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.

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.

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.