Objective:To explore the value of lumbar volumetric bone mineral density (vBMD) measured by quantitative computed tomography (QCT) in predicting coronary artery calcification (CAC).Methods:This retrospective, cross-sectional study included a total of 991 patients (504 male and 487 female) who underwent coronary artery CT angiography (CTA) and chest, abdomen, or lumbar spine CT examinations at Taihe County People′s Hospital from January 2023 to June 2024. Lumbar vBMD was measured by QCT. The coronary artery calcification score (CACS) was calculated using an artificial intelligence-assisted diagnostic system. Patients were categorized into a low calcification group (CACS≤100, 592 cases) and a moderate-to-severe calcification group (CACS>100, 399 cases). Independent-sample t-tests, Mann-Whitney U tests, or χ2 tests were employed to analyze the differences in clinical data and lumbar vBMD between the two groups. Binary logistic regression was employed to control confounding factors and analyze the correlation between lumbar vBMD and moderate-to-severe CAC. Multivariate binary logistic regression was used to identify independent predictors of moderate-to-severe CAC and construct a combined prediction model. The receiver operating characteristic curve was used to evaluate the efficacy of lumbar vBMD and the combined model in predicting moderate-to-severe CAC. The comparison of the area under the curve (AUC) was conducted using the DeLong test. Results:The age, incidence of diabetes, hypertension, and osteoporosis were significantly higher in the moderate-to-severe calcification group than in the low calcification group, while lumbar vBMD was significantly lower in the former group (all P<0.05). The body mass index, smoking history, and hyperlipidemia had no statistical differences between the two groups (all P>0.05). Following adjustment for potential confounding variables, the results of binary logistic regression analysis revealed that lumbar vBMD was not significantly associated with the presence of moderate-to-severe CAC in the overall study population or in male ( OR=0.998, 95% CI 0.993-1.003, P=0.379; OR=1.000, 95% CI 0.993-1.006, P=0.918). However, lumbar vBMD was a statistically significant predictor in females ( OR=0.992, 95% CI 0.985-0.999, P=0.032). Multivariate binary logistic regression analysis further demonstrated that age ( OR=1.048, 95% CI 1.019-1.077, P=0.001), hypertension ( OR=2.649, 95% CI 1.719-4.082, P<0.001), and lumbar vBMD ( OR=0.992, 95% CI 0.984-0.999, P=0.022) were independent predictors of moderate-to-severe CAC in women. The AUC of lumbar vBMD for predicting moderate-to-severe CAC in female patients was 0.684 (95% CI 0.641-0.725), and the AUC of the combined model was 0.746 (95% CI 0.705-0.784), with a statistically significant difference ( Z=3.26, P=0.001). Conclusions:Lumbar vBMD measured by QCT is an independent predictor of moderate-to-severe CAC in women and demonstrates moderate predictive value. The predictive performance can be enhanced by integrating age and hypertension into a combined predictive model.

Vertebral refracture following percutaneous vertebral augmentation (PVA) is commonly seen in elderly patients with osteoporotic thoracolumbar compression fractures (OTLCF). It can lead to recurrent pain, loss of vertebral height, progression of kyphosis, and even neurological dysfunction, significantly impairing patients′ quality of life. Current diagnosis and treatment face multiple challenges, including high misdiagnosis rate, difficulty in choosing between surgical and non-surgical treatment options, lack of standardized surgical protocols, interference from intralesional bone cement during procedures, inadequate stability of internal fixation in osteoporotic bone, and suboptimal compliance of anti-osteoporotic therapy. Establishing a standardized diagnostic and therapeutic framework is urgently needed. To standardize the management process and improve outcomes for vertebral refractures after PVA in elderly OTLCF patients, Spinal Trauma Group of the Orthopedic Branch of Chinese Medical Doctor Association organized experts in the field to develop Guideline for the diagnosis and treatment of vertebral refracture after percutaneous vertebral augmentation in elderly patients with osteoporotic thoracolumbar compression fractures ( version 2025), based on current literature and clinical experience, and adhering to principles of scientific rigor and clinical applicability. A total of 11 recommendations were proposed, encompassing diagnosis, treatment, and rehabilitation of vertebral refracture after PVA in elderly patients with OTLCF, aiming to provide a foundation for a standardized management.

