the analyses to age and gender which remain the most important variables in risk analyses of morbidity and mortality, and further included variables with p-value < 0.1. Finally, pearson correlation was used to investigate whether there was a correlation between age and admission time.

      Data management

      Prospective data from all COVID-19 patients were registered in a REDCap database hosted by Open Patient data Explorative Network (OPEN)(OPEN). STATA version 15 (Stata Corp LP, Texas) was used for data processing and analyses.

      Approvals

      This study is approved as a quality study and registered on the Region of Southern Denmark’s record of data processing activities (j. nr. 20/16169). All data are handled in accordance with applicable laws: The General Data Protection Regulation (GDPR), the Danish Act on Data Protection, the Danish Act on Research Ethics Review of Health Research Projects and the Danish Health Act.

      Results

      Between March 10 and April 21, we included 83 patients in the study of whom 50.6% were transferred from other hospitals. Three patients were diagnosed with COVID-19 during admission for non-COVID-19 related illnesses.

      Patients characteristics

      Baseline characteristics of the study population are shown in Table 1. The median age was 62 years (Interquartile range; IQR 54-74), 62.7% were men and 84.3% were Caucasian. The median Body Mass Index (BMI) was 26.5 (IQR 23.7 – 30.1) and 25.6% were obese (BMI ≥ 30). In total, 54.9% were never smokers and five (6.1%) had a high alcohol consumption. More than half of the admitted COVID-19 patients had prior cardiovascular diseases (CVD) (55.4%) of which hypertension was the most dominant (42.2%). Other common comorbidities included chronic pulmonary diseases (16.9%), malignancies (16.9%), and diabetes (15.7%). Although, a total of 86.8% had at least one comorbidity, the majority of the patients (66.3%) had an ECOG performance score of 0 before onset of disease. The median duration of symptoms prior to hospitalization was 9 days (IQR: 5.5-11.0 days).
      Table 1Demographic characteristics of patients admitted to Odense University Hospital with COVID-19.
      Study populationNo. (%)
      No. of patients83
      Age (years), median (IQR)62 (54-74)
      18-4914 (16.9)
      50-6432 (38.6)
      65-7922 (26.5)
      ≥8015 (18.1)
      Sex
      Male52 (62.7)
      Female31 (37.3)
      Ethnicity
      Caucasian70 (84.3)
      Non-Caucasian13 (15.7)
      BMI, median (IRQ)26.5 (23.7-30.1)
      <2528 (34.2)
      ≥25- <3033 (40.2)
      ≥3021 (25.6)
      Smoking
      Current smoker5 (6.1)
      Former smoker32 (39.0)
      Never smoker45 (54.9)
      Units of alcohol per week
      >7 for women / >14 for men5 (6.1)
      ≤7 for women / ≤14 for men77 (93.9)
      Comorbidity
      Any cardiovascular disease46 (55.4)
      Hypertension35 (42.2)
      Atrial fibrillation13 (15.7)
      Ischemic heart disease7 (8.4)
      Valvular heart disease6 (7.2)
      Congestive heart failure5 (6.0)
      Stroke4 (4.8)
      Peripheral vascular disease3 (3.6)
      Chronic pulmonary disease14 (16.9)
      Malignancy14 (16.9)
      Diabetes mellitus I + II13 (15.7)
      Inflammatory bowel disease3 (3.6)
      Rheumatoid artritis3 (3.6)
      Chronic kidney disease2 (2.4)
      Medication prior to admission
      Antibiotic therapy30 (36.1)
      ACE inhibitor / ARBs31 (37.4)
      Immunosuppressive7 (8.4)
      ECOG Performance score
      055 (66.3)
      116 (19.3)
      25 (6.0)
      36 (7.2)
      41 (1.2)
      Time from onset of symptoms to admission (days), median (IQR)9 (5.5-11)
      Initial hospital of admission
      OUH41 (49.4)
      Community-hospital42 (50.6)
      Abbreviations: Odense University Hospital (OUH), Angiotensin-converting enzyme (ACE), Angiotensin II receptor blockers (ARBs), Eastern Cooperative Oncology Group (ECOG), Body Mass Index (BMI)
      Data on BMI, smoking and alcohol were available for 82/83 patients. Data on time from onset of symptoms to admission were available for 80/83 patients.

      Clinical status at admission

      The most frequent symptoms at admission were fever (90.4%), cough (85.5%) and dyspnea (69.9%) (Table 2). At the initial triage, 59.0% had a temperature ≥ 38.1 degrees Celsius and 21.7% had a respiratory rate greater than 24 breaths/min. At admission 31.3% needed oxygen therapy (80.8% via nasal cannula (median oxygen supplementation 2 l/min) and 19.2% on a mask with or without bag. Although 49.4% had a NEWS2 score of >4, less than 5% were hemodynamically unstable at presentation (systolic blood pressure ≤ 90 mmHg and/or heart rate >130 bpm).
      Table 2Clinical parameters at admission for patients admitted to Odense University Hospital with COVID-19.
      Study populationNo. (%)
      No. of patients83
      Symptoms
      Fever75 (90.4)
      Cough71 (85.5)
      Dyspnea58 (69.9)
      Headache44 (53.0)
      Myalgia44 (53.0)
      Fatigue38 (45.8)
      Nausea/vomiting32 (38.6)
      Dizziness24 (28.9)
      Diarrhea24 (28.9)
      Chest pain14 (16.9)
      Abdominal pain13 (15.7)
      Throat pain11 (13.3)
      Rhinitis9 (10.8)
      Change of taste9 (10.8)
      Change of smell5 (6.0)
      Vital signs
      Temperature, median (IQR)38.4 (37.7-39.0)
      <37.516 (19.3)
      37.5-38.018 (21.7)
      38.1-39.029 (34.9)
      >39.020 (24.1)
      Respiratory rate (breaths per minute), median (IQR)20 (18-24)
      ≤2465 (78.3)
      >2418 (21.7)
      Saturation(%) without supplemental oxygen, median (IQR)
      Data on saturation without supplemental oxygen were available for 78 patients.
      95 (92-97)

      <9217 (20.5)
      ≥9266 (79.5)
      Received supplemental oxygen26 (31.3)
      • Nasal cannulae
      21(80.8)
      • Mask with or without bag
      5 (19.2)
      Supplemental oxygen with nasal cannulae (liters/minute), median (IQR)2 (1.5-3.0)
      Systolic blood pressure (mmHg), median (IQR)134 (121-147)
      ≤ 902 (2.4)
      Heart rate (beats per minute), median (IQR)87 (77-99)
      >1304 (4.8)
      Glasgow Coma Scale
      1582 (98.8)
      <151 (1.2)
      National Early Warning Score 2, median (IQR)4 (3-6)
      0-4 (low)42 (50.6)
      5-6 (medium)21 (25.3)
      >6 (high)20 (24.1)
      Abbreviations: Interquartile range (IQR),
      * Data on saturation without supplemental oxygen were available for 78 patients.

      Paraclinical status at admission

      Laboratory and radiological findings at admission are summarized in Table 3. Notable values with medians outside of normal reference range included lymphocyte count (0.86 × 109/L), C-reactive protein (66 mg/L), ferritin (496 µg/L), d-dimer (0.87 mg/L), fibrinogen (16.0 µmol/L) and lactate dehydrogenase (294 U/L). Reference values are shown in Table 3. Almost all patients (97.5%) had a chest x-ray performed upon admission and of these, 66.7% had lung opacities. Pleural effusion was rare (12.4%). The pneumonic opacities were randomly distributed in all pulmonary lobes.
      Table 3Laboratory and radiological findings at admission for patients admitted to Odense University Hospital with COVID-19.
      Tests in study populationValue, median (IQR)Reference valuesNo. of patients tested (n = 83)
      Haemoglobin
      Hemoglobin; mmol of hemoglobin/L. Conversion factor from g/dL to mmol/L is 0.6202.
      (g/dl )
      13 ()(12.6-14.8)Female 11.8-15.3Male 13.4-16.9

