Retrospective Characterization of Circadian Rhythms Using Vital Signs and Other Routine Data From Patients in the Intensive Care Unit
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 25,000
- 试验地点
- 1
- 主要终点
- Characterization of circadian periodicities and classification of time-varying patterns based on routine clinical data
研究概览
简要总结
The goal of this retrospective exploratory study is to examine how the body's natural day-and-night rhythms and oscillatory patterns behave in people who were treated in the Intensive Care Unit (ICU). The study uses routine clinical data that were already collected during and after the ICU stay, including post-discharge follow-up measurements (where available), such as heart rate, temperature, blood pressure, blood sugar, and insulin. The researchers want to learn whether these measurements show daily patterns and whether patients can be grouped based on their oscillatory profiles. The study also examines whether certain clinical factors such as illness severity scores, sedation levels, organ support treatments, or medications are associated with changes in these oscillatory rhythms. The team will explore whether conditions that can develop during or after critical illness, such as delirium, muscle weakness, or Post-Intensive Care Syndrome (PICS), are linked to disturbed physiological rhythms. Another goal is to test whether it is technically possible to show a patient's rhythm profile in real time using routine data from the ICU information system, evaluated retrospectively. Digital tools called "Chrono-Apps" will be developed to display rhythmic information and may help clinicians adjust lighting to patient needs.
详细描述
The investigators will retrospectively analyze periodic oscillations in clinical data, such as heart rate, temperature, blood glucose, insulin, or blood pressure, to identify parameters suitable for characterizing the physiological rhythms in hospitalized Intensive Care Unit (ICU) patients. While these measurements typically show regular daily changes, also called oscillatory patterns, critical illness, continuous monitoring, and frequent medical interventions can disrupt them. The study will investigate whether these patterns can be used to group patients into distinct temporal or rhythmic profiles. The study will also examine whether certain clinical factors are linked to functioning or disrupted oscillatory patterns. These factors include illness severity scores, sedation levels, organ support treatments, and medications. Additionally, the researchers will explore whether temporal disruption is associated with key ICU complications, including delirium, ICU-acquired muscle weakness, and Post-Intensive Care Syndrome (PICS). Finally, in a small cohort of ICU patients, the investigators will test personalized lighting regimens adapted to individual chronotypes. This phase of the study aims to assess the robustness of routine clinical data for tailoring light exposure, validate a circadian entrainment algorithm, and evaluate potential sources of bias. Real-time analyses of clinical data will be visualized as rhythm profiles via digital "Chrono-Apps" integrated into the electronic patient record. These visualizations will support the day-to-day implementation of personalized lighting regimens that adapt dynamically to each patient's current rhythm status and clinical condition.
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
- 否
入选标准
- •Primary Retrospective Cohort:
- •Admitted to and treated in a participating Intensive Care Unit (ICU) between 2017 and 2024
- •Minimum ICU length of stay of 3 days (≥ 72 hours)
- •Age ≥ 18 years at the time of ICU admission
- •All genders (male, female, diverse)
- •Cohorts from previous studies:
- •Prior enrollment in one of the designated parent study protocols
- •Availability of supplementary biological or metabolic data (e.g., clock gene expression, melatonin levels) suitable for exploratory association analyses
排除标准
- 未提供
研究组 & 干预措施
TRG-N (ISRCTN77569430)
干预措施: Exposure to routine clinical parameters (Other)
TRG-N (ISRCTN77569430)
干预措施: Exposure to ICU clinical factors and treatments (Other)
TRG-N (ISRCTN77569430)
干预措施: Exposure to critical illness-related conditions (Other)
TRG-N (ISRCTN77569430)
干预措施: Exposure to molecular and hormonal circadian markers (Other)
TRG-N (ISRCTN77569430)
干预措施: Exposure to metabolic changes related to critical illness (Other)
CIPCIM2 (ISRCTN19392591)
干预措施: Exposure to routine clinical parameters (Other)
CIPCIM2 (ISRCTN19392591)
干预措施: Exposure to ICU clinical factors and treatments (Other)
CIPCIM2 (ISRCTN19392591)
干预措施: Exposure to critical illness-related conditions (Other)
CIPCIM2 (ISRCTN19392591)
干预措施: Exposure to molecular and hormonal circadian markers (Other)
