Comparison of CATs, CURB-65 and PMEWS as triage tools in pandemic influenza admissions to UK hospitals: case control analysis using retrospective data.
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ABSTRACT:
SUBMITTER: Myles PR
PROVIDER: S-EPMC3317953 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:DNA Methylation profiles were generated for retrospective cases to support work into investigation of the immune environment in pediatric ependymoma. Samples were analyzed using the Illumina 450k beadchip and processed using the Heidelberg classifier (v11.2b and subsequently v12.3 for subgrouping/subtyping). The aim of the study was to better understand the immune-tumor microenvironment in pediatric ependymoma and the methylation profiles support the diagnoses of each case.
2023-10-02 | GSE190798 | GEO
Project description:BackgroundAccurate severity assessment is crucial to the initial management of community-acquired pneumonia (CAP). The CURB-65 (confusion, uremia, respiratory rate, BP, age ? 65 years) score contains data that are entered routinely in electronic medical records and are, thus, electronically calculable. The aim of this study was to determine whether an electronically generated severity estimate using CURB-65 elements as continuous and weighted variables better predicts 30-day mortality than the traditional CURB-65.MethodsIn a retrospective cohort study at a US university-affiliated community teaching hospital, we identified 2,069 patients aged 18 years or older with CAP confirmed by radiographic findings in the ED. CURB-65 elements were extracted from the electronic medical record, and 30-day mortality was identified with the Utah Population Database. Performance of a severity prediction model using continuous and weighted CURB-65 variables was compared with the traditional CURB-65 in the US derivation population and validated in the original 1,048 patients from the CURB-65 international derivation study.ResultsThe traditional, binary CURB-65 score predicted mortality in the US cohort with an area under the curve (AUC) of 0.82. Our severity prediction model generated from continuous, weighted CURB-65 elements was superior to the traditional CURB-65, with an out-of-bag AUC of 0.86 (P < .001). This finding was validated in the international database, with an AUC of 0.85 for the electronic model compared with 0.80 for the traditional CURB-65 (P = .01).ConclusionsUsing CURB-65 elements as continuous and weighted data improved prediction of 30-day mortality and could be used as a real-time, electronic decision support tool or to adjust outcomes by severity when comparing processes of care.
| S-EPMC3198487 | biostudies-literature
Project description:BackgroundNatural disasters, conflict, and terrorism are major global causes of death and disability. Central to the healthcare response is triage, vital to ensure the right care is provided to the right patient at the right time. The ideal triage tool has high sensitivity for the highest priority (P1) patients with acceptably low over-triage. This study compared the performance of major incident triage tools in predicting P1 casualty status in adults in the prospective UK Trauma Audit and Research Network (TARN) registry.MethodsTARN patients aged 16+ years (January 2008-December 2017) were included. Ten existing triage tools were applied using patients' first recorded pre-hospital physiology. Patients were subsequently assigned triage categories (P1, P2, P3, Expectant or Dead) based on pre-defined, intervention-based criteria. Tool performance was assessed by comparing tool-predicted and intervention-based priority status.Findings195,709 patients were included; mortality was 7·0% (n=13,601); median Injury Severity Score (ISS) was 9 (IQR 9-17); 97·1% sustained blunt injuries. 22,144 (11·3%) patients fulfilled intervention-based criteria for P1 status, exhibiting higher mortality (12·8% vs. 5·0%, p<0.001), increased intensive care requirement (52·4% vs 5·0%, p<0.001), and more severe injuries (median ISS 21 vs 9, p<0.001) compared with P2 patients.In 16-64 year olds, the highest performing tool was the Battlefield Casualty Drills (BCD) Triage Sieve (Prediction of P1 status: 70·4% sensitivity, over-triage 70·9%, area under the receiver operating curve (AUC) 0·068 [95%CI 0·676-0·684]). The UK National Ambulance Resilience Unit (NARU) Triage Sieve had sensitivity of 44·9%; over-triage 56·4%; AUC 0·666 (95%CI 0·662-0·670). All tools performed poorly amongst the elderly (65+ years).InterpretationThe BCD Triage Sieve performed best in this nationally representative population; we recommend it supersede the NARU Triage Sieve as the UK primary major incident triage tool. Validated triage category definitions are recommended for appraising future major incidents.FundingThis study is funded by the National Institute for Health Research (NIHR) Surgical Reconstruction and Microbiology Research Centre. GVG also acknowledges support from the MRC Heath Data Research UK (HDRUK/CFC/01). The views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care, or the Ministry of Defence.
| S-EPMC8257989 | biostudies-literature
Project description:A recently developed pneumonia caused by SARS-CoV-2 has quickly spread across the world. Unfortunately, a simplified risk score that could easily be used in primary care or general practice settings has not been developed. The objective of this study is to identify a simplified risk score that could easily be used to quickly triage severe COVID-19 patients. All severe and critical adult patients with laboratory-confirmed COVID-19 on the West campus of Union Hospital, Wuhan, China, from 28 January 2020 to 29 February 2020 were included in this study. Clinical data and laboratory results were obtained. CURB-65 pneumonia score was calculated. Univariate logistic regressions were applied to explore risk factors associated with in-hospital death. We used the receiver operating characteristic curve and multivariate COX-PH model to analyse risk factors for in-hospital death. A total of 74 patients (31 died, 43 survived) were finally included in the study. We observed that compared with survivors, non-survivors were older and illustrated higher respiratory rate, neutrophil-to-lymphocyte ratio, D-dimer and lactate dehydrogenase (LDH), but lower SpO2 as well as impaired liver function, especially synthesis function. CURB-65 showed good performance for predicting in-hospital death (area under curve 0.81, 95% confidence interval (CI) 0.71-0.91). CURB-65 ⩾ 2 may serve as a cut-off value for prediction of in-hospital death in severe patients with COVID-19 (sensitivity 68%, specificity 81%, F1 score 0.7). CURB-65 (hazard ratio (HR) 1.61; 95% CI 1.05-2.46), LDH (HR 1.003; 95% CI 1.001-1.004) and albumin (HR 0.9; 95% CI 0.81-1) were risk factors for in-hospital death in severe patients with COVID-19. Our study indicates CURB-65 may serve as a useful prognostic marker in COVID-19 patients, which could be used to quickly triage severe patients in primary care or general practice settings.
| S-EPMC7573457 | biostudies-literature
Project description:We sought to estimate age-specific hospitalization rates attributed to influenza and other virus for adults.Admissions from Canada's national hospitalization database (Canadian Institute of Health Information), from 1994/95 to 1999/2000, were modeled as a function of proxy variables for influenza, respiratory syncytial virus (RSV) and other viral activity, seasonality and trend using a Poisson regression model and stratified by age group.The average annual influenza-attributed hospitalization rate for all adults, 20 years of age or older, over the study period, which included three severe seasons, was an estimated 65/100,000 population (95% CI 63-67). Among persons aged 65 and over, 270-340 admissions per 100,000 population per year were attributed to influenza, while 30-110, 60-90 and 130-350 per 100,000 were attributed to RSV, parainfluenza (PIV) and other respiratory viruses, respectively. Although marked season-to-season variation in age-specific hospitalization rates attributable to influenza was observed in persons 50 years of age and older, increasing risk with age was preserved at all time periods.Influenza, RSV, PIV and other respiratory viruses were all associated with morbidity requiring hospitalization, while influenza was responsible for peak respiratory admissions. The burden of health care utilization associated with respiratory viruses is appreciable beginning in the sixth decade and increases significantly with age.