      83
      White-cell count (109/L)6.8 (5.2-8.6)3.50-8.8083
      Neutrophils (109/L)4.4 (3.2-6.7)1.50-7.5083
      Lymphocytes (109/L)0.86 (0.63-1.31)1.00-4.0079
      Monocytes (109/L)0.47 (0.33-0.72)0.20-0.8079
      Basophiles (U/L)0.04 (0.04-0.04)<0.2079
      Eosinophils (109/L)0.04 (0.04-0.06)<0.5079
      Platelets (109/L)188 (163-254)Female 165-400

      Male 145-350
      82
      C-reactive protein (mg/L)66 (29-112)<6.083
      Pro-calcitonin (µg/L)0.15 (0.06-0.21)<0.1021
      Ferritin (µg/L)496 (201-1017)Female≤ 15-180

      Female > 50 years 15-450

      Male 15-560
      27
      D-dimer (mg/L)0.87 (0.39-1.87)< 55 years <0.50

      55-65 years <0.60

      65-75 years <0.70

      >75 years <0.80
      64
      Fibrinogen (µmol/L)16.0 (13.2-18.5)5.2-12.648
      Lactate dehydrogenase (U/L)294 (212-357)<70 years 105-205

      >70 years 115-255
      66
      Alanine aminotransferase (U/L)33 (24-50)Female 10-45

      Male 10-70
      79
      Creatine kinase (U/L)104 (55-197)Females 35-210

      Male 18-50 years 50-400

      Male >50 years 40-280
      57
      Creatinine (µmol/L)84 (68-99)Female 45-90

      Male 60-105
      83
      Urea (mmol/L)5.6 (4.3-7.8)Female 18-50 years 2.6-6.4

      Female >50 years 3.1-7.9

      Male 18-50 years 3.2-8.1

      Male >50 years 3.5-8.1
      62
      Albumin (g/L)38 (35-41)< 40 years 36-50

      40-70 years 36-48

      >70 years 34-45
      82
      Activated partial thromboplastin time (APTT) (s)26 (24-28)22-2857
      International Normalized Ratio (INR)1.06 (1.00-1.17)<1.2079
      Pancreatic amylase (U/L)34 (22-51)10-6555
      Bilirubin (µmol/L)8 (6-10)5-2581
      Troponin T (ng/L)8 (7-22)<1416
      Potassium (mmol/L)3.8 (3.6-4.1)3.5-4.481
      Sodium (mmol/L)136 (134-139)137-14583
      Lung opacities on chest x-ray.No. (%)81
      Unilateral13 (16.1)
      Bilateral41 (50.6)
      None27 (33.3)
      Pleural Effusion10 (12.4)
      Placement of opacity
      Right upper lobe33 (40.7)
      Right middle lobe32 (39.5)
      Right lower lobe44 (54.3)
      Left upper lobe29 (35.8)
      Left lower lobe42 (51.9)
      Microbiology results during admissionNo. positive / tested personsNo. positive/total tests
      Sputum culture
      In 70.4% (38/54) the positive sputum cultures included yeast.
      27/60 (45.0)54/123 (43.9)
      PCR for Legionella pneumophila, Mycoplasma Pneumoniae and Chlamydia pneumoniae0/46 (0)0/60 (0)
      PCR for influenza virus0/65 (0)0/71 (0)
      Blood culture4/80 (5.0)14/208 (6.7)
      Urine culture3/66 (19.7)22/143 (15.4)
      Faeces (PCR/culture)2/19 (10.5)2/26 (7.0)
      Cerebrospinal fluid (PCR/culture)0/1 (0)0/2 (0)
      Abbreviations: Interquartile range (IQR), Polymerase Chain Reaction (PCR)
      * Hemoglobin; mmol of hemoglobin/L. Conversion factor from g/dL to mmol/L is 0.6202.
      ** In 70.4% (38/54) the positive sputum cultures included yeast.
      Most patients had blood cultures performed and four patients had clinically relevant bacteremia with Klebsiella pneumoniae, Escherichia coli, Enterococcus faecalis or Staphylococcus lugdunensis.
      Sputum cultures were performed in 45% (27/60) of the patients of which 16 patients (59.3%) only had yeast. Only 11 patients had culture-verified bacterial pneumonia with Haemophilus influenza (n = 2), Streptococcus pneumoniae (n = 2) and Staphylococcus aureus (n = 7).

