In Shock (Augusta, Ga.)
BACKGROUND : Septic patients with atrial fibrillation (AF) are common in intensive care unit (ICU) accompanied by high mortality. The early prediction of prognosis of these patients is critical for clinical intervention. This study aimed to develop a model by using machine learning (ML) algorithms to predict the risk of 28-day mortality in septic patients with AF.
METHODS : In this retrospective cohort study, we extracted septic patients with AF from the Medical Information Mart for Intensive Care III (MIMIC III) and IV database. Afterward, only MIMIC IV cohort was randomly divided into training or internal validation set. External validation set was mainly extracted from MIMIC III database. Propensity score matching was used to reduce the imbalance between the external validation and internal validation dataset. The predictive factors for 28-day mortality were determined by using multivariate logistic regression. Then we constructed models by using ML algorithms. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve, sensitivity, specificity, recall, and accuracy.
RESULTS : A total of 5317 septic patients with AF were enrolled, with 3845 in the training set, 960 in the internal testing set and 512 in the external testing set, respectively. Then we established four prediction models by using ML algorithms. Adaboost showed the moderate performance and had a higher accuracy than the other three models. Compared with other severity scores, the Adaboost obtained more net benefit.
CONCLUSION : We established first ML model for predicting the 28-day mortality of septic patients with AF. Compared with conventional scoring systems, the Adaboost model performed moderate. The model established will have the potential to improve the level of clinical practice.
Wang Ziwen, Zhang Linna, Chao Yali, Xu Meng, Geng Xiaojuan, Hu Xiaoyi
2023-Jan-05
