ArticleClinical cardiology2025
The Scoring Model to Predict ICU Stay and Mortality After Emergency Admissions in Atrial Fibrillation: A Retrospective Study of 30 366 Patients.
Article in Clinical cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
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.
Funding
Abstract
backgroundThe rapid assessment of the conditions is crucial for the prognosis of atrial fibrillation (AF) patients admitted to the emergency department (ED). We aim to derive and validate a more accurate and simplified scoring model to optimize the triage of AF patients in the ED. MATERIALS AND
methodsWe conducted a retrospective study using data from the Medical Information Mart for Intensive Care (MIMIC-IV) database and developed scoring models employing the Random Forest algorithm. The area under the receiver operating characteristic (ROC) curve (AUC) was used to measure the performance of the prediction for intensive care unit (ICU) stay, and the death likelihood within 3, 7, and 30 days following the ED admission.
resultsThe study included 30 366 AF patients, randomly divided into training, validation, and testing cohorts at a 7:1:2 ratio. The training set consisted of 21 257 patients, the validation set included 3036 patients, and the remaining 6073 patients were classified as the validation set. Among the cohorts, 9594 patients (32%) required ICU transfers, with mortality rates of 1% at 3 days, 3% at 7 days, and 6% at 30 days. In the testing set, the scoring models demonstrated strong discriminative ability with AUCs of 0.724 for ICU stay, 0.782 for 3-day mortality, 0.755 for 7-day mortality, and 0.767 for 30-day mortality.
conclusionWe derived and validated novel simplified scoring models with good discriminative performance to predict the likelihood of ICU stay, 3-day, 7-day, and 30-day death in AF patients after ED admission.
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.