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IntroductionAmbulances services are asked to further reduce avoidable conveyances to emergency departments (EDs). Risk of Adverse Outcomes after a Suspected Seizure seeks to support this by: (1) clarifying the risks of conveyance and non-conveyance, and (2) developing a risk prediction tool for clinicians to use ‘on scene’ to estimate the benefits an individual would receive if conveyed to ED and risks if not.Methods and analysisMixed-methods, multi-work package (WP) project. For WP1 and WP2 we shall use an existing linked data set that tracks urgent and emergency care (UEC) use of persons served by one English regional ambulance service. Risk tools are specific to clinical scenarios. We shall use suspected seizures in adults as an exemplar.WP1: Form a cohort of patients cared for a seizure by the service during 2019/2020. It, and nested Knowledge Exchange workshops with clinicians and service users, will allow us to: determine the proportions following conveyance and non-conveyance that die and/or recontact UEC system within 3 (/30) days; quantify the proportion of conveyed incidents resulting in ‘avoidable ED attendances’ (AA); optimise risk tool development; and develop statistical models that, using information available ‘on scene’, predict the risk of death/recontact with the UEC system within 3 (/30) days and the likelihood of an attendance at ED resulting in an AA.WP2: Form a cohort of patients cared for a seizure during 2021/2022 to ‘temporally’ validate the WP1 predictive models.WP3: Complete the ‘next steps’ workshops with stakeholders. Using nominal group techniques, finalise plans to develop the risk tool for clinical use and its evaluation.Ethics and disseminationWP1a and WP2 will be conducted under database ethical approval (IRAS 307353) and Confidentiality Advisory Group (22/CAG/0019) approval. WP1b and WP3 have approval from the University of Liverpool Central Research Ethics Committee (11450). We shall engage in proactive dissemination and knowledge mobilisation to share findings with stakeholders and maximise evidence usage.

Lapses in concentration while driving can be fatal, so if a person experiences a seizure – whether behind the wheel or not – UK authorities require that person to take time off driving until the risk of another seizure falls below a set threshold. But how much time off is required? Laura Bonnett explains how statistics helped find an answer

Background Catheter ablation of ventricular tachycardia (VT) is associated with potential major complications, including mortality. The risk of acute complications in patients with ischaemic cardiomyopathy (ICM) and non-ischaemic cardiomyopathy (NICM) has not been systematically evaluated. Methods PubMed was searched for studies of catheter ablation of VT published between September 2009 and September 2019. Pre-specified primary outcomes were (1) rate of major acute complications, including death, and (2) mortality rate. Results A total of 7395 references were evaluated for relevance. From this, 50 studies with a total of 3833 patients undergoing 4319 VT ablation procedures fulfilled the inclusion criteria (mean age 59 years; male 82%; 2363 [62%] ICM; 1470 [38%] NICM). The overall major complication rate in ICM cohorts was 9.4% (95% CI, 8.1–10.7) and NICM cohorts was 7.1% (95% CI, 6.0–8.3). Reported complication rates were highly variable between studies (ICM I2 = 90%; NICM I2 = 89%). Vascular complications (ICM 2.5% [95% CI, 1.9–3.1]; NICM 1.2% [95% CI, 0.7–1.7]) and cerebrovascular events (ICM 0.5% [95% CI, 0.2–0.7]; NICM, 0.1% [95% CI, 0–0.2]) were significantly higher in ICM cohorts. Acute mortality rates in the ICM and NICM cohorts were low (ICM 0.9% [95% CI, 0.5–1.3]; NICM 0.6% [95% CI, 0.3–1.0]) with the majority of overall deaths (ICM 75%; NICM 80%) due to either recurrent VT or cardiogenic shock. Conclusion Overall acute complication rates of VT ablation are comparable between ICM and NICM patients. However, the pattern and predictors of complications vary depending on the underlying cardiomyopathy.

Identifying people who are at risk of being admitted to hospital (hospitalised) for heart failure and death, and particularly those who have not previously been hospitalised for heart failure, is a priority. We aimed to develop and externally validate a prognostic model involving contemporary deep phenotyping that can be used to generate individual risk estimates of hospitalisation for heart failure or all-cause mortality in patients with, or at risk of, heart failure, but who have not previously been hospitalised for heart failure.Between June 1, 2016, and May 31, 2018, 3019 consecutive adult patients (aged ≥16 years) undergoing cardiac magnetic resonance (CMR) at Manchester University National Health Service Foundation Trust, Manchester, UK, were prospectively recruited into a model development cohort. Candidate predictor variables were selected according to clinical practice and literature review. Cox proportional hazards modelling was used to develop a prognostic model. The final model was validated in an external cohort of 1242 consecutive adult patients undergoing CMR at the University of Pittsburgh Medical Center Cardiovascular Magnetic Resonance Center, Pittsburgh, PA, USA, between June 1, 2010, and March 25, 2016. Exclusion criteria for both cohorts included previous hospitalisation for heart failure. Our study outcome was a composite of first hospitalisation for heart failure or all-cause mortality after CMR. Model performance was evaluated in both cohorts by discrimination (Harrell's C-index) and calibration (assessed graphically).Median follow-up durations were 1118 days (IQR 950-1324) for the development cohort and 2117 days (1685-2446) for the validation cohort. The composite outcome occurred in 225 (7·5%) of 3019 patients in the development cohort and in 219 (17·6%) of 1242 patients in the validation cohort. The final, externally validated, parsimonious, multivariable model comprised the predictors: age, diabetes, chronic obstructive pulmonary disease, N-terminal pro-B-type natriuretic peptide, and the CMR variables, global longitudinal strain, myocardial infarction, and myocardial extracellular volume. The median optimism-adjusted C-index for the externally validated model across 20 imputed model development datasets was 0·805 (95% CI 0·793-0·829) in the development cohort and 0·793 (0·766-0·820) in the external validation cohort. Model calibration was excellent across the full risk profile. A risk calculator that provides an estimated risk of hospitalisation for heart failure or all-cause mortality at 3 years after CMR for individual patients was generated.We developed and externally validated a risk prediction model that provides accurate, individualised estimates of the risk of hospitalisation for heart failure and all-cause mortality in patients with, or at risk of, heart failure, before first hospitalisation. It could be used to direct intensified therapy and closer follow-up to those at increased risk.The UK National Institute for Health Research, Guerbet Laboratories, and Roche Diagnostics International.

