1. Epidemiology and Global Health
  2. Medicine

Ten months of temporal variation in the clinical journey of hospitalised patients with COVID-19: An observational cohort

  1. ISARIC Clinical Characterisation Group
  2. Matthew D Hall  Is a corresponding author
  3. Joaquín Baruch
  4. Gail Carson
  5. Barbara Wanjiru Citarella
  6. Andrew Dagens
  7. Emmanuelle A Dankwa
  8. Christl A Donnelly
  9. Jake Dunning
  10. Martina Escher
  11. Christiana Kartsonaki
  12. Laura Merson
  13. Mark Pritchard
  14. Jia Wei
  15. Peter W Horby
  16. Amanda Rojek
  17. Piero L Olliaro
  1. Big Data Institute, Nuffield Department of Medicine, University of Oxford, United Kingdom
  2. ISARIC Global Support Centre, Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, United Kingdom
  3. Department of Statistics, University of Oxford, United Kingdom
  4. MRC Centre for Global Infectious Disease Analysis, Abdul Latif Jameel Institute for Disease and Emergency Analytics and Department of Infectious Disease Epidemiology, Imperial College London, United Kingdom
  5. MRC Population Health Research Unit, Clinical Trials Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, United Kingdom
  6. Infectious Diseases Data Observatory, Centre for Tropical Medicine and Global Health, University of Oxford, United Kingdom
  7. Royal Melbourne Hospital, Melbourne, Australia Centre for Integrated Critical Care, University of Melbourne, Australia
https://doi.org/10.7554/eLife.70970
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Cite this article
  1. ISARIC Clinical Characterisation Group
  2. Matthew D Hall
  3. Joaquín Baruch
  4. Gail Carson
  5. Barbara Wanjiru Citarella
  6. Andrew Dagens
  7. Emmanuelle A Dankwa
  8. Christl A Donnelly
  9. Jake Dunning
  10. Martina Escher
  11. Christiana Kartsonaki
  12. Laura Merson
  13. Mark Pritchard
  14. Jia Wei
  15. Peter W Horby
  16. Amanda Rojek
  17. Piero L Olliaro
(2021)
Ten months of temporal variation in the clinical journey of hospitalised patients with COVID-19: An observational cohort
eLife 10:e70970.
https://doi.org/10.7554/eLife.70970
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5 figures, 4 tables and 10 additional files

Figures

Figure 1
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Time from reported symptom onset to hospital admission, by week of reported symptom onset.

(A) Blue cells represent binned patients, with darker colours corresponding to more individuals. The black line represents the mean. (B)-(D) Mean time to admission plotted by patient characteristics: (B) age group, (C) final outcome, (D) number of the four most common symptoms (cough, fatigue, fever, and shortness of breath) present upon admission.

Figure 1—source data 1

Number of individuals for each combination of week of symptom onset and count of days from symptom onset to admission.

https://cdn.elifesciences.org/articles/70970/elife-70970-fig1-data1-v3.zip
Figure 1—source data 2

Mean number of days from symptom onset to admission by week of symptom onset.

https://cdn.elifesciences.org/articles/70970/elife-70970-fig1-data2-v3.zip
Figure 1—source data 3

Mean number of days from symptom onset to admission by week of symptom onset, by age group.

https://cdn.elifesciences.org/articles/70970/elife-70970-fig1-data3-v3.zip
Figure 1—source data 4

Mean number of days from symptom onset to admission by week of symptom onset, by final outcome (death or discharge).

https://cdn.elifesciences.org/articles/70970/elife-70970-fig1-data4-v3.zip
Figure 1—source data 5

Mean number of days from symptom onset to admission by week of symptom onset, by number of common symptoms recorded at admission.

https://cdn.elifesciences.org/articles/70970/elife-70970-fig1-data5-v3.zip
Figure 2
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Patients entering ICU/HDU within 13 days of COVID-19 admission (A) and time from COVID-19 admission to ICU/HDU admission (B) over time.

