COVID-19 Vaccine Provider Access and Vaccination Coverage Among Children Aged 5-11 Years - United States, November 2021-January 2022.

On October 29, 2021, the Pfizer-BioNTech pediatric COVID-19 vaccine received Emergency Use Authorization for children aged 5-11 years in the United States.† For a successful immunization program, both access to and uptake of the vaccine are needed. Fifteen million doses were initially made available to pediatric providers to ensure the broadest possible access for the estimated 28 million eligible children aged 5-11 years, especially those in high social vulnerability index (SVI)§ communities. Initial supply was strategically distributed to maximize vaccination opportunities for U.S. children aged 5-11 years. COVID-19 vaccination coverage among persons aged 12-17 years has lagged (1), and vaccine confidence has been identified as a concern among parents and caregivers (2). Therefore, COVID-19 provider access and early vaccination coverage among children aged 5-11 years in high and low SVI communities were examined during November 1, 2021-January 18, 2022. As of November 29, 2021 (4 weeks after program launch), 38,732 providers were enrolled, and 92% of U.S. children aged 5-11 years lived within 5 miles of an active provider. As of January 18, 2022 (11 weeks after program launch), 39,786 providers had administered 13.3 million doses. First dose coverage at 4 weeks after launch was 15.0% (10.5% and 17.5% in high and low SVI areas, respectively; rate ratio [RR] = 0.68; 95% CI = 0.60-0.78), and at 11 weeks was 27.7% (21.2% and 29.0% in high and low SVI areas, respectively; RR = 0.76; 95% CI = 0.68-0.84). Overall series completion at 11 weeks after launch was 19.1% (13.7% and 21.7% in high and low SVI areas, respectively; RR = 0.67; 95% CI = 0.58-0.77). Pharmacies administered 46.4% of doses to this age group, including 48.7% of doses in high SVI areas and 44.4% in low SVI areas. Although COVID-19 vaccination coverage rates were low, particularly in high SVI areas, first dose coverage improved over time. Additional outreach is critical, especially in high SVI areas, to improve vaccine confidence and increase coverage rates among children aged 5-11 years.

Predictive accuracy of enhanced versions of the on-admission National Early Warning Score in estimating the risk of COVID-19 for unplanned admission to hospital: a retrospective development and validation study.

BACKGROUND: The novel coronavirus SARS-19 produces 'COVID-19' in patients with symptoms. COVID-19 patients admitted to the hospital require early assessment and care including isolation. The National Early Warning Score (NEWS) and its updated version NEWS2 is a simple physiological scoring system used in hospitals, which may be useful in the early identification of COVID-19 patients. We investigate the performance of multiple enhanced NEWS2 models in predicting the risk of COVID-19. METHODS: Our cohort included unplanned adult medical admissions discharged over 3 months (11 March 2020 to 13 June 2020 ) from two hospitals (YH for model development; SH for external model validation). We used logistic regression to build multiple prediction models for the risk of COVID-19 using the first electronically recorded NEWS2 within ± 24 hours of admission. Model M0' included NEWS2; model M1' included NEWS2 + age + sex, and model M2' extends model M1' with subcomponents of NEWS2 (including diastolic blood pressure + oxygen flow rate + oxygen scale). Model performance was evaluated according to discrimination (c statistic), calibration (graphically), and clinical usefulness at NEWS2 ≥ 5. RESULTS: The prevalence of COVID-19 was higher in SH (11.0 %=277/2520) than YH (8.7 %=343/3924) with a higher first NEWS2 scores ( SH 3.2 vs YH 2.8) but similar in-hospital mortality (SH 8.4 % vs YH 8.2 %). The c-statistics for predicting the risk of COVID-19 for models M0',M1',M2' in the development dataset were: M0': 0.71 (95 %CI 0.68-0.74); M1': 0.67 (95 %CI 0.64-0.70) and M2': 0.78 (95 %CI 0.75-0.80)). For the validation datasets the c-statistics were: M0' 0.65 (95 %CI 0.61-0.68); M1': 0.67 (95 %CI 0.64-0.70) and M2': 0.72 (95 %CI 0.69-0.75) ). The calibration slope was similar across all models but Model M2' had the highest sensitivity (M0' 44 % (95 %CI 38-50 %); M1' 53 % (95 %CI 47-59 %) and M2': 57 % (95 %CI 51-63 %)) and specificity (M0' 75 % (95 %CI 73-77 %); M1' 72 % (95 %CI 70-74 %) and M2': 76 % (95 %CI 74-78 %)) for the validation dataset at NEWS2 ≥ 5. CONCLUSIONS: Model M2' appears to be reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions.

