Charles Reynard

Objectives We evaluated the accuracy of using routine health service data to identify hospital-acquired thrombosis (HAT) and major bleeding events (MBE) compared with a reference standard of case note review. Design A multicentre observational cohort study. Setting Four acute hospitals in the UK. Participants A consecutive unselective cohort of general medical and surgical patients requiring hospitalisation for a period of >24 hours during the calendar year 2021. We excluded paediatric, obstetric and critical care patients due to differential risk profiles. Interventions We compared preidentified sources of routinely collected information (using hospital coding data and local contractually mandated thrombosis datasets) to data extracted from case notes using a predesigned workflow methodology. Primary and secondary outcome measures We defined HAT as objectively confirmed venous thromboembolism occurring during hospital stay or within 90 days of discharge and MBE as per international consensus. Results We were able to source all necessary routinely collected outcome data for 87% of 2008 case episodes reviewed. The sensitivity of hospital coding data (International Classification of Diseases 10th Revision, ICD-10) for the diagnosis of HAT and MBE was 62% (95% CI, 54 to 69) and 38% (95% CI, 27 to 50), respectively. Sensitivity improved to 81% (95% CI, 75 to 87) when using local thrombosis data sets. Conclusions Using routinely collected data appeared to miss a substantial proportion of outcome events, when compared with case note review. Our study suggests that currently available routine data collection methods in the UK are inadequate to support efficient study designs in venous thromboembolism research. Trial registration number NIHR127454.

One problem with CPMs is that their predictive performance can deteriorate with time. For example, in 2013, Hickey et al3 investigated the temporal change in prognostic performance of a CPM called EUROSCORE examining mortality after cardiothoracic surgery. They found that the model became increasingly mis-calibrated over time, in the sense that the observed mortality proportion diverged from the predicted mortality over time.

Background: In response to the global COVID-19 pandemic, many in vitro diagnostic (IVD) tests for SARS-CoV-2 have been developed. Given the urgent clinical demand, researchers must balance the desire for precise estimates of sensitivity and specificity against the need for rapid implementation. To complement estimates of precision used for sample size calculations, we aimed to estimate the probability that an IVD will fail to perform to expected standards after implementation, following clinical studies with varying sample sizes. Methods: We assumed that clinical validation study estimates met the ‘desirable’ performance (sensitivity 97%, specificity 99%) in the target product profile (TPP) published by the Medicines and Healthcare products Regulatory Agency (MHRA). To estimate the real-world impact of imprecision imposed by sample size we used Bayesian posterior calculations along with Monte Carlo simulations with 10,000 independent iterations of 5,000 participants. We varied the prevalence between 1 and 15% and the sample size between 30 and 2,000. For each sample size, we estimated the probability that diagnostic accuracy would fail to meet the TPP criteria after implementation. Results: For a validation study that demonstrates ‘desirable’ sensitivity within a sample of 30 participants who test positive for COVID-19 using the reference standard, the probability that real-world performance will fail to meet the ‘desirable’ criteria is 10.7–13.5%, depending on prevalence. Theoretically, demonstrating the 'desirable' performance in 90 positive participants would reduce that probability to below 5%. A marked reduction in the probability of failure to hit ‘desirable’ specificity occurred between samples of 100 (19.1–21.5%) and 160 (4.3–4.8%) negative participants. There was little further improvement above sample sizes of 160 negative participants. Conclusion: Based on imprecision alone, small evaluation studies can lead to the acceptance of diagnostic tests which are likely to fail to meet performance targets when deployed. There is diminished return on uncertainty surrounding an accuracy estimate above a total sample size of 250 (90 positive and 160 negative).

Introduction Cardiovascular disease (CVD) remains one of the leading causes of preventable death in Europe, therefore any opportunity to intervene and improve care should be maximised. Known CVD risk factors are routinely collected in the emergency department (ED), yet they are often not acted on. If the risk factors have prognostic value and a pathway can be created, then this would provide more holistic care for patients and reduce health system inefficiency. Methods and analysis In this mixed-methods study, we will use quantitative methods to investigate the prognostic characteristics of routinely collected data for long-term CVD outcomes, and qualitative methods to investigate how to use and implement this knowledge. The quantitative arm will use a database of approximately 21 000 chest pain patient episodes with a mean follow-up of 7.3 years. We will use Cox regression to evaluate the prognostic characteristics of routinely collected ED data for long-term CVD outcomes. We will also use a series of semi-structured interviews to co-design a prototype care pathway with stakeholders via thematic analysis. To enable the development of prototypes, themes will be structured into a logic model consisting of situation, inputs, outputs and mechanism. Ethics and dissemination This work has been approved by Research Ethics Committee (Wales REC7) and the Human Research Authority under reference 19/WA/0312 and 19/WA/0311. It has also been approved by the Confidentiality Advisory Group reference 19/CAG/0209. Dissent recorded in the NHS’ opt-out scheme will be applied to the dataset by NHS Digital. This work will be disseminated through peer-review publication, conference presentation and a public dissemination strategy. Trial registration number ISRCTN41008456. Protocol version V.1.0—7 June 2021.

