Digital Decision Support in the Management of Patients With Chest Pain

Condition(s) studied

Investigational drug(s) / intervention(s)

Troponin hs-cTnI: One sample of Troponin (hs-cTnI) is obtained and analyzed at the scene. Capillary blood sample or venous (lithium heparin) if peripheral venous catheter is inserted for standard care reasons. The sample is disposed after the result has been recorded in the clinical report form. The clinicians participating in the study are instructed and have been educated about the study to only collect the data (report form with measurements) and provide care as constituted by the guidelines. Patients will be monitored by board members that standard clinical care is provided.

Plasma glucose: One sample of plasma glucose is obtained and analyzed at the scene. The sample is then disposed and the measured value is recorded in the clinical report form. P-glucose measurement is clinical practice in standard care and is measured on all patients with diabetes and patients with altered mental status or at the ambulance nurse discretion.

Study summary

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.

Eligibility

Primary outcome measure(s)

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

Trial sites (3)

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