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Continuous Temperature Measurement by Thermal Imaging Camera

NCT06256978 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 224

Last updated 2024-02-13

No results posted yet for this study

Summary

This study explores the significance of body temperature monitoring in hospitalized patients, particularly in critical care environments. With body temperature exhibiting considerable variability, fever, defined at a central temperature of 38.3°C, serves as a pertinent indicator across diverse medical conditions. Temperature measurement methods in Intensive Care Units (ICUs) range from routine peripheral measurements to more invasive central temperature monitoring.

Critical patients with fever often receive antibiotic treatment, even without conclusive evidence of infection, as early intervention is linked to improved survival in septic patients. However, the complexity of individual variability, circadian rhythms, medication effects, and methodological limitations underscores the impracticality of defining fever with a singular temperature value. The thermal curve, representing the temporal evolution of temperature, emerges as a nuanced parameter in this context.

This study seeks to establish the correlation between axillary temperature measurements, a conventional method, and temperatures recorded by thermal imaging cameras. Widely employed during the Covid-19 pandemic, these cameras offer non-invasive and contactless measurement, mitigating pathogen transmission risks, particularly in patients colonized by multidrug-resistant microorganisms or those with compromised skin integrity. The study also endeavors to evaluate the diagnostic validity of thermal imaging cameras for fever and hypothermia.

The integration of thermal imaging cameras into a system capable of automated, real-time peripheral temperature acquisition suggests a potential paradigm shift in ICU temperature monitoring practices. Beyond immediate clinical applications, the amassed data from this system holds promise for training intelligent systems through machine learning algorithms. This strategic integration aims to predict critical events, such as the onset of fever, nosocomial infections, or shock, marking a forward-looking approach to patient management.

Conditions

  • Critically Ill

Sponsors & Collaborators

  • Fundación ASISA

    collaborator UNKNOWN

  • Universidad Europea de Madrid

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
110 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-03-31
Primary Completion
2024-12-31
Completion
2025-01-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06256978 on ClinicalTrials.gov