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BackgroundUpdatable understanding of the onset and progression of individuals COVID-19 trajectories underpins pandemic mitigation efforts. In order to identify and characterize individual trajectories, we defined and validated ten COVID-19 phenotypes from linked electronic health records (EHR) on a nationwide scale using an extensible framework. MethodsCohort study of 56.6 million people in England alive on 23/01/2020, followed until 31/05/2021, using eight linked national datasets spanning COVID-19 testing, vaccination, primary & secondary care and death registrations data. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity using a combination of international clinical terminologies (e.g. SNOMED-CT, ICD-10) and bespoke data fields; positive test, primary care diagnosis, hospitalisation, critical care (four phenotypes), and death (three phenotypes). Using these phenotypes, we constructed patient trajectories illustrating the transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FindingsWe identified 3,469,528 infected individuals (6.1%) with 8,825,738 recorded COVID-19 phenotypes. Of these, 364,260 (11%) were hospitalised and 140,908 (4%) died. Of those hospitalised, 38,072 (10%) were admitted to intensive care (ICU), 54,026 (15%) received non-invasive ventilation and 21,404 (6%) invasive ventilation. Amongst hospitalised patients, first wave mortality (30%) was higher than the second (23%) in non-ICU settings, but remained unchanged for ICU patients. The highest mortality was for patients receiving critical care outside of ICU in wave 1 (51%). 13,083 (9%) COVID-19 related deaths occurred without diagnoses on the death certificate, but within 30 days of a positive test while 10,403 (7%) of cases were identified from mortality data alone with no prior phenotypes recorded. We observed longer patient trajectories in the second pandemic wave compared to the first. InterpretationOur analyses illustrate the wide spectrum of severity that COVID-19 displays and significant differences in incidence, survival and pathways across pandemic waves. We provide an adaptable framework to answer questions of clinical and policy relevance; new variant impact, booster dose efficacy and a way of maximising existing data to understand individuals progression through disease states. Research in ContextO_ST_ABSEvidence before the studyC_ST_ABSWe searched PubMed on October 14, 2021, for publications with the terms "COVID-19" or "SARS-CoV-2", "severity", and "electronic health records" or "EHR" without date or language restrictions. Multiple studies explore factors associated with severity of COVID-19 infection, and model predictions of outcome for hospitalised patients. However, most work to date focused on isolated facets of the healthcare system, such as primary or secondary care only, was conducted in subpopulations (e.g. hospitalised patients) of limited sample size, and often utilized dichotomised outcomes (e.g. mortality or hospitalisation) ignoring the full spectrum of disease. We identified no studies which comprehensively detailed severity of infections while describing disease severity across pandemic waves, vaccination status, and patient trajectories. Added value of this studyTo our knowledge, this is the first study providing a comprehensive view of COVID-19 across pandemic waves using national data and focusing on severity, vaccination, and patient trajectories. Drawing on linked electronic health record (EHR) data on a national scale (56.6 million people alive and registered with GP in England), we describe key demographic factors, frequency of comorbidities, impact of the two main waves in England, and effect of full vaccination on COVID-19 severities. Additionally, we identify and describe patient trajectory networks which illustrate the main transition pathways of COVID-19 patients in the healthcare system. Finally, we provide reproducible COVID-19 phenotyping algorithms reflecting clinically relevant stages of disease severity i.e. positive tests, primary care diagnoses, hospitalisation, critical care treatments (e.g. ventilatory support) and mortality. Implications of all the available evidenceThe COVID-19 phenotypes and trajectory analysis framework outlined produce a reproducible, extensible and repurposable means to generate national-scale data to support critical policy decision making. By modelling patient trajectories as a series of interactions with healthcare systems, and linking these to demographic and outcome data, we provide a means to identify and prioritise care pathways associated with adverse outcomes and highlight healthcare system touch points which may act as tangible targets for intervention.
