ReviewHeart failure clinics2020
Predicting High-Risk Patients and High-Risk Outcomes in Heart Failure.
Review in Heart failure clinics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effect of Home-Based Exercise and Influence of Training Principles on Cardiorespiratory Fitness in Sedentary Healthy People and Patients With Cardiovascular Disease: A Systematic Review With Meta-Analysis.European journal of sport science · 2026Pooled it
- Article
- The Risks to Patients Associated with Using Artificial Intelligence Tools in Pharmacy Practice: A Scoping Review.Pharmacy (Basel, Switzerland) · 2026Review
- Review
- Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.Diagnostics (Basel, Switzerland) · 2026Review
- Risk Stratification Tools in Acute Heart Failure and Their Roles in Personalized Follow-Up.Journal of clinical medicine · 2025Review
- Streamlining heart failure patient care with machine learning of thoracic cavity sound data.World journal of cardiology · 2025Review
- Prognostic Models in Heart Failure: Hope or Hype?Journal of personalized medicine · 2025Review
- Predicting death or readmission following heart failure hospitalisation: the VancOuver CoastAL Acute Heart Failure (VOCAL-AHF) registry.Open heart · 2025Article
- Prediction models and risk scores in different types of heart failure: a review.Frontiers in medicine · 2025Review
- Exploring the Relationship Between Ejection Fraction, Arterial Stiffness, NT-proBNP, and Hospitalization Risk in Heart Failure Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Mitigating Algorithmic Bias in AI-Driven Cardiovascular Imaging for Fairer Diagnostics.Diagnostics (Basel, Switzerland) · 2024Article
- Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system.Clinical research in cardiology : official journal of the German Cardiac Society · 2024Article
- Prognostic Role of Ultrasound Diagnostic Methods in Patients with Acute Decompensated Heart Failure.Oman medical journal · 2024Article
- Association between performance measures and clinical outcomes in patients with heart failure in China: Results from the HERO study.Clinical cardiology · 2024Article
- Remotely Monitored Cardiac Implantable Electronic Device Data Predict All-Cause and Cardiovascular Unplanned Hospitalization.Journal of the American Heart Association · 2022Observational
- Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction.Heart failure clinics · 2022Review
- Liver stiffness for predicting adverse cardiac events in chinese patients with heart failure: a two-year prospective study.BMC cardiovascular disorders · 2022Article
- Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19.NPJ digital medicine · 2021Article
- Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data.BioData mining · 2021Article
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
Abstract
Identifying patients with heart failure at high risk for poor outcomes is important for patient care, resource allocation, and process improvement. Although numerous risk models exist to predict mortality, hospitalization, and patient-reported health status, they are infrequently used for several reasons, including modest performance, lack of evidence to support routine clinical use, and barriers to implementation. Artificial intelligence has the potential to enhance the performance of risk prediction models, but has its own limitations and remains unproved.
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.