Role of hospital strain in determining outcomes for people hospitalised with COVID-19 in England.

BACKGROUND: In England, reported COVID-19 mortality rates increased during winter 2020/21 relative to earlier summer and autumn months. This study aimed to examine the association between COVID-19-related hospital bed-strain during this time and patient outcomes. METHODS: This was a retrospective observational study using Hospital Episode Statistics data for England. All unique patients aged ≥18 years in England with a diagnosis of COVID-19 who had a completed (discharged alive or died in hospital) hospital stay with an admission date between 1 July 2020 and 28 February 2021 were included. Bed-strain was calculated as the number of beds occupied by patients with COVID-19 divided by the maximum COVID-19 bed occupancy during the study period. Bed-strain was categorised into quartiles for modelling. In-hospital mortality was the primary outcome of interest and length of stay a secondary outcome. RESULTS: There were 253 768 unique hospitalised patients with a diagnosis of COVID-19 during a hospital stay. Patient admissions peaked in January 2021 (n=89 047), although the crude mortality rate peaked slightly earlier in December 2020 (26.4%). After adjustment for covariates, the mortality rate in the lowest and highest quartile of bed-strain was 23.6% and 25.3%, respectively (OR 1.13, 95% CI 1.09 to 1.17). For the lowest and the highest quartile of bed-strain, adjusted mean length of stay was 13.2 days and 11.6 days, respectively in survivors and was 16.5 days and 12.6 days, respectively in patients who died in hospital. CONCLUSIONS: High levels of bed-strain were associated with higher in-hospital mortality rates, although the effect was relatively modest and may not fully explain increased mortality rates during winter 2020/21 compared with earlier months. Shorter hospital stay during periods of greater strain may partly reflect changes in patient management over time.

Safety of day-case endoscopic sinus surgery in England: An observational study using an administrative dataset.

INTRODUCTION: As elective surgical services recover from the COVID-19 pandemic a movement towards day-case surgery may reduce waiting lists. However, evidence is needed to show that day-case surgery is safe for endoscopic sinus surgery (ESS). The aim of this study was to investigate the safety of day-case ESS in England. DESIGN: Secondary analysis of administrative data. METHODS: We extracted data from the Hospital Episodes Statistics database for the 5 years from 1 April 2014 to 31 March 2019. Patients undergoing elective ESS procedures aged ≥17 years were included. Exclusion criteria included malignant neoplasm, complex systemic disease and trans-sphenoidal pituitary surgery. The primary outcome was readmission within 30 days post-discharge. Multilevel, multivariable logistic regression modelling was used to compare outcomes for those operated on as day-cases and those with an overnight stay after adjusting for demographic, frailty, comorbidity and procedural covariates. RESULTS: Data were available for 49 223 patients operated on across 129 NHS hospital trusts. In trusts operating on more than 50 patients in the study period, rates of day-case surgery varied from 20.6% to 100%. Nationally, rates of day-case surgery increased from 64.0% in the financial year 2014/2015 to 78.7% in 2018/2019. Day-case patients had lower rates of 30-day emergency readmission (odds ratio 0.71, 95% confidence interval 0.62 to 0.81). Outcomes for patients operated on in trusts with ≥80% day-case rates compared with patients operated on in trusts with <50% rates of day-case surgery were similar. CONCLUSIONS: Our data support the view that ESS can safely be performed as day-case surgery in most cases, although it will not be suitable for all patients. There appears to be scope to increase rates of day-case ESS in some hospital trusts in England.

Frailty, comorbidity and associations with in-hospital mortality in older COVID-19 patients: an exploratory study of administrative data.