Epilepsy, characterised by a predisposition towards unprovoked seizures, is one of the most common neurological disorders globally. Whilst 60-70% of individuals diagnosed with epilepsy will gain seizure control through anti-seizure medication, the mechanisms underlying seizure persistence are unclear. Intractability can significantly degrade a patient’s quality of life amongst other things; the use of predictive modelling of epilepsy outcomes in deciding on treatment therefore offers a tangible benefit to the patient. Early indicators of pharmacoresistance may discourage certain treatment options, and save time in what has been indicated to be a critical stage for newly-diagnosed epilepsy. Primarily, this paper aims to evaluate existing predictive models to identify demographic, clinical, physiological (e.g. EEG), and neuroimaging (e.g. MRI) factors that may be predictive of treatment outcomes in newly-diagnosed epilepsy. Two electronic databases, MEDLINE and EMBASE, will be searched with terms related to prognosis in newly-diagnosed epilepsy, and identified studies will be included for review if they have combined at least two demographic, clinical, neuroimaging, and/or physiological factors to predict treatment outcome in people with newly-diagnosed epilepsy. Papers will be screened by two independent reviewers via titles, abstracts and then full text against the inclusion criteria for eligibility. Data will be extracted by reviewers using standardised forms, assessed for risk of bias using the PROBAST tool and synthesised narratively. If considered appropriate the authors will carry out a meta-analysis on the available data.Prospero registration number– CRD42022329936

Highlights • Model predicts risk of seizure recurrence after single fit or epilepsy diagnosis. • Model performs well in independent data. • Future work required to ensure the model is adopted in clinical practice. • Model can improve the lives of people with single seizures and early epilepsy., Purpose Following a single seizure, or recent epilepsy diagnosis, it is difficult to balance risk of medication side effects with the potential to prevent seizure recurrence. A prediction model was developed and validated enabling risk stratification which in turn informs treatment decisions and individualises counselling. Methods Data from a randomised controlled trial was used to develop a prediction model for risk of seizure recurrence following a first seizure or diagnosis of epilepsy. Time-to-event data was modelled via Cox's proportional hazards regression. Model validity was assessed via discrimination and calibration using the original dataset and also using three external datasets – National General Practice Survey of Epilepsy (NGPSE), Western Australian first seizure database (WA) and FIRST (Italian dataset of people with first tonic-clonic seizures). Results People with neurological deficit, focal seizures, abnormal EEG, not indicated for CT/MRI scan, or not immediately treated have a significantly higher risk of seizure recurrence. Discrimination was fair and consistent across the datasets (c-statistics: 0.555 (NGPSE); 0.558 (WA); 0.597 (FIRST)). Calibration plots showed good agreement between observed and predicted probabilities in NGPSE at one and three years. Plots for WA and FIRST showed poorer agreement with the model underpredicting risk in WA, and over-predicting in FIRST. This was resolved following model recalibration. Conclusion The model performs well in independent data especially when recalibrated. It should now be used in clinical practice as it can improve the lives of people with single seizures and early epilepsy by enabling targeted treatment choices and more informed patient counselling.

The cardiac manifestations of Fabry disease are the leading cause of death, but risk stratification remains inadequate. Identifying patients who are at risk of adverse cardiac outcome may facilitate more evidence-based treatment guidance. Contemporary cardiovascular cardiac magnetic resonance biomarkers have become widely adopted, but their prognostic value remains unclear.The objective of this study was to develop, internally validate, and evaluate the performance of, a prognostic model, including contemporary deep phenotyping, which can be used to generate individual risk estimates for adverse cardiac outcome in patients with Fabry disease.This longitudinal prospective cohort study consisted of 200 consecutive patients with Fabry disease undergoing clinical cardiac magnetic resonance. Median follow-up was 4.5 years (IQR: 2.7-6.3 years). Prognostic models were developed using Cox proportional hazards modeling. Outcome was a composite of adverse cardiac events. Model performance was evaluated. A risk calculator, which provides 5-year estimated risk of adverse cardiac outcome for individual patients, including men and women, was generated.The highest performing, internally validated, parsimonious multivariable model included age, native myocardial TThis study developed and internally validated a risk prediction model that accurately predicts 5-year risk of adverse cardiac outcome for individual patients with Fabry disease, including men and women, which could easily be integrated into clinical care. External validation is warranted.

Introduction: Accurately diagnosing asthma can be challenging. We aimed to derive and validate a prediction model to support primary care clinicians assess the probability of an asthma diagnosis in children and young people. Methods: The derivation dataset was created from the Avon Longitudinal Study of Parents and Children (ALSPAC) linked to electronic health records. Participants with at least three inhaled corticosteroid prescriptions in 12-months and a coded asthma diagnosis were designated as having asthma. Demographics, symptoms, past medical/family history, exposures, investigations, and prescriptions were considered as candidate predictors. Potential candidate predictors were included if data were available in ≥60% of participants. Multiple imputation was used to handle remaining missing data. The prediction model was derived using logistic regression. Internal validation was completed using bootstrap re-sampling. External validation was conducted using health records from the Optimum Patient Care Research Database (OPCRD). Results: Predictors included in the final model were wheeze, cough, breathlessness, hay-fever, eczema, food allergy, social class, maternal asthma, childhood exposure to cigarette smoke, prescription of a short acting beta agonist and the past recording of lung function/reversibility testing. In the derivation dataset, which comprised 11,972 participants aged Conclusions: We derived and validated a prediction model for clinicians to calculate the probability of asthma diagnosis for a child or young person up to 25 years of age presenting to primary care. Following further evaluation of clinical effectiveness, the prediction model could be implemented as a decision support software.

Objectives To examine the association between subjective health complaints, sleep quantity and new injury within an endurance sport population. Design Prospective cohort study. Methods Ninety-five endurance sporting participants were recruited from running, triathlon, swimming, cycling and rowing disciplines. Over 52-week period participants submitted weekly data regarding subjective health complaints (SHCs) (cardiorespiratory, gastrointestinal and psychological/lifestyle), sleep quantity, training load and new injury episodes. Applying a 7- and 14-day lag period, a shared frailty model was used to explore new injury risk associations with total SHCs and sleep quantity. Results 92.6% of 95 participants completed all 52 weeks of data submission and the remainder of the participants completed ≥30 weeks. Seven-day lag psychological/lifestyle SHCs were significantly associated with new injury risk (Hazard ratio (HR) = 1.32; CI 95% = 1.01–1.72, p 7 h/day sleep quantity (HR = 0.63, CI 95% = 0.45–0.87, p

IntroductionEndoscopic mucosal resection (EMR) is an invaluable technique, however it is associated with significant risks. In the elderly in particular, the long-term survival benefits of polyp resection with EMR are unknown. The aim of this study was to determine the long-term outcomes in elderly patients who had undergone EMR and to identify any adverse factors.MethodA retrospective observational study on patients of 75 years of age or greater, who underwent EMR of colorectal polyps, in a single tertiary centre, from 2005 to 2014. Demographics of the patients, including Charlson Comorbidity Index (CCI), endoscopic and histological data, were reviewed to identify potential factors predicting outcomes.ResultsThe patients’ median age was 80 years. In total 239 procedures were performed in 206 unique patients. The complication rate was 1.6%. Mean overall survival was 6.7 years with only one patient dying from metastatic colorectal cancer (0.5%) and 49 dying from non-colorectal cancer conditions (24%). Age more than 79 years and CCI more than 2 were independent predictors of significantly shorter survival (p=ConclusionEMR of colonic polyps is safe even for elderly patients. However, the decision to proceed to complex endoscopic therapy should be individualised considering the patients’ age and comorbidities. CCI can help to objectively assess the comorbid state of a patient prior to such decisions.