CIPCIM2 (ISRCTN19392591)
干预措施: Exposure to metabolic changes related to critical illness (Other)
CGM (NCT02296372)
干预措施: Exposure to routine clinical parameters (Other)
CGM (NCT02296372)
干预措施: Exposure to ICU clinical factors and treatments (Other)
CGM (NCT02296372)
干预措施: Exposure to critical illness-related conditions (Other)
HELIA-ICU (NCT05556811)
干预措施: Exposure to routine clinical parameters (Other)
HELIA-ICU (NCT05556811)
干预措施: Exposure to ICU clinical factors and treatments (Other)
HELIA-ICU (NCT05556811)
干预措施: Exposure to critical illness-related conditions (Other)
HELIA-ICU (NCT05556811)
干预措施: Exposure to molecular and hormonal circadian markers (Other)
PICS-Analyse (EA1/229/24)
干预措施: Exposure to routine clinical parameters (Other)
PICS-Analyse (EA1/229/24)
干预措施: Exposure to ICU clinical factors and treatments (Other)
PICS-Analyse (EA1/229/24)
干预措施: Exposure to critical illness-related conditions (Other)
All retrospective patients between 2017 and 2024
Relates to retrospective data from all patients who spent at least three days in participating intensive care units at Charite University, Berlin, Germany between 2017 and 2024
干预措施: Exposure to routine clinical parameters (Other)
All retrospective patients between 2017 and 2024
Relates to retrospective data from all patients who spent at least three days in participating intensive care units at Charite University, Berlin, Germany between 2017 and 2024
干预措施: Exposure to ICU clinical factors and treatments (Other)
All retrospective patients between 2017 and 2024
Relates to retrospective data from all patients who spent at least three days in participating intensive care units at Charite University, Berlin, Germany between 2017 and 2024
干预措施: Exposure to critical illness-related conditions (Other)
Pa-COVID (DRKS00021688)
干预措施: Exposure to routine clinical parameters (Other)
Pa-COVID (DRKS00021688)
干预措施: Exposure to ICU clinical factors and treatments (Other)
Pa-COVID (DRKS00021688)
干预措施: Exposure to critical illness-related conditions (Other)
Pa-COVID (DRKS00021688)
干预措施: Exposure to molecular and hormonal circadian markers (Other)
SeptiCLOCK (NCT02044575)
干预措施: Exposure to routine clinical parameters (Other)
SeptiCLOCK (NCT02044575)
干预措施: Exposure to ICU clinical factors and treatments (Other)
SeptiCLOCK (NCT02044575)
干预措施: Exposure to critical illness-related conditions (Other)
SeptiCLOCK (NCT02044575)
干预措施: Exposure to molecular and hormonal circadian markers (Other)
VITALITY (NCT02143661)
干预措施: Exposure to routine clinical parameters (Other)
VITALITY (NCT02143661)
干预措施: Exposure to ICU clinical factors and treatments (Other)
VITALITY (NCT02143661)
干预措施: Exposure to critical illness-related conditions (Other)
VITALITY (NCT02143661)
干预措施: Exposure to molecular and hormonal circadian markers (Other)
结局指标
主要结局
Characterization of circadian periodicities and classification of time-varying patterns based on routine clinical data
时间窗: From ICU admission to ICU discharge or death, whichever comes first (expected average duration of 4 weeks).
Assessment of periodicities and time-varying patterns in routine clinical parameters, including heart rate, body temperature, blood pressure, blood glucose, and insulin levels. Time-series analysis will be conducted to extract continuous metrics (amplitude, period, and phase). Based on these metrics, patients will be classified into distinct categorical groups according to their periodic oscillation profiles.
次要结局
- Association between circadian rhythm category and clinical severity scores(From ICU admission to ICU discharge or death, whichever comes first (expected average duration of 4 weeks).)
- Association between circadian rhythm disruption and critical illness-related conditions(From ICU admission to ICU discharge or death, whichever comes first (expected average duration of 4 weeks), and post-ICU period (up to 3 and 6 months).)
- Association between time-varying clinical patterns and external biological markers (from other studies)(Depending on individual study protocols, from ICU admission through 24 months post-discharge. Sampled daily to weekly during the ICU stay, followed by post-discharge examinations conducted at variable monthly intervals (a maximum of 5 visits).)
- Feasibility and Data Infrastructure for Real-Time Circadian Visualization (Chrono-Apps)(From ICU admission to ICU discharge or death, whichever comes first (expected average duration of 4 weeks).)
- Performance of Algorithm-Based Light Therapy Recommendations(From ICU admission to ICU discharge or death, whichever comes first (expected average duration of 4 weeks).)
研究者
Felix Balzer
Professor for Medical Data Science & Chief Medical Information Officer (IMI | CMIO)
Charite University, Berlin, Germany