BACKGROUND: Older adults have worse outcomes following hospitalisation with COVID-19, but within this group there is substantial variation. Although frailty and comorbidity are key determinants of mortality, it is less clear which specific manifestations of frailty and comorbidity are associated with the worst outcomes. OBJECTIVE: We aimed to identify the key comorbidities and domains of frailty that were associated with in-hospital mortality in older patients with COVID-19 using models developed using machine learning algorithms. METHODS: This was a retrospective study that used the Hospital Episode Statistics administrative dataset from 1st March 2020 to 28th February 2021 for hospital patients in England aged 65 years and over. The dataset was split into separate training (70%), test (15%) and validation (15%) datasets during model development. Global frailty was assessed using the Hospital Frailty Risk Score (HFRS) and specific domain of frailty identified using the Dr Foster Global Frailty Scale (GFS). Comorbidity was assessed using the Charlson Comorbidity Index (CCI). Additional features employed in the random forest algorithms included age, sex, deprivation, ethnicity, discharge month and year, geographical region, hospital trust, disease severity, International Statistical Classification of Disease and Related Health Problems 10th edition codes recorded during the admission. Features were selected, pre-processed and inputted into a series of random forest classification algorithms developed to identify factors strongly associated with in-hospital mortality. Two models were developed, the first model included the demographic, hospital-related and disease related items described above and individual GFS domains and CCI items. The second model was as the first but replaced the GFS domains and CCI items with the HFRS as a global measure of frailty. Model performance was assessed using the area under the receiver operating characteristic (AUROC) curve and measures of model accuracy. RESULTS: In total 215,831 patients were included. The model containing the individual GFS domains and CCI items had an AUROC curve for in-hospital mortality of 90% and a predictive accuracy of 83%. The model containing the HFRS had a similar performance (AUROC curve 90%, predictive accuracy 82%). The most important frailty items in the GFS were dementia/delirium, falls/fractures and pressure ulcers/weight loss. The most-important comorbidity items in the CCI were cancer, heart failure and renal disease. CONCLUSIONS: The physical manifestation of frailty and comorbidity, particularly a history of cognitive impairment and falls, may be useful in identification of patients who may need additional support during hospitalization with COVID-19.

Respiratory support for patients with COVID-19: A national survey of respiratory departments in England.

OBJECTIVE: We developed a national survey to assess the changes implemented by respiratory departments across England in response to the first wave of the COVID-19 pandemic. METHODS: An online survey was sent to the respiratory clinical leads in 132 NHS trusts in England. The survey was open between 10 August 2020 and 25 September 2020. RESULTS: Fifty-three responses (42%) are included in our results. The total number of non-critical care led Level 2 beds (requiring care for single organ failure-capable of managing continuous positive airways pressure, CPAP) increased by 159% at peak COVID activity from levels prior to COVID-19. CPAP was used solely in side-rooms in 9% of sites, and 57% and 31% of sites used CPAP in closed bays and closed wards, respectively. Fifteen sites (28%) reported shortages of non-vented non-invasive ventilation (NIV) masks and 12 sites (23%) CPAP machines. There was regional variation. CONCLUSIONS: The number of beds capable of managing patients requiring CPAP increased significantly. We found deviations from previous standards of care, which likely reflects the pressure faced by hospitals in managing patients with COVID-19. The regional variation in equipment shortages suggests moving resources between regions may have been beneficial.

Venous thromboembolism in patients hospitalised with COVID-19 in England.

BACKGROUND: The aim of this study was to detail the incidence of venous thromboembolism (VTE) in patients hospitalised with COVID-19 in England. METHODS: This was an exploratory retrospective analysis of observational data from the Hospital Episode Statistics dataset for England. All patients aged ≥18 years in England with a diagnosis of COVID-19 who had a hospital stay that was completed between 1st March 2020 and 31st March 2021 were included. A recorded diagnosis of VTE during the index stay or during a subsequent admission in the six weeks following discharge was the primary outcome in the main analysis. In secondary analysis, VTE diagnosis was the primary exposure and in-hospital mortality the primary outcome. RESULTS: Over the 13 months, 374,244 unique patients had a diagnosis of COVID-19 during a hospital stay, of whom 17,346 (4.6%) had a recorded diagnosis of VTE. VTE was more commonly recorded in patients aged 40-79 years, males and in patients of Black ethnicity, even after adjusting for covariates. Recorded VTE diagnosis was associated with longer hospital stay and higher adjusted in-hospital mortality (odds ratio 1.35 (95% confidence interval 1.29 to 1.41)). CONCLUSIONS: VTE was a common complication of hospitalisation with COVID-19 in England. VTE was associated with both increased length of stay and mortality rate.