      Outcome

      ARDS

      ARDS criteria was met in 33.7% (28/83) of the patients corresponding to an incidence rate of 7.1 per 100 person days. Of these, 25% (7/28) were characterized as severe ARDS. Of the patients transferred to the ICU, 92.3% (12/13) were diagnosed with ARDS, of which it was characterized as severe in 41.7% (5/12). Overall, 75% (21/28) of the patients with ARDS were men. We found no difference in median age between patients with severe ARDS and patients without ARDS. Of patients with ARDS, 57.1% (4/7) received immunosuppressive treatment prior the COVID-19 admission (supplemental 2a). As shown in Figure 1, we found a trend towards a higher cumulative incidence for ARDS for male gender, age ≤ 70 years and a BMI ≥ 25; however, this was not statistically significant (rank test > 0.05). We found a moderately positive correlation between admission time and age (Pearson’s correlation coefficient r = 0.32, p = 0.003). When investigating predictors for ARDS, male sex, BMI ≥ 25 and prior cardiovascular morbidity it showed a trend towards an increased risk of ARDS in the univariate analyses, but none of the variables deemed statistically significance (supplemental 3). In the multivariate analyze, cardiovascular comorbidity was associated with statistically significant more than 2.6 higher risk of ARDS (supplemental 3).
      Figure 1
      Figure 1Cululative incidence for ARDS stratified by (a) gender, (b) age < / ≥ 70 years old, and (c) BMI < / ≥ 25
      Abbreviations: ARDS; Adult respiratory distress syndrome, BMI; Body Mass Index. (Pearson’s correlation coefficient r = 0.32, p = 0.003)

      ICU admission

      At admission, 81.9% (68/83) were evaluated to be candidates for treatment in the ICU and 13 patients (15.7%) were transferred to the ICU. Median time from hospitalization until ICU admission was 1 day (range 0-10 days) and from onset of symptoms until ICU admission 10 days (range 4-24 days). Almost all patients (92.3%) received mechanical ventilation. Only one patient received noninvasive ventilation at the ICU. The median age was 63 years (IQR 60-69) and the majority was male (76.9%). The average number of comorbidities was 2.5 (range 1-4), the most frequent being hypertension (61.5%) (Supplemental Table 2b). None of the ICU patients died, and by the end of the study period, all but one patient was discharged from hospital.

      Treatment

      Antibiotic therapy was prescribed empirically to 69 (83.1%) patients during hospitalization, of whom 74.7% of the patients received broad-spectrum antibiotics (piperacillin-tazobactam, meropenem, cephalosporines, Table 4). Steroids were prescribed to 35.7% (10/28) of the patients with ARDS. In total, 8 patients received dexamethasone of which all were admitted to the ICU and diagnosed with either moderate or severe ARDS. In addition 3 patients admitted to ICU received another high dose corticosteroids.
      Table 4Outcomes in patients admitted to Odense University Hospital with COVID-19.
      Study populationNo. (%)

      N = 83
      Person days at riskMedian person days at risk (IQR)Incidence rate per 100 person days

