ArticleJournal of personalized medicine2022
Clinical Phenotypes of Atrial Fibrillation and Mortality Risk-A Cluster Analysis from the Nationwide Italian START Registry.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Prevalence, Incidence, and Impact of Cancer in Atrial Fibrillation: A Systematic Review and Meta-Analysis of 70,000,000 Patients.Health science reports · 2026Review
- AI-Based Phenotyping of Atrial Fibrillation Through Generative Topographic Mapping: Prospective Murcia Atrial Fibrillation Project III Cohort Study.Journal of medical Internet research · 2026Observational
- Deriving novel atrial fibrillation phenotypes using a tree-based artificial intelligence-enhanced electrocardiography approach.NPJ digital medicine · 2025Article
- Comorbidity patterns and mortality in atrial fibrillation: a latent class analysis of the EURopean study of Older Subjects with Atrial Fibrillation (EUROSAF).Annals of medicine · 2025Article
- Comorbidity and medication patterns in atrial fibrillation patients: association with adverse clinical outcomes.Internal and emergency medicine · 2025Article
- Clustering of electronic health records in atrial fibrillation patients and impact on prognosis and patient trajectories: a UK linked-dataset study.European heart journal. Digital health · 2025Article
- Clinical phenotypes of severe atrial cardiomyopathy and their outcome: A cluster analysis.International journal of cardiology. Heart & vasculature · 2025Article
- Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysis.International journal of cardiology. Heart & vasculature · 2025Article
- Outcomes in Atrial Fibrillation Patients with Different Clinical Phenotypes: Insights from the French Population.Journal of clinical medicine · 2025Article
- The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment.Reviews in cardiovascular medicine · 2024Review
- A Comparison of the Impact of Pharmacological Treatments on Cardioversion, Rate Control, and Mortality in Data-Driven Atrial Fibrillation Phenotypes in Critical Care.Bioengineering (Basel, Switzerland) · 2024Article
- Phenotyping of atrial fibrillation with cluster analysis and external validation.Heart (British Cardiac Society) · 2023Article
- Progressions in Cardiac Arrhythmia: Specific Populations and the Need for Precision Medicine.Journal of personalized medicine · 2023Article
- Clinical phenotypes of atrial fibrillation: A review of machine learning applications in personalized treatment.JRSM cardiovascular diseaseReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Patients with atrial fibrillation (AF) still experience a high mortality rate despite optimal antithrombotic treatment. We aimed to identify clinical phenotypes of patients to stratify mortality risk in AF. Cluster analysis was performed on 5171 AF patients from the nationwide START registry. The risk of all-cause mortality in each cluster was analyzed. We identified four clusters. Cluster 1 was composed of the youngest patients, with low comorbidities; Cluster 2 of patients with low cardiovascular risk factors and high prevalence of cancer; Cluster 3 of men with diabetes and coronary disease and peripheral artery disease; Cluster 4 included the oldest patients, mainly women, with previous cerebrovascular events. During 9857 person-years of observation, 386 deaths (3.92%/year) occurred. Mortality rates increased across clusters: 0.42%/year (cluster 1, reference group), 2.12%/year (cluster 2, adjusted hazard ratio (aHR) 3.306, 95% confidence interval (CI) 1.204−9.077, p = 0.020), 4.41%/year (cluster 3, aHR 6.702, 95%CI 2.433−18.461, p < 0.001), and 8.71%/year (cluster 4, aHR 8.927, 95%CI 3.238−24.605, p < 0.001). We identified four clusters of AF patients with progressive mortality risk. The use of clinical phenotypes may help identify patients at a higher risk of mortality.
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.