Benefits of electronic charts in intensive care and during a world health pandemic: advantages of the technology age.

AIMS AND OBJECTIVES: This study sets out to describe benefits from the implementation of electronic observation charting in intensive care units (ICU). This was an extension to the existing hospital wide digital health system. We evaluated error reduction, time-savings and the costs associated with conversion from paper to digital records. The world health emergency of COVID-19 placed extraordinary strain on ICU and staff opinion was evaluated to test how well the electronic system performed. METHODS: A clinically led project group working directly with programmers developed an electronic patient record for intensive care. Data error rates, time to add data and to make calculations were studied before and after the introduction of electronic charts. User feedback was sought pre and post go-live (during the COVID-19 pandemic) and financial implications were calculated by the hospital finance teams. RESULTS: Error rates equating to 219 000/year were avoided by conversion to electronic charts. Time saved was the equivalent of a nursing shift each day. Recurrent cost savings per year were estimated to be £257k. Staff were overwhelmingly positive about electronic charts in ICU, even during a health pandemic and despite redeployment into intensive care where they were using the electronic charts for the first time. DISCUSSION: Electronic ICU charts have been successfully introduced into our institution with benefits in terms of patient safety through error reduction and improved care through release of nursing time. Costs have been reduced. Staff feel supported by the digital system and report it to be helpful even during redeployment and in the unfamiliar environment of intensive care.

Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential.

Clinical outcomes for patients with COVID-19 are heterogeneous and there is interest in defining subgroups for prognostic modeling and development of treatment algorithms. We obtained 28 demographic and laboratory variables in patients admitted to hospital with COVID-19. These comprised a training cohort (n = 6099) and two validation cohorts during the first and second waves of the pandemic (n = 996; n = 1011). Uniform manifold approximation and projection (UMAP) dimension reduction and Gaussian mixture model (GMM) analysis was used to define patient clusters. 29 clusters were defined in the training cohort and associated with markedly different mortality rates, which were predictive within confirmation datasets. Deconvolution of clinical features within clusters identified unexpected relationships between variables. Integration of large datasets using UMAP-assisted clustering can therefore identify patient subgroups with prognostic information and uncovers unexpected interactions between clinical variables. This application of machine learning represents a powerful approach for delineating disease pathogenesis and potential therapeutic interventions.

Rapid adaptation of a local healthcare digital system to COVID-19: The experience in Birmingham (UK).

BACKGROUND: The COVID-19 pandemic created unprecedented pressure on hospitals globally. Digital tools developed before the crisis provided novel aspects of management, and new digital tools were rapidly developed as the crisis progressed. In our institution, a digitally mature NHS Trust in England which builds software systems, development during the early months of the crisis allowed increased patient safety and care, efficient management of the hospital and publication of data. The aim of this paper is to present this experience as a case study, describing development and lessons learned applicable to wider electronic healthcare record development. METHODS: Request, triage, build and test processes for the digital systems were altered in response to the pandemic. Senior Responsible Officers appointed for the emergency triaged all changes and were supported by expert opinion and research active clinicians. Build and test cycles were compressed. New tools were built or existing ones modified in the central Electronic Healthcare Record, PICS (Prescribing, Information and Communication System), Clinical Dashboards and video platforms for remote consultation were developed. FINDINGS: 2236 patients were admitted to UHB with suspected COVID-19 between March and May 2020. Dashboards and visualisation tools enabled by efficient real-time data collection for all new patients, contributed to strategic, operational and clinical decision making.Over 70 urgent changes were made to digital systems, including a screening proforma, improved infection control functions, help and order panels, data dashboards, and updated prescribing features. Novel uses were found for existing functions. INTERPRETATION: Digital tools contributed to a co-ordinated response to COVID-19 in an area with a high disease burden. Change management processes were modified during the pandemic and successfully delivered rapid software modifications and new tools. Principal benefits came from the ability to adapt systems to rapidly changing clinical situations. Lessons learned from this intense development period are widely applicable to EHR development. LAY SUMMARY: Digital tools, which are well designed, can help clinicians and safeguard patients. Health crises such as the COVID pandemic drove rapid development of digital tools. This case study outlines accelerated development within a governance framework that successfully reused existing tools and built new ones. The lessons from this development are generalizable to digital developments in healthcare.

Increase in recruitment upon integration of trial into a clinical care pathway: an observational study.

INTRODUCTION: Many respiratory clinical trials fail to reach their recruitment target and this problem exacerbates existing funding issues. Integration of the clinical trial recruitment process into a clinical care pathway (CCP) may represent an effective way to significantly increase recruitment numbers. METHODS: A respiratory support unit and a CCP for escalation of patients with severe COVID-19 were established on 11 January 2021. The recruitment process for the Randomised Evaluation of COVID-19 Therapy-Respiratory Support trial was integrated into the CCP on the same date. Recruitment data for the trial were collected before and after integration into the CCP. RESULTS: On integration of the recruitment process into a CCP, there was a significant increase in recruitment numbers. Fifty patients were recruited over 266 days before this process occurred whereas 108 patients were recruited over 49 days after this process. There was a statistically significant increase in both the proportion of recruited patients relative to the number of COVID-19 hospital admissions (change from 2.8% to 9.1%, p<0.0001) and intensive therapy unit admissions (change from 17.8% to 50.2%, p<0.001) over the same period, showing that this increase in recruitment was independent of COVID-19 prevalence. DISCUSSION: Integrating the trial recruitment process into a CCP can significantly boost recruitment numbers. This represents an innovative model that can be used to maximise recruitment without impacting on the financial and labour costs associated with the running of a respiratory clinical trial.

Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study.

BACKGROUND: The National Early Warning Score (NEWS2) is currently recommended in the UK for the risk stratification of COVID-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate NEWS2 for the prediction of severe COVID-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of NEWS2 alone for medium-term risk stratification. METHODS: Training cohorts comprised 1276 patients admitted to King's College Hospital National Health Service (NHS) Foundation Trust with COVID-19 disease from 1 March to 30 April 2020. External validation cohorts included 6237 patients from five UK NHS Trusts (Guy's and St Thomas' Hospitals, University Hospitals Southampton, University Hospitals Bristol and Weston NHS Foundation Trust, University College London Hospitals, University Hospitals Birmingham), one hospital in Norway (Oslo University Hospital), and two hospitals in Wuhan, China (Wuhan Sixth Hospital and Taikang Tongji Hospital). The outcome was severe COVID-19 disease (transfer to intensive care unit (ICU) or death) at 14 days after hospital admission. Age, physiological measures, blood biomarkers, sex, ethnicity, and comorbidities (hypertension, diabetes, cardiovascular, respiratory and kidney diseases) measured at hospital admission were considered in the models. RESULTS: A baseline model of 'NEWS2 + age' had poor-to-moderate discrimination for severe COVID-19 infection at 14 days (area under receiver operating characteristic curve (AUC) in training cohort = 0.700, 95% confidence interval (CI) 0.680, 0.722; Brier score = 0.192, 95% CI 0.186, 0.197). A supplemented model adding eight routinely collected blood and physiological parameters (supplemental oxygen flow rate, urea, age, oxygen saturation, C-reactive protein, estimated glomerular filtration rate, neutrophil count, neutrophil/lymphocyte ratio) improved discrimination (AUC = 0.735; 95% CI 0.715, 0.757), and these improvements were replicated across seven UK and non-UK sites. However, there was evidence of miscalibration with the model tending to underestimate risks in most sites. CONCLUSIONS: NEWS2 score had poor-to-moderate discrimination for medium-term COVID-19 outcome which raises questions about its use as a screening tool at hospital admission. Risk stratification was improved by including readily available blood and physiological parameters measured at hospital admission, but there was evidence of miscalibration in external sites. This highlights the need for a better understanding of the use of early warning scores for COVID.

Monitoring indirect impact of COVID-19 pandemic on services for cardiovascular diseases in the UK.

OBJECTIVE: To monitor hospital activity for presentation, diagnosis and treatment of cardiovascular diseases during the COVID-19) pandemic to inform on indirect effects. METHODS: Retrospective serial cross-sectional study in nine UK hospitals using hospital activity data from 28 October 2019 (pre-COVID-19) to 10 May 2020 (pre-easing of lockdown) and for the same weeks during 2018-2019. We analysed aggregate data for selected cardiovascular diseases before and during the epidemic. We produced an online visualisation tool to enable near real-time monitoring of trends. RESULTS: Across nine hospitals, total admissions and emergency department (ED) attendances decreased after lockdown (23 March 2020) by 57.9% (57.1%-58.6%) and 52.9% (52.2%-53.5%), respectively, compared with the previous year. Activity for cardiac, cerebrovascular and other vascular conditions started to decline 1-2 weeks before lockdown and fell by 31%-88% after lockdown, with the greatest reductions observed for coronary artery bypass grafts, carotid endarterectomy, aortic aneurysm repair and peripheral arterial disease procedures. Compared with before the first UK COVID-19 (31 January 2020), activity declined across diseases and specialties between the first case and lockdown (total ED attendances relative reduction (RR) 0.94, 0.93-0.95; total hospital admissions RR 0.96, 0.95-0.97) and after lockdown (attendances RR 0.63, 0.62-0.64; admissions RR 0.59, 0.57-0.60). There was limited recovery towards usual levels of some activities from mid-April 2020. CONCLUSIONS: Substantial reductions in total and cardiovascular activities are likely to contribute to a major burden of indirect effects of the pandemic, suggesting they should be monitored and mitigated urgently.

Ethnicity and risk of death in patients hospitalised for COVID-19 infection in the UK: an observational cohort study in an urban catchment area.

BACKGROUND: Studies suggest that certain black and Asian minority ethnic groups experience poorer outcomes from COVID-19, but these studies have not provided insight into potential reasons for this. We hypothesised that outcomes would be poorer for those of South Asian ethnicity hospitalised from a confirmed SARS-CoV-2 infection, once confounding factors, health-seeking behaviours and community demographics were considered, and that this might reflect a more aggressive disease course in these patients. METHODS: Patients with confirmed SARS-CoV-2 infection requiring admission to University Hospitals Birmingham NHS Foundation Trust (UHB) in Birmingham, UK between 10 March 2020 and 17 April 2020 were included. Standardised admission ratio (SAR) and standardised mortality ratio (SMR) were calculated using observed COVID-19 admissions/deaths and 2011 census data. Adjusted HR for mortality was estimated using Cox proportional hazard model adjusting and propensity score matching. RESULTS: All patients admitted to UHB with COVID-19 during the study period were included (2217 in total). 58% were male, 69.5% were white and the majority (80.2%) had comorbidities. 18.5% were of South Asian ethnicity, and these patients were more likely to be younger and have no comorbidities, but twice the prevalence of diabetes than white patients. SAR and SMR suggested more admissions and deaths in South Asian patients than would be predicted and they were more likely to present with severe disease despite no delay in presentation since symptom onset. South Asian ethnicity was associated with an increased risk of death, both by Cox regression (HR 1.4, 95% CI 1.2 to 1.8), after adjusting for age, sex, deprivation and comorbidities, and by propensity score matching, matching for the same factors but categorising ethnicity into South Asian or not (HR 1.3, 95% CI 1.0 to 1.6). CONCLUSIONS: Those of South Asian ethnicity appear at risk of worse COVID-19 outcomes. Further studies need to establish the underlying mechanistic pathways.