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ObjectivesTo determine if there is an association between survival rates in intensive care units (ICU) and occupancy of the unit on the day of admission. DesignNational retrospective observational cohort study during the COVID-19 pandemic. Setting90 English hospital trusts (i.e. groups of hospitals functioning as single operational units). Participants6,686 adults admitted to an ICU in England between 2nd April and 1st December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. InterventionsN/A Main Outcomes and MeasuresA Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible) bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease). Results121,151 patient-days were observed, with a mortality rate of 20.8 per 1,000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (>85% occupancy versus the baseline of 45 to 85%) [OR 1.18 (95% posterior credible interval (PCI): 1.00 to 1.38)]. In contrast, mortality was decreased for admissions during periods of low occupancy (<45% relative to the baseline) [OR 0.79 (95% PCI: 0.69 to 0.90)]. Conclusion and RelevanceIncreasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU. Public health interventions (such as expeditious vaccination programmes and non-pharmaceutical interventions) to control both incidence and prevalence of COVID-19, and therefore keep ICU occupancy low in the context of the pandemic, are necessary to mitigate the impact of this type of resource saturation. O_TEXTBOXSummary Box What is already known on this topicPre-pandemic, higher occupancy of intensive care units was shown to be associated with increased mortality risk. However, there is limited data on the extent to which occupancy levels impacted patient outcomes during the COVID-19 pandemic, especially in light of the mobilisation of significant additional resources. A recent study from Belgium reported a 42% higher mortality during periods of ICU surge capacity deployment, although in the analysis surge capacity was evaluated only as a binary variable, and notably this contradicts earlier results from smaller studies in Australia and Wales, where no association between ICU occupancy and mortality was identified. What this study addsThe results of this study suggest that survival rates for patients with COVID-19 in intensive care settings appears to deteriorate as the occupancy of (surge capacity) beds compatible with mechanical ventilation (a proxy for operational pressure), increases. Moreover, this risk doesnt occur above a specific threshold, but rather appears linear; whereby going from 0% occupancy to 100% occupancy increases risk of mortality by 69% (after adjusting for relevant individual-level factors). Furthermore, risk of mortality based on occupancy on the date of recorded outcome is even higher; OR 2.98 (95% posterior credible interval: 2.33 - 3.83). As such, this national-level cohort study of England provides compelling evidence for a relationship between occupancy and critical care mortality, and highlights the needs for decisive action to control the incidence and prevalence of COVID-19. C_TEXTBOX
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ObjectivesTo determine the trend in mortality risk over time in people with severe COVID-19 requiring critical care (high intensive unit [HDU] or intensive care unit [ICU]) management. MethodsWe accessed national English data on all adult COVID-19 specific critical care admissions from the COVID-19 Hospitalisation in England Surveillance System (CHESS), up to the 29th June 2020 (n=14,958). The study period was 1st March until 30th May, meaning every patient had 30 days of potential follow-up available. The primary outcome was in-hospital 30-day all-cause mortality. Hazard ratios for mortality were estimated for those admitted each week using a Cox proportional hazards models, adjusting for age (non-linear restricted cubic spline), sex, ethnicity, comorbidities, and geographical region. Results30-day mortality peaked for people admitted to critical care in early April (peak 29.1% for HDU, 41.5% for ICU). There was subsequently a sustained decrease in mortality risk until the end of the study period. As a linear trend from the first week of April, adjusted mortality risk decreased by 11.2% (adjusted HR 0.89 [95% CI 0.87 - 0.91]) per week in HDU, and 9.0% (adjusted HR 0.91 [95% CI 0.88 - 0.94]) in ICU. ConclusionsThere 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 compared to 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 clinical trials and a falling critical care burden.