Point-of-care tests for SARS-CoV-2 could enable rapid rule-in and/or rule-out of COVID-19, allowing rapid and accurate patient cohorting and potentially reducing the risk of nosocomial transmission. As COVID-19 begins to circulate with other more common respiratory viruses, there is a need for rapid diagnostics to help clinicians test for multiple potential causative organisms simultaneously.However, the different technologies available have strengths and weaknesses that must be understood to ensure that they are used to the benefit of the patient and healthcare system. Device performance is related to the deployed context, and the diagnostic characteristics may be affected by user experience.This practice review is written by members of the UK’s COVID-19 National Diagnostic Research and Evaluation programme. We discuss relative merits and test characteristics of various commercially available technologies. We do not advocate for any given test, and our coverage of commercially supplied tests is not intended to be exhaustive.

Background: Patients presenting with chest pain represent a large proportion of attendances to emergency departments. In these patients clinicians often consider the diagnosis of acute myocardial infarction (AMI), the timely recognition and treatment of which is clinically important. Clinical prediction models (CPMs) have been used to enhance early diagnosis of AMI. The Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid is currently in clinical use across Greater Manchester. CPMs have been shown to deteriorate over time through calibration drift. We aim to assess potential calibration drift with T-MACS and compare methods for updating the model. Methods: We will use routinely collected electronic data from patients who were treated using TMACS at two large NHS hospitals. This is estimated to include approximately 14,000 patient episodes spanning June 2016 to October 2020. The primary outcome of acute myocardial infarction will be sourced from NHS Digital’s admitted patient care dataset. We will assess the calibration drift of the existing model and the benefit of updating the CPM by model recalibration, model extension and dynamic updating. These models will be validated by bootstrapping and one step ahead prequential testing. We will evaluate predictive performance using calibrations plots and c-statistics. We will also examine the reclassification of predicted probability with the updated TMACS model. Discussion: CPMs are widely used in modern medicine, but are vulnerable to deteriorating calibration over time. Ongoing refinement using routinely collected electronic data will inevitably be more efficient than deriving and validating new models. In this analysis we will seek to exemplify methods for updating CPMs to protect the initial investment of time and effort. If successful, the updating methods could be used to continually refine the algorithm used within TMACS, maintaining or even improving predictive performance over time. Trial registration: ISRCTN number: ISRCTN41008456

Objectives: The objective was to assess the prognostic value of hypertension detected in the emergency department (ED).Methods: The ED presents a unique opportunity to predict long‐term cardiovascular disease (CVD) outcomes with its potential for high‐footfall, and large‐scale routine data collection applied to underserved patient populations. A systematic review and meta‐analyses were conducted to assess the prognostic performance and feasibility of ED‐measured hypertension as a risk factor for long‐term CVD outcomes. We searched MEDLINE and Embase databases and gray literature sources. The target populations were undifferentiated ED patients. The prognostic factor of interest was hypertension. Feasibility outcomes included prevalence, reliability, and follow‐up attendance. Meta‐analyses were performed for feasibility using a random effect and exact likelihood.Results: The searches identified 1072 studies after title and abstract review, 53 studies had their full text assessed for eligibility, and 26 studies were included. Significant heterogeneity was identified, likely due to the international populations and differing study design. The meta‐analyses estimate of prevalence for ED‐measured hypertension was 0.31 (95% confidence interval 0.25–0.37). ED hypertension was persistent outside the ED (FE estimate of 0.50). The proportion of patients attending follow‐up was low with an exact likelihood estimate of 0.41. Three studies examined the prognostic performance of hypertension and demonstrated an increased risk of long‐term CVD outcomes.Conclusion: Hypertension can be measured feasibly in the ED and consequently used in a long‐term cardiovascular risk prediction model. There is an opportunity to intervene in targeted individuals, using routinely collected data.

Objective To systematically appraise the available evidence to determine the accuracy of decision aids for emergency medical services (EMS) telephone triage of patients with chest pain suspected to be caused by acute coronary syndrome (ACS) or life-threatening conditions. Design Systematic review. Data sources Electronic searches were performed in Embase 1974, Medline 1946 and CINAHL 1937 databases from 3 March 2020 to 4 March 2020. Eligibility criteria The review included all types of original studies that included adult patients (>18 years) who called EMS with a primary complaint of chest pain and evaluated dispatch triage priority by telephone. Outcomes of interest were a final diagnosis of ACS, acute myocardial infarction or other life-threatening conditions. Data extraction and synthesis Two authors independently extracted data on study design, population, study period, outcome and all data for assessment of accuracy, including cross-tabulation of triage priority against the outcomes of interest. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 assessment tool. Results Searches identified 553 papers, of which 3 were eligible for inclusion. Those reports described the evaluation of three different prediction models with variation in the variables used to detect ACS. The overall results showed that dispatch triage tools have good sensitivity to detect ACS and life-threatening conditions, even though they are used to triage signs and symptoms rather than diagnosing the patients. On the other hand, prediction models were built to detect ACS and life-threatening conditions, and therefore, prediction models showed better sensitivity and negative predictive value than dispatch triage tools. Conclusion We have identified three prediction models for telephone triage of patients with chest pain. While they have been found to have greater accuracy than standard EMS dispatch systems, prospective external validation is essential before clinical use is considered. PROSPERO registration number This systematic review was pre-registered on the International prospective register of systematic reviews (PROSPERO) database (reference CRD42020171184).