关键词: 胸腔镜肺癌根治术, 苏醒期躁动, 机器学习, 风险因素, 预测模型, 护理

Abstract:

Objective To construct and verify a risk prediction model of emergence agitation in patients undergoing thoracoscopic radical resection of lung cancer，and to screen the optimal model by using machine learning algorithm，so as to provide references for clinical formulation of a nursing risk management plan. Methods The convenience sampling method was used to retrospectively select 476 patients who underwent thoracoscopic radical resection of lung cancer in a tertiary hospital in Hangzhou，Zhejiang Province from January to December 2023 as a construction group. Logistic regression，decision tree，random forest and naive Bayesian model were constructed by SPSS 29.0 and R 4.3.0 software. The prediction performance of each model was compared by accuracy，precision，recall，F1 score and area under the receiver operating characteristic curve，and the optimal model was screened. From January to June 2024，204 patients in the unit were prospectively selected as the research subjects of an external validation group. The discrimination and calibration of the optimal model were evaluated by AUC value and calibration curve. Results A total of 680 patients completed the survey. All 4 models showed that multimodal analgesia，thoracic drainage tube type，pain score，tracheal intubation type，state anxiety and catheter indwelling time were the influencing factors of emergence agitation in patients undergoing thoracoscopic radical resection of lung cancer（P<0.05）. The 4 risk prediction models showed that the random forest prediction model had the best comprehensive performance. The external verification results showed that the AUC value was 0.913，and the calibration curve fitted well with the 45° ideal line. Conclusion Among the 4 risk prediction models，the random forest prediction model has the best performance，which is more suitable for the assessment of the risk of emergence agitation in patients undergoing thoracoscopic radical resection of lung cancer，and has good generalization and clinical application value.

Key words: Thoracoscopic Radical Resection of Lung Cancer, Emergence Agitation, Machine Learning, Risk Factors, Predictive Model, Nursing Care