Improving Survival of Critical Care Patients With Coronavirus Disease 2019 in England: A National Cohort Study, March to June 2020*
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Abstract
To measure temporal trends in survival over time in people with severe coronavirus disease 2019 requiring critical care (high dependency unit or ICU) management, and to assess whether temporal variation in mortality was explained by changes in patient demographics and comorbidity burden over time.
Design:
Retrospective observational cohort; based on data reported to the COVID-19 Hospitalisation in England Surveillance System. The primary outcome was in-hospital 30-day all-cause mortality. Unadjusted survival was estimated by calendar week of admission, and Cox proportional hazards models were used to estimate adjusted survival, controlling for age, sex, ethnicity, major comorbidities, and geographical region.
Setting:
One hundred eight English critical care units.
Patients:
All adult (18 yr +) coronavirus disease 2019 specific critical care admissions between March 1, 2020, and June 27, 2020.
Interventions:
Not applicable.
Measurements and Main Results:
Twenty-one thousand eighty-two critical care patients (high dependency unit n = 15,367; ICU n = 5,715) were included. Unadjusted survival at 30 days was lowest for people admitted in late March in both high dependency unit (71.6% survival) and ICU (58.0% survival). By the end of June, survival had improved to 92.7% in high dependency unit and 80.4% in ICU. Improvements in survival remained after adjustment for patient characteristics (age, sex, ethnicity, and major comorbidities) and geographical region.
Conclusions:
There has been a substantial improvement in survival amongst people admitted to critical care with coronavirus disease 2019 in England, with markedly higher survival rates in people admitted in May and June compared with those admitted in March and April. Our analysis suggests this improvement is not due to temporal changes in the age, sex, ethnicity, or major comorbidity burden of admitted patients.
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SciScore for 10.1101/2020.07.30.20165134: (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 People aged 18-99 were eligible, pregnant women (n=88) were excluded. 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:A potential limitation of this analysis is the possibility of reporting delays leading to incomplete ascertainment of death in more recent weeks, although this was …
SciScore for 10.1101/2020.07.30.20165134: (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 People aged 18-99 were eligible, pregnant women (n=88) were excluded. 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:A potential limitation of this analysis is the possibility of reporting delays leading to incomplete ascertainment of death in more recent weeks, although this was mitigated by our study design which focused on in-hospital mortality and ensured all eligible patients had at least 30 days follow-up available. Whilst reporting delays might plausibly lead to under-ascertainment of mortality for people admitted in May, a clear mortality improvement was also observed in April. As CHESS is a daily mandatory collection for all hospitals in England we would expect reasonably accurate and timely capture of in-hospital mortality, and so feel it is unlikely our findings simply reflect reporting delays. Temporal changes in COVID-19 disease severity at admission, patient selection for critical care management, critical care treatment, hospital capacity, and COVID-19 testing, all offer potential explanations for our findings. Of particular note regarding treatment, the RECOVERY trial began nationwide recruitment in early April, and included interventions later shown to have mortality-specific, or length of ITU admission-specific benefits;[7] it is plausible recruitment to the trial might partially explain improved patient outcomes. Regarding capacity, it has been shown that bed saturation across England was at its highest in early April, and then progressively improved over the course of the 1st wave of the pandemic.[8] Therefore, the observed time trend could be a manifestation of the well...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
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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SciScore for 10.1101/2020.07.30.20165134: (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 People aged 18-99 were e pregnant women (n=88) were excluded. 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:
Discussion Our analysis, using the largest available COVID-19 specific national critical care database, shows a subst recent improvement in mortality for people admitted to …
SciScore for 10.1101/2020.07.30.20165134: (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 People aged 18-99 were e pregnant women (n=88) were excluded. 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:
Discussion Our analysis, using the largest available COVID-19 specific national critical care database, shows a subst recent improvement in mortality for people admitted to critical care with COVID-19 in England, with ma admitted to ITU for COVID-19 related illnesses.[6] The combination of these two results suggest that fur study is warranted to understand differences in mortality by hospital and geographical region across Engl A potential limitation of this analysis is the possibility of reporting delays leading to incomplete ascertain of death in more recent weeks, although this was mitigated by our study design which focused on in-hosp mortality and ensured all eligible patients had at least 30 days follow-up available. Whilst reporting delay might plausibly lead to under-ascertainment of mortality for people admitted in May, a clear mortality improvement was also observed in April. As CHESS is a daily mandatory collection for all hospitals in E we would expect reasonably accurate and timely capture of in-hospital mortality, and so feel it is unlikely findings simply reflect reporting delays. Temporal changes in COVID-19 disease severity at admission, patient selection for critical care managem critical care treatment, hospital capacity, and COVID-19 testing, all offer potential explanations for our findings. Of particular note regarding treatment, the RECOVERY trial began nationwide recruitment in e April, and included interventions later shown to have mortality-specific, or length of ITU admission-spec benefits;[7] it is plausible recruitment to the trial might partially explain improved patient outcomes. Reg capacity, it has been shown that bed saturation across England was at its highest in early April, and then progressively improved over the course of the 1st wave of the pandemic.[8] Therefore, the observed time could be a manifestation of the well established decline in patient-specific outcomes often observed at ‘u occupancy levels.[9] Further interrogation of possible quality-of-care based explanations is required. In conclusion, there has been a substantial mortality improvement in people admitted to critical care with COVID-19 in England, with markedly lower mortality in people admitted in mid-April and May compare earlier in the pandemic. This trend remains after adjustment for patient demographics and comorbidities suggesting this improvement is not due to changing patient characteristics. Possible causes include the introduction of effective treatments as part of the RECOVERY trial and a falling critical care burden.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
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