ALI/ARDS Clinical Sub-phenotyping Study

Official title

Acute Lung/ Acute Respiratory Distress Syndrome and Extra-pulmonary Organ Injury Clinical Sub-phenotyping Study

Overview

1. Construct a structured clinical data and biosample information platform for Chinese patients with acute lung injury/ acute respiratory distress syndrome. 2. By deciphering the heterogeneity of patients with acute lung injury/ acute respiratory distress syndrome, achieve clinical, longitudinal physiological, and biological sub-phenotyping to guide individualized precision treatment and improve prognosis.

Detailed description

Acute lung injury/ acute respiratory distress syndrome is one of the most common and complex critical illnesses in clinical practice, with a high mortality rate of 45% to 50%. Currently, effective therapeutic strategies for this condition are still lacking. Increasing evidence suggests that the significant heterogeneity of this disease plays a crucial role in the poor treatment outcomes and high mortality rates observed in patients. Therefore, this study aims to analyze the heterogeneity of acute lung injury/ acute respiratory distress syndrome patients and establish a clinical classification system for acute lung and extrapulmonary organ injuries.

The objectives of this study include establishing a nationwide clinical database and biobank for acute lung injury / acute respiratory distress syndrome by collecting clinical data and biological samples from various provinces. By overcoming the barriers posed by diverse and heterogeneous data sources, mathematical and machine learning models will be utilized to construct clinical, physiological, and biological classification systems for acute lung and extrapulmonary organ injuries. The proposed classification model will be validated multiple times using international public databases and prospective acute lung injury/acute respiratory distress syndrome cohorts to ensure its stability and generalizability. The mapping relationship between different classifications and patient prognosis as well as treatment responsiveness will be explored.

Moreover, a machine learning-based supervised technique will be applied to develop a bedside simplified model (Point-of-Care model) and establish a bedside clinical classification decision system. Ultimately, this research aims to provide a foundation for standardized and precision-guided clinical diagnostic and therapeutic pathways, promoting improved treatment outcomes and overall prognosis in acute lung injury/ acute respiratory distress syndrome.

Interventions

• Procedure Collect peripheral blood and bronchoalveolar lavage fluid
  Peripheral venous blood will be collected and stored from the study participants at a total of five time points: Day 0, Day 1, Day 2, Day 3 after admission to the ICU, and the day of ICU discharge/death. The collection will be conducted after assessing the patients' eligibility for ARDS diagnosis. Each time point will involve the collection of one tube of peripheral venous blood, totaling 5 mL per sample. Within 24 hours after patients' admission to the ICU and meeting the assessment criteria f

Primary outcome measures

• ICU mortality [Time frame: up to 12 weeks]
• hospital mortality [Time frame: up to 24 weeks]

Secondary outcome measures (4)

• 28 days without mechanical ventilation [Time frame: up to 28 days]
• length of stay in the ICU [Time frame: up to 12 weeks]
• Total length of hospital stay [Time frame: up to 24 weeks]
• Mortality at 1 year after discharge [Time frame: through study completion, an average of 1 year]

Eligibility criteria

Inclusion criteria

• Meet the diagnostic criteria for Acute Respiratory Distress Syndrome (ARDS) according to the updated global definition in 2023.
• The patient or their legal representative signs an informed consent form.

Exclusion criteria

• Individuals aged less than 18 years old.
• Those who refuse to participate in the study.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Cohort

Study locations

China · 1 center

• Jingen Xia — Beijing

Publications

• Thompson BT, Chambers RC, Liu KD. Acute Respiratory Distress Syndrome. N Engl J Med. 2017 Aug 10;377(6):562-572. doi: 10.1056/NEJMra1608077. No abstract available. PMID 28792873
• Huang X, Zhang R, Fan G, Wu D, Lu H, Wang D, Deng W, Sun T, Xing L, Liu S, Wang S, Cai Y, Tian Y, Zhang Y, Xia J, Zhan Q; CHARDSnet group. Incidence and outcomes of acute respiratory distress syndrome in intensive care units of mainland China: a multicentre prospective longitudinal study. Crit Care. 2020 Aug 20;24(1):515. doi: 10.1186/s13054-020-03112-0. PMID 32819400
• Reilly JP, Calfee CS, Christie JD. Acute Respiratory Distress Syndrome Phenotypes. Semin Respir Crit Care Med. 2019 Feb;40(1):19-30. doi: 10.1055/s-0039-1684049. Epub 2019 May 6. PMID 31060085
• Shah FA, Meyer NJ, Angus DC, Awdish R, Azoulay E, Calfee CS, Clermont G, Gordon AC, Kwizera A, Leligdowicz A, Marshall JC, Mikacenic C, Sinha P, Venkatesh B, Wong HR, Zampieri FG, Yende S. A Research Agenda for Precision Medicine in Sepsis and Acute Respiratory Distress Syndrome: An Official American Thoracic Society Research Statement. Am J Respir Crit Care Med. 2021 Oct 15;204(8):891-901. doi: 1 PMID 34652268
• Calfee CS, Delucchi K, Parsons PE, Thompson BT, Ware LB, Matthay MA; NHLBI ARDS Network. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir Med. 2014 Aug;2(8):611-20. doi: 10.1016/S2213-2600(14)70097-9. Epub 2014 May 19. PMID 24853585

Identifiers

NCT: NCT06123962 · 2022YFC2504401

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