Background Clinical prediction models are widely used to guide medical advice and therapeutic interventions. Asthma is one of the most common chronic diseases globally and is characterised by acute deteriorations. These exacerbations are largely preventable, so there is interest in using clinical prediction models in this area. The objective of this review was to identify studies which have developed such models, determine whether consistent and appropriate methodology was used and whether statistically reliable prognostic models exist. Methods We searched online databases MEDLINE (1948 onwards), CINAHL Plus (1937 onwards), The Cochrane Library, Web of Science (1898 onwards) and ClinicalTrials.gov, using index terms relating to asthma and prognosis. Data was extracted and assessment of quality was based on GRADE and an early version of PROBAST (Prediction study Risk of Bias Assessment Tool). A meta-analysis of the discrimination and calibration measures was carried out to determine overall performance across models. Results Ten unique prognostic models were identified. GRADE identified moderate risk of bias in two of the studies, but more detailed quality assessment via PROBAST highlighted that most models were developed using highly selected and small datasets, incompletely recorded predictors and outcomes, and incomplete methodology. None of the identified models modelled recurrent exacerbations, instead favouring either presence/absence of an event, or time to first or specified event. Preferred methodologies were logistic regression and Cox proportional hazards regression. The overall pooled c-statistic was 0.77 (95% confidence interval 0.73 to 0.80), though individually some models performed no better than chance. The meta-analysis had an I2 value of 99.75% indicating a high amount of heterogeneity between studies. The majority of studies were small and did not include internal or external validation, therefore the individual performance measures are likely to be optimistic. Conclusions Current prognostic models for asthma exacerbations are heterogeneous in methodology, but reported c-statistics suggest a clinically useful model could be created. Studies were consistent in lacking robust validation and in not modelling serial events. Further research is required with respect to incorporating recurrent events, and to externally validate tools in large representative populations to demonstrate the generalizability of published results.

Background: The ‘Prediction Of Survival in Advanced Sorafenib-treated HCC’ (PROSASH) model addressed the heterogeneous survival of patients with hepatocellular carcinoma (HCC) treated with sorafenib in clinical trials but requires validation in daily clinical practice. This study aimed to validate, compare and optimize this model for survival prediction. Methods: Patients treated with sorafenib for HCC at five tertiary European centres were retrospectively staged according to the PROSASH model. In addition, the optimized PROSASH-II model was developed using the data of four centres (training set) and tested in an independent dataset. These models for overall survival (OS) were then compared with existing prognostic models. Results: The PROSASH model was validated in 445 patients, showing clear differences between the four risk groups (OS 16.9-4.6months). A total of 920 patients (n=615 in training set, n=305 in validation set) were available to develop PROSASH-II. This optimized model incorporated fewer and less subjective parameters: the serum albumin, bilirubin and alpha-foetoprotein, and macrovascular invasion, extrahepatic spread and largest tumour size on imaging. Both PROSASH and PROSASH-II showed improved discrimination (C-index 0.62 and 0.63, respectively) compared with existing prognostic scores (C-index ≤0.59). Conclusions: In HCC patients treated with sorafenib, individualized prediction of survival and risk group stratification using baseline prognostic and predictive parameters with the PROSASH model was validated. The refined PROSASH-II model performed at least as good with fewer and more objective parameters. PROSASH-II can be used as a tool for tailored treatment of HCC in daily practice and to define pre-planned subgroups for future studies.

Background Pyrazinamide (PZA) is a key component of current and future regimens for tuberculosis (TB). Inclusion of PZA at higher doses and for longer durations may improve efficacy outcomes but must be balanced against the potential for worse safety outcomes. Methods We will search for randomised and quasi-randomised clinical trials in adult participants with and without the inclusion of PZA in TB treatment regimens in the Cochrane infectious diseases group’s trials register, Cochrane central register of controlled trials (CENTRAL), MEDLINE, EMBASE, LILACS, the metaRegister of Controlled Trials (mRCT) and the World Health Organization (WHO) international clinical trials registry platform. One author will screen abstracts and remove ineligible studies (10% of which will be double-screened by a second author). Two authors will review full texts for inclusion. Safety and efficacy data will be extracted to pre-piloted forms by one author (10% of which will be double-extracted by a second author). The Cochrane risk of bias tool will be used to assess study quality. The study has three objectives: the association of (1) inclusion, (2) dose and (3) duration of PZA with efficacy and safety outcomes. Risk ratios as relative measures of effect for direct comparisons within trials (all objectives) and proportions as absolute measures of effect for indirect comparisons across trials (for objectives 2 and 3) will be calculated. If there is insufficient data for direct comparisons within trials for objective 1, indirect comparisons between trials will be performed. Measures of effect will be pooled, with corresponding 95% confidence intervals and p values. Meta-analysis will be performed using the generalised inverse variance method for fixed effects models (FEM) or the DerSimonian-Laird method for random effects models (REM). For indirect comparisons, meta-regression for absolute measures against dose and duration data will be performed. Heterogeneity will be quantified through the I2-statistic for direct comparisons and the τ2 statistic for indirect comparisons using meta-regression. Discussion The current use of PZA for TB is based on over 60 years of clinical trial data, but this has never been synthesised to guide rationale use in future regimens and clinical trials. Systematic review registration: International Prospective Register of Systematic Reviews (PROSPERO) CRD42019138735

Introduction Selection of optimal image acquisition protocols in medical imaging remains a grey area, the superimposed use of the Likert scale in radiological image quality evaluations creates an additional challenge for the statistical analysis of image quality data. Using a simulation study, we have trialled a novel approach to analysing radiological image quality Likert scale data. Methods A simulation study was undertaken where simulated datasets were generated based on the distribution of Likert scale values according to varying image acquisition protocols from a real dataset. Simulated Likert scale values were pooled in four different ways; the mean, median, mode and the summation of patient Likert scale values of which the total was assigned a categorical Likert scale value. Estimates of bias, MAPE and RMSPE were then calculated for all four pooling approaches to determine which method most accurately represented an expert's opinion. Results When compared to an expert's opinion, the method of summation and categorisation of Likert scale values was most accurate 49 times out of the 114 (43.0%) tests. The mean 28 times out of 114 (24.6%), the median 23 times out of 114 (20.2%) and the mode 17 times out of 114 (14.9%). Conclusion We conclude that our method of summation and categorisation of Likert scale values is most often the best representation of the simulated data compared to the expert's opinion. Implications for practice There is scope to reproduce this simulation study with multiple observers to reflect clinical reality more accurately with the dynamic nature of multiple observers. This also prompts future investigation into other anatomical areas, to see if the same methods produce similar results.