Objective: To analyze the association between dietary diversity and cognitive function among the elderly using group-based trajectory model, so as to provide the basis for formulating dietary intervention strategies to prevent cognitive impairment. Methods: Based on the Chinese Longitudinal Healthy Longevity Survey (CLHLS) project, demographic information, lifestyle behaviors, psychological well-being, and activities of daily living of elderly individuals aged ≥65 years from 2008 to 2018 were collected. Dietary diversity was assessed using a food frequency questionnaire, with a score of <7 points defined as low dietary diversity. Cognitive function was evaluated using the Chinese version of the Mini-Mental State Examination (MMSE). A group-based trajectory model was established based on cognitive function scores from 2008 to 2018 to analyze the trajectories of cognitive function change. The association between dietary diversity and cognitive function was analyzed using a multinomial logistic regression model. Results: A total of 1 613 individuals were collected, with a median age was 72.00 (interquartile range, 10.00) years. There were 810 males (50.22%) and 803 females (49.78%). The group-based trajectory model analysis categorized the participants into three groups: the low-level normal group, the high-level normal group, and the slow-then-rapid decline group, comprising 796 (49.35%), 585 (36.27%), and 232 (14.38%) individuals, respectively. Among these groups, the numbers of individuals with low dietary diversity were 497 (62.44%), 311 (53.16%), and 166 (71.55%), respectively, with a statistically significant difference (P<0.05). Multinomial logistic regression analysis showed that after adjusting for demographic information, lifestyle behaviors, psychological well-being, and activities of daily living, compared with the high-level normal group, low dietary diversity was statistically associated with cognitive function in the slow-then-rapid decline group (OR=1.622, 95%CI: 1.103-2.384). Conclusion Low dietary diversity may increase the risk of cognitive impairment among the elderly.

Objective To explore the correlation between preoperative airway preparation and postoperative day 1 drainage volume in patients undergoing radical esophagectomy and the occurrence of postoperative pulmonary complications.Methods Analyzing the clinical data of esophageal cancer 147 patients who underwent thoracoscopic and laparoscopic radical surgery from January 2022 to June 2024,they were divided into PPCs group and non-PPCs group based on whether PPCs occurred postoperatively.Clinical data of the two groups were compared.Multivariate logistic regression analysis was applied to analyze the influencing factors for the occurrence of PPCs after radical resection of esophageal cancer,and a decision tree prediction model for postoperative PPCs was constructed.Receiver operating characteristic(ROC)curve was used to analyze the value of related factors in predicting the occurrence of PPCs after radical resection of esophageal cancer.Results Univariate analysis showed that age,albumin/fibrinogen ratio,neutrophil/lymphocyte ratio,preoperative respiratory preparation,and drainage volume on the first day after surgery were associated with the occurrence of PPCs after radical resection of esophageal cancer(P＜0.05).Multivariate logistic regression analysis indicated that age,albumin/fibrinogen ratio and drainage volume on the first day after surgery were independent influencing factors for the occurrence of PPCs after radical resection of esophageal cancer(P＜0.05).The decision tree model identified drainage volume on the first day after surgery,albumin/fibrinogen ratio,age and neutrophil/lymphocyte ratio as predictive factors.The incidence rate of PPCs in the group without preoperative respiratory preparation was significantly higher than that in the group with preoperative respiratory preparation(62.5％vs.37.5％,P＜0.05).A drainage volume of 246.59 ml on the first day after surgery was the optimal cutoff value for predicting the occurrence of PPCs after radical resection of esophageal cancer,and specificity was 93.04％,sensitivity was 87.5％,area under ROC curve(AUC)was 0.958,95％CI was 0.911-0.984.Moreover,the incidence rate of PPCs in patients with a drainage volume≤246.59 ml on the first day after surgery was significantly higher than that in patients with a drainage volume＞246.59 ml(77.78％vs.3.60％,P＜0.01).Conclusion Preoperative airway preparation and postoperative drainage volume are closely related to the occurrence of postoperative pulmonary complications(PPCs)after thoracoscopic radical surgery for esophageal cancer.Therefore,targeted measures should be taken for airway preparation before surgery,and changes in the patient's condition should be closely monitored after surgery to accurately determine the timing of drainage tube removal,which plays an important role in effectively preventing and treating PPCs.

