Weekend hospitalization and additional risk of death: An analysis of inpatient data

Objective To assess whether weekend admissions to hospital and/or already being an inpatient on weekend days were associated with any additional mortality risk.Design Retrospective observational survivorship study. We analysed all admissions to the English National Health Service (NHS) during the financial year 2009/10, following up all patients for 30 days after admission and accounting for risk of death associated with diagnosis, co-morbidities, admission history, age, sex, ethnicity, deprivation, seasonality, day of admission and hospital trust, including day of death as a time dependent covariate. The principal analysis was based on time to in-hospital death.Participants National Health Service Hospitals in England.Main Outcome Measures 30 day mortality (in or out of hospital).Results There were 14,217,640 admissions included in the principal analysis, with 187,337 in-hospital deaths reported within 30 days of admission. Admission on weekend days was associated with a considerable increase in risk of subsequent death compared with admission on weekdays, hazard ratio for Sunday versus Wednesday 1.16 (95% CI 1.14 to 1.18; P < .0001), and for Saturday versus Wednesday 1.11 (95% CI 1.09 to 1.13; P < .0001). Hospital stays on weekend days were associated with a lower risk of death than midweek days, hazard ratio for being in hospital on Sunday versus Wednesday 0.92 (95% CI 0.91 to 0.94; P < .0001), and for Saturday versus Wednesday 0.95 (95% CI 0.93 to 0.96; P < .0001). Similar findings were observed on a smaller US data set.Conclusions Admission at the weekend is associated with increased risk of subsequent death within 30 days of admission. The likelihood of death actually occurring is less on a weekend day than on a mid-week day

Development of risk prediction models to predict urine culture growth for adults with suspected urinary tract infection in the emergency department: protocol for an electronic health record study from a single UK university hospital

Background: Urinary tract infection (UTI) is a leading cause of hospital admissions and is diagnosed based on urinary symptoms and microbiological cultures. Due to lags in the availability of culture results of up to 72 h, and the limitations of routine diagnostics, many patients with suspected UTI are started on antibiotic treatment unnecessarily. Predictive models based on routinely collected clinical information may help clinicians to rule out a diagnosis of bacterial UTI in low-risk patients shortly after hospital admission, providing additional evidence to guide antibiotic treatment decisions. / Methods: Using electronic hospital records from Queen Elizabeth Hospital Birmingham (QEHB) collected between 2011 and 2017, we aim to develop a series of models that estimate the probability of bacterial UTI at presentation in the emergency department (ED) among individuals with suspected UTI syndromes. Predictions will be made during ED attendance and at different time points after hospital admission to assess whether predictive performance may be improved over time as more information becomes available about patient status. All models will be externally validated for expected future performance using QEHB data from 2018/2019. / Discussion: Risk prediction models using electronic health records offer a new approach to improve antibiotic prescribing decisions, integrating clinical and demographic data with test results to stratify patients according to their probability of bacterial infection. Used in conjunction with expert opinion, they may help clinicians to identify patients that benefit the most from early antibiotic cessation

OnabotulinumtoxinA in the treatment of overactive bladder: a cost-effectiveness analysis versus best supportive care in England and Wales

The cost-effectiveness of onabotulinumtoxinA (BOTOX®) 100 U + best supportive care (BSC) was compared with BSC alone in the management of idiopathic overactive bladder in adult patients who are not adequately managed with anticholinergics. BSC included incontinence pads and, for a proportion of patients, anticholinergics and/or occasional clean intermittent catheterisation. A five-state Markov model was used to estimate total costs and outcomes over a 10-year period. The cohort was based on data from two placebo-controlled trials and a long-term extension study of onabotulinumtoxinA. After discontinuation of initial treatment, a proportion of patients progressed to downstream sacral nerve stimulation (SNS). Cost and resource use was estimated from a National Health Service perspective in England and Wales using relevant reference sources for 2012 or 2013. Results showed that onabotulinumtoxinA was associated with lower costs and greater health benefits than BSC in the base case, with probabilistic sensitivity analysis indicating an 89 % probability that the incremental cost-effectiveness ratio would fall below £20,000. OnabotulinumtoxinA remained dominant over BSC in all but two scenarios tested; it was also economically dominant when compared directly with SNS therapy. In conclusion, onabotulinumtoxinA appears to be a cost-effective treatment for overactive bladder compared with BSC alone

On the pooling and subgrouping of data from percutaneous coronary intervention versus coronary artery bypass grafting trials: a call to circumspection

In the modern era, treatment choice is guided by scientific evidence, usually gathered from well-conducted clinical trials, and often followed by the pooling of their data. In this article, we review the most recent pooled evidence regarding myocardial revascularization strategies and discuss how these meta-analyses have inherent shortcomings that should be better understood, prior to their purported conclusions potentially influencing clinical decisions. Properly conducted randomized trials comparing percutaneous coronary intervention (PCI) with coronary artery bypass grafting (CABG) can provide unbiased estimates of treatment effects. However, the design of such trials has often involved a primary outcome that is a composite measure and thus open to challenges with regard to the appropriate interpretation of each individual components. Quantitative synthesis of such data from multiple trials can enable estimates of individual components of the composite outcomes (e.g. all-cause mortality). Where individual patient data are available, a full investigation of mediating effects, or subgroups analyses, may also be undertaken. Relevant to this article, we now have multiple meta-analyses of trials available, which provide an opportunity to assess the appropriateness of criteria for patient selection between PCI and CABG. Consequently, we will appraise the robustness of these meta-analytic methods in answering the question at stake: does PCI provide equivalent results to CABG for the treatment of unprotected left main coronary artery stenosis and multivessel coronary artery disease

National administrative data produces an accurate and stable risk prediction model for short-term and 1-year mortality following cardiac surgery

OBJECTIVES: Various risk models exist to predict short-term risk-adjusted outcomes after cardiac surgery. Statistical models constructed using clinical registry data usually perform better than those based on administrative datasets. We constructed a procedure-specific risk prediction model based on administrative hospital data for England and we compared its performance with the EuroSCORE (ES) and its variants. METHODS: The Hospital Episode Statistics (HES) risk prediction model was developed using administrative data linked to national mortality statistics register of patients undergoing CABG (35,115), valve surgery (18,353) and combined CABG and valve surgery (8392) from 2008 to 2011 in England and tested using an independent dataset sampled for the financial years 2011-2013. Specific models were constructed to predict mortality within 1-year post discharge. Comparisons with EuroSCORE models were performed on a local cohort of patients (2580) from 2008 to 2013. RESULTS: The discrimination of the HES model demonstrates a good performance for early and up to 1-year following surgery (c-stats: CABG 81.6%, 78.4%; isolated valve 78.6%, 77.8%; CABG & valve 76.4%, 72.0%), respectively. Extended testing in subsequent financial years shows that the models maintained performance outside the development period. Calibration of the HES model demonstrates a small difference (CABG 0.15%; isolated valve 0.39%; CABG & valve 0.63%) between observed and expected mortality rates and delivers a good estimate of risk. Discrimination for the HES model for in-hospital deaths is similar for CABG (logistic ES 79.0%) and combined CABG and valve surgery (logistic ES 71.6%) patients and superior for valve patients (logistic ES 70.9%) compared to the EuroSCORE models. The C-statistics of the EuroSCORE models for longer periods are numerically lower than that of the HES model. CONCLUSION: The national administrative dataset has produced an accurate, stable and clinically useful early and 1-year mortality prediction after cardiac surgery

85 Prediction of response to biventricular pacing from dyssynchrony indices: the absolute limit on predictability, and its clinical implications

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