Background Routinely recorded data held in electronic health records can be used to inform the conduct of randomised controlled trials (RCTs). However, limitations with access and accuracy have been identified. Objective: Using epilepsy as an exemplar condition, we assessed the attributes and agreement of routinely recorded data compared to data collected using case report forms in a UK RCT assessing antiepileptic drug treatments for individuals newly diagnosed with epilepsy. Methods The case study RCT is the Standard and New Antiepileptic Drugs II (SANAD II) trial, a pragmatic, UK multicentre RCT assessing the clinical and cost-effectiveness of antiepileptic drugs as treatments for epilepsy. Ninety-eight of 470 eligible participants provided consent for access to routinely recorded secondary care data that were retrieved from NHS Digital Hospital Episode Statistics (N=71) and primary and secondary care data from The Secure Anonymised Information Linkage Databank (N=27). We assessed data items relevant to the identification of individuals eligible for inclusion in SANAD II, baseline and follow-up visits. The attributes of routinely recorded data were assessed including the degree of missing data. The agreement between routinely recorded data and data collected on case report forms in SANAD II was assessed using calculation of Cohen’s kappa for categorical data and construction of Bland-Altman plots for continuous data. Results There was a significant degree of missing data in the routine record for 15 of the 20 variables assessed, including all clinical variables. Agreement was poor for the majority of comparisons, including the assessments of seizure occurrence and adverse events. For example, only 23/62 (37%) participants had a date of first-ever seizure identified in routine datasets. Agreement was satisfactory for the date of prescription of antiepileptic drugs and episodes of healthcare resource use. Conclusions There are currently significant limitations preventing the use of routinely recorded data for participant identification and assessment of clinical outcomes in epilepsy, and potentially other chronic conditions. Further research is urgently required to assess the attributes, agreement, additional benefits, cost-effectiveness and ‘optimal mix’ of routinely recorded data compared to data collected using standard methods such as case report forms at clinic visits for people with epilepsy. Trial registration Standard and New Antiepileptic Drugs II (SANAD II (EudraCT No: 2012-001884-64, registered 05/09/2012; ISRCTN Number: ISRCTN30294119, registered 03/07/2012))

Highlights • After a clinical prediction model is developed, it is usually necessary to undertake an external validation study that examines the model's performance in new data from the same or different population. External validation studies should have an appropriate sample size, in order to estimate model performance measures precisely for calibration, discrimination and clinical utility. • Rules-of-thumb suggest at least 100 events and 100 nonevents. Such blanket guidance is imprecise, and not specific to the model or validation setting. • Our works shows that precision of performance estimates is affected by the model's linear predictor (LP) distribution, in addition to number of events and total sample size. Furthermore, sample sizes of 100 (or even 200) events and non-events can give imprecise estimates, especially for calibration. • Our new proposal uses a simulation-based sample size calculation, which accounts for the LP distribution and (mis)calibration in the validation sample, and calculates the sample size (and events) required conditional on these factors. • The approach requires the researcher to specify the desired precision for each performance measure of interest (calibration, discrimination, net benefit, etc), the model's anticipated LP distribution in the validation population, and whether or not the model is well calibrated. Guidance for how to specify these values is given, and R and Stata code is provided., Introduction Sample size “rules-of-thumb” for external validation of clinical prediction models suggest at least 100 events and 100 non-events. Such blanket guidance is imprecise, and not specific to the model or validation setting. We investigate factors affecting precision of model performance estimates upon external validation, and propose a more tailored sample size approach. Methods Simulation of logistic regression prediction models to investigate factors associated with precision of performance estimates. Then, explanation and illustration of a simulation-based approach to calculate the minimum sample size required to precisely estimate a model's calibration, discrimination and clinical utility. Results Precision is affected by the model's linear predictor (LP) distribution, in addition to number of events and total sample size. Sample sizes of 100 (or even 200) events and non-events can give imprecise estimates, especially for calibration. The simulation-based calculation accounts for the LP distribution and (mis)calibration in the validation sample. Application identifies 2430 required participants (531 events) for external validation of a deep vein thrombosis diagnostic model. Conclusion Where researchers can anticipate the distribution of the model's LP (eg, based on development sample, or a pilot study), a simulation-based approach for calculating sample size for external validation offers more flexibility and reliability than rules-of-thumb.

Background: It would be clinically useful to prospectively identify the risk of disease progression in chronic myeloid leukaemia (CML). Overexpression of cancerous inhibitor of protein phosphatase 2A (PP2A) (CIP2A) protein is an adverse prognostic indicator in many cancers. Methods: We examined CIP2A protein levels in diagnostic samples from the SPIRIT2 trial in 172 unselected patients, of whom 90 received imatinib and 82 dasatinib as first-line treatment. Results: High CIP2A levels correlated with inferior progression-free survival (p = 0.04) and with worse freedom from progression (p = 0.03), and these effects were confined to dasatinib recipients. High CIP2A levels were associated with a six-fold higher five-year treatment failure rate than low CIP2A levels (41% vs. 7.5%, p = 0.0002), in both imatinib (45% vs. 11%, p = 0.02) and dasatinib recipients (36% vs. 4%, p = 0.007). Imatinib recipients with low CIP2A levels had a greater risk of treatment failure (p = 0.0008). CIP2A levels were independent of Sokal, Hasford, EUTOS (EUropean Treatment and Outcome Study), or EUTOS long-term survival scores (ELTS) or the presence of major route cytogenetic abnormalities. No association was seen between CIP2A levels and time to molecular response or the levels of the CIP2A-related proteins PP2A, SET, SET binding protein 1 (SETBP1), or AKT. Conclusions: These data confirm that high diagnostic CIP2A levels correlate with subsequent disease progression and treatment failure. CIP2A is a simple diagnostic biomarker that may be useful in planning treatment strategies.

Aim Making an accurate diagnosis of asthma can be challenging. Approaches used to assess the probability of asthma vary between clinicians; a prediction model could help to standardise clinical assessment. We aimed to derive and internally validate a clinical prediction model to support health professionals in primary care to assess the probability of an asthma diagnosis in children and young people presenting with symptoms suggestive of asthma. Methods We created a dataset from the Avon Longitudinal Study of Parents and Children (ALSPAC) enhanced with data from linked primary care electronic health records. Individuals with at least three inhaled corticosteroid prescriptions in one year and a ‘specific’ asthma Read code were designated as having asthma. Potential candidate predictors were included if data were available in at least 60% of participants. Remaining missing data were handled using multiple imputation. The prediction model was derived using logistic regression. Bootstrap re-sampling was used to internally validate the model. Results 11972 individuals aged Conclusion Information readily available from a patient’s electronic health records can support primary care clinicians weigh up the likelihood of a child/young person having asthma. We plan to externally validate the prediction model in a dataset created from primary care electronic health records. We will then develop the prediction model into a clinical decision support system (CDSS), co-produced with clinicians and patients, and test the feasibility of using the CDSS in clinical practice prior to prospective evaluation.