Computer-aided National Early Warning Score to predict the risk of sepsis following emergency medical admission to hospital: a model development and external validation study.

BACKGROUND: In hospitals in England, patients' vital signs are monitored and summarized into the National Early Warning Score (NEWS); this score is more accurate than the Quick Sepsis-related Organ Failure Assessment (qSOFA) score at identifying patients with sepsis. We investigated the extent to which the accuracy of the NEWS is enhanced by developing and comparing 3 computer-aided NEWS (cNEWS) models (M0 = NEWS alone, M1 = M0 + age + sex, M2 = M1 + subcomponents of NEWS + diastolic blood pressure) to predict the risk of sepsis. METHODS: We included all emergency medical admissions of patients 16 years of age and older discharged over 24 months from 2 acute care hospital centres (York Hospital [YH] for model development and a combined data set from 2 hospitals [Diana, Princess of Wales Hospital and Scunthorpe General Hospital] in the Northern Lincolnshire and Goole National Health Service Foundation Trust [NH] for external model validation). We used a validated Canadian method for defining sepsis from administrative hospital data. RESULTS: The prevalence of sepsis was lower in YH (4.5%, 1596/35 807) than in NH (8.5%, 2983/35 161). 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). For NEWS of 5 or higher, sensitivity increased (YH: 47.24% v. 50.56% v. 52.69%; NH: 37.91% v. 43.35% v. 48.07%), the positive likelihood ratio increased (YH: 2.77 v. 2.99 v. 3.06; NH: 3.18 v. 3.32 v. 3.45) and the positive predictive value increased (YH: 11.44% v. 12.24% v. 12.49%; NH: 22.75% v. 23.55% v. 24.21%). INTERPRETATION: From the 3 cNEWS models, model M2 is the most accurate. Given that it places no additional burden of data collection on clinicians and can be automated, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

Development and External Validation of an Automated Computer-Aided Risk Score for Predicting Sepsis in Emergency Medical Admissions Using the Patient's First Electronically Recorded Vital Signs and Blood Test Results.

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.

Accuracy of automated computer-aided risk scoring systems to estimate the risk of COVID-19: a retrospective cohort study.

BACKGROUND: In the UK National Health Service (NHS), the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) score. A set of computer-aided risk scoring systems (CARSS) was developed and validated for predicting in-hospital mortality and sepsis in unplanned admission to hospital using NEWS and routine blood tests results. We sought to assess the accuracy of these models to predict the risk of COVID-19 in unplanned admissions during the first phase of the pandemic. METHODS: Adult ( > = 18 years) non-elective admissions discharged (alive/deceased) between 11-March-2020 to 13-June-2020 from two acute hospitals with an index NEWS electronically recorded within ± 24 h of admission. We identified COVID-19 admission based on ICD-10 code 'U071' which was determined by COVID-19 swab test results (hospital or community). We assessed the performance of CARSS (CARS_N, CARS_NB, CARM_N, CARM_NB) for predicting the risk of COVID-19 in terms of discrimination (c-statistic) and calibration (graphically). RESULTS: The risk of in-hospital mortality following emergency medical admission was 8.4% (500/6444) and 9.6% (620/6444) had a diagnosis of COVID-19. For predicting COVID-19 admissions, the CARS_N model had the highest discrimination 0.73 (0.71 to 0.75) and calibration slope 0.81 (0.72 to 0.89) compared to other CARSS models: CARM_N (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.47 (0.41 to 0.54)), CARM_NB (discrimination:0.68 (0.65 to 0.70) and calibration slope 0.37 (0.31 to 0.43)), and CARS_NB (discrimination:0.68 (0.66 to 0.70) and calibration slope 0.56 (0.47 to 0.64)). CONCLUSIONS: The CARS_N model is reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned admissions because it requires no additional data collection and is readily automated.

Implementation and clinical utility of a Computer-Aided Risk Score for Mortality (CARM): a qualitative study.

