The impact of atypical intrahospital transfers on patient outcomes: a mixed methods study.

The architectural design of hospitals worldwide is centred around individual departments, which require the movement of patients between wards. However, patients do not always take the simplest route from admission to discharge, but can experience convoluted movement patterns, particularly when bed availability is low. Few studies have explored the impact of these rarer, atypical trajectories. Using a mixed-method explanatory sequential study design, we firstly used three continuous years of electronic health record data prior to the Covid-19 pandemic, from 55,152 patients admitted to a London hospital network to define the ward specialities by patient type using the Herfindahl-Hirschman index. We explored the impact of 'regular transfers' between pairs of wards with shared specialities, 'atypical transfers' between pairs of wards with no shared specialities and 'site transfers' between pairs of wards in different hospital site locations, on length of stay, 30-day readmission and mortality. Secondly, to understand the possible reasons behind atypical transfers we conducted three focus groups and three in-depth interviews with site nurse practitioners and bed managers within the same hospital network. We found that at least one atypical transfer was experienced by 12.9% of patients. Each atypical transfer is associated with a larger increase in length of stay, 2.84 days (95% CI 2.56-3.12), compared to regular transfers, 1.92 days (95% CI 1.82-2.03). No association was found between odds of mortality, or 30-day readmission and atypical transfers after adjusting for confounders. Atypical transfers appear to be driven by complex patient conditions, a lack of hospital capacity, the need to reach specific services and facilities, and more exceptionally, rare events such as major incidents. Our work provides an important first step in identifying unusual patient movement and its impacts on key patient outcomes using a system-wide, data-driven approach. The broader impact of moving patients between hospital wards, and possible downstream effects should be considered in hospital policy and service planning.

Patient Biochemistry and Treatment Need in Chronic Hepatitis B Virus Infection Across Three Continents: Retrospective Cross-Sectional Cohort Studies.

INTRODUCTION: Chronic hepatitis B virus (HBV) infection is associated with significant global morbidity and mortality. Low treatment rates are observed in patients living with HBV; the reasons for this are unclear. This study sought to describe patients' demographic, clinical and biochemical characteristics across three continents and their associated treatment need. METHODS: This retrospective cross-sectional post hoc analysis of real-world data used four large electronic databases from the United States, United Kingdom and China (specifically Hong Kong and Fuzhou). Patients were identified by first evidence of chronic HBV infection in a given year (their index date) and characterized. An algorithm was designed and applied, wherein patients were categorized as treated, untreated but indicated for treatment and untreated and not indicated for treatment based on treatment status and demographic, clinical, biochemical and virological characteristics (age; evidence of fibrosis/cirrhosis; alanine aminotransferase [ALT] levels, HCV/HIV coinfection and HBV virology markers). RESULTS: In total, 12,614 US patients, 503 UK patients, 34,135 patients from Hong Kong and 21,614 from Fuzhou were included. Adults (99.4%) and males (59.0%) predominated. Overall, 34.5% of patients were treated at index (range 15.9-49.6%), with nucleos(t)ide analogue monotherapy most commonly prescribed. The proportion of untreated-but-indicated patients ranged from 12.9% in Hong Kong to 18.2% in the UK; almost two-thirds of these patients (range 61.3-66.7%) had evidence of fibrosis/cirrhosis. A quarter (25.3%) of untreated-but-indicated patients were aged ≥ 65 years. CONCLUSION: This large real-world dataset demonstrates that chronic hepatitis B infection remains a global health concern; despite the availability of effective suppressive therapy, a considerable proportion of predominantly adult patients apparently indicated for treatment are currently untreated, including many patients with fibrosis/cirrhosis. Causes of disparity in treatment status warrant further investigation.

Increase in hypoglycaemia and hyperglycaemia in people with diabetes admitted to hospital during COVID-19 pandemic.