Objective To explore the correlation between preoperative airway preparation and postoperative day 1 drainage volume in patients undergoing radical esophagectomy and the occurrence of postoperative pulmonary complications.Methods Analyzing the clinical data of esophageal cancer 147 patients who underwent thoracoscopic and laparoscopic radical surgery from January 2022 to June 2024,they were divided into PPCs group and non-PPCs group based on whether PPCs occurred postoperatively.Clinical data of the two groups were compared.Multivariate logistic regression analysis was applied to analyze the influencing factors for the occurrence of PPCs after radical resection of esophageal cancer,and a decision tree prediction model for postoperative PPCs was constructed.Receiver operating characteristic(ROC)curve was used to analyze the value of related factors in predicting the occurrence of PPCs after radical resection of esophageal cancer.Results Univariate analysis showed that age,albumin/fibrinogen ratio,neutrophil/lymphocyte ratio,preoperative respiratory preparation,and drainage volume on the first day after surgery were associated with the occurrence of PPCs after radical resection of esophageal cancer(P＜0.05).Multivariate logistic regression analysis indicated that age,albumin/fibrinogen ratio and drainage volume on the first day after surgery were independent influencing factors for the occurrence of PPCs after radical resection of esophageal cancer(P＜0.05).The decision tree model identified drainage volume on the first day after surgery,albumin/fibrinogen ratio,age and neutrophil/lymphocyte ratio as predictive factors.The incidence rate of PPCs in the group without preoperative respiratory preparation was significantly higher than that in the group with preoperative respiratory preparation(62.5％vs.37.5％,P＜0.05).A drainage volume of 246.59 ml on the first day after surgery was the optimal cutoff value for predicting the occurrence of PPCs after radical resection of esophageal cancer,and specificity was 93.04％,sensitivity was 87.5％,area under ROC curve(AUC)was 0.958,95％CI was 0.911-0.984.Moreover,the incidence rate of PPCs in patients with a drainage volume≤246.59 ml on the first day after surgery was significantly higher than that in patients with a drainage volume＞246.59 ml(77.78％vs.3.60％,P＜0.01).Conclusion Preoperative airway preparation and postoperative drainage volume are closely related to the occurrence of postoperative pulmonary complications(PPCs)after thoracoscopic radical surgery for esophageal cancer.Therefore,targeted measures should be taken for airway preparation before surgery,and changes in the patient's condition should be closely monitored after surgery to accurately determine the timing of drainage tube removal,which plays an important role in effectively preventing and treating PPCs.

Objective:To explore the value of lumbar volumetric bone mineral density (vBMD) measured by quantitative computed tomography (QCT) in predicting coronary artery calcification (CAC).Methods:This retrospective, cross-sectional study included a total of 991 patients (504 male and 487 female) who underwent coronary artery CT angiography (CTA) and chest, abdomen, or lumbar spine CT examinations at Taihe County People′s Hospital from January 2023 to June 2024. Lumbar vBMD was measured by QCT. The coronary artery calcification score (CACS) was calculated using an artificial intelligence-assisted diagnostic system. Patients were categorized into a low calcification group (CACS≤100, 592 cases) and a moderate-to-severe calcification group (CACS>100, 399 cases). Independent-sample t-tests, Mann-Whitney U tests, or χ2 tests were employed to analyze the differences in clinical data and lumbar vBMD between the two groups. Binary