RESUMEN
We have previously described an open-source data-driven modelling technique that has been used to model critical care resource provision as well as expanded to elective surgery and even whole-hospital modelling. Here, we describe the use of this technique to model patient flow and resource use across the West Yorkshire Critical Care Network, with the advantage that recommendations can be made at an individual unit level for future resource provision, taking into account changes in population numbers and demography over the coming decade. We will be using this approach in other regions around the UK to help predict future critical care capacity requirements.
RESUMEN
The richness of linked population data provides exciting opportunities to understand local health needs, identify and predict those in most need of support and evaluate health interventions. There has been extensive investment to unlock the potential of clinical data for health research in the UK. However, most of the determinants of our health are social, economic, education, environmental, housing, food systems and are influenced by local authorities. The Connected Bradford Whole System Data Linkage Accelerator was set up to link health, education, social care, environmental and other local government data to drive learning health systems, prevention and population health management. Data spanning a period of over forty years has been linked for 800,000 individuals using the pseudonymised NHS number and other data variables. This prospective data collection captures near real time activity. This paper describes the dataset and our Connected Bradford Whole System Data Accelerator Framework that covers public engagement; practitioner and policy integration; legal and ethical approvals; information governance; technicalities of data linkage; data curation and guardianship; data validity and visualisation.
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Background: Guidance in COVID-19 respiratory failure has favoured early intubation, with concerns over the use of CPAP. We adopted early CPAP and self-proning, and evaluated the safety and efficacy of this approach. Methods: This retrospective observational study included all patients with a positive COVID-19 PCR, and others with high clinical suspicion. Our protocol advised early CPAP and self-proning for severe cases, aiming to prevent rather than respond to deterioration. CPAP was provided outside critical care by ward staff supported by physiotherapists and an intensive critical care outreach program. Data were analysed descriptively and compared against a large UK cohort (ISARIC). Results: 559 patients admitted before 1 May 2020 were included. 376 were discharged alive, and 183 died. 165 patients (29.5%) received CPAP, 40 (7.2%) were admitted to critical care and 28 (5.0%) were ventilated. Hospital mortality was 32.7%, and 50% for critical care. Following CPAP, 62% of patients with S:F or P:F ratios indicating moderate or severe ARDS, who were candidates for escalation, avoided intubation. Figures for critical care admission, intubation and hospital mortality are lower than ISARIC, whilst critical care mortality is similar. Following ISARIC proportions we would have admitted 92 patients to critical care and intubated 55. Using the described protocol, we intubated 28 patients from 40 admissions, and remained within our expanded critical care capacity. Conclusion: Bradford's protocol produced good results despite our population having high levels of co-morbidity and ethnicities associated with poor outcomes. In particular we avoided overloading critical care capacity. We advocate this approach as both effective and safe.
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Background: The coronavirus disease 2019 (COVID-19) pandemic has resulted in thousands of deaths in the UK. Those with existing comorbidities and minority ethnic groups have been found to be at increased risk of mortality. We wished to determine if there were any differences in intensive care unit (ICU) admission and 30-day hospital mortality in a city with high levels of deprivation and a large community of people of South Asian heritage. Methods: Detailed information on 622 COVID-19-positive inpatients in Bradford and Calderdale between February-August 2020 were extracted from Electronic Health Records. Logistic regression and Cox proportional hazards models were used to explore the relationship between ethnicity with admission to ICU and 30-day mortality, respectively accounting for the effect of demographic and clinical confounders. Results: The sample consisted of 408 (70%) White, 142 (24%) South Asian and 32 (6%) other minority ethnic patients. Ethnic minority patients were younger, more likely to live in deprived areas, and be overweight/obese, have type 2 diabetes, hypertension and asthma compared to white patients, but were less likely to have cancer (South Asian patients only) and COPD. Male and obese patients were more likely to be admitted to ICU, and patients of South Asian ethnicity, older age, and those with cancer were less likely. Being male, older age, deprivation, obesity, and cancer were associated with 30-day mortality. The risk of death in South Asian patients was the same as in white patients HR 1.03 (0.58, 1.82). Conclusions: Despite South Asian patients being less likely to be admitted to ICU and having a higher prevalence of diabetes and obesity, there was no difference in the risk of death compared to white patients. This contrasts with other findings and highlights the value of studies of communities which may have different ethnic, deprivation and clinical risk profiles.
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Modelling is an under-used tool in the NHS operationally; this is primarily due to a lack of familiarity, but also due to the complex nature of the healthcare system, lack of sufficiently detailed data, and difficulties trying to distil the heterogeneity of individual patient experience into manageable groups. This paper describes a model of patient flow and resource use on the critical care unit at Bradford Royal Infirmary, -produced using a novel technique which helps avoid these issues by using genuine routinely collected historical data in lieu of trying to model individual patients. This has had -unexpected benefits in terms of engagement with the model as it is much easier to justify its validity when it is based directly on real people. Going forward, we will use this approach to model an entire hospital.
