Influencing Factors of New-Onset Atrial Fibrillation in Sepsis Patients and Construction of a Nomogram Prediction Model: Based on LASSO Regression.

Objective: To investigate the influencing factors of new-onset atrial fibrillation (AF) in patients with sepsis and to construct a nomogram prediction model. Methods: A retrospective analysis of 245 sepsis patients admitted to our hospital from March 2021 to March 2024 was used as the training set. An additional 107 sepsis patients admitted to our hospital from April 2024 to April 2025 were included as the validation set. The training and validation sets were divided into an AF group and a non-AF group based on the occurrence of new-onset AF. Results: In the training set, there were significant differences between the two groups in terms of age, mechanical ventilation, APACHE II score, acute kidney injury, metabolic disorders, theophylline medication, TNF-α, E/e', and NT-proBNP (P < 0.05). LASSO regression analysis was used to screen for 7 predictive factors. Logistic regression analysis identified age, mechanical ventilation, APACHE II score, theophylline medication, TNF-α, E/e', and NT-proBNP as risk factors for new-onset AF in sepsis patients (P < 0.05). The area under the curve (AUC) of the ROC curve for the training set was 0.869, and the Hosmer-Lemeshow test yielded χ2=7.346 (P=0.713). The decision curve analysis (DCA) showed that the model has high clinical application value when the threshold probability is between 0.10 and 0.89. For external validation, the AUC of the ROC curve was 0.875, the Hosmer-Lemeshow test yielded χ2=6.992 (P=0.703), and the DCA curve showed that the model has high clinical application value when the threshold probability is between 0.12 and 0.83. Conclusion: Age, mechanical ventilation, APACHE II score, theophylline medication, TNF-α, E/e', and NT-proBNP are influencing factors for new-onset AF in patients with sepsis. The nomogram prediction model constructed on the basis of these factors has good clinical applicability.