ObjectiveTo explore ways so as to improve smoking cessation rates by studying relevant cases in Hong Kong.MethodsPatients attending the clinical pilot project in Hong Kong from 2010 to 2022 were retrospectively surveyed and analyzed. Information such as patients' general information, reasons for smoking for the first time, situations that enable smoking, barriers to smoking cessation, and withdrawal symptoms were obtained using a pre-designed case report form and analyzed.ResultsA total of 10436 patients, 6936 males and 3500 females, were included. Influenced by friends (67.70%), relieving mental stress (33.12%) and curiosity (30.52%) were the main reasons for smoking for the first time; depression (57.14%), after meals (49.08%) and nervousness (41.26%) were the situations that enable smoking; the main barriers to smoking cessation were physiologic dependence (87.06%) friends or colleagues smoking (37.03%) and compulsiveness to use tobacco (32.45%), top withdrawal symptoms smoking stoppage were craving for cigarettes (50.33%), restlessness (38.33%), and difficulty concentrating (26.63%).ConclusionsThe proportion of patients actively choosing to quit smoking is high in Hong Kong, and smoking cessation methods should be publicized to prompt smokers to take effective measures to quit. A majority of people are influenced by friends to smoke for the first time; thus, adolescent smoking behavior should be supervised to reduce first-time smokers. Moreover, as the most difficult thing to overcome in the process of quitting smoking is psychological addiction, behavioral interventions must be promoted to improve the rate of successful quitting, Steps should be taken to enable the management of withdrawal symptoms to prevent relapse.
ObjectiveTo analyze the effectiveness of fast track protocol of geriatric intertrochanteric fracture on operative waiting time, operation time, perioperative blood loss, providing data support for clinical therapy.MethodsThe clinical data of 240 elderly patients with intertrochanteric fracture admitted between January 2015 and December 2018 were retrospectively analyzed. They were divided into traditional protocol group (148 cases, group A) and fast track group (92 cases, group B). All patients were treated with closed reduction intramedullary nail (proximal femoral nail antirotation) surgery. There was no significant difference in gender, age, sides, fracture classification, fracture type, complications, the proportion of patients with more than 3 kinds of medical diseases, and the time from injury to admission between the two groups (P>0.05). Analysis index included operative waiting time (hospitalization to operation time), operation time, percentage of operation performing in 48 and 72 hours, percentage of transfusion, changes of hematocrit (Hct) at different stage (admission, operation day, and postoperative 1, 3 days), blood loss by fracture and cephalomedullary nail, intraoperative dominant blood loss, total blood loss in perioperative period were recorded and compared.ResultsThe operative waiting time, operation time, Hct on operation day and postoperative 3 days, blood loss by fracture, transfusion volume, and total blood loss in perioperative period in group B were significantly less than those in group A (P<0.05), and the percentage of operation performing in 48 and 72 hours in group B were significantly higher than those in group A (P<0.05). There was no signifcant difference in Hct on admission and postoperative 1 day, intraoperative dominant blood loss, percentage of transfusion, blood loss by cephalomedullary nail between the two groups (P>0.05).ConclusionFast track can shorten the operative waiting time of geriatric intertrochanteric fracture, reduce the blood loss by fracture, total blood loss in perioperative period, and transfusion volume. Early operation is conducive to improve the anemia status of patients during perioperative period.
ObjectiveTo develop and validate a machine learning model based on preoperative clinical characteristics, laboratory indices, and radiological features for the non-invasive prediction of spread through air spaces (STAS) in patients with early-stage lung adenocarcinoma. Methods Preoperative data from patients with early-stage lung adenocarcinoma who underwent surgical resection at Northern Jiangsu People's Hospital between January 2020 and August 2025 were retrospectively collected. The data included clinical characteristics, laboratory indices, and radiological features. Patients were divided into a STAS-positive and a STAS-negative group based on postoperative pathological findings. The dataset was randomly split into a training set and a testing set at a 7 : 3 ratio. Feature variables were selected using the maximum relevance and minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator (LASSO) regression. Five machine learning models were constructed: logistic regression (LR), random forest (RF), support vector machine (SVM), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The shapley additive explanations (SHAP) method was employed to interpret the optimal prediction model. Results A total of 377 patients were included, comprising 177 (46.9%) males and 200 females (53.1%), with a mean age of (63.31±9.73) years. There were 261 patients in the training set and 116 patients in the testing set. In the training set, statistically significant differences were observed between the STAS-positive group (n=130) and STAS-negative group (n=131) across multiple features, including age, sex, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), clinical T stage, and maximum solid component diameter (P<0.05). A final set of 10 feature variables was selected by combining mRMR and LASSO regression, and five machine learning models (LR, RF, SVM, LightGBM, XGBoost) were developed. The XGBoost model demonstrated superior predictive performance in both the training and testing sets, achieving AUCs of 0.947 [95%CI (0.920, 0.975)] and 0.943 [95%CI (0.894, 0.993)], respectively, and achieved the optimal level in the testing set. DCA indicated that the XGBoost model provided a high net clinical benefit across a wide range of threshold probabilities. SHAP analysis revealed that the vessel convergence sign, clinical T stage, age, consolidation-to-tumor ratio (CTR), and MLR were the features with the highest contributions to STAS prediction. Conclusion The XGBoost model effectively predicts preoperative STAS status in early-stage lung adenocarcinoma, exhibiting excellent discriminative performance and good clinical interpretability. Key predictors such as the vessel convergence sign, clinical T stage, age and CTR provide a crucial reference for preoperative risk assessment and the individualized selection of surgical strategies, ultimately benefiting patients.