Estimating nosocomial infection and its outcomes in hospital patients in England with a diagnosis of COVID-19 using machine learning (preprint)

Purpose. Our aim was to provide a comprehensive account of COVID-19 nosocomial infections (NIs) in England and identify their characteristics and outcomes using machine learning.Methods. From the Hospital Episodes Statistics database, 374,244 adult hospital patients in England with a diagnosis of COVID-19 and discharged between March 1st 2020 and March 31st 2021 were identified. A cohort of suspected COVID-19 NIs was identified using four empirical methods linked to hospital coding. A random forest classifier was designed to model the characteristics of these infections.Results. The model estimated a mean NI rate of 10.5%, with a peak close to 18% during the first wave, but much lower rates (7%) thereafter. NIs were highly correlated with longer lengths of stay, high trust capacity strain, greater age and a higher degree of patient frailty, and associated with higher mortality rates and more severe COVID-19 sequelae, including pneumonia, kidney disease and sepsis.Conclusions. Identification of the characteristics of patients who acquire NIs should help trusts to identify those most at risk. The evolution of the NI rate over time may reflect the impact of changes in hospital management practices and vaccination efforts. Variations in NI rates across trusts may partly reflect different data recording and coding practice.

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

Background: 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 many ENT operations including endoscopic sinus surgery (ESS). We aimed to investigate the safety of ESS in England. Methods: This was an observational, secondary analysis of administrative data. Participants were all patients in England undergoing elective ESS procedure aged ≥ 17 years during for the five years from 1st April 2014 to 31st March 2019. The exposure variable was day-case or overnight stay. The primary outcome was emergency readmission within 30 days post-discharge. 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 100% to 20.6%. Rates of day-case surgery increased from 64.0% in 2014/15 to 78.7% in 2018/19. Day-case patients had lower rates of 30-day emergency readmission (odds ratio 0.71, 95% confidence interval 0.62 to 0.81). For secondary outcomes measures, there was no evidence of poorer outcomes for day-case patients. Outcomes for patients operated on in trusts with ≥80% day-case rates compared to patients operated on in trusts with <50% rates of day-case surgery were similar. Conclusions: ESS can safely be performed as day-case surgery at current rates. There is a potential to increase rates of day-case ESS in England, especially in departments that currently have low rates of day-case ESS.

Comparison of vertebroplasty and kyphoplasty for osteoporotic spinal fractures: An observational study using administrative data from England (preprint)

Purpose To investigate the variation in the use of, and outcomes for, vertebroplasty (VP) and balloon kyphoplasty (BKP) techniques for osteoporotic spinal fracture for patients operated on within the National Health Service in England.Methods This was an observational analysis of administrative data. Data were extracted from the Hospital Episodes Statistics database for the period 1st April 2011 to 31st March 2018 for all VP and BKP procedures. Patients aged < 19 years, with metastatic carcinoma and undergoing other decompression procedures were excluded. The primary outcome was repeat spinal surgery within one year. Secondary outcomes were 30-day emergency readmission, death within one year, extended hospital stay, post-procedural pain within 30 days and post-procedural haemorrhage or infection within 30 days. Multilevel, multivariable logistic regression was used to adjust for covariates.Results Data were available for 5,792 VP and 3,136 BKP patients operated on at 96 hospital trusts. In the 63 trusts that conducted more than 20 procedures during the study period, the proportion of procedures conducted as BKP varied from 0–100%. There was no difference in any of the outcomes between VP and BKP patients or between trusts performing ≥ 70% and ≤ 30% of procedures as BKP.Conclusions There is no evidence that VP is associated with poorer outcomes than VP. The greater costs of performing BKP may be hard to justify as healthcare budgets continue to be put under pressure as they recover from the coronavirus pandemic.

Utilisation of tracheostomy in patients with COVID-19 in England: patient characteristics, timing and outcomes (preprint)

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 March 1st and October 31st, 2020 were included. Main outcomes and measures: This was a retrospective exploratory analysis using the Hospital Episode Statistics administrative dataset. 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, 2,200 hospitalised COVID-19 patients had a tracheostomy. Tracheostomy utilisation varied substantially 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.

