ArticleEuropean heart journal. Digital health2022
Current state of artificial intelligence-based algorithms for hospital admission prediction in patients with heart failure: a scoping review
Article in European heart journal. Digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- A systematic review on the associations between the built environment and adult's physical activity in global tropical and subtropical climate regions.The international journal of behavioral nutrition and physical activity · 2024Pooled it
- Artificial intelligence to support clinical practice across the inpatient heart failure care pathway: a systematic review.European heart journal. Digital health · 2026Review
- Comparative accuracy of a predictive algorithm versus cardiologist assessment for 90-day readmission in decompensated chronic heart failure.BMC cardiovascular disorders · 2026Observational
- Machine Learning Models Incorporating Nursing Care Needs to Predict 180-Day Prognosis in Patients With Heart Failure: Validation With Discrimination and Calibration Analyses.Circulation reports · 2026Article
- Revolutionizing Multimorbidity Care: A Narrative Review on Artificial Intelligence Applications.Health science reports · 2026Article
- Advancing Heart Failure Care Through Disease Management Programs: A Comprehensive Framework to Improve Outcomes.Journal of cardiovascular development and disease · 2025Review
- Noninvasive biometric monitoring technologies for patients with heart failure.Heart failure reviews · 2025Review
- The emerging role of AI in transforming cardiovascular care.Future cardiology · 2025Article
- Health Care Professionals and Data Scientists' Perspectives on a Machine Learning System to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Qualitative Interview Study.Journal of medical Internet research · 2025Article
- Fluid status assessment in heart failure patients: pilot validation of the Maastricht Decompensation Questionnaire.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2025Article
- Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review protocol.BMJ open · 2024Article
- Artificial Intelligence Applied to Electrical and Non-Invasive Hemodynamic Markers in Elderly Decompensated Chronic Heart Failure Patients.Biomedicines · 2024Article
- Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review.Cureus · 2024Review
- Clinical Predictive Modeling of Heart Failure: Domain Description, Models' Characteristics and Literature Review.Diagnostics (Basel, Switzerland) · 2024Review
- Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities.International journal of environmental research and public health · 2024Review
- Digital Health Technologies for Post-Discharge Care after Heart Failure Hospitalisation to Relieve Symptoms and Improve Clinical Outcomes.Journal of clinical medicine · 2023Review
- Most Recent Trials and Advances in Hypertension.European cardiology · 2022Review
- Harnessing Artificial Intelligence in Cardiology: Advancements in Diagnosis, Treatment, and Patient Care.Heart views : the official journal of the Gulf Heart AssociationReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Patients with congestive heart failure (HF) are prone to clinical deterioration leading to hospital admissions, burdening both patients and the healthcare system. Predicting hospital admission in this patient group could enable timely intervention, with subsequent reduction of these admissions. To date, hospital admission prediction remains challenging. Increasing amounts of acquired data and development of artificial intelligence (AI) technology allow for the creation of reliable hospital prediction algorithms for HF patients. This scoping review describes the current literature on strategies and performance of AI-based algorithms for prediction of hospital admission in patients with HF. Methods and results: PubMed, EMBASE, and the Web of Science were used to search for articles using machine learning (ML) and deep learning methods to predict hospitalization in patients with HF. After eligibility screening, 23 articles were included. Sixteen articles predicted 30-day hospital (re-)admission resulting in an area under the curve (AUC) ranging from 0.61 to 0.79. Six studies predicted hospital admission over longer time periods ranging from 6 months to 3 years, with AUC's ranging from 0.65 to 0.78. One study prospectively evaluated performance of a disposable sensory patch at home after hospitalization which resulted in an AUC of 0.89 for unplanned hospital admission prediction. Conclusion: AI has the potential to enable prediction of hospital admission in HF patients. Improvement of data management, adding new data sources such as telemonitoring data and ML models and prospective and external validation of current models must be performed before clinical applicability is possible.
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.