OBJECTIVES: The Computer-Aided Risk Score for Mortality (CARM) estimates the risk of in-hospital mortality following acute admission to the hospital by automatically amalgamating physiological measures, blood tests, gender, age and COVID-19 status. Our aims were to implement the score with a small group of practitioners and understand their first-hand experience of interacting with the score in situ. DESIGN: Pilot implementation evaluation study involving qualitative interviews. SETTING: This study was conducted in one of the two National Health Service hospital trusts in the North of England in which the score was developed. PARTICIPANTS: Medical, older person and ICU/anaesthetic consultants and specialist grade registrars (n=116) and critical outreach nurses (n=7) were given access to CARM. Nine interviews were conducted in total, with eight doctors and one critical care outreach nurse. INTERVENTIONS: Participants were given access to the CARM score, visible after login to the patients' electronic record, along with information about the development and intended use of the score. RESULTS: Four themes and 14 subthemes emerged from reflexive thematic analysis: (1) current use (including support or challenge clinical judgement and decision making, communicating risk of mortality and professional curiosity); (2) barriers and facilitators to use (including litigation, resource needs, perception of the evidence base, strengths and limitations), (3) implementation support needs (including roll-out and integration, access, training and education); and (4) recommendations for development (including presentation and functionality and potential additional data). Barriers and facilitators to use, and recommendations for development featured highly across most interviews. CONCLUSION: Our in situ evaluation of the pilot implementation of CARM demonstrated its scope in supporting clinical decision making and communicating risk of mortality between clinical colleagues and with service users. It suggested to us barriers to implementation of the score. Our findings may support those seeking to develop, implement or improve the adoption of risk scores.

Patterns of admissions in an acute medical unit: priorities for service development and education.

An Acute Medical Unit has recently been established at York Hospital. The present study sought to characterise the case mix of acutely unwell medical patients to allow identification of priorities for ongoing service development and to assess educational opportunities for trainees in the region. Data were collected for 16001 admission episodes between January 2010 and April 2011 inclusive. These allowed characterisation of the case mix, and identified key priorities where clinical pathway do not yet exist, namely heart failure, urinary tract infection, and acute diarrhoea. Good educational opportunities exist for most aspects of the Acute Medicine curriculum; several weaknesses were identified, and trainees might address these by undertaking a specific period of specialty training in endocrinology and neurology.

Development and validation of automated computer-aided risk scores to predict in-hospital mortality for emergency medical admissions with COVID-19: a retrospective cohort development and validation study.

OBJECTIVES: There are no established mortality risk equations specifically for unplanned emergency medical admissions which include patients with SARS-19 (COVID-19). We aim to develop and validate a computer-aided risk score (CARMc19) for predicting mortality risk by combining COVID-19 status, the first electronically recorded blood test results and the National Early Warning Score (NEWS2). DESIGN: Logistic regression model development and validation study. SETTING: Two acute hospitals (York Hospital-model development data; Scarborough Hospital-external validation data). PARTICIPANTS: Adult (aged ≥16 years) medical admissions discharged over a 24-month period with electronic NEWS and blood test results recorded on admission. We used logistic regression modelling to predict the risk of in-hospital mortality using two models: (1) CARMc19_N: age+sex+NEWS2 including subcomponents+COVID19; (2) CARMc19_NB: CARMc19_N in conjunction with seven blood test results and acute kidney injury score. Model performance was evaluated according to discrimination (c-statistic), calibration (graphically) and clinical usefulness at NEWS2 thresholds of 4+, 5+, 6+. RESULTS: The risk of in-hospital mortality following emergency medical admission was similar in development and validation datasets (8.4% vs 8.2%). The c-statistics for predicting mortality for CARMc19_NB is better than CARMc19_N in the validation dataset (CARMc19_NB=0.88 (95% CI 0.86 to 0.90) vs CARMc19_N=0.86 (95% CI 0.83 to 0.88)). Both models had good calibration (CARMc19_NB=1.01 (95% CI 0.88 to 1.14) and CARMc19_N:0.95 (95% CI 0.83 to 1.06)). At all NEWS2 thresholds (4+, 5+, 6+) model, CARMc19_NB had better sensitivity and similar specificity. CONCLUSIONS: We have developed a validated CARMc19 scores with good performance characteristics for predicting the risk of in-hospital mortality. Since the CARMc19 scores place no additional data collection burden on clinicians, it may now be carefully introduced and evaluated in hospitals with sufficient informatics infrastructure.

