Post-covid syndrome in individuals admitted to hospital with covid-19: retrospective cohort study
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Abstract
Objective
To quantify rates of organ specific dysfunction in individuals with covid-19 after discharge from hospital compared with a matched control group from the general population.
Design
Retrospective cohort study.
Setting
NHS hospitals in England.
Participants
47 780 individuals (mean age 65, 55% men) in hospital with covid-19 and discharged alive by 31 August 2020, exactly matched to controls from a pool of about 50 million people in England for personal and clinical characteristics from 10 years of electronic health records.
Main outcome measures
Rates of hospital readmission (or any admission for controls), all cause mortality, and diagnoses of respiratory, cardiovascular, metabolic, kidney, and liver diseases until 30 September 2020. Variations in rate ratios by age, sex, and ethnicity.
Results
Over a mean follow-up of 140 days, nearly a third of individuals who were discharged from hospital after acute covid-19 were readmitted (14 060 of 47 780) and more than 1 in 10 (5875) died after discharge, with these events occurring at rates four and eight times greater, respectively, than in the matched control group. Rates of respiratory disease (P<0.001), diabetes (P<0.001), and cardiovascular disease (P<0.001) were also significantly raised in patients with covid-19, with 770 (95% confidence interval 758 to 783), 127 (122 to 132), and 126 (121 to 131) diagnoses per 1000 person years, respectively. Rate ratios were greater for individuals aged less than 70 than for those aged 70 or older, and in ethnic minority groups compared with the white population, with the largest differences seen for respiratory disease (10.5 (95% confidence interval 9.7 to 11.4) for age less than 70 years v 4.6 (4.3 to 4.8) for age ≥70, and 11.4 (9.8 to 13.3) for non-white v 5.2 (5.0 to 5.5) for white individuals).
Conclusions
Individuals discharged from hospital after covid-19 had increased rates of multiorgan dysfunction compared with the expected risk in the general population. The increase in risk was not confined to the elderly and was not uniform across ethnicities. The diagnosis, treatment, and prevention of post-covid syndrome requires integrated rather than organ or disease specific approaches, and urgent research is needed to establish the risk factors.
Article activity feed
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SciScore for 10.1101/2021.01.15.21249885: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and limitations: The main strength of our study lies in its size and completeness, as it includes all individuals in England in hospital with COVID-19 observed over a follow-up period of up to several months. Use of a …
SciScore for 10.1101/2021.01.15.21249885: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and limitations: The main strength of our study lies in its size and completeness, as it includes all individuals in England in hospital with COVID-19 observed over a follow-up period of up to several months. Use of a matched control group allowed rates of post-discharge adverse events in individuals with COVID-19 to be compared against counterfactual outcomes – what might have been observed given the background risk in these individuals. Like all observational studies, we cannot rule out the possibility of residual confounding (for example, due to biomarkers or socio-economic exposures omitted from our matching set) which precludes our ability to draw definitive causal conclusions. The limited number of events in the control group meant we were unable to disaggregate rate ratios stratified by age and ethnicity beyond broad ‘<70 years verses ≥ 70 years’ and ‘White versus Non-White’ comparisons, despite the likelihood of heterogeneity in outcomes within these groups. The threshold for hospital admission may be lower among individuals with a recent history of COVID-19 than in the general population, and rates of diagnoses in general may have decreased as an indirect result of the pandemic, particularly among people who were not admitted to hospital with COVID-19. We did not have access to testing data and thus were unable to remove individuals infected with COVID-19 who did not require hospitalisation from our control group. Moreover, our results are unlikely to fully...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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Our take
In this study, available as a preprint and thus not yet peer reviewed, of patients hospitalized with COVID-19 in England (N=47,780), 30% were readmitted, 21% were diagnosed with new-onset respiratory disease, and 12% died following discharge; rates much higher than in a matched control population. Organ impairment, including new-onset of cardiovascular, metabolic, kidney, and liver complications were also more common in hospitalized COVID-19 cases than controls. Because there is heightened attention to post-discharge outcomes of COVID-19 patients, these events may have been over-reported, but regardless, findings are striking and warrant further consideration.
Study design
retrospective-cohort
Study population and setting
This study detailed a matched, retrospective cohort study using national …
Our take
In this study, available as a preprint and thus not yet peer reviewed, of patients hospitalized with COVID-19 in England (N=47,780), 30% were readmitted, 21% were diagnosed with new-onset respiratory disease, and 12% died following discharge; rates much higher than in a matched control population. Organ impairment, including new-onset of cardiovascular, metabolic, kidney, and liver complications were also more common in hospitalized COVID-19 cases than controls. Because there is heightened attention to post-discharge outcomes of COVID-19 patients, these events may have been over-reported, but regardless, findings are striking and warrant further consideration.
