A novel nomogram prediction model for postoperative atrial fibrillation in patients undergoing laparotomy.

BACKGROUND

Postoperative atrial fibrillation (POAF) is an ordinary complication of surgery, particularly cardiac surgery. It significantly increases in-hospital mortality and costs. This study aimed to establish a nomogram prediction model for POAF in patients undergoing laparotomy. The model is expected to identify individuals at a high risk of POAF before surgery in clinical practice.

METHODS

A retrospective observational case-control study involving 230 adult patients (60 patients with POAF, 120 patients in the control group, and 50 patients in the validation group) who underwent laparotomy was retrieved from two hospitals. Independent risk variables for POAF were investigated using logistic regression and the least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, a nomogram model for POAF was constructed by multivariate logistic regression equations. The prediction model was internally validated by bootstrap method and externally validated with the validation group data. To assess the discriminative ability of the nomogram model, a receiver operating characteristic (ROC) curve was generated and a calibration curve was employed to assess the concentricity between the model's probability curve and the ideal curve. Subsequently, decision curve analysis (DCA) was performed to assess the clinical effectiveness of the model.

RESULTS

C-reactive protein (CRP), lymphocyte-to-monocyte ratio(LMR), blood urea nitrogen (BUN), and Macruz index were independent risk variables for POAF in patients who underwent laparotomy. A user-friendly and efficient prediction nomogram was visualized using R software. This nomogram exhibited strong discrimination, as evidenced by an area under the ROC curve (AUC) of 0.90 (95% CI 0.8509-0.9488) for the training set, 0.86 (95% CI 0.7142-1) for the test set, and 0.9792 (95% CI 0.9293-1) for the validation group data. The C-index of the bootstrap nomogram model was 0.8998. Furthermore, DCA revealed that this model displayed excellent fit and calibration, as well as positive net benefits.

CONCLUSIONS

A nomogram prediction model was constructed for POAF in patients who underwent abdominal surgery. The nomogram prediction model is expected to identify individuals at high risk of POAF in clinical practice for prophylactic therapeutic intervention prior to surgery.