Estimating Nosocomial Infection and its Outcomes in Hospital Patients in England with a Diagnosis of COVID-19 Using Machine Learning (preprint)

Background: COVID-19 nosocomial infections (NIs) may have played a significant role in the dynamics of the pandemic in England, but analysis of their impact at the national scale has been lacking. Our aim was to provide a comprehensive account of NIs, identify their characteristics and outcomes in patients with a diagnosis of COVID-19 and use machine learning modelling to refine these estimates.Methods: From the Hospital Episodes Statistics database all adult hospital patients in England with a diagnosis of COVID-19 and discharged between March 1st 2020 and March 31st 2021 were identified. A cohort of suspected COVID-19 NIs was identified using four empirical methods linked to hospital coding. A random forest classifier was designed to model the relationship between acquiring NIs and the covariates: patient characteristics, comorbidities, frailty, trust capacity strain and severity of COVID-19 infections.Findings: In total, 374,244 adult patients with COVID-19 were discharged during the study period. The four empirical methods identified 29,896 (8.0%) patients with NIs. The random forest classifier estimated a mean NI rate of 10.5%, with a peak close to 18% during the first wave, but much lower rates thereafter and around 7% in early spring 2021. NIs were highly correlated with longer lengths of stay, high trust capacity strain, greater age and a higher degree of patient frailty. NIs were also found to be associated with higher mortality rates and more severe COVID-19 sequelae, including pneumonia, kidney disease and sepsis.Interpretation: Identification of the characteristics of patients who acquire NIs should help trusts to identify those most at risk. The evolution of the NI rate over time may reflect the impact of changes in hospital management practices and vaccination efforts. Variations in NI rates across trusts may partly reflect different data recording and coding practice.Funding: None to declare. Declaration of Interest: None to declare.

The Relationship Between Frailty, Comorbidity and COVID-19 Mortality in Older COVID-19 Patients: Analysis of an Administrative Dataset Using Machine Learning Techniques (preprint)

Introduction: Older adults have disproportionally poor 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 poorest outcomes. The aim of this study was to identify the key comorbidities and functional manifestations of frailty that were associated with in-hospital mortality in older patients with COVID-19.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. Frailty was assessed using the Dr Foster Global Frailty Scale (GFS) and comorbidity using the Charlson Comorbidity Index (CCI). Exploratory analysis techniques were used to determine mortality according to the demographic, frailty and comorbidity profile of patients. Features were selected, pre-processed and inputted into a random forest classification algorithm to predict in-hospital mortality.Results: In total 215,831 patients were included. The frailty and comorbidity measures significantly improved the model’s ability to predict mortality in patients. 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 diabetes (without complications), pulmonary disease, heart failure and renal failure. The best-performing model had a predictive accuracy of 70% as well as an area under the curve of 0.78.Discussion: Frailty and comorbidity are associated with poorer COVID-19 outcomes in older adults, even after adjusting for chronological age. 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 their hospital stay.Funding: None to declare.Declaration of Interest: None to declare.

COVID-19 Hospital Activity and Outcomes During the First and Second Waves of the Pandemic in England: Analysis of the Hospital Episodes Statistics Database (preprint)

Background: The aim of this study was 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 a retrospective observational study that used 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 (discharged alive or died) between 1st March 2020 and 31st March 2021 were included. In-hospital mortality was the primary outcome of interest. The first wave was defined as 1st March-31st August 2020 and the second wave as 1st September 2020 to 31st March 2021. Multivariable logistic regression was used to model the relationship between mortality and the covariates: age, sex, deprivation, ethnicity, discharge/admission date and a number of comorbidities.Findings: Over the 13 months, 374,244 unique patients had a diagnosis of COVID-19 during a hospital stay, of whom 93,701 (25.0%) 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, with all having higher odds of mortality during the second wave than White ethnicity patients.Interpretation: 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.Funding Information: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.Declaration of Interests: The authors declare that there is no conflict of interest.Ethics Approval Statement: Consent from individuals involved in this study was not required for this analysis of the Hospital Episodes Statistics (HES) administrative dataset. The analysis and presentation of data follows current NHS Digital guidance for the use of HES data for research purposes. Reported data are anonymised to the level required by ISB1523 Anonymisation Standard for Publishing Health and Social Care Data.

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 (preprint)

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, 30 days emergency hospital readmission, length of stay and mortality within 30 days of discharge were also investigated. 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 mortality rates between hospital trusts and regions was relatively modest. Trusts with a larger baseline number of beds had better outcomes than those with a smaller number of beds.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 for future hospital management of COVID-19 patients during future case-surges.Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.Declaration of Interests: The authors declare that there is no conflict of interest.Ethics Approval Statement: Consent from individuals involved in this study was not required. The analysis and presentation of data follows current NHS Digital guidance for the use of HES data for research purposes and is anonymised to the level required by ISB1523 Anonymisation Standard for Publishing Health and Social Care Data.