Translating knowledge on best practice into improving quality of RRT care: a systematic review of implementation strategies.

Recent studies showed wide variation in the extent to which guidelines and other types of best practice have been implemented as part of routine health care. This is also true for the delivery of renal replacement therapy (RRT) for ESRD patients. Increasing uptake of best practice within such complex care systems requires an understanding of implementation strategies and specific quality improvement (QI) techniques. Therefore, we systematically reviewed over 5000 titles published since 1990 and included papers describing planned attempts to accelerate uptake of best RRT practice into daily care. This resulted in a list of 93 QI initiatives, categorized in order to expedite shared learning. The majority of the initiatives were executed within the domains of vascular access, nutrition, and anemia management. Strategies oriented at patients were most common and many initiatives pre-defined an improvement target before starting implementation. Of the 93 initiatives, 22 were sufficiently robust methodologically to be analyzed in more detail. Our results tend to support previous findings that multifaceted strategies are more effective than single strategies. Improving our understanding of how to successfully implement best practice can inform system-level change and is the only way to close the gap between knowledge on what works and the actual care delivered to ESRD patients. Research into implementation, using specific QI techniques, should therefore be given priority in future.

Use of the first National Early Warning Score recorded within 24 hours of admission to estimate the risk of in-hospital mortality in unplanned COVID-19 patients: a retrospective cohort study.

OBJECTIVES: Although the National Early Warning Score (NEWS) and its latest version NEWS2 are recommended for monitoring deterioration in patients admitted to hospital, little is known about their performance in COVID-19 patients. We aimed to compare the performance of the NEWS and NEWS2 in patients with COVID-19 versus those without during the first phase of the pandemic. DESIGN: A retrospective cross-sectional study. SETTING: Two acute hospitals (Scarborough and York) are combined into a single dataset and analysed collectively. PARTICIPANTS: Adult (≥18 years) non-elective admissions discharged between 11 March 2020 and 13 June 2020 with an index or on-admission NEWS2 electronically recorded within ±24 hours of admission to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) in COVID-19 versus non-COVID-19 admissions. RESULTS: Out of 6480 non-elective admissions, 620 (9.6%) had a diagnosis of COVID-19. They were older (73.3 vs 67.7 years), more often male (54.7% vs 50.1%), had higher index NEWS (4 vs 2.5) and NEWS2 (4.6 vs 2.8) scores and higher in-hospital mortality (32.1% vs 5.8%). The c-statistics for predicting in-hospital mortality in COVID-19 admissions was significantly lower using NEWS (0.64 vs 0.74) or NEWS2 (0.64 vs 0.74), however, these differences reduced at 72hours (NEWS: 0.75 vs 0.81; NEWS2: 0.71 vs 0.81), 48 hours (NEWS: 0.78 vs 0.81; NEWS2: 0.76 vs 0.82) and 24hours (NEWS: 0.84 vs 0.84; NEWS2: 0.86 vs 0.84). Increasing NEWS2 values reflected increased mortality, but for any given value the absolute risk was on average 24% higher (eg, NEWS2=5: 36% vs 9%). CONCLUSIONS: The index or on-admission NEWS and NEWS2 offers lower discrimination for COVID-19 admissions versus non-COVID-19 admissions. The index NEWS2 was not proven to be better than the index NEWS. For each value of the index NEWS/NEWS2, COVID-19 admissions had a substantially higher risk of mortality than non-COVID-19 admissions which reflects the increased baseline mortality risk of COVID-19.

COVID-19 vaccination coverage among hospital-based healthcare personnel reported through the Department of Health and Human Services Unified Hospital Data Surveillance System, United States, January 20, 2021-September 15, 2021.

To protect both patients and staff, healthcare personnel (HCP) were among the first groups in the United States recommended to receive the COVID-19 vaccine. We analyzed data reported to the U.S. Department of Health and Human Services (HHS) Unified Hospital Data Surveillance System on COVID-19 vaccination coverage among hospital-based HCP. After vaccine introduction in December 2020, COVID-19 vaccine coverage rose steadily through April 2021, but the rate of uptake has since slowed; as of September 15, 2021, among 3,357,348 HCP in 2,086 hospitals included in this analysis, 70.0% were fully vaccinated. Additional efforts are needed to improve COVID-19 vaccine coverage among HCP.

