ArticleJournal of the Society for Cardiovascular Angiography & Interventions
Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients With Acute Heart Failure.
Article in Journal of the Society for Cardiovascular Angiography & Interventions. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Cardiogenic Shock Detection Using Electronic Medical Records: A Review and Blueprint for Clinical Implementation and Future Research.Journal of the American Heart Association · 2026Review
- AI-Based Predictive Models for Cardiogenic Shock in STEMI: Real-World Data for Early Risk Assessment and Prognostic Insights.Journal of clinical medicine · 2025Article
- Comparison of different AI systems for diagnosing sepsis, septic shock, and cardiogenic shock: a retrospective study.Scientific reports · 2025Article
- Temporary Mechanical Support in Cardiogenic Shock Secondary to Heart Failure: An Evolving Paradigm.Journal of personalized medicine · 2025Review
- Profiling of Cardiogenic Shock: Incorporating Machine Learning Into Bedside Management.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease.Biomedicines · 2025Review
- Hemodynamic management of cardiogenic shock in the intensive care unit.The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation · 2024Review
- Development and external validation of a dynamic risk score for early prediction of cardiogenic shock in cardiac intensive care units using machine learning.European heart journal. Acute cardiovascular care · 2024Article
- 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
- Review
- Early Prediction of Cardiogenic Shock Using Machine Learning.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Background: Despite technological and treatment advancements over the past 2 decades, cardiogenic shock (CS) mortality has remained between 40% and 60%. Our objective was to develop an algorithm that can continuously monitor heart failure patients and partition them into cohorts of high and low risk for CS. Methods: We retrospectively studied 24,461 patients hospitalized with acute decompensated heart failure, 265 of whom developed CS, in the Johns Hopkins Health System. Our cohort identification approach is based on logistic regression and makes use of vital signs, lab values, and medication administrations recorded during the normal course of care. Results: Our algorithm identified patients at high risk of CS. Patients in the high-risk cohort had 10.2 times (95% confidence interval, 6.1-17.2) higher prevalence of CS than those in the low-risk cohort. Patients who experienced CS while in the high-risk cohort were first deemed high risk a median of 1.7 days (interquartile range, 0.8-4.6) before CS diagnosis was made by their clinical team. To evaluate Conclusions: This risk model was able to predict patients at higher risk of CS in a time frame that allowed a change in clinical care. The actionability evaluation demonstrates a possible opportunity to intervene as part of a CS algorithm for escalation of care.
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