The Scoring Model to Predict ICU Stay and Mortality After Emergency Admissions in Atrial Fibrillation: A Retrospective Study of 30 366 Patients

Abstract

Background

The 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 Methods

We 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.

Results

The 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.

Conclusion

We 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.