Construction of prediction models for prolonged length of postoperative hospital stay in patients undergoing thoracoscopic lobectomy / 中华麻醉学杂志

ABSTRACT

Objective:To construct the prediction model for the prolonged length of postoperative hospital stay in the patients undergoing thoracoscopic lobectomy.Methods:The patients of both sexes, aged ≥18 yr, of American Society of Anesthesiologists Physical Status classification Ⅰ-Ⅲ, who received elective thoracoscopic lobectomy with general anesthesia from March 2016 to February 2019 in our hospital, were selected, their clinical data were collected, and the patients were pathologically diagnosed with non-small-cell lung cancer after operation.Basic information (sex, age, smoking history), previous history (dyslipidemia, hypertension, diabetes, cardiovascular and cerebrovascular diseases, peripheral vascular diseases, chronic obstructive pulmonary diseases), allergy history, other tumor history, surgical resection site, anesthetic factors (intraoperative use of non-steroidal anti-inflammatory drugs and glucocorticoids, duration of anesthesia, intraoperative epidural anesthesia + postoperative epidural analgesia) and postoperative complications (pleural effusion, pneumothorax, atelectasis) was collected.The patients were divided into 2 groups according to whether the length of postoperative hospital stay was prolonged normal group (≤ 7 days) and prolonged group (>7 days).Logistic regression analysis was used to identify the predictors for prolonged length of postoperative hospital stay.The regression model for prediction of prolonged length of postoperative hospital stay was constructed based on the TensorFlow deep learning framework, and the efficacy of prediction was evaluated.A deep neural network was further established based on the TensorFlow framework to construct a classification prediction model for prolonged length of postoperative hospital stay, and the efficacy of prediction was assessed, further comparing it with the prediction model constructed by the traditional machine learning method.Results:A total of 428 patients were finally enrolled in the study.The results of multivariate logistic regression analysis showed that age and anesthesia duration were the risk factors for the prolonged length of postoperative hospital stay, and female, other tumor history and resection of right middle lobe were the protective factors ( P<0.05).The performance of the regression model proved ineffective, getting 2.16 mean absolute error and 11.05 mean square error on the training set, 2.14 mean absolute error and 11.73 mean square error on the test set.The classification model achieved better score with accuracy 75.58%, F1-measure 0.553 and area under the receiver operating characteristic curve 0.702 on the test set, however, it showed no better performance than that of 4 other prediction models established by 4 traditional machine learning methods, specifically Logistic Regression, Random Forest, Gradient Boosting and Support Vector Machine. Conclusions:Sex, age, surgical resection site, other tumor history and duration of anesthesia can serve as the predictors, and a classification prediction model for prolonged length of postoperative hospital stay is constructed based on a deep neural network in the patients undergoing thoracoscopic lobectomy.