Clinical trials: Early Warning Score
Severe Sepsis, Cardiac Arrest
Examination of the Impact of Better Surveillance and Communication of Patient Deterioration on Patient Related Outcomes
A hospitals manual method of patient monitoring will be implemented in an automated system and supported by an early patient deterioration detection for timely escalation.
- Conditions
- Severe Sepsis, Cardiac Arrest, Respiratory Failure
- Trial name
- NCT01692847
- Where
- United Kingdom
- Volunteers
- 678 people
- Sponsor
- Philips Healthcare
Sepsis, Renal Failure
The Prevention of Failure to Rescue Using Early Warning Scoring
The purpose of this study is to assess if, compared with standard paper-based systems, an automated Early Warning System (EWS) resident in a spot check patient monitor, can help to identify deteriorating patients.
- Conditions
- Sepsis, Renal Failure, Pulmonary Edema, Cardio-pulmonary Arrest
- Trial name
- VitalCare (NCT01197326)
- Testing
- use of the MP5 EWS patient monitor
- Where
- Australia
- Volunteers
- 414 people, healthy volunteers welcome
- Sponsor
- Philips Healthcare
Clinical Assessment
CHroniSense National Early Warning Score Study
ChroniSense Medical Ltd. is developing a wearable medical multi-parametric monitoring system, Polso™. Polso™ provides a number of the parameters employed in various Early Warning Scores (EWS), such as the National Early Warning…
- Trial name
- CHESS (NCT03448861)
- Testing
- Polso Wearebale watch
- Volunteers
- 250 people
- Sponsor
- BlueWind Medical
Cardiac Arrest, Renal Failure
The Vital Signs to Identify, Target, and Assess Level (VITAL) Care Study III
To evaluate the effect of automated recording on frequency of recorded scores, number of automated notifications and serious events.
- Conditions
- Cardiac Arrest, Renal Failure, Respiration Failure, Myocardial Infarction
- Trial name
- NCT02973243
- Testing
- Automatic RR Measurement
- Where
- United Kingdom
- Volunteers
- 446 people
- Sponsor
- Philips Healthcare
Sepsis, Septicemia
A Rapid Diagnostic of Risk in Hospitalized Patients Using Machine Learning
In this study, the investigators will deploy a software-based clinical decision support tool (eCARTv5) into the electronic health record (EHR) workflow of multiple hospital wards.
- Conditions
- Sepsis, Septicemia, Respiratory Failure, Hemodynamic Instability
- Trial name
- NCT05893420
- Testing
- eCARTv5 clinical deterioration monitoring, Standard of care control
- Where
- 3 sites in United States
- Volunteers
- 30,000 people
- Sponsor
- AgileMD, Inc.