Defining the roles of Data Manager and Epidemiologist in emergency medical teams

Funding: Hong Kong Jockey Club Charity Trust within the collaborative project “Training and Research Development for Emergency Medical Teams with reference to the WHO Global EMTs initiative, classification, and standards” between the Humanitarian and Conflict Response Institute (HCRI; Manchester, United Kingdom) and the Hong Kong Jockey Club Disaster Preparedness and Response Institute (HKJCDPRI; Aberdeen, Hong Kong).Medical and epidemiological documentation in disasters is pivotal: the former for recording patient care and the latter for providing real-time information to the host country. Furthermore, documentation informs post-hoc analysis to improve the effectiveness of future deployments. Although documentation is considered important and indeed integral to health care response, there are many barriers and challenges. Some of these challenges include: working without well-established standards for medical documentation; and working with international guidelines which provide minimal guidance as to how health data should be managed practically to ensure accuracy and completion. Furthermore, there is a shift in mindset in disaster contexts wherein most health care focus shifts to direct clinical care and diverts almost all attention from quality documentation. This report distinguishes between the tasks of the epidemiologist and the data manager (DM) in an emergency medical team (EMT) and discusses the importance of data collection in the specific case of an EMT deployment. While combining these roles is sometimes possible if resources are limited, it is better to separate them, as the two are quite distinct. Although there is overlap, to achieve the goals of either role, preferentially they should be carried out by two people working closely together with complementary skill sets. The main objective of this report is to provide guidance and task descriptions to EMTs and field hospitals when training, recruiting, and preparing DMs and epidemiologists to work within their teams. Clear delineation of tasks will lead to better quality data, as it commits DMs to being concerned with the provision of real-time documentation from patient arrival through to compiling daily reports. It also commits epidemiologists to providing enhanced disease surveillance; outbreak investigation; and a source of reliable and actionable information for decision makers and stakeholders in the disaster management cycle.PostprintPeer reviewe

Advanced cardiovascular risk prediction in the emergency department: updating a clinical prediction model - a large database study protocol.

From Europe PMC via Jisc Publications RouterHistory: ppub 2021-10-01, epub 2021-10-07Publication status: PublishedFunder: Department of Health; Grant(s): NIHR300246Funder: national institute for health research; Grant(s): NIHR300246BackgroundPatients 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.MethodsWe 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.DiscussionCPMs 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 registrationISRCTN number: ISRCTN41008456

Cross-cultural adaptation and its impact on research in emergency care

The perspective of patients is increasingly recognised as important to care improvement and innovation. Patient questionnaires such as patient-reported outcome measures may often require cross-cultural adaptation (CCA) to gather their intended information most effectively when used in cultures and languages different to those in which they were developed. The use of CCA could be seen as a practical step in addressing the known problems of inclusion, diversity and access in medical research. An example of the recent adaptation of a patient-reported outcome measure for use with ED patients is used to explore some key features of CCA, introduce the importance of CCA to emergency care practitioners and highlight the limitations of CCA

Advanced cardiovascular risk prediction in the emergency department: updating a clinical prediction model – a large database study protocol

From Springer Nature via Jisc Publications RouterHistory: received 2021-07-27, accepted 2021-09-27, registration 2021-09-28, online 2021-10-07, pub-electronic 2021-10-07, collection 2021-12Publication status: PublishedFunder: national institute for health research; doi: http://dx.doi.org/10.13039/501100000272; Grant(s): NIHR300246Abstract: 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: ISRCTN4100845