| S-EPMC4634329 | biostudies-other
Project description:The COVID-19 pandemic has strong effects on most health care systems. Forecasting of admissions can help for the efficient organisation of hospital care. We aimed to forecast the number of admissions to psychiatric hospitals before and during the COVID-19 pandemic and we compared the performance of machine learning models and time series models. This would eventually allow to support timely resource allocation for optimal treatment of patients. We used admission data from 9 psychiatric hospitals in Germany between 2017 and 2020. We compared machine learning models with time series models in weekly, monthly and yearly forecasting before and during the COVID-19 pandemic. A total of 90,686 admissions were analysed. The models explained up to 90% of variance in hospital admissions in 2019 and 75% in 2020 with the effects of the COVID-19 pandemic. The best models substantially outperformed a one-step seasonal naïve forecast (seasonal mean absolute scaled error (sMASE) 2019: 0.59, 2020: 0.76). The best model in 2019 was a machine learning model (elastic net, mean absolute error (MAE): 7.25). The best model in 2020 was a time series model (exponential smoothing state space model with Box-Cox transformation, ARMA errors and trend and seasonal components, MAE: 10.44). Models forecasting admissions one week in advance did not perform better than monthly and yearly models in 2019 but they did in 2020. The most important features for the machine learning models were calendrical variables. Model performance did not vary much between different modelling approaches before the COVID-19 pandemic and established forecasts were substantially better than one-step seasonal naïve forecasts. However, weekly time series models adjusted quicker to the COVID-19 related shock effects. In practice, multiple individual forecast horizons could be used simultaneously, such as a yearly model to achieve early forecasts for a long planning period and weekly models to adjust quicker to sudden changes.
| S-EPMC9508170 | biostudies-literature
Project description:checklists are increasingly proposed as a means to enhance safety and quality of care. However, their use has been met with variable levels of success. The Frailsafe project focused on introducing a checklist with the aim to increase completion of key clinical assessments and to facilitate communication for the care of older patients in acute admissions.to examine the use of the Frailsafe checklist, including potential to contribute to improved safety, quality and reliability of care.110 qualitative interviews and group discussions with healthcare professionals and other specialties, 172 h of ethnographic observation in 12 UK hospitals and reporting of high-level process data (completion of checklist and relevant frailty assessments). Qualitative analysis followed a thematic and theory-driven approach.through use of the checklist, hospital teams identified limitations in their existing assessments (e.g. absence of delirium protocols) and practices (e.g. unnecessary catheter use). This contributed to hospitals reporting just 24.0% of sampled patients as having received all clinical assessments across key domains for this population for the duration of the project (1,687/7,021 checklists as fully completed). Staff perceptions and experiences of using the checklist varied significantly, primarily driven by the extent to which the aims of this quality improvement project aligned with local service priorities and pre-existing team communications styles.the Frailsafe checklist highlighted limitations with frailty assessment in acute care and motivated teams to review routine practices. Further work is needed to understand whether and how checklists can be embedded in complex, multidisciplinary care.
| S-EPMC6016694 | biostudies-other
Project description:Aim of this study was to develop a new simpler and more effective severity score for community-acquired pneumonia (CAP) patients. A total of 1640 consecutive hospitalized CAP patients in Second Affiliated Hospital of Zhejiang University were included. The effectiveness of different pneumonia severity scores to predict mortality was compared, and the performance of the new score was validated on an external cohort of 1164 patients with pneumonia admitted to a teaching hospital in Italy. Using age ≥ 65 years, LDH > 230 u/L, albumin < 3.5 g/dL, platelet count < 100 × 10(9)/L, confusion, urea > 7 mmol/L, respiratory rate ≥ 30/min, low blood pressure, we assembled a new severity score named as expanded-CURB-65. The 30-day mortality and length of stay were increased along with increased risk score. The AUCs in the prediction of 30-day mortality in the main cohort were 0.826 (95% CI, 0.807-0.844), 0.801 (95% CI, 0.781-0.820), 0.756 (95% CI, 0.735-0.777), 0.793 (95% CI, 0.773-0.813) and 0.759 (95% CI, 0.737-0.779) for the expanded-CURB-65, PSI, CURB-65, SMART-COP and A-DROP, respectively. The performance of this bedside score was confirmed in CAP patients of the validation cohort although calibration was not successful in patients with health care-associated pneumonia (HCAP). The expanded CURB-65 is objective, simpler and more accurate scoring system for evaluation of CAP severity, and the predictive efficiency was better than other score systems.