Study design
retrospective-cohort
Study population and setting
This study detailed a matched, retrospective cohort study using national electronic health records and death registration data in England, including 47,780 hospitalized COVID-19 cases (10% in ICU, mean age: 64.5, 55% male) matched 1:1 to hospitalized non-COVID-19 controls on age group, sex, ethnicity, region of residence, index of multiple deprivation quintile, smoking status, BMI group, and chronic disease history (hypertension, major adverse cardiovascular event, respiratory disease, chronic kidney disease, chronic liver disease, diabetes, and cancer). Laboratory-confirmed or clinically-diagnosed COVID-19 cases who were discharged alive from their first hospital episode between January 1 and August 31, 2020 were identified from the Hospital Episode Statistics Admitted Patient Care records in England. Controls were identified from the General Practice Extraction Service Data for Pandemic Planning and Research (GDPPR) dataset, as individuals with at least one GDPPR record from January 1 2019 to September 30, 2020, who did not meet the COVID-19 case definition, and who were alive on the COVID-19 case’s index date. The index date was defined as the date of discharge following first hospitalization with COVID-19, and individuals were followed from that date until September 30, 2020 or date of death. Outcomes of interest included death, readmission, or any diagnoses of severe organ impairment (respiratory, cardiovascular, metabolic, kidney, and liver diseases) following hospital discharge.
Summary of main findings
Follow-up times were relatively similar between cases and matched-controls with mean follow-up of 140 days (SD: 50 days) and 153 days (SD: 33 days), respectively. Among the 47,780 hospitalized COVID-19 patients, 29.4% (766 per 1000 person-years (PY)) were readmitted and 12.3% (320 per 1000 PY) died following discharge, which were 3.5 and 7.7 times higher than rates in matched controls, respectively. Among COVID-19 cases, post-discharge new-onset of respiratory disease (21.5%, 538.9 per 1000 PY), major adverse cardiovascular events (MACE: 2.6%, 65.9 per 1000 PY), diabetes (1.1%, 28.7 per 1000 PY), chronic kidney disease (CKD: 0.6%, 14.6 per 1000 PY), and chronic liver disease (CLD: 0.2%, 4 per 1000 PY) occurred at higher rates than controls, with rate ratios 27.3 (respiratory disease), 3.0 (MACE), 1.5 (diabetes), 1.9 (CKD), and 2.8 (CLD) times higher, respectively, than matched controls. The increased rates of long-term outcomes (death, readmission, respiratory disease, diabetes, MACE, CKD, CLD) in COVID-19 patients compared to matched controls were even more pronounced among non-white individuals and individuals <70 years, but relatively consistent by sex.
Study strengths
This was a very large study using national electronic health records and death registration data across England. The study used a matched cohort design, including a comparison group of non-COVID controls, who were matched on several factors that could be related to COVID-19 and post-discharge outcomes of interest. Outcomes of interest were presented as prevalence and rates, the latter of which accounts for differences in follow-up times between COVID-19 patients and matched controls.
Limitations
Hospitalized COVID-19 patients, who were discharged alive, but who had missing age or sex or those who could not be matched to a control were excluded from the analysis (9.2% of all eligible patients), which may have resulted in selection bias if the excluded participants were different than those included in terms of their risk of outcomes. With increased attention on long-term outcomes of COVID-19, post-discharge follow-up of severe COVID-19 cases may be systematically different than that of the matched controls, leading to a lower threshold for readmission or increased rates of post-discharge diagnoses, compared to the matched controls (detection bias, resulting in a bias away from the null or potential exaggeration of the association). The population included only individuals who were hospitalized for COVID-19 (or a control condition, which are not well described), so the results are unlikely generalizable to the population of COVID-19 cases who do not require hospitalization. This study focused solely on re-admission, mortality, and organ dysfunction, following hospitalization, rather than symptom longevity, which is of additional interest in COVID-19 research. The control group is somewhat poorly defined, limiting complete assessment of residual confounding and bias.
Value added
This is the largest known study of post-discharge follow-up among COVID-19 patients, using a matched control population for comparison.
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SciScore for 10.1101/2021.01.15.21249885: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable 47,780 individuals (mean age 65 years, 55% male) in hospital with COVID-19 and discharged alive by 31 August 2020, matched to controls on demographic and clinical characteristics. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
Strengths and limitations The main strength of our study …
SciScore for 10.1101/2021.01.15.21249885: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable 47,780 individuals (mean age 65 years, 55% male) in hospital with COVID-19 and discharged alive by 31 August 2020, matched to controls on demographic and clinical characteristics. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
Strengths and limitations The main strength of our study lies in its size and completeness, as it includes all individuals in England in hospital with COVID-19 observed over a follow-up period of up to several months. Use of a matched control group allowed rates of post-discharge adverse events in individuals with COVID-19 to be compared against counterfactual outcomes – what might have been observed given the background risk in these individuals. Like all observational studies, we cannot rule out the possibility of residual confounding (for example, due to biomarkers or socio-economic exposures omitted from our matching set) which precludes our ability to draw definitive causal conclusions. The limited number of events in the control group meant we were unable to disaggregate rate ratios stratified by age and ethnicity beyond broad ‘<70 years verses ≥70 years’ and ‘White versus Non-White’ comparisons, despite the likelihood of heterogeneity in outcomes within these groups. The threshold for hospital admission may be lower among individuals with a recent history of COVID-19 than in the general population, and rates of diagnoses in general may have decreased as an indirect result of the pandemic, particularly among people who were not admitted to hospital with COVID-19. We did not have access to testing data and thus were unable to remove individuals infected with COVID-19 who did not require hospitalisation from our control group. Moreover, our results are unlikely to fully c...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).
Results from JetFighter: We did not find any issues relating to colormaps.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.
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