ReviewJACC. Advances2022
Machine Learning Approaches for Phenotyping in Cardiogenic Shock and Critical Illness: Part 2 of 2.
Review in JACC. Advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- A narrative review on the use of artificial intelligence in cardiovascular medicine.Cardiovascular diagnosis and therapy · 2026Review
- Acid-Base Status During Venoarterial Extracorporeal Membrane Oxygenator Support in Patients With Cardiogenic Shock.Journal of the American Heart Association · 2026Article
- Can algorithms come to the rescue of a failing heart? Machine learning, artificial intelligence, and decision-making in cardiogenic shock.Journal of anesthesia, analgesia and critical care · 2026Article
- Artificial Intelligence- and Machine Learning-Assisted Subphenotyping for Personalized Immunotherapy in Sepsis.Journal of personalized medicine · 2026Review
- Unsupervised cardiometabolic phenotyping unmasks residual MACCE risk beyond LDL-C in acute myocardial infarction after revascularization.Frontiers in cardiovascular medicine · 2026Article
- Phenotyping cardiogenic shock: an insight from the gulf cardiogenic shock registry.Frontiers in artificial intelligence · 2026Article
- Cardiac Critical Care in 2025 and in 2035.JACC. Advances · 2025Article
- Cardiogenic shock syndrome unraveled: understanding the different layers of heterogeneity.Intensive care medicine · 2025Article
- Comparison of different AI systems for diagnosing sepsis, septic shock, and cardiogenic shock: a retrospective study.Scientific reports · 2025Article
- Profiling of Cardiogenic Shock: Incorporating Machine Learning Into Bedside Management.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Key Concepts in Machine Learning and Clinical Applications in the Cardiac Intensive Care Unit.Current cardiology reports · 2025Review
- Identifying biomarker-driven subphenotypes of cardiogenic shock: analysis of prospective cohorts and randomized controlled trials.EClinicalMedicine · 2025Article
- Mixed Cardiogenic-Vasodilatory Shock: Current Insights and Future Directions.JACC. Advances · 2025Review
- Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.ESC heart failure · 2024Article
- Management of cardiogenic shock: state-of-the-art.Intensive care medicine · 2024Review
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
- Clinical phenotypes of cardiogenic shock survivors: insights into late host responses and long-term outcomes.ESC heart failure · 2024Observational
- Editorial: Cardiogenic shock: basic and clinical consideration, volume II.Frontiers in cardiovascular medicine · 2024Article
- Admission Total Leukocyte Count as a Predictor of Mortality in Cardiac Intensive Care Unit Patients.JACC. Advances · 2024Article
- Research Priorities in Critical Care Cardiology: JACC Expert Panel.Journal of the American College of Cardiology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Progress in improving cardiogenic shock (CS) outcomes may have been limited by failure to embrace the heterogeneity of pathophysiologic processes driving the underlying syndrome. To better understand the variability inherent to CS populations, recent algorithms for describing underlying CS disease subphenotypes have been described and validated. These strategies hope to identify specific patient subgroups with more favorable responses to standard therapies, as well as those who require novel treatment approaches. This paper is part 2 of a 2-part state-of-the-art review. In this second article, we present machine learning-based statistical approaches to identifying subphenotypes and discuss their strengths and limitations, as well as evidence from other critical illness syndromes and emerging applications in CS. We then discuss how staging and stratification may be considered in CS clinical trials and finally consider future directions for this emerging area of research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.