BACKGROUND: We used detailed information on patients with diabetes admitted to hospital to determine differences in clinical outcomes before and during the COVID-19 pandemic in the UK. METHODS: The study used electronic patient record data from Imperial College Healthcare NHS Trust. Hospital admission data for patients coded for diabetes was analysed over three time periods: pre-pandemic (31st January 2019-31st January 2020), Wave 1 (1st February 2020-30th June 2020), and Wave 2 (1st September 2020-30th April 2021). We compared clinical outcomes including glycaemia and length of stay. RESULTS: We analysed data obtained from 12,878, 4008 and 7189 hospital admissions during the three pre-specified time periods. The incidence of Level 1 and Level 2 hypoglycaemia was significantly higher during Waves 1 and 2 compared to the pre-pandemic period (25 % and 25.1 % vs. 22.9 % for Level 1 and 11.7 % and 11.5 % vs. 10.3 % for Level 2). The incidence of hyperglycaemia was also significantly higher during the two waves. The median hospital length of stay increased significantly (4.1[1.6, 9.8] and 4.0[1.4, 9.4] vs. 3.5[1.2, 9.2] days). CONCLUSIONS: During the COVID-19 pandemic in the UK, hospital in-patients with diabetes had a greater number of hypoglycaemic/hyperglycaemic episodes and an increased length of stay when compared to the pre-pandemic period. This highlights the necessity for a focus on improved diabetes care during further significant disruptions to healthcare systems and ensuring minimisation of the impact on in-patient diabetes services. SUMMARY: Diabetes is associated with poorer outcomes from COVID-19. However the glycaemic control of inpatients before and during the COVID-19 pandemic is unknown. We found the incidence of hypoglycaemia and hyperglycaemia was significantly higher during the pandemic highlighting the necessity for a focus on improved diabetes care during further pandemics.

Nurse staffing and inpatient mortality in the English National Health Service: a retrospective longitudinal study.

OBJECTIVE: To examine the impact of nursing team size and composition on inpatient hospital mortality. DESIGN: A retrospective longitudinal study using linked nursing staff rostering and patient data. Multilevel conditional logistic regression models with adjustment for patient characteristics, day and time-invariant ward differences estimated the association between inpatient mortality and staffing at the ward-day level. Two staffing measures were constructed: the fraction of target hours worked (fill-rate) and the absolute difference from target hours. SETTING: Three hospitals within a single National Health Service Trust in England. PARTICIPANTS: 19 287 ward-day observations with information on 4498 nurses and 66 923 hospital admissions in 53 inpatient hospital wards for acutely ill adult patients for calendar year 2017. MAIN OUTCOME MEASURE: In-hospital deaths. RESULTS: A statistically significant association between the fill-rate for registered nurses (RNs) and inpatient mortality (OR 0.9883, 95% CI 0.9773 to 0.9996, p=0.0416) was found only for RNs hospital employees. There was no association for healthcare support workers (HCSWs) or agency workers. On average, an extra 12-hour shift by an RN was associated with a reduction in the odds of a patient death of 9.6% (OR 0.9044, 95% CI 0.8219 to 0.9966, p=0.0416). An additional senior RN (in NHS pay band 7 or 8) had 2.2 times the impact of an additional band 5 RN (fill-rate for bands 7 and 8: OR 0.9760, 95% CI 0.9551 to 0.9973, p=0.0275; band 5: OR 0.9893, 95% CI 0.9771 to 1.0017, p=0.0907). CONCLUSIONS: RN staffing and seniority levels were associated with patient mortality. The lack of association for HCSWs and agency nurses indicates they are not effective substitutes for RNs who regularly work on the ward.

Establishing a colorectal cancer research database from routinely collected health data: the process and potential from a pilot study.

