Advanced monitoring system for sedation in ICU patients
Official title: Sistema de monitorización multiparámetros Para sedación de Pacientes críticos en UCI (ROMANTIC: multipaRameter mOnitoring systeM for sedAtion iNThe ICu)
Run by Pontificia Universidad Catolica de Chile · NCT06667869 · Data updated 2026-11-12
This study is testing a new monitoring system that uses machine learning to see if it can better track sedation levels in critically ill patients in the ICU compared to standard methods.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 80 (estimated) |
| Sponsor | Pontificia Universidad Catolica de Chile Academic / other |
| Locations | 2 sites (Santiago, Metropolitana de Santiago and 1 other locations) |
| Status | Recruiting |
| Started | February 14, 2025 |
| Main results expected | July 1, 2026 |
| Study ends | December 15, 2026 |
| Registry record updated | June 8, 2025 |
| Trial ID | NCT06667869 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates for this study are adult patients over 18 years old who are admitted to critical care units and require sedation and mechanical ventilation for more than 24 hours.
✕Probably not a fit
Patients with neurological pathologies, psychiatric disabilities, or those under 18 years old may not benefit from this study.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This project aims to develop a multiparameter monitoring system called ROMANTIC that utilizes machine learning algorithms to continuously assess sedation levels in critically ill patients in the ICU. The study will collect various clinical, demographic, and pharmacological data from patients requiring sedation and mechanical ventilation for over 24 hours. The performance of this machine learning approach will be compared to traditional clinical sedation scales to improve the accuracy of sedation management. The study is divided into two phases: an observational cohort study followed by the development of a predictive model based on the collected data.
Why it matters
Potential benefit: If successful, this system could significantly enhance the safety and effectiveness of sedation management in ICU patients, potentially reducing complications and improving outcomes.
How similar studies have gone: While the use of machine learning in sedation monitoring is an emerging field, similar studies have shown promise in improving patient outcomes, indicating potential success for this novel approach.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Patients admitted to adult critical care units * Individuals over 18 years old * Requiring sedation for more than 24 hours * Patients needing mechanical ventilation for more than 24 hours Exclusion Criteria: * Patients with neurological pathology as the cause of mechanical ventilation (including recovered cardiocirculatory arrest, fulminant liver failure, and neurocritical conditions, except subdural hematomas or subarachnoid hemorrhages). * Pregnancy * Presence of psychiatric or intellectual disability prior to hospitalization * Drug dependency * History of chronic liver damage with hepatic encephalopathy * Second period of mechanical ventilation during hospitalization * Early limitation of therapeutic effort * Patients under 18 years old
Where it's running
-
Hospital Clínico Pontificia Universidad Católica de Chile
Santiago, Santiago Metropolitan, Chile Recruiting -
Hospital Dr. Sotero del Rio
Santiago, Metropolitana de Santiago, Chile Not yet recruiting
Study contacts
- Study coordinator: Juan C. Pedemonte, M.D.
- Email: jcpedemo@uc.cl
- Phone: +56 99 879 8836
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.