Prediction of Sepsis in the Emergency Room with Pulse Wave applied Machine Learning

Prediction of Sepsis in the Emergency Room with Pulse Wave applied Machine Learning - RADAR study

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

NL-OMON

Registry ID

NL-OMON51532

Enrollment

1500

Registered

2023-05-04

Start date

2023-06-19

Completion date

Unknown

Last updated

2026-08-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

spoedeisende hulp bloedvergifiting sepsis

Interventions

None listed

Eligibility

Age

18 Years to 99 Years

Inclusion criteria

Inclusion criteria: - >18 years of age - Informed consent - Admitted to the emergency department

Exclusion criteria

Exclusion criteria: - Patients admitted to the trauma room - Subjects will be excluded if noninvasive blood pressure cannot be measured with the finger cuff according to the Instructions for Use of the CS/EV1000 system.

Design outcomes

Primary

Measure	Time frame
The primary aim of this study is to predict deterioration in patients admitted to the ED. More specifically, we aim to predict sepsis, septic shock and cardiovascular instability based on the hemodynamic profile of the patient. Therefore, we will collect the continuous noninvasive arterial pressure waveform signals with the ClearSight (CS) finger cuff. In combination with the electronic medical record (EMR) data of the patient, we will develop a machine learning framework for the predictive tasks.	—

Measure

Time frame

The primary aim of this study is to predict deterioration in patients admitted to the ED. More specifically, we aim to predict sepsis, septic shock and cardiovascular instability based on the hemodynamic profile of the patient. Therefore, we will collect the continuous noninvasive arterial pressure waveform signals with the ClearSight (CS) finger cuff. In combination with the electronic medical record (EMR) data of the patient, we will develop a machine learning framework for the predictive tasks.

—

Secondary

Measure	Time frame
The secondary aim of this study is to determine the optimal patient-specific therapeutic pathway and thereby aiming to determine fluid responsiveness of the patient. Furthermore, within the machine learning framework, we will investigate whether hospitalization (ICU or general ward) of ED patients can be predicted.	—

Countries

Netherlands

Outcome results