OBJECTIVE: Colorectal cancer is a common cause of death and morbidity. A significant amount of data are routinely collected during patient treatment, but they are not generally available for research. The National Institute for Health Research Health Informatics Collaborative in the UK is developing infrastructure to enable routinely collected data to be used for collaborative, cross-centre research. This paper presents an overview of the process for collating colorectal cancer data and explores the potential of using this data source. METHODS: Clinical data were collected from three pilot Trusts, standardised and collated. Not all data were collected in a readily extractable format for research. Natural language processing (NLP) was used to extract relevant information from pseudonymised imaging and histopathology reports. Combining data from many sources allowed reconstruction of longitudinal histories for each patient that could be presented graphically. RESULTS: Three pilot Trusts submitted data, covering 12 903 patients with a diagnosis of colorectal cancer since 2012, with NLP implemented for 4150 patients. Timelines showing individual patient longitudinal history can be grouped into common treatment patterns, visually presenting clusters and outliers for analysis. Difficulties and gaps in data sources have been identified and addressed. DISCUSSION: Algorithms for analysing routinely collected data from a wide range of sites and sources have been developed and refined to provide a rich data set that will be used to better understand the natural history, treatment variation and optimal management of colorectal cancer. CONCLUSION: The data set has great potential to facilitate research into colorectal cancer.

Comparing the longer-term effectiveness of a single dose of the Pfizer-BioNTech and Oxford-AstraZeneca COVID-19 vaccines across the age spectrum.

Background: A single dose strategy may be adequate to confer population level immunity and protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, especially in low- and middle-income countries where vaccine supply remains limited. We compared the effectiveness of a single dose strategy of the Oxford-AstraZeneca or Pfizer-BioNTech vaccines against SARS-CoV-2 infection across all age groups and over an extended follow-up period. Methods: Individuals vaccinated in North-West London, UK, with either the first dose of the Oxford-AstraZeneca or Pfizer-BioNTech vaccines between January 12, 2021 and March 09, 2021, were matched to each other by demographic and clinical characteristics. Each vaccinated individual was additionally matched to an unvaccinated control. Study outcomes included SARS-CoV-2 infection of any severity, COVID-19 hospitalisation, COVID-19 death, and all-cause mortality. Findings: Amongst matched individuals, 63,608 were in each of the vaccine groups and 127,216 were unvaccinated. Between 14 and 84 days of follow-up after matching, there were 534 SARS-CoV-2 infections, 65 COVID-19 hospitalisations, and 190 deaths, of which 29 were categorized as due to COVID-19. The incidence rate ratio (IRR) for SARS-CoV-2 infection was 0.85 (95% confidence interval [CI], 0.69 to 1.05) for Oxford-Astra-Zeneca, and 0.69 (0.55 to 0.86) for Pfizer-BioNTech. The IRR for both vaccines was the same at 0.25 (0.09 to 0.55) and 0.14 (0.02 to 0.58) for reducing COVID-19 hospitalization and COVID-19 mortality, respectively. The IRR for all-cause mortality was 0.25 (0.15 to 0.39) and 0.18 (0.10 to 0.30) for the Oxford-Astra-Zeneca and Pfizer-BioNTech vaccines, respectively. Age was an effect modifier of the association between vaccination and SARS-CoV-2 infection of any severity; lower hazard ratios for increasing age. Interpretation: A single dose strategy, for both vaccines, was effective at reducing COVID-19 mortality and hospitalization rates. The magnitude of vaccine effectiveness was comparatively lower for SARS-CoV-2 infection, although this was variable across the age range, with higher effectiveness seen with older adults. Our results have important implications for health system planning -especially in low resource settings where vaccine supply remains constrained.

Incidence, Risk Factors, and Effect on Allograft Survival of Glomerulonephritis Post-transplantation in a United Kingdom Population: Cohort Study.

Background: Post-transplant glomerulonephritis (PTGN) has been associated with inferior long-term allograft survival, and its incidence varies widely in the literature. Methods: This is a cohort study of 7,623 patients transplanted between 2005 and 2016 at four major transplant UK centres. The diagnosis of glomerulonephritis (GN) in the allograft was extracted from histology reports aided by the use of text-mining software. The incidence of the four most common GN post-transplantation was calculated, and the risk factors for disease and allograft outcomes were analyzed. Results: In total, 214 patients (2.8%) presented with PTGN. IgA nephropathy (IgAN), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), and membranoproliferative/mesangiocapillary GN (MPGN/MCGN) were the four most common forms of post-transplant GN. Living donation, HLA DR match, mixed race, and other ethnic minority groups were associated with an increased risk of developing a PTGN. Patients with PTGN showed a similar allograft survival to those without in the first 8 years of post-transplantation, but the results suggest that they do less well after that timepoint. IgAN was associated with the best allograft survival and FSGS with the worst allograft survival. Conclusions: PTGN has an important impact on long-term allograft survival. Significant challenges can be encountered when attempting to analyze large-scale data involving unstructured or complex data points, and the use of computational analysis can assist.

Impact of the COVID-19 pandemic on routine surveillance for adults with chronic hepatitis B virus (HBV) infection in the UK.

Background: To determine the impact of the COVID-19 pandemic on the population with chronic Hepatitis B virus (HBV) infection under hospital follow-up in the UK, we quantified the coverage and frequency of measurements of biomarkers used for routine surveillance (alanine transferase [ALT] and HBV viral load). Methods: We used anonymized electronic health record data from the National Institute for Health Research (NIHR) Health Informatics Collaborative (HIC) pipeline representing five UK National Health Service (NHS) Trusts. Results: We report significant reductions in surveillance of both biomarkers during the pandemic compared to pre-COVID-19 years, both in terms of the proportion of patients who had ≥1 measurement annually, and the mean number of measurements per patient. Conclusions: These results demonstrate the real-time utility of HIC data in monitoring health-care provision, and support interventions to provide catch-up services to minimise the impact of the pandemic. Further investigation is required to determine whether these disruptions will be associated with increased rates of adverse chronic HBV outcomes.

Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre.

Importance: The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation. Objective: To automate lung nodule identification in a tertiary cancer centre. Methods: This retrospective cohort study used Electronic Healthcare Records to identify CT reports generated between 31st October 2011 and 24th July 2020. A structured query language/natural language processing tool was developed to classify reports according to lung nodule status. Performance was externally validated. Sentences were used to train machine-learning classifiers to predict concerning nodule features in 2,000 patients. Results: 14,586 patients with lung nodules were identified. The cancer types most commonly associated with lung nodules were lung (39%), neuro-endocrine (38%), skin (35%), colorectal (33%) and sarcoma (33%). Lung nodule patients had a greater proportion of metastatic diagnoses (45 vs. 23%, p < 0.001), a higher mean post-baseline scan number (6.56 vs. 1.93, p < 0.001), and a shorter mean scan interval (4.1 vs. 5.9 months, p < 0.001) than those without nodules. Inter-observer agreement for sentence classification was 0.94 internally and 0.98 externally. Sensitivity and specificity for nodule identification were 93 and 99% internally, and 100 and 100% at external validation, respectively. A linear-support vector machine model predicted concerning sentence features with 94% accuracy. Conclusion: We have developed and validated an accurate tool for automated lung nodule identification that is valuable for service evaluation and research data acquisition.

Assessing COVID-19 Vaccine Uptake and Effectiveness Through the North West London Vaccination Program: Retrospective Cohort Study.

BACKGROUND: On March 11, 2020, the World Health Organization declared SARS-CoV-2, causing COVID-19, as a pandemic. The UK mass vaccination program commenced on December 8, 2020, vaccinating groups of the population deemed to be most vulnerable to severe COVID-19 infection. OBJECTIVE: This study aims to assess the early vaccine administration coverage and outcome data across an integrated care system in North West London, leveraging a unique population-level care data set. Vaccine effectiveness of a single dose of the Oxford/AstraZeneca and Pfizer/BioNTech vaccines were compared. METHODS: A retrospective cohort study identified 2,183,939 individuals eligible for COVID-19 vaccination between December 8, 2020, and February 24, 2021, within a primary, secondary, and community care integrated care data set. These data were used to assess vaccination hesitancy across ethnicity, gender, and socioeconomic deprivation measures (Pearson product-moment correlations); investigate COVID-19 transmission related to vaccination hubs; and assess the early effectiveness of COVID-19 vaccination (after a single dose) using time-to-event analyses with multivariable Cox regression analysis to investigate if vaccination independently predicted positive SARS-CoV-2 in those vaccinated compared to those unvaccinated. RESULTS: In this study, 5.88% (24,332/413,919) of individuals declined and did not receive a vaccination. Black or Black British individuals had the highest rate of declining a vaccine at 16.14% (4337/26,870). There was a strong negative association between socioeconomic deprivation and rate of declining vaccination (r=-0.94; P=.002) with 13.5% (1980/14,571) of individuals declining vaccination in the most deprived areas compared to 0.98% (869/9609) in the least. In the first 6 days after vaccination, 344 of 389,587 (0.09%) individuals tested positive for SARS-CoV-2. The rate increased to 0.13% (525/389,243) between days 7 and 13, before then gradually falling week on week. At 28 days post vaccination, there was a 74% (hazard ratio 0.26, 95% CI 0.19-0.35) and 78% (hazard ratio 0.22, 95% CI 0.18-0.27) reduction in risk of testing positive for SARS-CoV-2 for individuals that received the Oxford/AstraZeneca and Pfizer/BioNTech vaccines, respectively, when compared with unvaccinated individuals. A very low proportion of hospital admissions were seen in vaccinated individuals who tested positive for SARS-CoV-2 (288/389,587, 0.07% of all patients vaccinated) providing evidence for vaccination effectiveness after a single dose. CONCLUSIONS: There was no definitive evidence to suggest COVID-19 was transmitted as a result of vaccination hubs during the vaccine administration rollout in North West London, and the risk of contracting COVID-19 or becoming hospitalized after vaccination has been demonstrated to be low in the vaccinated population. This study provides further evidence that a single dose of either the Pfizer/BioNTech vaccine or the Oxford/AstraZeneca vaccine is effective at reducing the risk of testing positive for COVID-19 up to 60 days across all age groups, ethnic groups, and risk categories in an urban UK population.

Association between intrahospital transfer and hospital-acquired infection in the elderly: a retrospective case-control study in a UK hospital network.

BACKGROUND: Intrahospital transfers have become more common as hospital staff balance patient needs with bed availability. However, this may leave patients more vulnerable to potential pathogen transmission routes via increased exposure to contaminated surfaces and contacts with individuals. OBJECTIVE: This study aimed to quantify the association between the number of intrahospital transfers undergone during a hospital spell and the development of a hospital-acquired infection (HAI). METHODS: A retrospective case-control study was conducted using data extracted from electronic health records and microbiology cultures of non-elective, medical admissions to a large urban hospital network which consists of three hospital sites between 2015 and 2018 (n=24 240). As elderly patients comprise a large proportion of hospital users and are a high-risk population for HAIs, the analysis focused on those aged 65 years or over. Logistic regression was conducted to obtain the OR for developing an HAI as a function of intrahospital transfers until onset of HAI for cases, or hospital discharge for controls, while controlling for age, gender, time at risk, Elixhauser comorbidities, hospital site of admission, specialty of the dominant healthcare professional providing care, intensive care admission, total number of procedures and discharge destination. RESULTS: Of the 24 240 spells, 2877 cases were included in the analysis. 72.2% of spells contained at least one intrahospital transfer. On multivariable analysis, each additional intrahospital transfer increased the odds of acquiring an HAI by 9% (OR=1.09; 95% CI 1.05 to 1.13). CONCLUSION: Intrahospital transfers are associated with increased odds of developing an HAI. Strategies for minimising intrahospital transfers should be considered, and further research is needed to identify unnecessary transfers. Their reduction may diminish spread of contagious pathogens in the hospital environment.

