Yes / Objectives The Computer-Aided Risk Score (CARS) estimates the risk of death following emergency admission to medical wards using routinely collected vital signs and blood test data. Our aim was to elicit the views of healthcare practitioners (staff) and service users and carers (SU/C) on (1) the potential value, unintended consequences and concerns associated with CARS and practitioner views on (2) the issues to consider before embedding CARS into routine practice. Setting This study was conducted in two National Health Service (NHS) hospital trusts in the North of England. Both had in-house information technology (IT) development teams, mature IT infrastructure with electronic National Early Warning Score (NEWS) and were capable of integrating NEWS with blood test results. The study focused on emergency medical and elderly admissions units. There were 60 and 39 acute medical/elderly admissions beds at the two NHS hospital trusts. Participants We conducted eight focus groups with 45 healthcare practitioners and two with 11 SU/Cs in two NHS acute hospitals. Results Staff and SU/Cs recognised the potential of CARS but were clear that the score should not replace or undermine clinical judgments. Staff recognised that CARS could enhance clinical decision-making/judgments and aid communication with patients. They wanted to understand the components of CARS and be reassured about its accuracy but were concerned about the impact on intensive care and blood tests. Conclusion Risk scores are widely used in healthcare, but their development and implementation do not usually involve input from practitioners and SU/Cs. We contributed to the development of CARS by eliciting views of staff and SU/Cs who provided important, often complex, insights to support the development and implementation of CARS to ensure successful implementation in routine clinical practice. / Health Foundation, National Institute for Health Research (NIHR) Yorkshire and Humber Patient Safety Translational Research Centre (NIHR Yorkshire and Humber PSTRC)

Yes / Objectives There are no established mortality risk equations specifically for emergency medical patients who are admitted to a general hospital ward. Such risk equations may be useful in supporting the clinical decision-making process. We aim to develop and externally validate a computer-aided risk of mortality (CARM) score by combining the first electronically recorded vital signs and blood test results for emergency medical admissions. Design Logistic regression model development and external validation study. Setting Two acute hospitals (Northern Lincolnshire and Goole NHS Foundation Trust Hospital (NH)—model development data; York Hospital (YH)—external validation data). Participants Adult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic National Early Warning Score(s) and blood test results recorded on admission. Results The risk of in-hospital mortality following emergency medical admission was 5.7% (NH: 1766/30 996) and 6.5% (YH: 1703/26 247). The C-statistic for the CARM score in NH was 0.87 (95% CI 0.86 to 0.88) and was similar in an external hospital setting YH (0.86, 95% CI 0.85 to 0.87) and the calibration slope included 1 (0.97, 95% CI 0.94 to 1.00). Conclusions We have developed a novel, externally validated CARM score with good performance characteristics for estimating the risk of in-hospital mortality following an emergency medical admission using the patient’s first, electronically recorded, vital signs and blood test results. Since the CARM score places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure. / The Health Foundation, National Institute for Health Research (NIHR) Yorkshire and Humberside Patient Safety Translational Research Centre

Yes / Routine administrative data have been used to show that patients admitted to hospitals over the weekend appear to have a higher mortality compared to weekday admissions. Such data do not take the severity of sickness of a patient on admission into account. Our aim was to incorporate a standardized vital signs physiological-based measure of sickness known as the National Early Warning Score to investigate if weekend admissions are: sicker as measured by their index National Early Warning Score; have an increased mortality; and experience longer delays in the recording of their index National Early Warning Score. Methods: We extracted details of all adult emergency medical admissions during 2014 from hospital databases and linked these with electronic National Early Warning Score data in four acute hospitals. We analysed 47,117 emergency admissions after excluding 1657 records, where National Early Warning Score was missing or the first (index) National Early Warning Score was recorded outside ±24 h of the admission time. Results: Emergency medical admissions at the weekend had higher index National Early Warning Score (weekend: 2.53 vs. weekday: 2.30, p

Yes / Objectives: To develop a logistic regression model to predict the risk of sepsis following emergency medical admission using the patient’s first, routinely collected, electronically recorded vital signs and blood test results and to validate this novel computer-aided risk of sepsis model, using data from another hospital. Design: Cross-sectional model development and external validation study reporting the C-statistic based on a validated optimized algorithm to identify sepsis and severe sepsis (including septic shock) from administrative hospital databases using International Classification of Diseases, 10th Edition, codes. Setting: Two acute hospitals (York Hospital - development data; Northern Lincolnshire and Goole Hospital - external validation data). Patients: Adult emergency medical admissions discharged over a 24-month period with vital signs and blood test results recorded at admission. Interventions: None. Main Results: The prevalence of sepsis and severe sepsis was lower in York Hospital (18.5% = 4,861/2,6247; 5.3% = 1,387/2,6247) than Northern Lincolnshire and Goole Hospital (25.1% = 7,773/30,996; 9.2% = 2,864/30,996). The mortality for sepsis (York Hospital: 14.5% = 704/4,861; Northern Lincolnshire and Goole Hospital: 11.6% = 899/7,773) was lower than the mortality for severe sepsis (York Hospital: 29.0% = 402/1,387; Northern Lincolnshire and Goole Hospital: 21.4% = 612/2,864). The C-statistic for computer-aided risk of sepsis in York Hospital (all sepsis 0.78; sepsis: 0.73; severe sepsis: 0.80) was similar in an external hospital setting (Northern Lincolnshire and Goole Hospital: all sepsis 0.79; sepsis: 0.70; severe sepsis: 0.81). A cutoff value of 0.2 gives reasonable performance. Conclusions: We have developed a novel, externally validated computer-aided risk of sepsis, with reasonably good performance for estimating the risk of sepsis for emergency medical admissions using the patient’s first, electronically recorded, vital signs and blood tests results. Since computer-aided risk of sepsis places no additional data collection burden on clinicians and is automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure. / Health Foundation

