跳至主要内容
临床试验/NCT05767619
NCT05767619招募中不适用

Development of a Decision Support System in the Assessment of Patients With Chest Pain in the Prehospital Setting

Vastra Gotaland Region3 个研究点 分布在 1 个国家目标入组 2,000 人开始时间: 2023年5月15日最近更新:
适应症

试验速览

阶段
不适用
状态
招募中
发起方
入组人数
2,000
试验地点
3
主要终点
Clinical outcome prediction of the decision support model based on a composite outcome

研究概览

简要总结

The goal of this observational study is to develop a decision support system in patients presenting with chest pain in the prehospital setting. The main question it aims to answer is:

• Performance of a machine learning based model for decision support of patients in contact with emergency medical services due to chest pain

Participants will be asked to:

  • respond to questions asked by the clinician at the scene regarding previous known risk factors and pain characteristics
  • consent to the collection of routinely available data from medical records
  • consent of taking one blood sample capillary or venous (if perifer catheter is placed for standard care reasons) troponin and glucose which is measured at the scene, disposed, and the result is entered in the clinical report form.

详细描述

Prehospital emergency care has undergone dramatic changes in recent decades. From the fact that the ambulance nurse has previously started care at the site of the illness, with the goal of transporting the patient to the nearest emergency room, prehospital care has become increasingly differentiated. This means that "care at the right level of care" has become a watchword and a number of different levels of care have become relevant, ranging from fast track and rapid investigation and treatment in hospital for heart attack, acute myocardial infarctions and strokes to the patient being left at the scene with advice on self-care.

In principle, there is three overall levels of care:1) Need for inpatient resources;2) Need for primary care contact or visit by mobile healthcare team within the next day and 3) Referral to home care, provide support for self-care or treatment on site.

This places competence requirements on the ambulance nurse with requirements for a prehospital assessed condition compatible with a level of care where the patient's needs can be met. This approach puts patient safety in focus in a different way than before. Because with this procedure, patients with time-sensitive conditions (such as stroke, heart attack, acute myocardial infarction and sepsis) run an increased risk of being left at the scene with advice on self-care, due to inappropriate prehospital assessment. In prehospital emergency care, the assessment of the severity of the patient's condition takes place in two stages: 1) At the emergency dispatch centre when the patient has called the national emergency number and 2) At the scene of the illness by the ambulance nurse after arrival at the patients side.

The basis for prehospital decision support is a) identification of time-sensitive conditions, i.e. conditions where the time to initiation of causal treatment can affect the prognosis, and b) identification of predictors, i.e. factors that are already prehospitally characteristic of the condition (disease or accident) itself, but also of the severity of the condition. The classic examples of time-critical conditions are manifestations of cardiovascular diseases such as heart attack ,acute myocardial infarction and stroke, but also serious infectious diseases such as sepsis and severe trauma. Predictors can be identified via measurement of vital parameters such as pulse, blood pressure and oxygen saturation, medical history (previously known diseases and current onset), current symptom picture, clinical manifestations ( pallor) examination findings (ECG) and analysis of biochemical markers by capillary blood test (glucose, troponin).

Current studies indicate that for many patients in contact with the emergency medical service due to acute chest pain, other options than the emergency department, e.g. follow-up in primary care, may be more beneficial for the patient and less resource-intensive for the ambulance and the emergency care. In these cases, a decision support system based on gender, medical history, symptoms and clinical observations including ECG and and biochemical markers, (troponin) could provide support for the ambulance nurse. Chest pain is one of the most common search causes and constitutes about 10% of assignments in the Swedish emergency medical service.

研究设计

研究类型
Observational
观察模型
Cohort
时间视角
Prospective

入排标准

年龄范围
18 Years 至 —(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • Patient in contact with the emergency medical service and patient main symptom is chest pain or chest discomfort
  • Primary assignment (not assessed by physician in primary care, hospital)

排除标准

  • Assignment taking place outside participating emergency medical service organisation geographical catchment area
  • Under 18 years of age
  • Unwillingness to participate
  • Unable to participate (language, dementia, etc.)

结局指标

主要结局

Clinical outcome prediction of the decision support model based on a composite outcome

时间窗: Time-sensitive condition: from EMS inclusion to hospital discharge follow-up time 1 day up to 100 days; Death: from EMS inclusion up to seven days; Adverse events: from EMS inclusion up to 72 hours.

The models predictive ability for classifying low risk and high risk patients. Including features collected from the clinician at the scene, patient characteristics, previous medical history, pain characteristics, time duration of pain/discomfort, ECG interpretation, vital signs,Troponin and Glucose measurements. A composite outcome is based on the following: 1. Time-sensitive diagnose at hospital discharge 2. Death within seven days 3. Adverse events within 72 hours Adverse events is defined as : cardiac arrest, ventricular arrhythmias, shock, convulsions, heart failure ,hypotension, syncope and loss of consciousness and associated with a grade 4 or 5 according to the CCTAE v 6.0. Multiple models will be developed with an ensemble of classifiers and evaluated determining the best model performance. Measures reported are discriminative performance of the model roc-AUC, calibration, sensitivity, specificity, accuracy, positive predictive value, negative predictive value.

次要结局

未报告次要终点

研究者

发起方
Vastra Gotaland Region
申办方类型
Other Gov
责任方
Principal Investigator
主要研究者

Carl Magnusson

Head prehospital nurse, PhD

Vastra Gotaland Region

研究点 (3)

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