Key result
IL-6-based risk model predicts new-onset AF in sepsis with a validation AUC of ~0.87.
Why the study?
Patients with sepsis face a high risk of new-onset atrial fibrillation, which increases mortality, highlighting the need for a risk prediction model for early stratification.
Can a risk prediction model incorporating Log IL-6, BUN, and HR effectively predict new-onset atrial fibrillation in sepsis patients?
Observational (n=423)
No
Can a risk prediction model incorporating Log IL-6, BUN, and HR effectively predict new-onset atrial fibrillation in sepsis patients?
Effect estimate: AUC 0.925
A simple risk prediction model using Log IL-6, BUN, and heart rate can accurately identify sepsis patients at high risk for new-onset atrial fibrillation, facilitating early intervention.
May support closer monitoring of high-risk sepsis patients; leaves open prospective validation before practice change.
Background: Sepsis patients face a high risk of new-onset atrial fibrillation (NOAF), which increases mortality. Thus, it is significant to construct a risk prediction model for early risk stratification. Objective: To construct and validate a risk prediction model for NOAF in sepsis. Methods: A total of 423 sepsis patients were randomly divided into training (n=299) and validation (n=124) cohorts. Predictors were selected using least absolute shrinkage and selection operator (LASSO) regression, and independent risk factors were identified by multivariate logistic regression to construct a nomogram. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow test, and calibration curves. Clinical utility was evaluated using decision curve analysis (DCA) and clinical impact curves (CIC). Results: Log interleukin-6 (Log IL-6), blood urea nitrogen (BUN), and heart rate (HR) were identified as independent risk factors for NOAF. The nomogram demonstrated strong discriminative ability, with AUCs of 0.925 in the training cohort and 0.866 in the validation cohort. Calibration was good in both cohorts, and DCA and CIC indicated favorable clinical utility across a range of threshold probabilities. Conclusion: A risk prediction model incorporating Log IL-6, BUN, and HR effectively could predict NOAF in sepsis patients, with good discrimination, calibration, and potential clinical applicability for early risk identification. However, prior to further clinical application, additional multicenter, prospective studies are required for external validation.
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Gou et al. (2025) conducted an observational in Sepsis (n=423). Risk prediction model (Log IL-6, BUN, HR) was evaluated on Discriminative ability (AUC) for new-onset atrial fibrillation (AUC 0.925). A risk prediction model incorporating Log IL-6, blood urea nitrogen, and heart rate effectively predicted new-onset atrial fibrillation in sepsis patients, achieving an AUC of 0.925 in the training cohort and 0.866 in the validation cohort.
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