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 algorit