跳至主要内容
临床试验/NCT07510776
NCT07510776招募中不适用

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

Lisbon Academic Medical Center - Centro Académico de Medicina de Lisboa7 个研究点 分布在 1 个国家目标入组 7,000 人开始时间: 2025年1月13日最近更新:
适应症

试验速览

阶段
不适用
状态
招募中
发起方
入组人数
7,000
试验地点
7
主要终点
Need for one or more continuous organ support treatment at Day 10

研究概览

简要总结

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.

研究设计

研究类型
Observational
观察模型
Cohort
时间视角
Retrospective

入排标准

年龄范围
18 Years 至 —(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • Adult patients aged 18 years or older;
  • ICU length of stay equal to or greater than 5 days.

排除标准

  • Patients with an ICU stay < 5 days;
  • Patients discharged from the ICU early due to lack of ward availability, rather than clinical recovery;
  • Patients who do not survive the early phase of critical illness (i.e., early ICU deaths).

研究组 & 干预措施

Adult patients (≥18 years) admitted to participating ICUs for more than 5 days.

Adult patients (≥18 years) admitted to participating intensive care units (ICUs) who remained in the ICU for more than 5 days.

结局指标

主要结局

Need for one or more continuous organ support treatment at Day 10

时间窗: The first 10 days in the ICU

Data from patients who remain in the ICU for more than 10 days requiring ongoing organ support, such as invasive mechanical ventilation, renal replacement therapy or vasopressors, will be used to identify those who develop Persistent Critical Illness and to enable subsequent cluster analysis of their clinical trajectories.

次要结局

  • All cause mortality stratified by Persistent Critical Illness (PCI) clusters(From cluster identification (Day 10) until hospital discharge or up to 1-year follow-up if available.)
  • Organ dysfunction stratified by Persistent Critical Illness(From cluster identification (Day 10) until hospital discharge or up to 1-year follow-up if available.)

研究者

发起方
Lisbon Academic Medical Center - Centro Académico de Medicina de Lisboa
申办方类型
Network
责任方
Principal Investigator
主要研究者

Susana Mendes Fernandes

Prof. Dr.

Lisbon Academic Medical Center - Centro Académico de Medicina de Lisboa

研究点 (7)

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