Staffing and Capacity Planning for SARS-CoV-2 Monoclonal Antibody Infusion Facilities: A Performance Estimation Calculator Based on Discrete-Event Simulations.

Background: The COVID-19 pandemic has significantly stressed healthcare systems. The addition of monoclonal antibody (mAb) infusions, which prevent severe disease and reduce hospitalizations, to the repertoire of COVID-19 countermeasures offers the opportunity to reduce system stress but requires strategic planning and use of novel approaches. Our objective was to develop a web-based decision-support tool to help existing and future mAb infusion facilities make better and more informed staffing and capacity decisions. Materials and Methods: Using real-world observations from three medical centers operating with federal field team support, we developed a discrete-event simulation model and performed simulation experiments to assess performance of mAb infusion sites under different conditions. Results: 162,000 scenarios were evaluated by simulations. Our analyses revealed that it was more effective to add check-in staff than to add additional nurses for middle-to-large size sites with ≥2 infusion nurses; that scheduled appointments performed better than walk-ins when patient load was not high; and that reducing infusion time was particularly impactful when load on resources was only slightly above manageable levels. Discussion: Physical capacity, check-in staff, and infusion time were as important as nurses for mAb sites. Health systems can effectively operate an infusion center under different conditions to provide mAb therapeutics even with relatively low investments in physical resources and staff. Conclusion: Simulations of mAb infusion sites were used to create a capacity planning tool to optimize resource utility and allocation in constrained pandemic conditions, and more efficiently treat COVID-19 patients at existing and future mAb infusion sites.

UK Renal Registry 12th Annual Report (December 2009): chapter 9: anaemia variables in UK adult dialysis patients in 2008: national and centre-specific analyses.

BACKGROUND: The UK Renal Association (RA) and National Institute for Health and Clinical Excellence (NICE) have published Clinical Practice Guidelines which include recommendations for management of anaemia in established renal failure. AIMS: To determine the extent to which the guidelines for anaemia management are met in the UK. METHODS: Quarterly data (haemoglobin (Hb) and factors that influence Hb) extracts from renal centres in England, Wales, Northern Ireland (EWNI), and annual data from the Scottish Renal Registry for incident and prevalent renal replacement therapy (RRT) cohorts for 2008 were analysed by the UK Renal Registry (UKRR). RESULTS: In the UK, in 2008 57% of patients commenced dialysis therapy with Hb >or= 10.0 g/dl (median Hb 10.2 g/dl). For incident patients the Hb at 3 and 6 months of dialysis treatment was 11.4 and 11.7 g/dl respectively. The median Hb of haemodialysis (HD) patients was 11.6 g/dl with an interquartile range (IQR) of 10.6-12.5 g/dl. Of HD patients 85% had a Hb >or= 10.0 g/dl. The median Hb of peritoneal dialysis (PD) patients in the UK was 11.7 g/dl (IQR 10.8-12.6 g/dl). Of UK PD patients 89% had a Hb >or= 10.0 g/dl. The median ferritin in HD patients in EWNI was 436 mg/L (IQR 289-622) and 95% of HD patients had a ferritin >or= 100 mg/L. The median ferritin in PD patients was 246 mg/L (IQR 141-399) with 84% of PD patients having a ferritin >or= 100 mg/L. In EWNI the mean ESA dose was higher for HD than PD patients (9,166 vs. 6,302 IU/week). CONCLUSIONS: Last year for the first time a small fall (from 85.9% in 2006 to 85.6% in 2007) in the % of HD patients with a Hb of >or= 10 g/dl which was thought to be related to the implementation of the new Hb Standard which has a target range of 10.5-12.5 g/dl was seen. This year attainment of Hb >or= 10 g/dl in HD patients fell again slightly to 85.3%. In HD patients, 54% of patients had a Hb >or= 10.5 and

Deep brain stimulation of the orbitofrontal projections for the treatment of intermittent explosive disorder.