Each line is the proportion (A) or mean value (B) amongst all patients (black, dotted) or patients in each age group (coloured).

Figure 2—source data 1

Proportion of individuals entering ICU by week of COVID-19 admission, according to age group and overall.

https://cdn.elifesciences.org/articles/70970/elife-70970-fig2-data1-v3.zip
Figure 2—source data 2

Mean time in days from COVID-19 admission to ICU admission by week of COVID-19 admission, according to age group and overall.

https://cdn.elifesciences.org/articles/70970/elife-70970-fig2-data2-v3.zip
Figure 3 with 4 supplements
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Temporal trends in outcome and time to outcome.

(A) Case fatality ratio in patients experiencing death or discharge within 45 days of COVID-19 admission, by recorded ICU/HDU admission. (B) Mean time from COVID-19 admission to the outcome of death or discharge, further faceted by ICU/HDU admission. Error bars represent 95 % confidence intervals. Numbers along the x-axis indicate the numbers of patients involved in each category.

Figure 3—source data 1

Estimate and 95 % confidence interval for hCFR by week of COVID-19 admission, according to ICU/HDU admission status.

https://cdn.elifesciences.org/articles/70970/elife-70970-fig3-data1-v3.zip
Figure 3—source data 2

Estimate and 95 % confidence interval for time from COVID-19 admission to outcome by week of COVID-19 admission, according to ICU/HDU admission status and outcome.

https://cdn.elifesciences.org/articles/70970/elife-70970-fig3-data2-v3.zip
Figure 3—source data 3

Estimated overall hCFR by week of COVID-19 admission.

https://cdn.elifesciences.org/articles/70970/elife-70970-fig3-data3-v3.zip
Figure 3—source data 4

Estimated mean time from COVID-19 admission to outcome by week of COVID-19 admission.

https://cdn.elifesciences.org/articles/70970/elife-70970-fig3-data4-v3.zip
Figure 3—source data 5

Estimate and 95 % confidence interval for hCFR by week of COVID-19 admission, according to ICU/HDU admission status and age group.

https://cdn.elifesciences.org/articles/70970/elife-70970-fig3-data5-v3.zip
Figure 3—source data 6

Estimate and 95 % confidence interval for time from COVID-19 admission to outcome by week of COVID-19 admission, according to ICU/HDU admission status and outcome, and age group.

https://cdn.elifesciences.org/articles/70970/elife-70970-fig3-data6-v3.zip
Figure 3—figure supplement 1
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Temporal trends in case fatality rate amongst all patients.
Figure 3—figure supplement 2
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Temporal trends in mean time from COVID-19 admission to final outcome (death or discharge).
Figure 3—figure supplement 3
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Temporal trends in case fatality rate, faceted by ICU/HDU admission and further separated by age group.
Figure 3—figure supplement 4
Download asset Open asset
Temporal trends in mean time from COVID-19 admission to final outcome, faceted by outcome and ICU/HDU admission and further separated by age group.
Figure 4
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Regression model predictions for hospital CFR (A), predicted time to death in fatal cases (B) and predicted time to discharge in non-fatal cases (C) in a set of hypothetical typical patients.

Lines are plotted by month of COVID-19 admission (y-axis), age group (facets, left to right), sex (red: female, blue: male), and ICU admission (solid lines: at least once, dotted lines: never). The inset table (D) lists the comorbidities assigned to the individuals in each combination of sex and age group.

Figure 4—source data 1

Predicted hCFR, time to death and time to discharge for all hypothetical patients.

https://cdn.elifesciences.org/articles/70970/elife-70970-fig4-data1-v3.zip
Figure 5 with 1 supplement
Download asset Open asset
Sankey diagrams depicting the progress through the inpatient journey for patients with COVID-19 admission in April, June, August and October 2020, and subdivided by age.

Bars are presented for the day of admission (A), 3 and 7 days later (A + 3 and A + 7), and the day after final outcome (O + 1).

