ReviewInternational journal of cardiology. Cardiovascular risk and prevention2025
The efficacy of artificial intelligence in predicting mortality rate and cardiogenic shock in acute coronary syndrome patients.
Review in International journal of cardiology. Cardiovascular risk and prevention, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Comment on "The efficacy of artificial intelligence in predicting mortality rate and cardiogenic shock in acute coronary syndrome patients".International journal of cardiology. Cardiovascular risk and prevention · 2026Article
- Clinical Characteristics and Outcomes of Patients Admitted in Cardiac Intensive Care Unit with Cardiogenic Shock: A Single-Center Study.Diseases (Basel, Switzerland) · 2025Article
- Artificial intelligence: Enhancing diagnosis and treatment plans for acute coronary syndrome.International journal of cardiology. Cardiovascular risk and prevention · 2025Article
- Harnessing artificial intelligence for early prediction of cardiogenic shock and mortality in acute coronary syndrome patients.International journal of cardiology. Cardiovascular risk and prevention · 2025Article
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
Objective: In 2023, the Ministry of Health of the Republic of Indonesia reported that deaths due to coronary heart disease reached 245,343 per year. Currently, many studies are focused on developing AI or to predict mortality rates and complications from Acute Coronary Syndrome (ACS). Therefore, the purpose of this systematic review and meta-analysis is to summarize recent research findings regarding the efficacy of AI in predicting mortality rates and the incidence of cardiogenic shock in ACS patients. Data sources: A systematic literature search using across five databases-PubMed, Cochrane, Plos One, Scopus, and Springer-up to May 3, 2024, with additional studies included through hand searching and reference list checking. Study selection: Study selection was done independently by two reviewers using pre-specified inclusion and exclusion criteria. Data extraction: Critical appraisal was conducted using the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool for AI diagnostic studies. Data synthesis: The reporting of this article follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement. We included 7 studies and analyzed the results using Revman v.5.4, MetaDTA, and STATA v.17 to construct forest plots for sensitivity and specificity. Conclusion: The 91 % (95 % CI: 0.87-0.94) pooled specificity and 0.94 (95 % CI: 0.92-0.96) pooled AUC highlight the potential of ML models to provide more accurate risk stratification to predicting mortality rate and cardiogenic shock in ACS patients.
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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.