ObjectivesTo devise an assessment tool to aid discharge and admission decision-making in relation to children and young people in hospital urgent and emergency care facilities, and thereby improve the quality of care that patients receive, using a clinical prediction modelling approach.DesignObservational cohort study with internal and external validation of a predictive tool.SettingTwo general emergency departments (EDs) and an urgent care centre in the North of England.ParticipantsThe eligibility criteria were children and young people 0–16 years of age who attended one of the three hospital sites within one National Health Service (NHS) organisation. Children were excluded if they opted out of the study, were brought to the ED following their death in the community or arrived in cardiac arrest when the heart rate and respiratory rate would be unmeasurable.Main outcome measuresAdmission or discharge. A participant was defined as being admitted to hospital if they left the ED to enter the hospital for further assessment, (including being admitted to an observation and assessment unit or hospital ward), either on first presentation or with the same complaint within 7 days. Those who were not admitted were defined as having been discharged.ResultsThe study collected data on 36 365 participants. 15 328 participants were included in the final analysis cohort (21 045 observations) and 17 710 participants were included in the validation cohort (23 262 observations). There were 14 variables entered into the regression analysis. Of the 13 that remained in the final model, 10 were present in all 500 bootstraps. The resulting Paediatric Admission Guidance in the Emergency Department (PAGE) score demonstrated good internal validity. The C-index (area under the ROC) was 0.779 (95% CI 0.772 to 0.786).ConclusionsFor units without the immediate availability of paediatricians the PAGE score can assist staff to determine risk of admission. Cut-off values will need to be adjusted to local circumstance.Study protocolThe study protocol has been published in an open access journal: Riazet alRefining and testing the diagnostic accuracy of an assessment tool (Pennine Acute Hospitals NHS Trust-Paediatric Observation Priority Score) to predict admission and discharge of children and young people who attend an ED: protocol for an observational study. BMC Pediatr 18, 303 (2018).https://doi.org/10.1186/s12887-018-1268-7.Trial registration numberThe protocol has been published and the study registered (NIHR RfPB Grant: PB-PG-0815–20034; ClinicalTrials.gov:213469).

Background Bariatric surgery promotes weight loss and improves co-morbid conditions, with patients who are more physically active having better outcomes. However, levels of physical activity and sedentary behaviour often remain unchanged following surgery. Objectives To identify interventions and components thereof that are able to facilitate changes in physical activity and sedentary behaviour. Eligibility Physical activity and/or sedentary behaviour must have been measured, pre and post intervention, in patients who have undergone bariatric surgery. Study appraisal and synthesis methods : Four databases were searched with key-words. Two researchers conducted paper screening, data extraction and risk-of-bias assessment. Results Twelve studies were included; eleven were randomised. Two were delivered pre-surgery and ten post-surgery; five found positive effect. Moderate to vigorous physical activity increased in three studies, two of which also found a significant increase in step count. The fourth found a significant increase in strenuous activity and the fifth a significant increase in metabolic equivalent of task/day and reduced time spent watching television. Funding Jennifer James is funded by a NIHR ICA Clinical Doctoral Research Fellowship. Limitations Meta-analysis could not be conducted due to heterogeneity of outcomes and the tools used. Conclusion and implications of key findings This review has identified interventions and components thereof that were able to provoke positive effect. However, intervention and control conditions were not always well described particularly in terms of behaviour change techniques and the rationale for their use. Systematic review registration number PROSPERO (CRD42019121372)

Background Non-traumatic coma is a common acute childhood presentation to healthcare facilities in Africa and is associated with high morbidity and mortality. Historically, the majority of cases were attributed to cerebral malaria (CM). With the recent drastic reduction in malaria incidence, non-malarial coma is becoming a larger proportion of cases and determining the aetiology is diagnostically challenging, particularly in resource-limited settings. The purpose of this study will be to evaluate the aetiology and prognosis of non-traumatic coma in African children. Methods With no date restrictions, systematic searches of MEDLINE, Embase, and Scopus will identify prospective and retrospective studies (including randomised controlled trials, cluster randomised trials, cohort studies, cross-sectional, and case-control studies) recruiting children (1 month–16 years) with non-traumatic coma (defined by Blantyre Coma Score ≤ 2 or comparable alternative) from any African country. Disease-specific studies will be included if coma is associated and reported. The primary outcome is to determine the aetiology (infectious and non-infectious) of non-traumatic coma in African children, with pooled prevalence estimates of causes (e.g., malaria). Secondary outcomes are to determine overall estimates of morbidity and mortality of all-cause non-traumatic coma and disease-specific states of non-traumatic coma, where available. Random effects meta-analysis will summarise aetiology data and in-hospital and post-discharge mortality. Heterogeneity will be quantified with τ2, I2, and Cochran’s Q test. Discussion This systematic review will provide a summary of the best available evidence on the aetiology and outcome of non-traumatic coma in African children. Systematic review registration PROSPERO CRD42020141937

Background Complicated intra-abdominal infections (cIAIs) are associated with significant morbidity and mortality. The aim of this study was to describe the clinical characteristics of patients with cIAI in a multicentre study and to develop clinical prediction models (CPMs) to help identify patients at risk of mortality or relapse. Methods A multicentre observational study was conducted from August 2016 to February 2017 in the UK. Adult patients diagnosed with cIAI were included. Multivariable logistic regression was performed to develop CPMs for mortality and cIAI relapse. The c-statistic was used to test model discrimination. Model calibration was tested using calibration slopes and calibration in the large (CITL). The CPMs were then presented as point scoring systems and validated further. Results Overall, 417 patients from 31 surgical centres were included in the analysis. At 90 days after diagnosis, 17.3 per cent had a cIAI relapse and the mortality rate was 11.3 per cent. Predictors in the mortality model were age, cIAI aetiology, presence of a perforated viscus and source control procedure. Predictors of cIAI relapse included the presence of collections, outcome of initial management, and duration of antibiotic treatment. The c-statistic adjusted for model optimism was 0.79 (95 per cent c.i. 0.75 to 0.87) and 0.74 (0.73 to 0.85) for mortality and cIAI relapse CPMs. Adjusted calibration slopes were 0.88 (95 per cent c.i. 0.76 to 0.90) for the mortality model and 0.91 (0.88 to 0.94) for the relapse model; CITL was −0.19 (95 per cent c.i. −0.39 to −0.12) and − 0.01 (− 0.17 to −0.03) respectively. Conclusion Relapse of infection and death after complicated intra-abdominal infections are common. Clinical prediction models were developed to identify patients at increased risk of relapse or death after treatment, these now require external validation.