Figure 5—source data 1

Number of patients occupying a ward bed, occupying an ICU/HDU bed, dead, discharged and with unknown outcome on the day of admission (A), 3 and 7 days later (A + 3 and A + 7), and the day after final outcome (O + 1), by age group and month of COVID-19 admission.

https://cdn.elifesciences.org/articles/70970/elife-70970-fig5-data1-v3.zip
Figure 5—figure supplement 1
Download asset Open asset
Expanded version of Figure 5, showing Sankey diagrams for all months.

Tables

Table 1
Baseline characteristics of the included patients.

*Some patients admitted in early 2021 are included in order to fully represent patients with symptom onset in December 2020.

VariableValueCount%
Month of admissionMarch27,10819.4
 April42,26730.3
 May12,3118.82
 June5,3423.83
 July2,8112.01
 August2,2181.59
 September5,2653.77
 October13,8229.91
 November15,15510.9
 December13,2059.47
SexFemale59,71942.8
 Male79,55057
 Unknown2350.168
Age group0–192,6971.93
 20–399,3026.67
 40–5930,39921.8
 60–6922,81516.4
 70–7929,90121.4
 80+41,57129.8
 Unknown2,8192.02
Symptom onset post-admissionNo118,87485.2
 Yes11,6958.38
 Unknown8,9356.4
Table 2
Prevalence of symptoms at hospital admission and comorbidities.

The final column gives the number of times the condition is recorded as present over the number of times its presence or absence is recorded (i.e. the data is non-missing). Designated “common” symptoms are indicated with a (C); the number and percentages of patients presenting with combinations of these are separately presented.

Name% presentN (present)/n (data recorded)
Symptoms at admissionCough (C)66.6(87218/131002)
 Shortness of breath (C)64.4(89611/139244)
 Fever (C)63.4(84665/133494)
 Fatigue (C)44.7(52837/118184)
 Confusion24.9(31167/125123)
 Vomiting19.9(24577/123625)
 Myalgia18.8(20921/111419)
 Diarrhoea18.2(22375/123121)
 Headache12(13424/112069)
 Abdominal pain11.1(13294/120175)
 Ageusia8.8(6758/76396)
 Wheezing7.7(8846/115511)
 Anosmia6.8(5281/77751)
 Runny nose3.4(3704/108623)
 Ulcers2.2(2291/105394)
 Bleeding1.8(2093/119266)
 Rash1.5(1713/113636)
 Seizures1.5(1801/120755)
 Lymphadenopathy0.7(774/112245)
 Conjunctivitis0.5(553/113083)
 Ear pain0.5(484/94873)
Number of recorded ‘common’ symptoms (C)07.6(10836/142540)
 120.5(29257/142540)
 226.4(37681/142540)
 329(41359/142540)
 416.4(23407/142540)
ComorbiditiesHypertension47.6(50174/105433)
 Chronic cardiac disease29.7(38175/128374)
 Diabetes16.8(20037/119155)
 Chronic pulmonary disease16.5(22040/133662)
 Chronic kidney disease15.7(20894/133256)
 Obesity14.4(16624/115463)
 Asthma13.2(17656/133341)
 Dementia12.9(16404/127239)
 Smoking12.8(7299/57164)
 Chronic neurological disorder11.5(15248/132789)
 Rheumatological disorder11.2(13814/123453)
 Malignant neoplasm9.3(12343/132537)
 Chronic haemotologic disease4.1(5117/123739)
 Liver disease3.5(4443/128733)
 Malnutrition2.6(3094/119518)
 HIV/AIDS0.4(515/119235)
Table 3
Summary of the components of the inpatient journey and their variation over the course of 2020.

All time periods are in days. Patients are categorised by month of symptom onset for onset to admission, and by month of COVID admission in all other cases. Patients with COVID admission in 2021, who are included in the analysis of time from onset to admission if their onset date was in 2020, are not listed here as they are excluded from any analysis where the outcome variable is not time from onset to admission. “Outcome” is either death or discharge, and the ‘admission to outcome’ column gives the total length of hospital stay. For all durations, the top 2.5 % of values are excluded as potentially mis-entered.

MonthOnset to hospital admissionProportion entering ICU/HDUCOVID-19 admission to ICU/HDUhCFRCOVID-19 admission to deathCOVID-19 admission to dischargeCOVID-19 admission to outcome
 MeanSDMeanSDMeanSDMeanSDMeanSD
March6.825.150.251.782.340.33118.4110.99.7110.89.29
April4.274.530.161.632.360.339.17.9610.39.149.918.78
May4.094.580.171.452.450.3108.41119.6210.99.28
June4.44.510.31.042.120.27108.6510.58.8310.58.78
July4.774.220.351.12.290.21118.689.538.379.888.46
August5.494.60.361.412.490.22128.828.887.889.448.16
September6.35.010.241.382.210.22149.819.258.6410.29.08
October5.724.890.191.692.590.26128.699.788.7510.58.81
November5.174.750.181.482.490.26128.389.18.089.788.24
December4.424.210.221.512.480.291189.528.16108.15
Table 4
Combined results of a logistic regression analysis identifying predictors of death as an outcome, and two linear regression analyses identifying correlates of time to death and time to discharge.

All analyses are multivariable. For brevity, the country variable, as well as the ‘unknown’ class for each comorbidity (representing patients with missing data for that condition) are omitted here; see Supplementary file 7 for a version with them included. The p-values of Wald tests for the inclusion of each variable in each regression are included as a separate column; these were calculated including the ‘unknown’ class for comorbidities.

Odds ratio (death v discharge)Time to death (% change, days)Time to discharge (% change, days)
Estimate95 % confidence intervalWald test p-valueEstimate95 % confidence intervalWald test p-valueEstimate95 % confidence intervalWald test p-value
Month of COVID admission (ref: April)< 0.001< 0.001< 0.001
March1.1(1.1, 1.2)14.7(12.2, 17.3)3.7(2.1, 5.2)
May0.7(0.7, 0.8)15.5(12.1, 19.1)1.3(–0.6, 3.3)
June0.5(0.5, 0.6)20.8(14.2, 27.7)–2.8(−5.6,–0.02)
July0.3(0.3, 0.4)28.1(14.9, 42.8)–8.5(−12.2,–4.7)
August0.4(0.3, 0.5)47.2(29.5, 67.3)–10.8(−15.0,–6.4)
September0.6(0.5, 0.6)40.7(32.6, 49.3)–3.2(−5.8,–0.5)
October0.6(0.6, 0.7)33.2(28.9, 37.7)–1.4(–3.2, 0.4)
November0.6(0.6, 0.7)26.7(22.8, 30.8)–3.5(−5.2,–1.8)
December0.8(0.8, 0.9)26.3(22.3, 30.4)2.2(0.2, 4.2)
Age group (ref: 40–59)< 0.001< 0.001< 0.001
10–190.3(0.2, 0.4)–0.5(–24.3, 30.6)–33.6(−35.7,–31.4)
20–390.3(0.2, 0.3)–1.7(–15.9, 14.8)–16.9(−18.5,–15.3)
60–692.9(2.7, 3.2)–2.1(–6.8, 3.0)17.4(15.6, 19.2)
70–796.1(5.7, 6.6)1.9(–2.6, 6.6)36.4(34.3, 38.6)
80+10.9(10.1, 11.8)4.3(–0.3, 9.0)52.9(50.4, 55.4)
ICU/HDU admission7.6(6.8, 8.4)< 0.00168.3(59.1, 78.0)< 0.001140.1(133.0, 147.3)< 0.001
Sex (ref: female)< 0.001< 0.0010.0059
Male1.3(1.3, 1.4)2.9(1.4, 4.5)1.2(0.2, 2.1)
Pregnant (ref: female, no)0.0970.069< 0.001
Female, yes0.6(0.4, 1.0)–5.4(–27.4, 23.3)–18.6(−22.3,–14.6)
Days from symptom onset to hospital admission (ref: 0–6)< 0.001< 0.001< 0.001
Symptom onset post-admission1.3(1.2, 1.3)20.7(17.9, 23.5)45.7(42.9, 48.6)
7–130.7(0.7, 0.8)–1.7(–3.5, 0.1)–3.6(−4.6,–2.5)
14+0.7(0.7, 0.8)–0.2(–2.9, 2.5)–6.1(−7.6,–4.5)
Comorbidities
Asthma0.9(0.9, 1.0)< 0.0012.5(0.2, 4.9)0.11–1.6(−2.9,–0.3)0.048
Chronic cardiac disease1.2(1.2, 1.3)< 0.001–2.8(−4.4,–1.3)< 0.0011.3(0.1, 2.6)0.075
Chronic haemotologic disease1.2(1.1, 1.3)< 0.0010.2(–3.1, 3.6)0.625.8(3.1, 8.6)< 0.001
Chronic kidney disease1.4(1.4, 1.5)< 0.001–2.4(−4.1,–0.6)0.0125.8(4.2, 7.4)< 0.001
Chronic neurological disorder1.4(1.3, 1.4)< 0.001–0.7(–2.7, 1.4)0.6115.3(13.4, 17.2)< 0.001
Chronic pulmonary disease1.4(1.3, 1.4)< 0.001–2.2(−3.9,–0.5)0.0343.9(2.4, 5.3)< 0.001
Dementia1.5(1.4, 1.6)< 0.001–1.2(–3.1, 0.8)0.439(7.0, 11.0)< 0.001
Diabetes1.1(1.1, 1.2)< 0.001–4.2(−6.0,–2.3)< 0.0011.8(0.4, 3.2)< 0.001
HIV/AIDS1.2(1.0, 1.6)0.0054(–8.2, 17.8)0.582.2(–5.1, 10.1)0.0025
Hypertension1(0.9, 1.0)< 0.0010.2(–1.5, 1.9)0.142(0.8, 3.2)< 0.001
Liver disease1.4(1.3, 1.6)< 0.0015.2(1.1, 9.5)< 0.00112(9.0, 15.1)< 0.001
Malignant neoplasm1.4(1.4, 1.5)< 0.0013.5(1.2, 5.8)0.00592.2(0.3, 4.1)< 0.001
Malnutrition1.3(1.2, 1.5)< 0.0010.6(–3.6, 5.0)0.8111.5(7.5, 15.7)< 0.001
Obesity1.1(1.1, 1.2)< 0.001–5.5(−7.7,–3.2)< 0.0016.3(4.8, 7.9)< 0.001
Rheumatological disorder1(0.9, 1.0)0.0452.3(0.1, 4.7)0.0520.6(–1.0, 2.2)0.44
Smoking1.1(1.1, 1.2)< 0.0014.2(0.5, 8.0)0.12.2(0.04, 4.4)0.034
Interaction: ICU/HDU admission _ month of admission (ref: April)< 0.001< 0.001< 0.001
March1(0.9, 1.1)–14.3(−18.1,–10.4)0.6(–3.1, 4.5)
May1.1(0.9, 1.3)–12(−17.6,–6.1)-9(−13.9,–3.9)
June1.2(1.0, 1.5)–9.1(−16.8,–0.8)–11.3(−17.1,–5.1)
July1.4(1.1, 1.9)–14.8(−25.8,–2.1)–18.8(−25.2,–11.8)
August1.1(0.8, 1.6)–27.9(−38.4,–15.6)–15.6(−23.2,–7.3)
September1.1(0.9, 1.4)–16.6(−24.1,–8.4)–6.9(−13.0,–0.3)
October1.1(1.0, 1.3)–13.7(−18.9,–8.2)–11.6(−15.9,–7.0)
November1.1(1.0, 1.3)–6.6(−12.1,–0.7)–15.9(−20.0,–11.6)
December0.8(0.7, 0.9)–11.6(−17.0,–5.8)–27.9(−31.4,–24.2)
Interaction: ICU/HDU admission _ age group (ref: 40–59)< 0.001< 0.001< 0.00
10–190.8(0.4, 1.4)–42.8(−59.3,–19.6)1.7(–6.0, 10.2)
20–391.8(1.4, 2.4)–11.8(–25.9, 4.9)0.8(–3.6, 5.4)
60–690.7(0.7, 0.8)2.5(–3.7, 9.0)–7.2(−10.4,–3.9)
70–790.6(0.5, 0.7)–11.8(−16.8,–6.6)–21.8(−24.9,–18.5)
80+0.4(0.3, 0.5)–28.4(−32.9,–23.6)–40.1(−43.6,–36.4)
Observations102,14731,25070,897

