Increasing age was the dominant risk factor for new-onset atrial fibrillation in critically ill patients, with prediction models achieving AUCs >0.82 and >0.91.
Cohort (n=16,144)
What are the risk factors and predictive performance of a model for new-onset atrial fibrillation in critically ill patients?
A newly developed logistic regression model demonstrated excellent performance in predicting new-onset atrial fibrillation in critically ill patients, identifying age as the dominant risk factor.
Effect estimate: 10-fold increased risk per doubling of age
The occurrence of atrial fibrillation (AF) represents clinical deterioration in acutely unwell patients and leads to increased morbidity and mortality. Prediction of the development of AF allows early intervention. Using the AmsterdamUMCdb, clinically relevant variables from patients admitted in sinus rhythm were extracted over the full duration of the ICU stay or until the first recorded AF episode occurred. Multiple logistic regression was performed to identify risk factors for AF. Input variables were automatically selected by a sequential forward search algorithm using cross-validation. We developed three different models: For the overall cohort, for ventilated patients and non-ventilated patients. 16,144 out of 23,106 admissions met the inclusion criteria. 2,374 (12.8%) patients had at least one AF episode during their ICU stay. Univariate analysis revealed that a higher percentage of AF patients were older than 70 years (60% versus 32%) and died in ICU (23.1% versus 7.1%) compared to non-AF patients. Multivariate analysis revealed age to be the dominant risk factor for developing AF with doubling of age leading to a 10-fold increased risk. Our logistic regression models showed excellent performance with AUC.ROC > 0.82 and > 0.91 in ventilated and non-ventilated cohorts, respectively. Increasing age was the dominant risk factor for the development of AF in both ventilated and non-ventilated critically ill patients. In non-ventilated patients, risk for development of AF was significantly higher than in ventilated patients. Further research is warranted to identify the role of ventilatory settings on risk for AF in critical illness and to optimise predictive models.
Ortega‐Martorell et al. (Fri,) conducted a cohort in Medical-Surgical Critical Illness (n=16,144). Increasing age vs. Younger age was evaluated on New episodes of atrial fibrillation (10-fold increased risk per doubling of age). Increasing age was the dominant risk factor for new-onset atrial fibrillation in critically ill patients, with prediction models achieving AUCs >0.82 and >0.91.
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