BackgroundPredictive models within epilepsy are frequently developed via Cox’s proportional hazards models. These models estimate risk of a specified event such as 12-month remission. They are relatively simple to produce, have familiar output, and are useful to answer questions about short-term prognosis. However, the Cox model only considers time to first event rather than all seizures after starting treatment for example. This makes assessing change in seizure rates over time difficult. Variants to the Cox model exist enabling recurrent events to be modelled. One such variant is the Prentice, Williams and Peterson – Total Time (PWP-TT) model. An alternative is the negative binomial model for event counts. This study aims to demonstrate the differences between the three approaches, and to consider the benefits of the PWP-TT approach for assessing change in seizure rates over time.MethodsTime to 12-month remission and time to first seizure after randomisation were modelled using the Cox model. Risk of seizure recurrence was modelled using the PWP-TT model, including all seizures across the whole follow-up period. Seizure counts were modelled using negative binomial regression. Differences between the approaches were demonstrated using participants recruited to the UK-based multi-centre Standard versus New Antiepileptic Drug (SANAD) study.ResultsResults from the PWP-TT model were similar to those from the conventional Cox and negative binomial models. In general, the direction of effect was consistent although the variables included in the models and the significance of the predictors varied. The confidence intervals obtained via the PWP-TT model tended to be narrower due to the increase in statistical power of the model.ConclusionsThe Cox model is useful for determining the initial response to treatment and potentially informing when the next intervention may be required. The negative binomial model is useful for modelling event counts. The PWP-TT model extends the Cox model to all included events. This is useful in determining the longer-term effects of treatment policy. Such a model should be considered when designing future clinical trials in medical conditions typified by recurrent events to improve efficiency and statistical power as well as providing evidence regarding changes in event rates over time.

The statistical concept of sampling is often given little direct attention, typically reduced to the mantra "take a random sample". This low resource and adaptable activity demonstrates sampling and explores issues that arise due to biased sampling.

Probability and chance are essential concepts, not just in statistics but in real life. We present an adaptable activity which investigates what we mean by bias, how we can identify bias, and how we can use it to our advantage!

HLA-G is a non-classical class I HLA antigen, normally expressed in high levels only on extravillous cytotrophoblast. It has immunosuppressive properties in pregnancy and has also been found to be upregulated on leucocytes in viral infection. In this study, proportions of all leucocyte subsets expressing HLA-G were found to be low in healthy subjects positive or negative for cytomegalovirus (CMV). Significantly greater proportions of CD4+ CD69+ and CD56+ T cells expressed HLA-G compared to other T cells. However, following stimulation with CMV antigens or intact CMV, proportions of CD4+, CD8+, CD69+ and CD56+ T cells, and also B cells expressing HLA-G, were significantly increased in CMV+ subjects. Despite some subjects having alleles of HLA-G associated with high levels of expression, no relationship was found between HLA-G genotype and expression levels. Purified B cells from CMV+ subjects stimulated in mixed culture with CMV antigens showed significantly increased HLA-G mRNA expression by real-time polymerase chain reaction. Serum levels of soluble HLA-G were similar in CMV- and CMV+ subjects but levels in culture supernatants were significantly higher in cells from CMV+ than from CMV- subjects stimulated with CMV antigens. The HLA-G ligand KIR2DL4 was mainly expressed on NK cells and CD56+ T cells with no differences between CMV+ and CMV- subjects. Following stimulation with IL-2, an increase in the proportion of CD56+ T cells positive for KIR2DL4 was found, together with a significant decrease in CD56dimCD16+ NK cells. The results show that CMV influences HLA-G expression in healthy subjects and may contribute to viral immune evasion.

Objectives The goal of this study was to determine whether a strategy of early re-isolation of pulmonary vein (PV) reconnection in all patients, regardless of symptoms, would reduce the recurrence of atrial fibrillation (AF) and improve quality of life. Background Lasting pulmonary vein isolation (PVI) remains elusive. PV reconnection is strongly linked to the recurrence of arrhythmia. Methods A total of 80 patients with paroxysmal AF were randomized 1:1 after contact force-guided PVI to receive either standard care or undergo a repeat electrophysiology study after 2 months regardless of symptoms (repeat study). At the initial procedure, PVI was demonstrated by entrance/exit block and adenosine administration after a minimum 20-min wait. At the repeat study, all sites of PV reconnection were re-ablated. Patients recorded electrocardiograms daily and whenever symptomatic for 12 months using a handheld monitor. Recurrence was defined as ≥30 s of atrial tachyarrhythmia (AT) after a 3-month blanking period. The Atrial Fibrillation Effect on Quality-of-Life Questionnaire was completed at baseline and at 6 and 12 months. Results All 40 patients randomized to repeat study attended for this after 62 ± 6 days, of whom 25 (62.5%) had reconnection of 41 (26%) PVs. There were no complications related to these procedures. Subjects recorded a total of 32,203 electrocardiograms (380 [335 to 447] per patient) during 12.6 (12.2 to 13.2) months of follow-up. AT recurrence was significantly lower for the repeat study group (17.5% vs. 42.5%; p = 0.03), as was AT burden (p = 0.03). Scores on the Atrial Fibrillation Effect on Quality-of-Life Questionnaire were higher in the repeat study group at 6 months (p Conclusions A strategy of routine repeat assessment with re-isolation of PV reconnection improved freedom from AT recurrence, AT burden, and quality of life compared with current standard care. (The Effect of Early Repeat Atrial Fibrillation [AF] on AF Recurrence [PRESSURE]; NCT01942408)

SummaryBackgroundCOVID-19 pandemic has developed rapidly and the ability to stratify the most vulnerable patients is vital. However, routinely used severity scoring systems are often low on diagnosis, even in non-survivors. Therefore, clinical prediction models for mortality are urgently required.MethodsWe developed and internally validated a multivariable logistic regression model to predict inpatient mortality in COVID-19 positive patients using data collected retrospectively from Tongji Hospital, Wuhan (299 patients). External validation was conducted using a retrospective cohort from Jinyintan Hospital, Wuhan (145 patients). Nine variables commonly measured in these acute settings were considered for model development, including age, biomarkers and comorbidities. Backwards stepwise selection and bootstrap resampling were used for model development and internal validation. We assessed discrimination via the C statistic, and calibration using calibration-in-the-large, calibration slopes and plots.FindingsThe final model included age, lymphocyte count, lactate dehydrogenase and SpO2as independent predictors of mortality. Discrimination of the model was excellent in both internal (c=0·89) and external (c=0·98) validation. Internal calibration was excellent (calibration slope=1). External validation showed some over-prediction of risk in low-risk individuals and under-prediction of risk in high-risk individuals prior to recalibration. Recalibration of the intercept and slope led to excellent performance of the model in independent data.InterpretationCOVID-19 is a new disease and behaves differently from common critical illnesses. This study provides a new prediction model to identify patients with lethal COVID-19. Its practical reliance on commonly available parameters should improve usage of limited healthcare resources and patient survival rate.FundingThis study was supported by following funding: Key Research and Development Plan of Jiangsu Province (BE2018743 and BE2019749), National Institute for Health Research (NIHR) (PDF-2018-11-ST2-006), British Heart Foundation (BHF) (PG/16/65/32313) and Liverpool University Hospitals NHS Foundation Trust in UK.Research in contextEvidence before this studySince the outbreak of COVID-19, there has been a pressing need for development of a prognostic tool that is easy for clinicians to use. Recently, a Lancet publication showed that in a cohort of 191 patients with COVID-19, age, SOFA score and D-dimer measurements were associated with mortality. No other publication involving prognostic factors or models has been identified to date.Added value of this studyIn our cohorts of 444 patients from two hospitals, SOFA scores were low in the majority of patients on admission. The relevance of D-dimer could not be verified, as it is not included in routine laboratory tests. In this study, we have established a multivariable clinical prediction model using a development cohort of 299 patients from one hospital. After backwards selection, four variables, including age, lymphocyte count, lactate dehydrogenase and SpO2remained in the model to predict mortality. This has been validated internally and externally with a cohort of 145 patients from a different hospital. Discrimination of the model was excellent in both internal (c=0·89) and external (c=0·98) validation. Calibration plots showed excellent agreement between predicted and observed probabilities of mortality after recalibration of the model to account for underlying differences in the risk profile of the datasets. This demonstrated that the model is able to make reliable predictions in patients from different hospitals. In addition, these variables agree with pathological mechanisms and the model is easy to use in all types of clinical settings.Implication of all the available evidenceAfter further external validation in different countries the model will enable better risk stratification and more targeted management of patients with COVID-19. With the nomogram, this model that is based on readily available parameters can help clinicians to stratify COVID-19 patients on diagnosis to use limited healthcare resources effectively and improve patient outcome.

