Ignite Creation Date:
2025-12-24 @ 2:56 PM
Ignite Modification Date:
2025-12-26 @ 6:24 PM
Study NCT ID:
NCT04654559
Status:
SUSPENDED
Last Update Posted:
2024-03-15
First Post:
2020-11-26
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Development of a Fever Detection Algorithm Based on Non-invasive Skin-based Sensor Values in Infants up to 18 Months of Age
Sponsor:
greenTEG AG
Organization:
Study Overview
Official Title:
Development of a Fever Detection Algorithm Based on Non-invasive Skin-based Sensor Values in Infants up to 18 Months of Age
Status:
SUSPENDED
Status Verified Date:
2024-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Temporary stop \& analyze current data. Study may be resumed.
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Fever of infants up to 18 months of age will be monitored in the hospital using the standard clinical methods and wearable research prototypes. These research prototypes will be measuring continuously and non-invasively skin based parameters, with which the feasibility of developing a fever detection algorithm will be investigated.
Detailed Description:
Background and Rationale:
In daily clinical practice, core body temperature (CBT) of infants with fever symptoms is monitored using sporadic rectal measurements. Because these sporadic invasive measurements are time consuming for the medical staff and displeasing for patients, an alternative method to assess CBT/fever is needed. The company greenTEG is developing a CBT algorithm which calculates CBT continuously form skin temperature (ST), corresponding heat flux (HF) and other skin-based parameters. The algorithm development will be achieved by collecting skin-based parameters and reference CBT values from infants having fever in a clinical setting.
Objective(s):
Develop and validate an algorithm that allows the detection of fever in infants through a non-invasive sensor system, which calculates CBT from ST, HF and other skin-based data streams, allowing a more effective patient management.
Statistical Considerations:
The measures of quality will be: 1) 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 patient. An aggregate performance measure over a group of patients is defined by averaging the MAD values of each patient in the group. 2) the 2σ (standard deviation) range of the Bland-Altmann-Plot between the CBT prediction and the reference signal. This is calculated either for individual patient data or for the combined data of all patients together. As we have defined a group of patients for algorithm validation, the total improvement will be defined by comparing the above aggregate performance measures of old and new algorithm for the validation group. We expect the factors age, sex to influence the algorithmic prediction. Balancing the probability of occurrence of the factors in the population and the overall size of the study, a final size of 50 patients is reasonable.
Study procedures:
Infants will be recruited and screened 1 days before the measurements starts. Two research prototypes will be applied to the patient on the left side of the body (lateral ribcage and foot), after being admitted to the hospital and parents having signed the informed consent. For all infants rectal measurement will be asses every 8 hours, as reference temperature. For infants older than 6 months, in addition to rectal temperature, ear temperature will be assessed every 4 hours with an infrared ear thermometer. The whole measurement procedure will last 18-72 hours, depending on the stationary stay of the individual patient.
In daily clinical practice, core body temperature (CBT) of infants with fever symptoms is monitored using sporadic rectal measurements. Because these sporadic invasive measurements are time consuming for the medical staff and displeasing for patients, an alternative method to assess CBT/fever is needed. The company greenTEG is developing a CBT algorithm which calculates CBT continuously form skin temperature (ST), corresponding heat flux (HF) and other skin-based parameters. The algorithm development will be achieved by collecting skin-based parameters and reference CBT values from infants having fever in a clinical setting.
Objective(s):
Develop and validate an algorithm that allows the detection of fever in infants through a non-invasive sensor system, which calculates CBT from ST, HF and other skin-based data streams, allowing a more effective patient management.
Statistical Considerations:
The measures of quality will be: 1) 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 patient. An aggregate performance measure over a group of patients is defined by averaging the MAD values of each patient in the group. 2) the 2σ (standard deviation) range of the Bland-Altmann-Plot between the CBT prediction and the reference signal. This is calculated either for individual patient data or for the combined data of all patients together. As we have defined a group of patients for algorithm validation, the total improvement will be defined by comparing the above aggregate performance measures of old and new algorithm for the validation group. We expect the factors age, sex to influence the algorithmic prediction. Balancing the probability of occurrence of the factors in the population and the overall size of the study, a final size of 50 patients is reasonable.
Study procedures:
Infants will be recruited and screened 1 days before the measurements starts. Two research prototypes will be applied to the patient on the left side of the body (lateral ribcage and foot), after being admitted to the hospital and parents having signed the informed consent. For all infants rectal measurement will be asses every 8 hours, as reference temperature. For infants older than 6 months, in addition to rectal temperature, ear temperature will be assessed every 4 hours with an infrared ear thermometer. The whole measurement procedure will last 18-72 hours, depending on the stationary stay of the individual patient.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: