ReviewJournal of the Society for Cardiovascular Angiography & Interventions2025
Profiling of Cardiogenic Shock: Incorporating Machine Learning Into Bedside Management.
Review in Journal of the Society for Cardiovascular Angiography & Interventions, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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
10 citing papers in PubMed.
- A narrative review on the use of artificial intelligence in cardiovascular medicine.Cardiovascular diagnosis and therapy · 2026Review
- Cardiogenic Shock Detection Using Electronic Medical Records: A Review and Blueprint for Clinical Implementation and Future Research.Journal of the American Heart Association · 2026Review
- Phenotyping cardiogenic shock: an insight from the gulf cardiogenic shock registry.Frontiers in artificial intelligence · 2026Article
- A Deep Learning Model to Guide Personalized Mechanical Circulatory Support Use in Cardiogenic Shock Patients Undergoing PCI.JACC. Advances · 2026Article
- A Proposed Algorithm for the Management of Patients with Cardiogenic Shock Based on Contemporary Knowledge and Gaps in Evidence.Journal of cardiovascular development and disease · 2025Review
- Clinical Characteristics and Outcomes of Patients Admitted in Cardiac Intensive Care Unit with Cardiogenic Shock: A Single-Center Study.Diseases (Basel, Switzerland) · 2025Article
- Invasive Hemodynamic Monitoring in Acute Heart Failure and Cardiogenic Shock.Reviews in cardiovascular medicine · 2025Review
- Temporary Mechanical Support in Cardiogenic Shock Secondary to Heart Failure: An Evolving Paradigm.Journal of personalized medicine · 2025Review
- Acute Myocardial Infarction and Stage E Shock: Insights From the RECOVER III Study.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Article
- Echocardiography for short-term mechanical circulatory support: a trans-Atlantic practical guide.European heart journal. Imaging methods and practice · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiogenic shock (CS) is a complex clinical syndrome with various etiologies and clinical presentations. Despite advances in therapeutic options, mortality remains high, and clinical trials in the field are complicated in part by the heterogeneity of CS patients. More individualized targeted therapeutic approaches might improve outcomes in CS, but their implementation remains challenging. The present review discusses current and emerging machine learning-based approaches, including unsupervised and supervised learning methods that use real-world clinical data to individualize therapeutic strategies for CS patients. We will discuss the rationale for each approach, potential advantages and disadvantages, and how these strategies can inform clinical trial design and management decisions.
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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.