logistic regression was employed to control confounding factors and analyze the correlation between lumbar vBMD and moderate-to-severe CAC. Multivariate binary logistic regression was used to identify independent predictors of moderate-to-severe CAC and construct a combined prediction model. The receiver operating characteristic curve was used to evaluate the efficacy of lumbar vBMD and the combined model in predicting moderate-to-severe CAC. The comparison of the area under the curve (AUC) was conducted using the DeLong test. Results:The age, incidence of diabetes, hypertension, and osteoporosis were significantly higher in the moderate-to-severe calcification group than in the low calcification group, while lumbar vBMD was significantly lower in the former group (all P<0.05). The body mass index, smoking history, and hyperlipidemia had no statistical differences between the two groups (all P>0.05). Following adjustment for potential confounding variables, the results of binary logistic regression analysis revealed that lumbar vBMD was not significantly associated with the presence of moderate-to-severe CAC in the overall study population or in male ( OR=0.998, 95% CI 0.993-1.003, P=0.379; OR=1.000, 95% CI 0.993-1.006, P=0.918). However, lumbar vBMD was a statistically significant predictor in females ( OR=0.992, 95% CI 0.985-0.999, P=0.032). Multivariate binary logistic regression analysis further demonstrated that age ( OR=1.048, 95% CI 1.019-1.077, P=0.001), hypertension ( OR=2.649, 95% CI 1.719-4.082, P<0.001), and lumbar vBMD ( OR=0.992, 95% CI 0.984-0.999, P=0.022) were independent predictors of moderate-to-severe CAC in women. The AUC of lumbar vBMD for predicting moderate-to-severe CAC in female patients was 0.684 (95% CI 0.641-0.725), and the AUC of the combined model was 0.746 (95% CI 0.705-0.784), with a statistically significant difference ( Z=3.26, P=0.001). Conclusions:Lumbar vBMD measured by QCT is an independent predictor of moderate-to-severe CAC in women and demonstrates moderate predictive value. The predictive performance can be enhanced by integrating age and hypertension into a combined predictive model.

Vertebral refracture following percutaneous vertebral augmentation (PVA) is commonly seen in elderly patients with osteoporotic thoracolumbar compression fractures (OTLCF). It can lead to recurrent pain, loss of vertebral height, progression of kyphosis, and even neurological dysfunction, significantly impairing patients′ quality of life. Current diagnosis and treatment face multiple challenges, including high misdiagnosis rate, difficulty in choosing between surgical and non-surgical treatment options, lack of standardized surgical protocols, interference from intralesional bone cement during procedures, inadequate stability of internal fixation in osteoporotic bone, and suboptimal compliance of anti-osteoporotic therapy. Establishing a standardized diagnostic and therapeutic framework is urgently needed. To standardize the management process and improve outcomes for vertebral refractures after PVA in elderly OTLCF patients, Spinal Trauma Group of the Orthopedic Branch of Chinese Medical Doctor Association organized experts in the field to develop Guideline for the diagnosis and treatment of vertebral refracture after percutaneous vertebral augmentation in elderly patients with osteoporotic thoracolumbar compression fractures ( version 2025), based on current literature and clinical experience, and adhering to principles of scientific rigor and clinical applicability. A total of 11 recommendations were proposed, encompassing diagnosis, treatment, and rehabilitation of vertebral refracture after PVA in elderly patients with OTLCF, aiming to provide a foundation for a standardized management.