      (95% CI)
      No. of patients83
      ARDS28 (33.7)3954.0 (2-9)7.1 (4.1-10.2)
      Mild14 (50)
      Moderate7 (25)
      Severe7 (25)
      In-hospital mortality of ARDS patients2 (7.1)
      ICU candidate at admission
      Yes68 (81.9)
      No15 (18.1)
      ICU13 (15.7)3544.0 (2-8)3.7 (2.1-6.3)
      IR for ICU is only calculated for the 68 ICU candidates.
      Invasive mechanical ventilation12 (92.3)
      Vasopressors12 (92.3)
      Renal replacement therapy4 (30.8)
      ECMO0 (0)
      In-hospital mortality of ICU patients.0/13 (0)
      Time in ICU (days), median (IQR)11 (IQR, 7-12)
      Status on May 1, 2020
      Died during admission4 (4.8)7947.0 (4-12)0.5 (0.2-1.3)
      Discharged alive from hospital78 (94)
      Still admitted in hospital1 (1.2)
      Time from hospital admission to dead (days), median (IQR)10 (6.5-13)
      Time from hospital admission to discharge (days), median (IQR)7 (3-12)
      Only calculated for the 82 patients whom were either dead or discharged at the end of follow-up.
      Therapy during admission
      Steroids for ARDS10 (36)
      Antibiotic therapy during admission69 (83.1)
      Piperacillin-tazobactam53 (63.9)
      Amoxicillin-clavulanate37 (44.6)
      Meropenem14 (16.7)
      Benzylpenicillin12 (14.5)
      Macrolide6 (7.2)
      Other
      Other, name (No. of patients): Fluconazol (5), Cefuroxim (3), Amoxicillin (2), Dicloxcillin (2), Ciprofloxacin (2), Metronidazol (2), Mecillinam (1), Vancomycin (1), Nitrofurantoin (1), Aciclovir (1).
      20 (24.1)
      Abbreviations: Number of patients (No.), Interquartile range (IQR), adult respiratory distress syndrome (ARDS), intensive care unit (ICU), extracorporeal membrane oxygenation (ECMO).
      * IR for ICU is only calculated for the 68 ICU candidates.
      ** Only calculated for the 82 patients whom were either dead or discharged at the end of follow-up.
      *** Other, name (No. of patients): Fluconazol (5), Cefuroxim (3), Amoxicillin (2), Dicloxcillin (2), Ciprofloxacin (2), Metronidazol (2), Mecillinam (1), Vancomycin (1), Nitrofurantoin (1), Aciclovir (1).

      Admission time and course of disease

      Median time of hospital stay until discharge was 7 days (IQR 3-12 days). The patients hospitalized for more than seven days had a higher median age (67 versus 58 years) and had more cardiovascular comorbidities (70.8% versus 34.3% (supplemental table 2c).
      We found an overall low mortality rate 0.5 per 100 person days (95%CI: 0.2-1.3). The four patients who died (4.8%) during admission had a median age of 83, of whom 75% were men (Supplemental 2d). Cause of mortality was respiratory failure in all cases. The median time of hospital stay until death was 10 days (IQR 6.5-13 days) (Table 4).

      Health system capacity

      During the study period, neither the regular COVID-19 units nor the ICU were at maximum capacity at any time. In the ICU, patients were cared for by the regular ICU staff at all times.

      Discussion

      This Danish hospital-based prospective cohort study is among the first to report COVID-19 data from a Scandinavian country where all inhabitants have free and equal access to health care and social support. The general patient characteristics in our study are similar to those reported by others with 17% requiring admission to the ICU (
      • Docherty A.B.
      • Harrison E.M.
      • Green C.A.
      • Hardwick H.E.
      • Pius R.
      • Norman L.
      • et al.
      Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study.
      ) and the same proportion of ARDS (
      • Zhou F.
      • Yu T.
      • Du R.
      • Fan G.
      • Liu Y.
      • Liu Z.
      • et al.
      Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study.
      ) however, we still found a low overall mortality of 4.8% and a mortality of 0% among patients treated in the ICU. This illustrates the excellent set-up with 1) high preparedness, 2) use of MDT’s and 3) the widespread use of dexamethasone for ARDS. These results are from a period in which the Danish government launched an aggressive lockdown of the society to reduce the spread of SARS-CoV-2. As it is apparent from the reduction of COVID-19 admissions during the study period, this lockdown was successful in reducing the number of COVID-19 admissions in Denmark. As a result, our tertiary center never got overwhelmed with severely ill COVID-19 patients.
      As observed in studies from other countries, the COVID-19 patients in need of hospital admission in this study were predominately male, overweight, middle-aged to elderly with frequent co-morbidities, in particular cardiovascular diseases (
      • Goyal P.
      • Choi J.J.
      • Pinheiro L.C.
      • Schenck E.J.
      • Chen R.
      • Jabri A.
      • et al.
      Clinical Characteristics of Covid-19 in New York City.
      ,
      • Grasselli G.
      • Zangrillo A.
      • Zanella A.
      • Antonelli M.
      • Cabrini L.
      • Castelli A.
      • et al.
      Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy.
      ,
      • Zhou F.
      • Yu T.
      • Du R.
      • Fan G.
      • Liu Y.
      • Liu Z.
      • et al.
      Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study.
      ). Regarding clinical symptoms, vital signs, and laboratory findings, we found no substantial difference between our results and findings in other studies (
      • Pascarella G.
      • Strumia A.
      • Piliego C.
      • Bruno F.
      • Del Buono R.
      • Costa F.
      • et al.
      COVID-19 diagnosis and management: a comprehensive review.
      ,
      • Wang D.
      • Hu B.
      • Hu C.
      • Zhu F.
      • Liu X.
      • Zhang J.
      • et al.
      Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China.
      ), but the patient’s NEWS2 score appeared to be higher in our cohort (
      • Carr E.
      • Bendayan R.
      • Bean D.
      • O’Gallagher K.
      • Pickles A.
      • Stahl D.
      • et al.
      Supplementing the National Early Warning Score (NEWS2) for anticipating early deterioration among patients with COVID-19 infection.
      ), however the score is not frequently used in other studies.
      ARDS has previously been reported as one of the most frequent causes of deterioration in COVID-19, with reported proportions between 15.6-30.0% (
      • Li X.
      • Ma X.
      Acute respiratory failure in COVID-19: is it "typical" ARDS?.
      ). Despite similar proportions of ARDS in our study, an overall low mortality was found among admitted patients when compared to other countries (
      • Suleyman G.
      • Fadel R.A.
      • Malette K.M.
      • Hammond C.
      • Abdulla H.
      • Entz A.
      • et al.
      Clinical Characteristics and Morbidity Associated With Coronavirus Disease 2019 in a Series of Patients in Metropolitan Detroit.
      ,
      • Zhou F.
      • Yu T.
      • Du R.
      • Fan G.
      • Liu Y.
      • Liu Z.
      • et al.
      Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study.
      ). One of the reason for this may be the use of corticosteroids to 84.6% of the ICU patients. A Randomized clinical trial and meta-analysis that since have been published have showed that use of dexamethasone or systemic corticosteroids in general reduce mortality for critically ill patients (
      • Horby P.
      • Lim W.S.
      • Emberson J.R.
      • Mafham M.
      • Bell J.L.
      • Linsell L.
      • et al.
      Dexamethasone in Hospitalized Patients with Covid-19 - Preliminary Report.
      ,
      • Sterne J.A.C.
      • Murthy S.
      • Diaz J.V.
      • Slutsky A.S.
      • Villar J.
      • Angus D.C.
      • et al.
      Association Between Administration of Systemic Corticosteroids and Mortality Among Critically Ill Patients With COVID-19: A Meta-analysis.
      ). Our population had a low ECOG performance score at admission. This might be a result of self-isolation of elderly persons and patients with many comorbidities as advised by the government in early March. As such, one could speculate that this could also have contributed to the overall low mortality. Although no statistically significant association between the ECOG score and ARDS was found in the univariate and multivariate regression analyses, these analyses are affected by lack of power. In addition, we found a moderately positive correlation between admission time and age independently of ARDS risk. This could indicate that the inverse relation between age and risk of ARDS was due to an increased admission time among patients with a higher age.
      The preparedness of each country and the characteristics of the population have shown to be predictors of the overall mortality (
      • Chaudhry R.
      • Dranitsaris G.
      • Mubashir T.
      • Bartoszko J.
      • Riazi S.
      A country level analysis measuring the impact of government actions, country preparedness and socioeconomic factors on COVID-19 mortality and related health outcomes.
      ).
      Results from the same period from another Danish hospital and a large German study, illustrated a mortality of above 20% however, the median age in these cohorts where somewhat higher than ours (
      • Israelsen S.B.
      • Kristiansen K.T.
      • Hindsberger B.
      • Ulrik C.S.
      • Andersen O.
      • Jensen M.
      • et al.
      Characteristics of patients with COVID-19 pneumonia at Hvidovre Hospital, March-April 2020.
      ,
      • Karagiannidis C.
      • Mostert C.
      • Hentschker C.
      • Voshaar T.
      • Malzahn J.
      • Schillinger G.
      • et al.
      Case characteristics, resource use, and outcomes of 10 021 patients with COVID-19 admitted to 920 German hospitals: an observational study.
      ). As our institution had comprised a MDT of doctors, all COVID-19 patients were triaged by specialists in ID, respiratory medicine and anesthesiology to ensure that fragile patients were not admitted to the ICU. Furthermore, decision on initiation of steroid treatment for ARDS was based on MDT consultations. However, in this context, it is worthy of note that among those deemed too fragile for the ICU, only four patients died. We speculate that our MDT approach led to a better evaluation and treatment of the patients.

