Objective To systematically evaluate the risk factors for postoperative delirium (POD) in patients undergoing pneumonectomy. Methods PubMed, Web of Science, Cochrane Library, CNKI, Wanfang, and VIP databases were searched from the inception to November 7, 2024 for cross-sectional studies, case-control studies, and cohort studies on POD in patients undergoing pneumonectomy. Two researchers independently screened the literature, extracted data, and evaluated the quality of the literature. RevMan 5.4.1 software was used for meta-analysis. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the literature. Results A total of 12 studies were included, with 5 574 patients. The NOS scores of the literature were all≥6 points. Meta-analysis results showed that age (≥60 years) [OR=2.43, 95%CI (2.01, 2.93), P<0.01], American Society of Anesthesiologists (ASA) classification (Ⅳ) [OR=8.74, 95%CI (5.23, 14.61), P<0.01], history of diabetes [OR=12.81, 95%CI (10.45, 15.71), P<0.01], history of cerebrovascular disease [OR=3.00, 95%CI (2.46, 3.67), P<0.01], depression [OR=7.27, 95%CI (5.46, 9.67), P<0.01], squamous cell carcinoma [OR=4.79, 95%CI (1.83, 12.51), P<0.01], malnutrition [OR=5.25, 95%CI (3.35, 8.25), P<0.01], sleep disorders [OR=2.79, 95%CI (2.28, 3.42), P<0.01], and duration of one-lung ventilation during surgery [OR=1.32, 95%CI (1.11, 1.57), P<0.01] were all risk factors for POD, while high body mass index (BMI) [OR=0.96, 95%CI (0.95, 0.97), P<0.01] was a protective factor for POD. Conclusion Age (≥60 years), ASA classification (Ⅳ), history of diabetes, history of cerebrovascular disease, depression, squamous cell carcinoma, malnutrition, sleep disorders, and duration of one-lung ventilation during surgery are independent risk factors for POD, while high BMI is a protective factor.

Objective To observe the effect of precision nursing based on visualization technolo-gy in treating patients with pulmonary nodule surgery.Methods A total of 186 patients with surgery for pulmonary nodules were randomly divided into control group and observation group,with 93 cases in each group.The control group was given routine nursing,while the observation group was given vi-sualization technology assisted precision nursing on the basis of the control group.The psychological status[the Self-rating Anxiety Scale(SAS)score and the Self-rating Depression Scale(SDS)score]and postoperative recovery[score of the Quality of Recovery Scale with 15 items(QoR-15)]were compared between two groups,and the nursing satisfaction degree was compared between the two groups as well.Results After the intervention,the scores of SAS and SDS in both groups de-creased,and the score of each item in the observation group was significantly lower than that in the control group(P＜0.05);after intervention,the each item of the QoR-15 and the total score in the observation group were significantly higher than those in the control group(P＜0.05);the nursing satisfaction degree of the observation group was 97.8％,which was significantly higher than 89.2％in the control group(P＜0.05).Conclusion Precision nursing based on visualization technology can improve the adverse psychological state of patients with pulmonary nodule surgery,enhance the quality of postoperative recovery,and increase nursing satisfaction degree.

Objective To observe the effect of precision nursing based on visualization technolo-gy in treating patients with pulmonary nodule surgery.Methods A total of 186 patients with surgery for pulmonary nodules were randomly divided into control group and observation group,with 93 cases in each group.The control group was given routine nursing,while the observation group was given vi-sualization technology assisted precision nursing on the basis of the control group.The psychological status[the Self-rating Anxiety Scale(SAS)score and the Self-rating Depression Scale(SDS)score]and postoperative recovery[score of the Quality of Recovery Scale with 15 items(QoR-15)]were compared between two groups,and the nursing satisfaction degree was compared between the two groups as well.Results After the intervention,the scores of SAS and SDS in both groups de-creased,and the score of each item in the observation group was significantly lower than that in the control group(P＜0.05);after intervention,the each item of the QoR-15 and the total score in the observation group were significantly higher than those in the control group(P＜0.05);the nursing satisfaction degree of the observation group was 97.8％,which was significantly higher than 89.2％in the control group(P＜0.05).Conclusion Precision nursing based on visualization technology can improve the adverse psychological state of patients with pulmonary nodule surgery,enhance the quality of postoperative recovery,and increase nursing satisfaction degree.

Objective:To investigate and compare the predictive effects of two postoperative nausea and vomiting (PONV) prediction models in elderly patients with thoracoscopic partial pneumonectomy.Methods:The total of 227 elderly patients who underwent thoracoscopic partial pneumonectomy in Brain Hospital Affiliated to Nanjing Medical University from October 2021 to January 2022 were collected. Apfel risk score and Koivuranta risk score were used to predict the risk of PONV in each patient. The area under the ROC curve was used to evaluate the discrimination ability of the two prediction models; Hosmer-Lemeshow goodness of fit test was used to evaluate the calibration of the two prediction models.Results:Among 227 patients, 74 had PONV, and the incidence of PONV was 32.6%. The area under the ROC curve of Apfel and Koivuranta risk scores in the risk prediction of PONV in elderly patients with thoracoscopic partial pneumonectomy were 0.640 and 0.683 respectively. There was no significant difference between the two ( Z=1.54, P>0.05). The Hosmer-Lemeshow goodness of fit test showed that the accuracy of the two models in predicting PONV risk in elderly patients with thoracoscopic partial pneumonectomy was better ( P>0.05). Conclusions:The difference between the two models in PONV risk prediction in elderly patients with thoracoscopic partial pneumonectomy is general, Koivuranta model is more suitable for predicting PONV in elderly patients with thoracoscopic partial pneumonectomy, but its applicability is still insufficient.