Tundra Space

Tundra Space

Clinical Research Directory

Browse clinical research sites, groups, and studies.

Back to Studies
RECRUITING
NCT07510776

UP STUDY - Decipher Persistent Critical Illness Through in Deep Clinical Phenotyping.

Sponsor: Lisbon Academic Medical Center - Centro Académico de Medicina de Lisboa

View on ClinicalTrials.gov

Summary

Persistent Critical Illness (PCI) is a condition that affects some patients who remain in the Intensive Care Unit (ICU) for a long time, usually more than 10-14 days. It is estimated to occur in 5-20% of critically ill patients. A recent Portuguese study found that more than 14% of ICU patients stayed longer than 14 days. PCI is often associated with ongoing need for life support, such as mechanical ventilation or medications to maintain blood pressure. However, patients may also experience severe muscle weakness, repeated infections, or other complications, which makes this group very diverse. One of the main risk factors for prolonged ICU stay is sepsis, a severe infection that affects the whole body. Other factors-such as prior health conditions, use of corticosteroids, sedation practices, early versus late mobilization, fluid and antibiotic management, and delirium treatment-may also influence the development and course of PCI. This study aims to identify different clinical patterns ("clusters") among critically ill patients who remain in the ICU for more than 10 days. Patients will be followed until hospital discharge, and up to one year if data are available. Understanding these different patterns will help develop more personalized and effective care strategies for each patient profile. The study is a multicenter retrospective cohort including adult patients (≥18 years) admitted to participating ICUs for more than 5 days between 2021 and 2023. Data collected will include demographic, clinical, and laboratory information, details of organ support (such as mechanical ventilation or vasopressors), medications, nutrition, and rehabilitation practices. Statistical and machine learning methods will be used to identify groups of patients with similar clinical trajectories and to assess how these groups are related to outcomes such as survival, recovery of organ function, or long-term disability. Expected results are the identification of distinct clinical clusters of PCI that combine clinical and laboratory data, and the development of tailored management strategies to improve recovery and outcomes for patients with PCI.

Key Details

Gender

All

Age Range

18 Years - Any

Study Type

OBSERVATIONAL

Enrollment

7000

Start Date

2025-01-13

Completion Date

2027-12-31

Last Updated

2026-04-03

Healthy Volunteers

No

Locations (7)

Centro Hospitalar de São João / ULS São João

Lisbon, Lisbon District, Portugal

Hospital de Vila Franca de Xira / ULS Estuário do Tejo

Lisbon, Lisbon District, Portugal

Hospital Garcia de Orta / ULS Almada-Seixal

Lisbon, Lisbon District, Portugal

Hospital Prof. Doutor Fernando Fonseca / ULS Amadora -Sintra

Lisbon, Lisbon District, Portugal

Hospital Santa Maria / ULS Santa Maria

Lisbon, Lisbon District, Portugal

Hospital São Francisco Xavier / Centro Hospitalar de Lisboa Ocidental

Lisbon, Lisbon District, Portugal

Hospital de VIla Nova de Gaia-Espinho / ULS Gaia e Espinho

Vila Nova de Gaia, Porto District, Portugal