      Strengths and limitations

      A major strength of this study is that it was performed in a country with free and equal access to health care. All patients experiencing symptoms and signs of COVID-19 infection could be seen quickly by a skilled physician and admitted to hospital if deemed necessary. Therefore, the patients in this study were probably in a better overall condition compared with patients from other settings. Another strength of this study is the MDT approach and the early recognition of the need for development of regional standardized guidelines for the management of COVID-19 patients, which secured best practice treatment. Finally, empirical therapy with antiviral or immunosuppressive drugs were not given outside RCTs. The exception was steroid therapy for ARDS, upon which the decision to treat was based on MDT discussions.
      The major limitation of this study is the small sample size and single center setting which to some degree may limit its generalizability. Moreover, the low sample size restricted the number of variables that could be included in the regression models in order to avoid an over-fitted model.
      Furthermore, the low ECOG score and lower median age when compared to other cohorts from comparable studies complicates comparison.

      Conclusion

      This study provides characteristics of Danish hospitalized COVID-19 patients. Despite similar proportions of ARDS in our study, an overall low mortality was found among admitted patients when compared to other countries. This suggest that the preparedness of the society and a timely and well-prepared strategy with an MDT approach to COVID-19 patients along with the use of dexamethasone may improve patient management and short-term prognosis including need of ICU treatment.

      Statement of role

      ISJ conceived the study with input from all co-authors. LWM, SOL, FCK, CBL, SLN collected the data. SLN did the data analyses and SLN, LWM and LDR performed the interpretation of the data. LWM and ISJ wrote the first draft of the manuscript and all authors contributed to the final version.

      Role of founding source

      This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

      Declaration of interest

      Lone Wulff Madsen, Susan Olaf Lindvig, Line Dahlerup Rasmussen, Fredrikke Christie Knudtzen, Anne Øvrehus, Christian B. Laursen, Stig Lønberg Nielsen, Isik Somuncu Johansen declares no conflict of interest.
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      Appendix A. Supplementary data

      The following is Supplementary data to this article:

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