The Role of Social Media in Improving Patient Recruitment for Research Studies on Persistent Post-Infectious Olfactory Dysfunction.

Background and Objectives: Since the COVID-19 pandemic, the number of cases of post-infectious olfactory dysfunction (PIOD) has substantially increased. Despite a good recovery rate, olfactory dysfunction (OD) becomes persistent in up to 15% of cases and further research is needed to find new treatment modalities for those patients who have not improved on currently available treatments. Social media has emerged as a potential avenue for patient recruitment, but its role in recruiting patients with smell dysfunction remains unexplored. We conducted a survey using the AbScent Facebook page to evaluate the feasibility of using this platform for future studies on smell dysfunction. Materials and Methods: Between 26 October and 4 November 2021, we conducted an online survey to evaluate propensity of patients with PIOD who would be willing to participate in research studies on smell dysfunction. Results: Sixty-five subjects were surveyed with a response rate of 90.7%. The median visual analogue scale (VAS) for sense of smell was 0 at infection and 2 at survey completion. The median length of OD was 1.6 years, and the main cause of OD was SARS-CoV-2 (57.6%). Parosmia was reported in 41 subjects (69.5%) whilst phantosmia in 22 (37.3%). The median length of olfactory training (OT) was 6 months but subjectively effective in 15 subjects (25.4%). Twenty-seven subjects (45.8%) tried other medications to improve olfaction, but only 6 participants (22.2%) reported an improvement. All subjects expressed their propensity to participate in future studies with most of them (38; 64.4%) willing to be enrolled either in medical and surgical studies or to be part of a randomised study design (11; 18.6%). Conclusions: Using the AbScent Facebook platform we successfully selected a population of subjects with persistent and severe OD that have failed to improve on available treatments and are willing to participate in further clinical trials.

Utilisation of tracheostomy in patients with COVID-19 in England: Patient characteristics, timing and outcomes.

OBJECTIVES: We aimed to characterise the use of tracheostomy procedures for all COVID-19 critical care patients in England and to understand how patient factors and timing of tracheostomy affected outcomes. DESIGN: A retrospective observational study using exploratory analysis of hospital administrative data. SETTING: All 500 National Health Service hospitals in England. PARTICIPANTS: All hospitalised COVID-19 patients aged ≥18 years in England between 1 March and 31 October 2020 were included. MAIN OUTCOMES AND MEASURES: This was a retrospective exploratory analysis using the Hospital Episode Statistics administrative data set. Multilevel modelling was used to explore the relationship between demographic factors, comorbidity and use of tracheostomy and the association between tracheostomy use, tracheostomy timing and the outcomes. RESULTS: In total, 2200 hospitalised COVID-19 patients had a tracheostomy. Tracheostomy utilisation varied across the study period, peaking in April-June 2020. In multivariable modelling, for those admitted to critical care, tracheostomy was most common in those aged 40-79 years, in males and in people of Black and Asian ethnic groups and those with a history of cerebrovascular disease. In critical care patients, tracheostomy was associated with lower odds of mortality (OR: 0.514 [95% CI 0.443 to 0.596], but greater length of stay OR: 41.143 [95% CI 30.979 to 54.642]). In patients that survived, earlier timing of tracheostomy (≤14 days post admission to critical care) was significantly associated with shorter length of stay. CONCLUSIONS: Tracheostomy is safe and advantageous for critical care COVID-19 patients. Early tracheostomy may be associated with better outcomes, such as shorter length of stay, compared to late tracheostomy.

COVID-19 hospital activity and in-hospital mortality during the first and second waves of the pandemic in England: an observational study.

INTRODUCTION: We aimed to examine the profile of, and outcomes for, all people hospitalised with COVID-19 across the first and second waves of the pandemic in England. METHODS: This was an exploratory retrospective analysis of observational data from the Hospital Episode Statistics data set for England. All patients aged ≥18 years in England with a diagnosis of COVID-19 who had a hospital stay that was completed between 1 March 2020 and 31 March 2021 were included. In-hospital mortality was the primary outcome of interest. The second wave was identified as starting on 1 September 2020. Multilevel logistic regression modelling was used to investigate the relationship between mortality and demographic, comorbidity and temporal covariates. RESULTS: Over the 13 months, 374 244 unique patients had a diagnosis of COVID-19 during a hospital stay, of whom 93 701 (25%) died in hospital. Adjusted mortality rates fell from 40%-50% in March 2020 to 11% in August 2020 before rising to 21% in January 2021 and declining steadily to March 2021. Improvements in mortality rates were less apparent in older and comorbid patients. Although mortality rates fell for all ethnic groups from the first to the second wave, declines were less pronounced for Bangladeshi, Indian, Pakistani, other Asian and black African ethnic groups. CONCLUSIONS: There was a substantial decline in adjusted mortality rates during the early part of the first wave which was largely maintained during the second wave. The underlying reasons for consistently higher mortality risk in some ethnic groups merits further study.

Changes in COVID-19 in-hospital mortality in hospitalised adults in England over the first seven months of the pandemic: An observational study using administrative data.

BACKGROUND: Previous research by our team identified factors associated with in-hospital mortality in patients with a diagnosis of COVID-19 in England between March and May 2020. The aim of the current paper was to investigate the changing role of demographics and co-morbidity, with a particular focus on ethnicity, as risk factors for in-hospital mortality over an extended period. METHODS: This was a retrospective observational study using the Hospital Episode Statistics administrative dataset. All patients aged ≥ 18 years in England with a diagnosis of COVID-19 who had a hospital stay that was completed (discharged alive or died) between 1st March and 30th September 2020 were included. In-hospital mortality was the primary outcome of interest. Multilevel logistic regression was used to model the relationship between in-hospital mortality with adjustment for the covariates: age, sex, deprivation, ethnicity, date of discharge and a number of comorbidities. FINDINGS: Compared to patients in March-May (n = 93,379), patients in June-September (n = 24,059) were younger, more likely to be female and of Asian ethnicity, but less likely to be of Black ethnicity. In-hospital mortality rates, adjusted for covariates, declined from 33-34% in March to 11-12% in September. Compared to the March-May period, Bangladeshi, Indian and Other Asian ethnicity patients had a lower relative odds of death (compared to White ethnicity patients) during June-September. For Pakistani patients, the decline in-hospital mortality rates was more modest across the same time periods with the relative odds of death increasing slightly (odds ratio (95% confidence interval)) 1.24 (1.10 to 1.40) and 1.35 (1.08 to 1.69) respectively. From March-May to June-September the relative odds of death in patients with a diagnosis of metastatic carcinoma increased (1.90 (1.73 to 2.08) vs 3.01 (2.55 to 3.54)) but decreased for male patients (1.44 (1.39 to 1.49) vs 1.27 (1.17 to 1.38)) and patients with obesity (1.42 (1.34 to 1.52) vs 0.97 (0.83 to 1.14)) and diabetes without complications (1.14 (1.10 to 1.19) vs 0.95 (0.87 to 1.05)). INTERPRETATION: In-hospital mortality rates for patients with a diagnosis of COVID-19 have fallen substantially and there is evidence that the relative importance of some covariates has changed since the start of the pandemic. These patterns should continue to be tracked as new variants of the virus emerge, vaccination programmes are rolled out and hospital pressures fluctuate.

Variability in COVID-19 in-hospital mortality rates between national health service trusts and regions in England: A national observational study for the Getting It Right First Time Programme.

BACKGROUND: A key first step in optimising COVID-19 patient outcomes during future case-surges is to learn from the experience within individual hospitals during the early stages of the pandemic. The aim of this study was to investigate the extent of variation in COVID-19 outcomes between National Health Service (NHS) hospital trusts and regions in England using data from March-July 2020. METHODS: This was a retrospective observational study using the Hospital Episode Statistics administrative dataset. Patients aged ≥ 18 years who had a diagnosis of COVID-19 during a hospital stay in England that was completed between March 1st and July 31st, 2020 were included. In-hospital mortality was the primary outcome of interest. In secondary analysis, critical care admission, length of stay and mortality within 30 days of discharge were also investigated. Multilevel logistic regression was used to adjust for covariates. FINDINGS: There were 86,356 patients with a confirmed diagnosis of COVID-19 included in the study, of whom 22,944 (26.6%) died in hospital with COVID-19 as the primary cause of death. After adjusting for covariates, the extent of the variation in-hospital mortality rates between hospital trusts and regions was relatively modest. Trusts with the largest baseline number of beds and a greater proportion of patients admitted to critical care had the lowest in-hospital mortality rates. INTERPRETATION: There is little evidence of clustering of deaths within hospital trusts. There may be opportunities to learn from the experience of individual trusts to help prepare hospitals for future case-surges.

Patient factors and temporal trends associated with COVID-19 in-hospital mortality in England: an observational study using administrative data.

BACKGROUND: Analysis of the effect of COVID-19 on the complete hospital population in England has been lacking. Our aim was to provide a comprehensive account of all hospitalised patients with COVID-19 in England during the early phase of the pandemic and to identify the factors that influenced mortality as the pandemic evolved. METHODS: This was a retrospective exploratory analysis using the Hospital Episode Statistics administrative dataset. All patients aged 18 years or older in England who completed a hospital stay (were discharged alive or died) between March 1 and May 31, 2020, and had a diagnosis of COVID-19 on admission or during their stay were included. In-hospital death was the primary outcome of interest. Multilevel logistic regression was used to model the relationship between death and several covariates: age, sex, deprivation (Index of Multiple Deprivation), ethnicity, frailty (Hospital Frailty Risk Score), presence of comorbidities (Charlson Comorbidity Index items), and date of discharge (whether alive or deceased). FINDINGS: 91 541 adult patients with COVID-19 were discharged during the study period, among which 28 200 (30·8%) in-hospital deaths occurred. The final multilevel logistic regression model accounted for age, deprivation score, and date of discharge as continuous variables, and sex, ethnicity, and Charlson Comorbidity Index items as categorical variables. In this model, significant predictors of in-hospital death included older age (modelled using restricted cubic splines), male sex (1·457 [1·408-1·509]), greater deprivation (1·002 [1·001-1·003]), Asian (1·211 [1·128-1·299]) or mixed ethnicity (1·317 [1·080-1·605]; vs White ethnicity), and most of the assessed comorbidities, including moderate or severe liver disease (5·433 [4·618-6·392]). Later date of discharge was associated with a lower odds of death (0·977 [0·976-0·978]); adjusted in-hospital mortality improved significantly in a broadly linear fashion, from 52·2% in the first week of March to 16·8% in the last week of May. INTERPRETATION: Reductions in the adjusted probability of in-hospital mortality for COVID-19 patients over time might reflect the impact of changes in hospital strategy and clinical processes. The reasons for the observed improvements in mortality should be thoroughly investigated to inform the response to future outbreaks. The higher mortality rate reported for certain ethnic minority groups in community-based studies compared with our hospital-based analysis might partly reflect differential infection rates in those at greatest risk, propensity to become severely ill once infected, and health-seeking behaviours. FUNDING: None.