Development and validation of a predictive model for survival outcomes in patients with paroxysmal versus persistent atrial fibrillation: a retrospective cohort study based on the MIMIC-IV database

Atrial fibrillation (AF) has been implicated in increasing all-cause mortality among patients in intensive care unit (ICU), with paroxysmal atrial fibrillation (PAF) often progressing over time to persistent atrial fibrillation (PersAF), which carries an even higher risk of death compared to PAF. Our study aims to analyze the survival disparities between patients with PAF and PersAF, and to develop a comprehensive model to predict the impact of life-threatening comorbidities on AF patients’ prognosis in the ICU. This endeavor is geared towards facilitating early assessment and timely intervention for AF patients, ultimately improving their clinical outcomes. Data were retrieved from the MIMIC-IV database for patients aged ≥ 18 years admitted to the ICU for the first time between 2008 and 2019. A total of 12,130 AF patients were identified and split into a training cohort (n = 8,490) and a validation cohort (n = 3,640). Cox regression analysis was performed to identify independent predictors of 90-day mortality. A nomogram was developed to predict survival probabilities at 30, 60, and 90 days. Kaplan-Meier survival curves were generated to visually compare survival outcomes between patients with PAF and PersAF. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and Decision Curve Analysis (DCA). The mean age of the study population was 74.60 ± 12.05 years, with 40.63% females. Independent predictors of 90-day mortality included age, persistent AF, cerebral infarction, intracranial injury, chronic heart failure (CHF), acute kidney failure (AKF), severe sepsis, cardiogenic shock, acute respiratory distress syndrome (ARDS), malignant neoplasm, and acute respiratory failure (ARF). Antiplatelet therapy and anticoagulants were protective factors. The nomogram demonstrated excellent discriminatory performance with AUC values ranging from 0.80 to 0.84. Calibration curves and DCA confirmed the model’s reliability and clinical usefulness. Kaplan-Meier curves showed higher survival rates in patients with PAF compared to those with PersAF. The developed and validated nomogram has demonstrated sufficient accuracy in predicting the risk of all-cause mortality and identifying prognostic factors in patients with AF admitted to ICU for the first time.