ClinicalTrials.gov
public full text
Public Domain
Identification of the Sick Patient in the ED
This non-interventional study will test scoring systems used to identify high risk patients when they are triaged upon presentation in the Emergency Department (ED) in a single hospital in the Northwest of England between May and July 2017.E The study will involve collecting data only from a cohort of 500 consecutive patients who arrive at the ED. Patients who have a traumatic, purely obstetric or purely psychiatric condition will be excluded. Patients will be triaged as is routine by nursing and medical staff using various scoring methods determine whether they are high risk and need urgent, life-saving treatment. In addition the impression (yes/no) of the triaging nurse and the treating clinician as to whether the patient will need a life-saving intervention will be collected. Patients will be followed up for 48 hours to see whether they needed any life-saving treatment, such as admission to ICU, life-saving surgery, cardiopulmonary resuscitation (CPR), or death. Each patient's Manchester Triage category, NEWS at presentation, nurse and treating clinician impressions and a novel score calculated from NEWS data will be collected together with the outcome data in order to compare the predictive power of the five scoring systems. In this way the study will test which is the best scoring system for identifying high risk patients in a timely manner. This is important as it can allow life saving treatment to be delivered quickly to those patients who need it most and can prevent inappropriate interventions on patients who do not immediately need them. The study will collect minimal patient information and will not interfere with or alter their treatment in any way. Only patient data recorded as part of routine practice is required, which will be collected by members of the direct care team and will be anonymised prior to analysis.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Trauma Follow-Up Prediction (Project 2: Aim 1)#979999
- A Rapid Diagnostic of Risk in Hospitalized Patients Using Machine Learning#895279
- AI to Improve Data From Danish Cardiac Arrest Registry#857030
- Clinical Registry on Sudden Death Primary Prevention at Latin America#1262380
- Non Attendance at Scheduled Appointments as a Marker of Mortality and Hospital Admission#853267
Record · ID 1323587
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive