Subphenotypes of Cardiac Arrest Patients Admitted to Intensive Care Unit: a latent profile analysis of a large critical care database.

Abstract

Cardiac arrest (CA) may occur due to a variety of causes with heterogeneity in their clinical presentation and outcomes. This study aimed to identify clinical patterns or subphenotypes of CA patients admitted to the intensive care unit (ICU). The clinical and laboratory data of CA patients in a large electronic healthcare database were analyzed by latent profile analysis (LPA) to identify whether subphenotypes existed. Multivariable Logistic regression was used to assess whether mortality outcome was different between subphenotypes. A total of 1,352 CA patients fulfilled the eligibility criteria were included. The LPA identified three distinct subphenotypes: Profile 1 (13%) was characterized by evidence of significant neurological injury (low GCS). Profile 2 (15%) was characterized by multiple organ dysfunction with evidence of coagulopathy (prolonged aPTT and INR, decreased platelet count), hepatic injury (high bilirubin), circulatory shock (low mean blood pressure and elevated serum lactate); Profile 3 was the largest proportion (72%) of all CA patients without substantial derangement in major organ function. Profile 2 was associated with a significantly higher risk of death (OR: 2.09; 95% CI: 1.30 to 3.38) whilst the mortality rates of Profiles 3 was not significantly different from Profile 1 in multivariable model. LPA using routinely collected clinical data could identify three distinct subphenotypes of CA; those with multiple organ failure were associated with a significantly higher risk of mortality than other subphenotypes. LPA profiling may help researchers to identify the most appropriate subphenotypes of CA patients for testing effectiveness of a new intervention in a clinical trial.

• Authors: Zhongheng Zhang, Min Yao, Kwok M Ho, Yucai Hong
• Journal: Scientific reports (Sci Rep) Vol. 9 Issue 1 Pg. 13644 (09 20 2019) ISSN: 2045-2322 [Electronic] England
• PMID: 31541172 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Topics

• Aged
• Aged, 80 and over
• Data Management
• Databases, Factual
• Female
• Heart Arrest (classification, mortality)
• Humans
• Intensive Care Units
• Logistic Models
• Male
• Middle Aged
• Mortality
• Multiple Organ Failure (epidemiology, mortality)
• Patient Admission (statistics & numerical data)