Intermittent explosive disorder (IED) is characterized by a dysfunction in the greater limbic system leading an individual to experience sudden aggressive behavior with little or no environmental perturbation. This report describes a procedure for the treatment of IED in a 19-year-old woman with a history of IED, having had episodes of severe violent attacks against family, dating to early childhood. Due to the severity and intractability of the illness, deep brain stimulation was performed, targeting the orbitofrontal projections to the hypothalamus. The patient's history and the procedure, management, and rationale are described in detail.

A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation.

We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients' first blood test results and physiological measurements using an external validation approach. We trained and tested each model using data from one hospital (n = 24,696) and compared the performance of these models in data from another hospital (n = 13,477). We used two performance measures - the calibration slope and area under the receiver operating characteristic curve. The logistic model performed reasonably well - calibration slope: 0.90, area under the receiver operating characteristic curve: 0.847 compared to the other machine learning methods. Given the complexity of choosing tuning parameters of these methods, the performance of logistic regression with transformations for in-hospital mortality prediction was competitive with the best performing alternative machine learning methods with no evidence of overfitting.

UK Renal Registry 11th Annual Report (December 2008): Chapter 9 Haemoglobin, ferritin and erythropoietin amongst patients receiving dialysis in the UK in 2007: national and centre-specific analyses.

BACKGROUND: The UK Renal Association (RA) and National Institute for Health and Clinical Excellence (NICE) have published Clinical Practice Guidelines which include recommendations for management of anaemia in established renal failure. AIMS: To determine the extent to which the guidelines for anaemia management are met in the UK. METHODS: Quarterly data (haemoglobin (Hb) and factors that influence Hb) extracts from renal centres in England, Wales and Northern Ireland (EWNI), and annual data from the Scottish Renal Registry for incident and prevalent renal replacement therapy (RRT) cohorts for 2007 were analysed by the UK Renal Registry (UKRR). RESULTS: In the UK, in 2007 58% of patients commenced dialysis therapy with Hb > or = 10.0 g/dl (median Hb 10.3 g/dl). Of incident patients 81% and 87% had a Hb > or = 10.0 g/dl by 3 and 6 months of dialysis treatment respectively. The median Hb of haemodialysis (HD) patients was 11.6 g/dl with an interquartile range (IQR) of 10.6-12.6 g/dl. Of HD patients 86% had a Hb > or = 10.0 g/dl. The median Hb of peritoneal dialysis (PD) patients in the UK was 11.9 g/dl (IQR 11.0-12.8 g/dl). 91% of UK PD patients had a Hb > or = 10.0 g/dl. The median ferritin in HD patients in EWNI was 417 microg/L (IQR 270-598) and 95% of HD patients had a ferritin > or = 100 microg/L. The median ferritin in PD patients was 255 microg/L (IQR 143-411) with 85% of PD patients having a ferritin > or = 100 microg/L. In EWNI the mean ESA dose was higher for HD than PD patients (9,300 vs. 6,100 IU/week). CONCLUSIONS: This year for the first time there has been a small fall (from 85.9% in 2006 to 85.6%) in the percentage of HD patients with an Hb of > or = 10 g/dl. This contrasts with previous annual improvements in this figure and is related to implementation of the new Hb Standard which has a target range of 10.5-12.5 g/dl.

Performance of externally validated enhanced computer-aided versions of the National Early Warning Score in predicting mortality following an emergency admission to hospital in England: a cross-sectional study.

OBJECTIVES: In the English National Health Service, the patient's vital signs are monitored and summarised into a National Early Warning Score (NEWS) to support clinical decision making, but it does not provide an estimate of the patient's risk of death. We examine the extent to which the accuracy of NEWS for predicting mortality could be improved by enhanced computer versions of NEWS (cNEWS). DESIGN: Logistic regression model development and external validation study. SETTING: Two acute hospitals (YH-York Hospital for model development; NH-Northern Lincolnshire and Goole Hospital for external model validation). PARTICIPANTS: Adult (≥16 years) medical admissions discharged over a 24-month period with electronic NEWS (eNEWS) recorded on admission are used to predict mortality at four time points (in-hospital, 24 hours, 48 hours and 72 hours) using the first electronically recorded NEWS (model M0) versus a cNEWS model which included age+sex (model M1) +subcomponents of NEWS (including diastolic blood pressure) (model M2). RESULTS: The risk of dying in-hospital following emergency medical admission was 5.8% (YH: 2080/35 807) and 5.4% (NH: 1900/35 161). The c-statistics for model M2 in YH for predicting mortality (in-hospital=0.82, 24 hours=0.91, 48 hours=0.88 and 72 hours=0.88) was higher than model M0 (in-hospital=0.74, 24 hours=0.89, 48 hours=0.86 and 72 hours=0.85) with higher Positive Predictive Value (PPVs) for in-hospital mortality (M2 19.3% and M0 16.6%). Similar findings were seen in NH. Model M2 performed better than M0 in almost all major disease subgroups. CONCLUSIONS: An externally validated enhanced computer-aided NEWS model (cNEWS) incrementally improves on the performance of a NEWS only model. Since cNEWS places no additional data collection burden on clinicians and is readily automated, it may now be carefully introduced and evaluated to determine if it can improve care in hospitals that have eNEWS systems.

