Fever Algorithm Development of a Non-invasive Wearable Core Body Temperature Sensor System in Intensive Care Unit Patients
Fever Algorithm Development of a Non-invasive Wearable Core Body Temperature Sensor System in Intensive Care Unit Patients
Background and Rationale:
The gold standard to assess fever, is to conduct invasive intravascular, esophageal or bladder thermistor core body temperature (CBT) measurements. Since these methods are time consuming for the medical staff and more importantly displeasing to the patients, alternative CBT/fever assessments is needed. greenTEG is developing a CBT algorithm that will be able to reliably calculate CBT continuously form skin temperature (ST) and the corresponding heat flux (HF). This can be achieved from subjects developing fever in intensive care unit, since the prevalence of fever is high and optimally assessed.
Objective(s):
The aim of this study is to develop and validation of an algorithm that allows the detection of fever in patients through a non-invasive wearable prototype sensor, which calculates CBT from ST, HF and heart rate (HR) data streams, allowing a more effective patient management
Endpoint(s):
ST, HF, HR and CBT data are collected for at least 24hrs until 3 day, depending on the ICU length of stay of the patient. ST, HF, HR and CBT values from different measurement positions (subclavicular, lateral ribcage, upper arm and wrist) of the non-invasive wearable research prototypes will be compared with each other and compared to clinical invasive method particularly CBT measured by blood temperature from a pulmonary catheter if present or vesical temperature
Study Design:
Interventional-Single Group Assignment, monocentric, open label.
Statistical Considerations:
The measure of quality will be the mean absolute difference (MAD) between the CBT prediction and the reference signal where the mean is taken over the whole measurement of a single candidate. An aggregate performance measure over a group of candidates is defined by averaging the MAD values of each candidate in the group. When a group of candidates for algorithm validation is defined, the total improvement will be defined by comparing aggregate performances of old and new algorithm for the validation group. Balancing the probability of occurrence of the factors in the population and the overall size of the study, a final size of 50 candidates is reasonable.
Inclusion- / Exclusion Criteria:
Inclusion criteria:
Exclusion criteria:
Number of Participants with Rationale:
Number of participants in the study :50 candidates. The study will be divided in two parts. In the first part data are collected from 38 patients. In the second part, data form 12 patients will be collected. The reason for the first part is to collect data in order to develop the algorithm. The reason to collect additional data from 12 candidates in the second part is to validate and adjust the algorithm that is develop in the first part of the study.
Study Intervention:
50% of the research prototypes (4 pieces) will be mounted to the patient on left side of the body (sub clavicula, lateral ribcage, upper arm and wrist), after being admitted to the ICU and having signed the informed consent. As soon as a patient gets fever, the other 50% of the research prototypes (4 pieces) will be applied on the right side of the body (sub clavicula, lateral ribcage, upper arm and wrist).
Control Intervention:
Not applicable
Study procedures:
Patients will be recruited and screened 1-3 days before the measurements starts. 50% of the research prototypes (4 pieces) will be applied to the patient on the left side of the body (sub clavicula, lateral ribcage, upper arm and wrist), after being admitted to the ICU and having signed the informed consent. After the development of the fever, the other 50% of the research prototypes (4 pieces) will be applied on the right side of the body (sub clavicula, lateral ribcage, upper arm and wrist). At the end of intervention all prototypes will be removed from the patient. A greenTEG employee will collect all the prototypes.
Inclusion Criteria:
Exclusion Criteria: