Development and validation of a nomogram for predicting atrial fibrillation in patients with acute heart failure admitted to the ICU: A retrospective cohort study

Abstract

Abstract Introduction Acute heart failure is a serious condition. Atrial fibrillation is the most frequent arrhythmia in patients with acute heart failure. The occurrence of atrial fibrillation occurs in heart failure patients worsen the prognosis and leads to substantially increase in treatment costs. Materials and Methods We retrospectively analyzed the MIMIC-IV database of patients admitted to the intensive care unit (ICU) for acute heart failure and who were initially sinus rhythm. Data on demographics, comorbidities, laboratory findings, vital signs, and treatment were extracted. The cohort was divided into a training set and a validation set. Variables selected by LASSO regression and multivariate logistic regression in the training set were used to develop a model for predicting the emergence of atrial fibrillation in acute heart failure in the ICU. A nomogram was drawn and an online calculator was developed. The performance of the model was tested using the validation set. Results This study enlisted 2342 patients with acute heart failure, 646 of whom developed atrial fibrillation during their ICU stay. Using LASSO and multiple logistic regression, we selected 6 variables: age, prothrombin time, heart rate, use of vasoactive drugs within 24 hours, SOFA score, and APSIII. The C-index of the model was 0.700 (95% confidence interval: 0.672–0.727) and 0.682 (95% confidence interval: 0.639–0.725) in the training and validation set, respectively. The calibration curves also performed well in both sets. Conclusion We developed a simple and effective model for predicting atrial fibrillation in patients with acute heart failure in the ICU.