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BackgroundNon-pharmacological interventions were introduced based on modelling studies which suggested that the English National Health Service (NHS) would be overwhelmed by the COVID-19 pandemic. In this study, we describe the pattern of bed occupancy across England during the first wave of the pandemic, January 31st to June 5th 2020. MethodsBed availability and occupancy data was extracted from daily reports submitted by all English secondary care providers, between 27-Mar and 5-June. Two thresholds for safe occupancy were utilized (85% as per Royal College of Emergency Medicine and 92% as per NHS Improvement). FindingsAt peak availability, there were 2711 additional beds compatible with mechanical ventilation across England, reflecting a 53% increase in capacity, and occupancy never exceeded 62%. A consequence of the repurposing of beds meant that at the trough, there were 8{middle dot}7% (8,508) fewer general and acute (G&A) beds across England, but occupancy never exceeded 72%. The closest to (surge) capacity that any trust in England reached was 99{middle dot}8% for general and acute beds. For beds compatible with mechanical ventilation there were 326 trust-days (3{middle dot}7%) spent above 85% of surge capacity, and 154 trust-days (1{middle dot}8%) spent above 92%. 23 trusts spent a cumulative 81 days at 100% saturation of their surge ventilator bed capacity (median number of days per trust = 1 [range: 1 to 17]). However, only 3 STPs (aggregates of geographically co-located trusts) reached 100% saturation of their mechanical ventilation beds. InterpretationThroughout the first wave of the pandemic, an adequate supply of all bed-types existed at a national level. Due to an unequal distribution of bed utilization, many trusts spent a significant period operating above safe-occupancy thresholds, despite substantial capacity in geographically co-located trusts; a key operational issue to address in preparing for a potential second wave. FundingThis study received no funding. Research In ContextO_ST_ABSEvidence Before This StudyC_ST_ABSWe identified information sources describing COVID-19 related bed and mechanical ventilator demand modelling, as well as bed occupancy during the first wave of the pandemic by performing regular searches of MedRxiv, PubMed and Google, using the terms COVID-19, mechanical ventilators, bed occupancy, England, UK, demand, and non-pharmacological interventions (NPIs), until June 20th, 2020. Two UK-specific studies were found that modelled the demand for mechanical ventilators, one of which incorporated sensitivity analysis based on the introduction of NPIs and found that their effects might prevent the healthcare system being overwhelmed. Separately, several news reports were found pertaining to a single hospital that reached ventilator capacity in England during the first wave of the pandemic, however, no single authoritative source was identified detailing impact across all hospital sites in England. Added Value of This StudyThis national study of hospital-level bed occupancy in England provides unique and timely insight into bed-specific resource utilization during the first wave of the COVID-19 pandemic, nationally, and by specific (geographically defined) health footprints. We found evidence of an unequal distribution of resource utilization across England. Although occupancy of beds compatible with mechanical ventilation never exceeded 62% at the national level, 52 (30%) hospitals across England reached 100% saturation at some point during the first wave of the pandemic. Close examination of the geospatial data revealed that in the vast majority of circumstances there was relief capacity in geographically co-located hospitals. Over the first wave it was theoretically possible to markedly reduce (by 95.1%) the number of hospitals at 100% saturation of their mechanical ventilator bed capacity by redistributing patients to nearby hospitals. Implications Of All The Available EvidenceNow-casting using routinely collected administrative data presents a robust approach to rapidly evaluate the effectiveness of national policies introduced to prevent a healthcare system being overwhelmed in the context of a pandemic illness. Early investment in operational field hospital and an independent sector network may yield more overtly positive results in the winter, when G&A occupancy-levels regularly exceed 92% in England, however, during the first wave of the pandemic they were under-utilized. Moreover, in the context of the non-pharmacological interventions utilized during the first wave of COVID-19, demand for beds and mechanical ventilators was much lower than initially predicted, but despite this many trust spent a significant period of time operating above safe-occupancy thresholds. This finding demonstrates that it is vital that future demand (prediction) models reflect the nuances of local variation within a healthcare system. Failure to incorporate such geographical variation can misrepresent the likelihood of surpassing availability thresholds by averaging out over regions with relatively lower demand, and presents a key operational issue for policymakers to address in preparing for a potential second wave.