The inclusion of delirium in version 2 of the National Early Warning Score will substantially increase the alerts for escalating levels of care: findings from a retrospective database study of emergency medical admissions in two hospitals .

BACKGROUND: The National Early Warning Score (NEWS) is being replaced with NEWS2 which adds 3 points for new confusion or delirium. We estimated the impact of adding delirium on the number of medium/high level alerts that are triggers to escalate care. METHODS: Analysis of emergency medical admissions in two acute hospitals (York Hospital (YH) and Northern Lincolnshire and Goole NHS Foundation Trust hospitals (NH)) in England. Twenty per cent were randomly assigned to have delirium. RESULTS: The number of emergency admissions (YH: 35584; NH: 35795), mortality (YH: 5.7%; NH: 5.5%), index NEWS (YH: 2.5; NH: 2.1) and numbers of NEWS recorded (YH: 879193; NH: 884072) were similar in each hospital. The mean number of patients with medium level alerts per day increased from 55.3 (NEWS) to 69.5 (NEWS2), a 25.7% increase in YH and 64.1 (NEWS) to 77.4 (NEWS2), a 20.7% increase in NH. The mean number of patients with high level alerts per day increased from 27.3 (NEWS) to 34.4 (NEWS2), a 26.0% increase in YH and 29.9 (NEWS) to 37.7 (NEWS2), a 26.1% increase in NH. CONCLUSIONS: The addition of delirium in NEWS2 will have a substantial increase in medium and high level alerts in hospitalised emergency medical patients. Rigorous evaluation of NEWS2 is required before widespread implementation because the extent to which staff can cope with this increase without adverse consequences remains unknown.

Understanding and applying practitioner and patient views on the implementation of a novel automated Computer-Aided Risk Score (CARS) predicting the risk of death following emergency medical admission to hospital: qualitative study.

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.

Management of post-traumatic stress disorder: A protocol for a multiple treatment comparison meta-analysis of randomized controlled trials.

BACKGROUND: Most systematic reviews have explored the efficacy of treatments on symptoms associated with post-traumatic stress disorder (PTSD), which is a chronic and often disabling condition. Previous network meta-analysis (NMA) had limitations such as focusing on pharmacological or psychotherapies. Our review is aims to explore the relative effectiveness of both pharmacological and psychotherapies and we will establish the differential efficacy of interventions for PTSD in consideration of both symptom reduction and functional recovery. METHODS: We will conduct a network meta-analysis of randomized controlled trials evaluating treatment interventions for PTSD. We will systematically search Medline, PILOT, Embase, CINHAL, AMED, Psychinfo, Health Star, DARE and CENTRAL to identify trials that: (1) enroll adult patients with PTSD, and (2) randomize them to alternative interventions or an intervention and a placebo/sham arm. Independent reviewers will screen trials for eligibility, assess risk of bias using a modified Cochrane instrument, and extract data. Our outcomes of interest include PTSD symptom reduction, quality of life, functional recovery, social and occupational impairment, return to work and all-cause drop outs. RESULTS: We will conduct frequentist random-effects network meta-analysis to assess relative effects of competing interventions. We will use a priori hypotheses to explore heterogeneity between studies, and assess the certainty of evidence using the GRADE approach. CONCLUSION: This network meta-analysis will determine the comparative effectiveness of therapeutic options for PTSD on both symptom reduction and functional recovery. Our results will be helpful to clinicians and patients with PTSD, by providing a high-quality evidence synthesis to guide shared-care decision making.