ArticleScientific reports2019
Subphenotypes of Cardiac Arrest Patients Admitted to Intensive Care Unit: a latent profile analysis of a large critical care database.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Who gets frustrated? Identifying individuals prone to frustration using a latent profile analysis.Frontiers in psychology · 2025Article
- Investigating the neuroprotective effect of heparin in improving mitochondrial function in rats with cardiac arrest cardiopulmonary resuscitation based on transcriptome sequencing.Frontiers in medicine · 2025Article
- Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis.JMIR aging · 2023Article
- Ginsenoside Rb1 Improves Post-Cardiac Arrest Myocardial Stunning and Cerebral Outcomes by Regulating the Keap1/Nrf2 Pathway.International journal of molecular sciences · 2023Article
- Phenotyping cardiogenic shock that showed different clinical outcomes and responses to vasopressor use: a latent profile analysis from MIMIC-IV database.Frontiers in medicine · 2023Article
- Identification of subphenotypes in critically ill thrombocytopenic patients with different responses to therapeutic interventions: a retrospective study.Frontiers in medicine · 2023Article
- Latent transition analysis of cardiac arrest patients treated in the intensive care unit.PloS one · 2021Article
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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