| S-EPMC4796818 | biostudies-other
Project description:IntroductionCanadian patients presenting to the emergency department (ED) typically undergo a triage process where they are assessed by a specially trained nurse and assigned a Canadian Triage and Acuity Scale (CTAS) score, indicating their level of acuity and urgency of assessment. We sought to assess the ability of patients to self-triage themselves through use of one of two of our proprietary self-triage tools, and how this would compare with the standard nurse-driven triage process.MethodsWe enrolled a convenience sample of ambulatory ED patients aged 17 years or older who presented with chief complaints of chest pain, abdominal pain, breathing problems, or musculoskeletal pain. Participants completed one, or both, of an algorithm generated self-triage (AGST) survey, or visual acuity scale (VAS) based self-triage tool which subsequently generated a CTAS score. Our primary outcome was to assess the accuracy of these tools to the CTAS score generated through the nurse-driven triage process.ResultsA total of 223 patients were included in our analysis. Of these, 32 (14.3%) presented with chest pain, 25 (11.2%) with shortness of breath, 75 (33.6%) with abdominal pain, and 91 (40.8%) with musculoskeletal pain. Of the total number of patients, 142 (47.2%) completed the AGST tool, 159 (52.8%) completed the VAS tool and 78 (25.9%) completed both tools. When compared to the nurse-driven triage standard, both the AGST and VAS tools had poor levels of agreement for each of the four presenting complaints.ConclusionsSelf-triage through use of an AGST or VAS tool is inaccurate compared to the established standard of nurse-driven triage. Although existing literature exists which suggests that self-triage tools developed for specific subsets of complaints may be feasible, our results would suggest that adopting the self-triage approach on a broader scale for all-comers to the ED does not appear to be a viable option to enhance the current triage process. Further study is required to show if self-triage can be used in the ED to optimize the triage process.
| S-EPMC11356451 | biostudies-literature
Project description:ObjectiveTo evaluate the association between weekend admission to hospital and 11 hospital acquired conditions recently considered by the Centers for Medicare and Medicaid as "never events" for which resulting healthcare costs are not reimbursed.DesignNational analysis.SettingUS Nationwide Inpatient Sample discharge database.Participants351 million patients discharged from US hospitals, 2002-10.Main outcome measuresUnivariate rates and multivariable likelihood of hospital acquired conditions among patients admitted on weekdays versus weekends, as well as the impacts of these events on prolonged length of stay and total inpatient charges.ResultsFrom 2002 to 2010, 351,170,803 patients were admitted to hospital, with 19% admitted on a weekend. Hospital acquired conditions occurred at an overall frequency of 4.1% (5.7% among weekend admissions versus 3.7% among weekday admissions). Adjusting for patient and hospital cofactors the probability of having one or more hospital acquired conditions was more than 20% higher in weekend admissions compared with weekday admissions (odds ratio 1.25, 95% confidence interval 1.24 to 1.26, P<0.01). Hospital acquired conditions have a negative impact on both hospital charges and length of stay. At least one hospital acquired condition was associated with an 83% (1.83, 1.77 to 1.90, P<0.01) likelihood of increased charges and 38% likelihood of prolonged length of stay (1.38, 1.36 to 1.41, P<0.01).ConclusionWeekend admission to hospital is associated with an increased likelihood of hospital acquired condition, cost, and length of stay. Future protocols and staffing regulations must be tailored to the requirements of this high risk subgroup.
| S-EPMC4398994 | biostudies-literature