Long-term care facilities (LTCF) were impacted disproportionately by the coronavirus (COVID-19), suggesting their high risk for community-spread pandemics. This three-article dissertation with publications aims to a) aggregate the emerging research evidence of factors for nursing home residents' COVID-19 infections; b) explore hospitalizations due to COVID-19 among emergency admissions and length of hospital stays for long-term care facility (LTCF) residents with dementia; and c). investigate how comorbidity index score mediates the relationship between COVID-19 hospitalization and discharge outcomes among LTCF residents with dementia. This dissertation consists of a three-article format: a mixed-methods systematic review and two retrospective cohort studies. The first study is a systematic review to summarize major factors of nursing home residents' COVID-19 infections over the pandemic period (January 1, 2020, to October 31, 2022) in the United States providing a context for the two empirical studies on COVID-19 hospitalization outcomes for LTCF residents with dementia. The second study is a cross-sectional study and utilizes Texas Inpatient Public Use Data File (PUDF) to compare COVID-19 hospitalization outcomes for LTCF residents with dementia aged over 60 years (n = 1,413) and those without dementia (n = 1,674) during period January 2020 to October 2022. Logistic regression is used to predict emergency admissions and length of hospital stay, with pre-existing conditions mediating the relationship. The third is a cross-sectional study and uses the same dataset and criterion from the second study. Logistic regression, mediation analysis, and moderation analysis are used to investigate the effect of comorbidity index score and health insurance status on the association between dementia status and place of live discharge, while controlling for sociodemographic factors such as age cohort, race, and gender. Findings from the mix-method systematic review of 48 articles yielded evidence to suggest risk factors associated with COVID-19 infections among nursing home residents in the USA by geography, demography, type of nursing home, staffing, resident's status, and COVID-19 vaccination status through 48 articles. The second study found that with COVID-19 hospitalization, a diagnosis of dementia and preexisting conditions was significantly associated with emergency admission (OR = 1.70; 95%CI = 1.40-2.06) and shorter hospital stays (OR = 0.64; 95%CI = 0.55-0.74) when considering, adjusting for confounders such as demographics, health insurance, and lifestyle. In the third study, dementia diagnosis with COVID-19 hospitalization increased the likelihood of discharge to hospice care (OR = 1.44, 95% CI = 1.16-1.80), followed by LTCF (OR = 1.42, 95% CI = 1.23-1.65), but decreased the likelihood of discharge to recovery hospitals (OR = 0.70, 95% CI = 0.52-0.94). The findings highlight the increased risk of COVID-19 hospitalization disparities among individuals with dementia. Targeted health support programs for LTCF residents with dementia would enhance their COVID-19 hospitalization outcomes. Discharge plans for COVID-19 patients with dementia should be customized to their care needs, including hospice care, to minimize healthcare disparities compared to other residents. Further study is needed as to why recovery hospitals are less preferred for live discharge of COVID-19 patients with dementia diagnosis.

Yes / In English hospitals, the patient’s vital signs are monitored and summarised into a National Early Warning Score (NEWS). NEWS is more accurate than the quick sepsis related organ failure assessment (qSOFA) score at identifying patients with sepsis. We investigate the extent to which the accuracy of the NEWS is enhanced by developing computer-aided NEWS (cNEWS) models. We compared three cNEWS models (M0=NEWS alone; M1=M0 + age + sex; M2=M1 + subcomponents of NEWS + diastolic blood pressure) to predict the risk of sepsis. Methods: All adult emergency medical admissions discharged over 24-months from two acute hospitals (YH–York Hospital for model development; NH–Northern Lincolnshire and Goole Hospital for external model validation). We used a validated Canadian method for defining sepsis from administrative hospital data. Findings: The prevalence of sepsis was lower in YH (4.5%=1596/35807) than NH (8.5%=2983/35161). The c-statistic increased across models (YH: M0: 0.705, M1:0.763, M2:0.777; NH:M0: 0.708, M1:0.777, M2:0.791). At NEWS 5+, sensitivity increased (YH: 47.24% vs 50.56% vs 52.69%; NH: 37.91% vs 43.35% vs 48.07%)., the positive likelihood ratio increased (YH: 2.77 vs 2.99 vs 3.06; NH: 3.18 vs 3.32 vs 3.45) and the positive predictive value increased (YH: 11.44% vs 12.24% vs 12.49%; NH: 22.75% vs 23.55% vs 24.21%). Interpretation: From the three cNEWS models, Model M2 is the most accurate. Since it places no additional data collection burden on clinicians and can be automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure. / The Health Foundation, National Institute for Health Research (NIHR) Yorkshire and Humberside Patient Safety Translational Research Centre / Research Development Fund Publication Prize Award winner, April 2019.