Background: Accurately diagnosing asthma can be challenging. Uncertainty about the best combination of clinical features and investigations for asthma diagnosis is reflected in conflicting recommendations from international guidelines. One solution could be a clinical prediction model to support health professionals estimate the probability of an asthma diagnosis. However, systematic review evidence identifies that existing models for asthma diagnosis are at high risk of bias and unsuitable for clinical use. Being mindful of previous limitations, this protocol describes plans to derive and validate a prediction model for use by healthcare professionals to aid diagnostic decision making during assessment of a child or young person with symptoms suggestive of asthma in primary care. Methods: A prediction model will be derived using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) and linked primary care electronic health records (EHR). Data will be included from study participants up to 25 years of age where permissions exist to use their linked EHR. Participants will be identified as having asthma if they received at least three prescriptions for an inhaled corticosteroid within a one-year period and have an asthma code in their EHR. To deal with missing data we will consider conducting a complete case analysis. However, if the exclusion of cases with missing data substantially reduces the total sample size, multiple imputation will be used. A multivariable logistic regression model will be fitted with backward stepwise selection of candidate predictors. Apparent model performance will be assessed before internal validation using bootstrapping techniques. The model will be adjusted for optimism before external validation in a dataset created from the Optimum Patient Care Research Database. Discussion: This protocol describes a robust strategy for the derivation and validation of a prediction model to support the diagnosis of asthma in children and young people in primary care.

Background Empirical evidence is uncertain regarding the value of nonblanchable erythema in predicting the incidence of stage 2 (or more severe) pressure ulcers. Objectives To investigate whether nonblanchable erythema is an independent prognostic factor for pressure ulcer incidence using individual patient data. Methods We performed an electronic database search in February 2017 to identify longitudinal studies that considered nonblanchable erythema for predicting pressure ulcer risk in any population. We collected individual participant data for the included studies, and assessed the risk of bias of these studies using the Quality In Prognosis Studies tool. We analysed individual participant data in Stata using mixed-effects logistic regression to investigate the association of interest. The certainty of evidence from individual participant data analysis was assessed using the Grades of Recommendation Assessment, Development and Evaluation. The study was registered with PROSPERO (CRD42017081151). Results From the 13 included studies (total 68 077 participants) we had access to individual participant data from four (n = 3223), and 11·9% of participants (383 of 3223) developed new pressure ulcers of stage 2 or above within 28 days. Mixed-effects logistic regression showed that participants with nonblanchable erythema had higher odds of developing new pressure ulcers of stage 2 or above within 28 days of follow-up than those without nonblanchable erythema (multivariable association: n = 2684; odds ratio 2·72, 95% confidence interval 2·02-3·69; τ2 = 0; moderate-certainty evidence). Conclusions This first prognostic factor review with individual-level data analysis in patients with pressure ulcers suggests that people with nonblanchable erythema are more likely to develop new pressure ulcers of stage 2 or above within 28 days than people without nonblanchable erythema. It is important to identify nonblanchable erythema in practice and to intervene appropriately to prevent pressure ulceration. What's already known about this topic? Pressure ulcer reduction is a high priority for healthcare systems. Regularly inspecting skin to identify skin abnormalities is one key practice for preventing ulceration. Nonblanchable erythema - discoloration of the skin that does not turn white when pressed - is one clinically important skin abnormality. Empirical evidence synthesized using conventional meta-analysis is uncertain regarding the value of nonblanchable erythema for predicting open pressure ulcer incidence; this is partly because the conventional technique has weakness in terms of pooling prognostic effects of different multivariable analyses across studies. What does this study add? This prognostic factor review used individual-level data analysis to overcome the limitations of the conventional meta-analysis technique. For the first time there is confirmatory and moderate-certainty evidence on the association of nonblanchable erythema with pressure ulcer incidence. People with nonblanchable erythema are more likely to develop new pressure ulcers of stage 2 or more severe within 28 days than people without nonblanchable erythema, regardless of their age, baseline pressure ulcer risk or received support surfaces.

Background\ud In sub-Saharan Africa, there is a dearth of epidemiologic data on the burden of cerebral ath-erosclerosis. This is explained by the limited availability and the high cost of standard vascu-\ud lar imaging techniques. Neurovascular ultrasound is portable, cheaper and non-invasive and could, therefore, represent a reasonable alternative to fill this knowledge gap. We explored the feasibility of neurovascular ultrasound in Malawian adults with acute stroke-like syndrome to inform the design of future large stroke studies comparing its diagnostic perfor-\ud mance to that of gold standard vascular imaging techniques in sub-Saharan Africa.\ud Methods\ud We enrolled consecutive patients diagnosed with acute stroke-like syndrome based on the World Health Organization definition. Clinical and demographic data were recorded, and a comprehensive neurovascular ultrasound was performed. Fisher’s exact and Kruskal-Wallis\ud tests were used to study the relationship between atherosclerosis and potential risk factors.\ud Results\ud Sixty-six patients were enrolled (mean age: 58.7 years). The frequency of extracranial ath-erosclerosis was 39.4% (n = 26, 95% CI: 28.6–52.2). There were 12 patients with abnormal carotid intima media thickness (18.2%, 95% CI: 9.8–29.6) and 14 patients with a carotid pla-que (21.2%, 95% CI: 12.1–33.0). The frequency of intracranial atherosclerosis was 19.2%(95%CI: 6.6–39.4) in 26 patients with successful transcranial insonation. Hypertension(80.8 versus 52.5%, p = 0.03) and hypercholesterolemia (11.5 versus 0.0%, p = 0.05) were more prevalent in patients with extracranial atherosclerosis.\ud Conclusions\ud This study demonstrates the feasibility of neurovascular ultrasound to assess cervical arter-ies in adults with stroke-like syndrome in sub-Saharan Africa. There is a high rate of tran-scranial insonation failure in this setting, highlighting the need for echocontrast agents.

