Frontiers in cardiovascular medicine · 2025
A novel scoring model for predicting prolonged mechanical ventilation in cardiac surgery patients: development and validation.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Liu Quan, Chen Pengfei, Wang Wuwei, Zhou Yifei, Xu Yichen, Cao Xu, Fan Rui, Chen Wen, Huang Fuhua, Chen Xin
Аннотация
OBJECTIVE: Prolonged mechanical ventilation (PMV) is a significant postoperative complication in cardiac surgery, associated with increased mortality and healthcare costs. This study aims to develop and validate a novel scoring model to predict the risk of PMV in cardiac surgery patients.
METHODS: A retrospective analysis was conducted using data from 14 comprehensive hospitals in Jiangsu Province, including adult patients who underwent coronary artery bypass grafting (CABG), valve surgery, and aortic surgery from January 2021 to December 2022. Predictive variables were selected based on clinical expertise and prior literature, and a nomogram was developed using LASSO regression and multiple logistic regression. Model performance was evaluated using the C-index, calibration plots, and decision curve analysis (DCA).
RESULTS: A total of 5,206 patients were included in the final analysis. The incidence rate of PMV were 11.83% in the training set, 8.65% in the internal validation set, and 15.4% in the external validation set. The nomogram identified 9 significant predictors, including age, gender, preoperative conditions, and surgical factors. The model demonstrated robust performance with C-index values of 0.79 in the training and internal validation sets and 0.75 in the external validation set, indicating good predictive capability. Calibration curves confirmed the accuracy of predicted probabilities, and DCA indicated substantial net benefits for clinical decision-making.
CONCLUSIONS: This study presents a validated scoring model for predicting PMV in cardiac surgery patients, integrating a comprehensive range of clinical variables. The model facilitates early identification of high-risk patients, enabling tailored perioperative strategies and potentially improving patient outcomes and resource utilization in cardiac surgery.
METHODS: A retrospective analysis was conducted using data from 14 comprehensive hospitals in Jiangsu Province, including adult patients who underwent coronary artery bypass grafting (CABG), valve surgery, and aortic surgery from January 2021 to December 2022. Predictive variables were selected based on clinical expertise and prior literature, and a nomogram was developed using LASSO regression and multiple logistic regression. Model performance was evaluated using the C-index, calibration plots, and decision curve analysis (DCA).
RESULTS: A total of 5,206 patients were included in the final analysis. The incidence rate of PMV were 11.83% in the training set, 8.65% in the internal validation set, and 15.4% in the external validation set. The nomogram identified 9 significant predictors, including age, gender, preoperative conditions, and surgical factors. The model demonstrated robust performance with C-index values of 0.79 in the training and internal validation sets and 0.75 in the external validation set, indicating good predictive capability. Calibration curves confirmed the accuracy of predicted probabilities, and DCA indicated substantial net benefits for clinical decision-making.
CONCLUSIONS: This study presents a validated scoring model for predicting PMV in cardiac surgery patients, integrating a comprehensive range of clinical variables. The model facilitates early identification of high-risk patients, enabling tailored perioperative strategies and potentially improving patient outcomes and resource utilization in cardiac surgery.