Head & neck · 2024
Intelligent method to predict intensive care unit admission after drainage operation in patients with deep neck space abscess: A multicenter retrospective study.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Lei Han, Lin Yu, Chen Weixiong, Liu Tianrun, Ye Jin, Cai Qian, Ye Fei, He Long, Xie Xingqiang, Xiong Guoping, Gao Wenxiang, Lei Wenbin
Аннотация
BACKGROUNDS: A deep neck space abscess (DNSA) is a critical condition resulting from infection of deep neck fascia and soft issue, leading to high morbidity and mortality. Therefore, intensive care can be very significant for patients with DNSA. This study aimed to develop models to predict the need for postoperative intensive care in patients with DNSA.
METHODS: We retrospectively analyzed the records of 332 patients with DNSA who received drainage operation between 2015 and 2020. Multivariate logistic regression analysis and the eXtrem Gradient Boosting (XGBoost) algorithm were used to develop predictive models.
RESULTS: We developed two predictive models, the nomogram and the XGBoost model. The area under the curve (AUC) of the nomogram was 0.911 and of the XGBoost model was 0.935.
CONCLUSION: We developed two predictive models for guiding clinical decision making for postoperative ICU admission for DNSA patients, which may help improve prognosis and optimize intensive care resource allocation.
METHODS: We retrospectively analyzed the records of 332 patients with DNSA who received drainage operation between 2015 and 2020. Multivariate logistic regression analysis and the eXtrem Gradient Boosting (XGBoost) algorithm were used to develop predictive models.
RESULTS: We developed two predictive models, the nomogram and the XGBoost model. The area under the curve (AUC) of the nomogram was 0.911 and of the XGBoost model was 0.935.
CONCLUSION: We developed two predictive models for guiding clinical decision making for postoperative ICU admission for DNSA patients, which may help improve prognosis and optimize intensive care resource allocation.