ArticleCritical care (London, England)2023
Prospective, multicenter validation of the deep learning-based cardiac arrest risk management system for predicting in-hospital cardiac arrest or unplanned intensive care unit transfer in patients admitted to general wards.
Article in Critical care (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04951973 (Comparison of Deep Learning Based Early Warning Score and Conventional Screening System in Rapid Response Team Activation in General Ward Patients), which is not on this map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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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.
Comparison of Deep Learning Based Early Warning Score and Conventional Screening System in Rapid Response Team Activation in General Ward Patients
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Applications of the Early Warning Score as an outcome measure: a systematic review of methodologies.BMJ health & care informatics · 2026Pooled it
- Implementation of an AI-Based Clinical Decision Support System Predicting In-Hospital Cardiac Arrest in General Wards: A Multicenter Staggered-Implementation Study in Secondary Hospitals in Korea.Diagnostics (Basel, Switzerland) · 2026Article
- From scores to signals: evolution and innovations in pediatric early warning systems.Acute and critical care · 2026Article
- A novel, standardised approach to balancing effectiveness, efficiency and utility of surveillance AI prediction models for hospitalised patients using sepsis prediction as an exemplar.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Clinical Effectiveness of an Artificial Intelligence-Based Prediction Model for Cardiac Arrest in General Ward-Admitted Patients: A Non-Randomized Controlled Trial.Diagnostics (Basel, Switzerland) · 2026Article
- DeepCARS-Identified High-Risk Patients: Clinical Interventions and Outcomes During the Korean Healthcare Crisis.Medicina (Kaunas, Lithuania) · 2025Article
- Current Perspectives on the Artificial Intelligence in Critical Care Medicine.Anesthesiology clinics · 2025Review
- Conceptual framework for prediction models of patient deterioration based on nursing documentation patterns: reproducibility and generalizability with a large number of hospitals across the United States.Journal of biomedical informatics · 2025Article
- Artificial intelligence in resuscitation: a scoping review.Resuscitation plus · 2025Review
- Using weak signals to predict spontaneous breathing trial success: a machine learning approach.Intensive care medicine experimental · 2025Article
- Multicenter validation of a machine learning model to predict intensive care unit readmission within 48 hours after discharge.EClinicalMedicine · 2025Article
- Enhancing Clinical Cardiac Care: Predicting In-Hospital Cardiac Arrest With Machine Learning.Annals of laboratory medicine · 2025Article
- Resident and nurse attitudes toward a rapid response team in a tertiary hospital in South Korea.Acute and critical care · 2025Article
- Transforming rapid response team through artificial intelligence.Acute and critical care · 2025Article
- Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models.Frontiers in artificial intelligence · 2025Review
- Rapid Response Teams in low and middle-income countries: a scoping review.Critical care science · 2025Article
- Prospective Human Validation of Artificial Intelligence Interventions in Cardiology: A Scoping Review.JACC. Advances · 2024Article
- Management and prevention of in-hospital cardiac arrest: present and future.NPJ cardiovascular health · 2024Review
- [Data-driven intensive care: a lack of comprehensive datasets].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2024Review
- Considerations for Developing Diagnostic Artificial Intelligence: Towards Real-World Application of an Asthma Detection Model.Allergy, asthma & immunology research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundRetrospective studies have demonstrated that the deep learning-based cardiac arrest risk management system (DeepCARS™) is superior to the conventional methods in predicting in-hospital cardiac arrest (IHCA). This prospective study aimed to investigate the predictive accuracy of the DeepCARS™ for IHCA or unplanned intensive care unit transfer (UIT) among general ward patients, compared with that of conventional methods in real-world practice.
methodsThis prospective, multicenter cohort study was conducted at four teaching hospitals in South Korea. All adult patients admitted to general wards during the 3-month study period were included. The primary outcome was predictive accuracy for the occurrence of IHCA or UIT within 24 h of the alarm being triggered. Area under the receiver operating characteristic curve (AUROC) values were used to compare the DeepCARS™ with the modified early warning score (MEWS), national early warning Score (NEWS), and single-parameter track-and-trigger systems.
resultsAmong 55,083 patients, the incidence rates of IHCA and UIT were 0.90 and 6.44 per 1,000 admissions, respectively. In terms of the composite outcome, the AUROC for the DeepCARS™ was superior to those for the MEWS and NEWS (0.869 vs. 0.756/0.767). At the same sensitivity level of the cutoff values, the mean alarm counts per day per 1,000 beds were significantly reduced for the DeepCARS™, and the rate of appropriate alarms was higher when using the DeepCARS™ than when using conventional systems.
conclusionThe DeepCARS™ predicts IHCA and UIT more accurately and efficiently than conventional methods. Thus, the DeepCARS™ may be an effective screening tool for detecting clinical deterioration in real-world clinical practice. Trial registration This study was registered at ClinicalTrials.gov ( NCT04951973 ) on June 30, 2021.
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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.