Additional files

Supplementary file 1

Description of variables used in regression analyses.

https://cdn.elifesciences.org/articles/70970/elife-70970-supp1-v3.docx
Supplementary file 2

Extended demographics table for the complete dataset, by month of COVID-19 admission and overall.

Numbers are raw counts with column-wise percentages in brackets.

https://cdn.elifesciences.org/articles/70970/elife-70970-supp2-v3.zip
Supplementary file 3

Prevalence of symptoms at admission amongst individuals with no reported “common” symptoms (cough, fatigue, fever or shortness of breath), by age group.

Numbers are percentages with fractions in brackets.

https://cdn.elifesciences.org/articles/70970/elife-70970-supp3-v3.zip
Supplementary file 4

Full results of the multivariable linear regression analysis identifying correlates of time from symptom onset to hospital admission (question 1).

Given are percentage predicted increased in time to admission (in days), 95 % confidence intervals, and the p-values of Wald tests for the inclusion of each variable in each regression.

https://cdn.elifesciences.org/articles/70970/elife-70970-supp4-v3.zip
Supplementary file 5

Full results of the multivariable logistic regression analysis identifying predictors of ICU/HDU admission (question 2).

The p-values of Wald tests for the inclusion of each variable in each regression are included as a separate column. Given are odds ratios for admission, 95 % confidence intervals, and the p-values of Wald tests for the inclusion of each variable in each regression.

https://cdn.elifesciences.org/articles/70970/elife-70970-supp5-v3.zip
Supplementary file 6

Full results of the multivariable logistic regression analysis identifying correlates of time to ICU/HDU admission amongst patients so admitted (question 3).

Given are percentage predicted increased in time to admission (in days), 95 % confidence intervals, and the p-values of Wald tests for the inclusion of each variable in each regression.

https://cdn.elifesciences.org/articles/70970/elife-70970-supp6-v3.zip
Supplementary file 7

Extended version of Table 4, including the country variable and the “unknown” class for comorbidities.

https://cdn.elifesciences.org/articles/70970/elife-70970-supp7-v3.zip
Supplementary file 8

Full details for the ISARIC Clinical Characterisation Group.

https://cdn.elifesciences.org/articles/70970/elife-70970-supp8-v3.docx
Transparent reporting form
https://cdn.elifesciences.org/articles/70970/elife-70970-transrepform1-v3.docx
Reporting standard 1

Strobe statement.

https://cdn.elifesciences.org/articles/70970/elife-70970-repstand1-v3.docx

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  1. ISARIC Clinical Characterisation Group
  2. Matthew D Hall
  3. Joaquín Baruch
  4. Gail Carson
  5. Barbara Wanjiru Citarella
  6. Andrew Dagens
  7. Emmanuelle A Dankwa
  8. Christl A Donnelly
  9. Jake Dunning
  10. Martina Escher
  11. Christiana Kartsonaki
  12. Laura Merson
  13. Mark Pritchard
  14. Jia Wei
  15. Peter W Horby
  16. Amanda Rojek
  17. Piero L Olliaro
(2021)
Ten months of temporal variation in the clinical journey of hospitalised patients with COVID-19: An observational cohort
eLife 10:e70970.
https://doi.org/10.7554/eLife.70970