A unified machine learning approach to time series forecasting applied to demand at emergency departments.

BACKGROUND: There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provide adequate quality of care while maintaining standards and productivity. Managing hospital demand effectively requires an adequate knowledge of the future rate of admission. We develop a novel predictive framework to understand the temporal dynamics of hospital demand. METHODS: We compare and combine state-of-the-art forecasting methods to predict hospital demand 1, 3 or 7 days into the future. In particular, our analysis compares machine learning algorithms to more traditional linear models as measured in a mean absolute error (MAE) and we consider two different hyperparameter tuning methods, enabling a faster deployment of our models without compromising performance. We believe our framework can readily be used to forecast a wide range of policy relevant indicators. RESULTS: We find that linear models often outperform machine learning methods and that the quality of our predictions for any of the forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE. Our approach is able to predict attendances at these emergency departments one day in advance up to a mean absolute error of ±14 and ±10 patients corresponding to a mean absolute percentage error of 6.8% and 8.6% respectively. CONCLUSIONS: Simple linear methods like generalized linear models are often better or at least as good as ensemble learning methods like the gradient boosting or random forest algorithm. However, though sophisticated machine learning methods are not necessarily better than linear models, they improve the diversity of model predictions so that stacked predictions can be more robust than any single model including the best performing one.

National Institute for Health Research Health Informatics Collaborative: development of a pipeline to collate electronic clinical data for viral hepatitis research.

OBJECTIVE: The National Institute for Health Research (NIHR) Health Informatics Collaborative (HIC) is a programme of infrastructure development across NIHR Biomedical Research Centres. The aim of the NIHR HIC is to improve the quality and availability of routinely collected data for collaborative, cross-centre research. This is demonstrated through research collaborations in selected therapeutic areas, one of which is viral hepatitis. DESIGN: The collaboration in viral hepatitis identified a rich set of datapoints, including information on clinical assessment, antiviral treatment, laboratory test results and health outcomes. Clinical data from different centres were standardised and combined to produce a research-ready dataset; this was used to generate insights regarding disease prevalence and treatment response. RESULTS: A comprehensive database has been developed for potential viral hepatitis research interests, with a corresponding data dictionary for researchers across the centres. An initial cohort of 960 patients with chronic hepatitis B infections and 1404 patients with chronic hepatitis C infections has been collected. CONCLUSION: For the first time, large prospective cohorts are being formed within National Health Service (NHS) secondary care services that will allow research questions to be rapidly addressed using real-world data. Interactions with industry partners will help to shape future research and will inform patient-stratified clinical practice. An emphasis on NHS-wide systems interoperability, and the increased utilisation of structured data solutions for electronic patient records, is improving access to data for research, service improvement and the reduction of clinical data gaps.

Quasispecies Changes with Distinctive Point Mutations in the Hepatitis C Virus Internal Ribosome Entry Site (IRES) Derived from PBMCs and Plasma.

The 5' untranslated region (UTR) of the hepatitis C virus (HCV) genome contains the internal ribosome entry site (IRES), a highly conserved RNA structure essential for cap-independent translation of the viral polyprotein. HCV, apart from the liver, is thought to be associated with lymphocyte subpopulations of peripheral blood mononuclear cells (PBMCs), in lymph nodes and brain tissue. In this study, RT-PCR, cloning, and sequence analysis were employed to investigate the quasispecies nature of the 5'UTR following extraction of viral RNA from PBMCs and plasma of HCV infected individuals. The nucleotide variation between IRES-derived sequences from PBMCs and plasma indicated the existence of polymorphic sites within the IRES. HCV isolates had divergent variants with unique mutations particularly at positions 107, 204, and 243 of the IRES. Most of the PBMC-derived sequences contained an A-A-A variant at these positions. The mutations associated with the IRESes suggested the presence of unique quasispecies populations in PBMCs compared with plasma.