Uveal melanoma (UM) is fatal in ~50% of patients as a result of disseminated disease. This study aims to externally validate the Liverpool Uveal Melanoma Prognosticator Online V3 (LUMPO3) to determine its reliability in predicting survival after treatment for choroidal melanoma when utilizing external data from other ocular oncology centers. Anonymized data of 1836 UM patients from seven international ocular oncology centers were analyzed with LUMPO3 to predict the 10-year survival for each patient in each external dataset. The analysts were masked to the patient outcomes. Model predictions were sent to an independent statistician to evaluate LUMPO3&rsquo, s performance using discrimination and calibration methods. LUMPO3&rsquo, s ability to discriminate between UM patients who died of metastatic UM and those who were still alive was fair-to-good, with C-statistics ranging from 0.64 to 0.85 at year 1. The pooled estimate for all external centers was 0.72 (95% confidence interval: 0.68 to 0.75). Agreement between observed and predicted survival probabilities was generally good given differences in case mix and survival rates between different centers. Despite the differences between the international cohorts of patients with primary UM, LUMPO3 is a valuable tool for predicting all-cause mortality in this disease when using data from external centers.

Background Prognostic models for repeated events of the same type are highly useful in predicting when a patient may have a recurrence of a chronic disease or illness. Whilst methods are currently available for analysing recurrent event data in prognostic models, to our knowledge, most are not widely known or applied in a medical setting. As a result, often only the first recurrence is analysed meaning valuable information for multiple recurrences is discarded. Therefore, the aim of this review is to systemically review models for repeated medical events of the same type, to determine what modelling techniques are available and how they are applied. Methods MEDLINE will be used as the primary method to search sources. Various databases from the Cochrane Library and EMBASE will also be searched. Trial registries such as Clinicaltrials.gov.uk will be searched, as will registered trials that are ongoing and not yet published. Abstracts submitted to conferences will also be searched, and non-English sources will also be considered. Studies to be included in the review will be decided based on PICO guidelines, where the study population and outcomes correspond to this study’s aims and target population. The prognostic models used in each study chosen for inclusion in the review will be summarised qualitatively. Discussion As recurrent event data is not widely analysed in prognostic models, the results from this systematic review will identify which methods are available and which are commonly used. It is also unknown if certain methods which will be identified in the review perform better given certain conditions. Therefore, if included studies assess predictive performance, the results of this review could also provide evidence to determine if certain models are better fitting dependant on the event rate of the chronic condition. The results will be used to determine if model selection varies across disease area. The review will also provide an insight into the development of any new methods used for analysing recurrent events. Trial registration The review has been registered on PROSPERO (CRD42019116031).

Background: Antibiotics are routinely given to COPD patients presenting with lower respiratory tract infection (LRTI) symptoms in primary care. Yet the optimal drug and duration for effective first line treatment is unclear. Aim: To characterise antibiotic prescriptions for LRTI in COPD patients and investigate factors associated with repeat prescriptions. Methods: A retrospective analysis of antibiotic prescriptions for non-pneumonic LRTI in COPD patients from 2010 to 2015 using the UK primary care Optimum Patient Care Research Database. Second antibiotic prescriptions for LRTI or all indications within 14 days were the primary and secondary outcomes respectively, a proxy of initial treatment failure. We derived a model for repeat courses using uni- and multivariable logistic regression analysis. Results: 8.4% of the 9,042 incident events received further antibiotics for LRTI, 15.5% further courses for any indication. Amoxicillin and doxycycline were the commonest index and second line drugs respectively (58.7% and 28.7%), mostly given for 7 days. Index drugs other than amoxicillin, cardiovascular disease, pneumococcal vaccination and more primary care consultations were significantly associated with repeat prescriptions for LRTI (p Conclusion: Almost one in twelve patients received two antibiotic courses for LRTI within two weeks. There was no consensus antibiotic strategy. The data supported the preference for amoxicillin as index drug but confirmation by interventional methods is needed.

IntroductionAntibiotics are routinely given to people with chronic obstructive pulmonary disease (COPD) presenting with lower respiratory tract infection (LRTI) symptoms in primary care. Population prescribing habits and their consequences have not been well-described.MethodsWe conducted a retrospective analysis of antibiotic prescriptions for non-pneumonic exacerbations of COPD from 2010 to 2015 using the UK primary care Optimum Patient Care Research Database. As a proxy of initial treatment failure, second antibiotic prescriptions for LRTI or all indications within 14 days were the primary and secondary outcomes, respectively. We derived a model for repeat courses using univariable and multivariable logistic regression analysis.ResultsA total of 8.4% of the 9042 incident events received further antibiotics for LRTI, 15.5% further courses for any indication. Amoxicillin and doxycycline were the most common index and second-line drugs, respectively (58.7% and 28.7%), mostly given for 7 days. Index drugs other than amoxicillin, cardiovascular disease, pneumococcal vaccination and more primary care consultations were statistically significantly associated with repeat prescriptions for LRTI (pDiscussionThe prescription of multiple antibiotic courses for COPD exacerbations was relatively common—one in twelve patients receiving antibiotics for LRTI had a further course within 2 weeks. The findings support the current preference for amoxicillin as index drug within the limitations of this observational study. Further clinical trials to determine best practice in this common clinical situation appear required.

Summary Probability and chance are essential concepts, not just in statistics but in real life. We present an adaptable activity which investigates what we mean by bias, how we can identify bias, and how we can use it to our advantage!

BackgroundOutcomes of reverse total shoulder arthroplasty (RTSA) have typically been assessed using the same instruments as anatomical shoulder arthroplasties. However, to date, there has been a lack of investigation with respect to the correlation of such scores and patient satisfaction in the RTSA population.MethodsThe Oxford Shoulder Score (OSS) and Quick Disabilities of the Arm, Shoulder and Hand (QD) score were prospectively collected in 38 RTSA patients (41 shoulders) postoperatively. Scores were then evaluated to establish whether or not they correlated with patient satisfaction at a minimum of 1 year postoperatively.ResultsThe correlation coefficient for the OSS and patient satisfaction was found to be 0.313 (p = 0.011) and the correlation coefficient for the QD score and patient satisfaction was -0.292 (p = 0.017), showing a statistically significant but moderately weak relationship between the OSS and QD scores with patient satisfaction (p ConclusionsThe present study showed no strongly significant relationship between patient-reported outcome measure (PROM) scores and patient satisfaction following elective RTSA. These findings emphasise the need to question the appropriateness of standard PROM scores for the assessment of outcome and success following elective RTSA.

For permission to use (where not already granted under a licence) please go to. Clinical prediction models estimate the risk of existing disease or future outcome for an individual, which is conditional on the values of multiple predictors such as age, sex, and biomarkers. In this article, Bonnett and colleagues provide a guide to presenting clinical prediction models so that they can be implemented in practice, if appropriate. They describe how to create four presentation formats and discuss the advantages and disadvantages of each format. A key message is the need for stakeholder engagement to determine the best presentation option in relation to the clinical context of use and the intended users.