Objective:To construct a nomogram for predicting unfavorable prognosis at 6 months after moderate and severe traumatic brain injury (msTBI) and validate its predictive effectiveness.Methods:A retrospective cohort study was conducted to analyze the clinical data of 387 patients with msTBI who were admitted to 904th Hospital of the Joint Logistic Support Force of PLA from January 2020 to December 2022, including 265 males and 122 females, aged 6-97 years [58(47, 68)years]. According to the Glasgow outcome scale (GOS) score at 6 months after injury, the patients were divided into favorable prognosis group (GOS 4-5 points, n=201) and unfavorable prognosis group (GOS 1-3 points, n=186). The clinical characteristics, imaging manifestations, and laboratory test results of the two groups on admission were recorded. Univariate analysis was applied to evaluate the correlation between the aforementioned indicators and the unfavorable prognosis of the msTBI patients at 6 months after injury. Receiver operating characteristic (ROC) curves of single variable and the correlation heatmap among continuous variables were plotted. Lasso regression was used to select variables and multivariate Logistic regression analysis was used to determine independent predictive factors so as to construct Logistic regression equation and plot the nomogram. The internal verification was carried out by means of random and non-random split of data. In random split, the data were divided randomly with a ratio of 6∶4 into training group ( n=232) and verification group ( n=155). In non-random split, the patients admitted from January 2020 to December 2021 were assigned to the training group ( n=260), while those admitted from January 2022 to December 2022 to the verification group ( n=127). Area under the curve (AUC) was used to evaluate the predictive ability of the model in the training group and verification group, calibration curve and Hosmer-Lemeshow (H-L) test to evaluate its goodness of fit, and decision curve analysis (DCA) to evaluate its clinical applicability. The influence of inclusion of neutrophil-to-lymphocyte ratio (NLR) model on the warning effectiveness of poor prognosis was analyzed in comparison with the model without inclusion of NLR. Results:Univariate analysis showed that there was a certain correlation between age, length of hospital stay, Glasgow coma scale (GCS), American Society of Anesthesiologists Physical Status (ASA-PS) classification, Injury severity score (ISS), prehospital tracheal intubation, hypotension, hypoxia, pupillary responsiveness, midline shift, basilar cisterna status, traumatic subarachnoid hemorrhage (tSAH), D-Dimer, prothrombin time activity (PTA), glucose, hemoglobin, K +, Cl -, Ca 2+, HCO -, creatinine, albumin, lactic acid, platelet, lymphocyte, systemic immune-inflammation index (SII), NLR, lymphocyte-to-monocyte ratio (LMR) and unfavorable prognosis of msTBI patients at 6 months after injury ( P<0.05 or 0.01). The ROC curve of single variable showed that GCS (AUC=0.82), ISS (AUC=0.81), pupillary responsiveness (AUC=0.76), basal cistern status (AUC=0.73) and NLR (AUC=0.73) had good predictive validity. The results of the correlation heatmap showed that there was a significant correlation and collinearity among the continuous variables, while no collinearity was found between ISS and NLR. Fourteen potential predictors selected by Lasso regression were included in multivariate Logistic regression analysis and its results showed that age ( OR=0.86, 95% CI 1.38, 5.19), GCS 6-8 points ( OR=3.13, 95% CI 1.06, 9.27), GCS 3-5 points ( OR=12.36, 95% CI 2.81, 54.27), ISS ( OR=3.68, 95% CI 1.38, 9.80), pupillary responsiveness ( OR=2.45, 95% CI 0.85, 7.07), and NLR ( OR=2.62, 95% CI 1.52, 4.51) were identified as the independent risk factors for unfavorable prognosis of msTBI patients at 6 months after injury ( P<0.05 or 0.01). The multivariate Logistic regression equation was Logit [P/(1-P)]=0.066×"age"+ 1.474×"GCS 6-8"+2.357×"GCS 3-5"+0.066×"ISS"+0.965×"absence of pupillary light reflex"+0.194×"NLR"-10.704. In the internal verification of random split of data, the AUC value of the model was 0.93 (95% CI 0.89, 0.96) in the training group and 0.93 (95% CI 0.89, 0.97) in the verification group. In the internal verification of non-random split, the AUC value was 0.94 (95% CI 0.91, 0.97) in the training group and 0.93 (95% CI 0.89, 0.97) in the verification group. The calibration curve and H-L test showed that the model had good calibration ability ( P>0.5). The results of DCA showed that the application of the nomogram would increase the net benefit of the patients (risk threshold probability of 0.0-0.8). Compared with the conventional model (AUC=0.90), inclusion of NLR model (AUC=0.93) enhanced the warning effectiveness. Conclusions:Age, GCS, ISS, pupillary responsiveness and NLR are independent risk factors affecting unfavorable prognosis in msTBI patients at 6 months after injury, based on which the nomogram constructed can better predict the clinical outcome of msTBI patients.

Abstract Survival analysis has been widely used in the field of medical research. The Cox proportional hazard model is commonly used, but its practical application is limited. Machine learning method can compensate for the shortcomings of the Cox proportional hazard model in terms of nonlinear data processing and prediction accuracy. This article reviewed the advance of machine learning methods represented by neural networks, within the field of survival analysis, and highlighted the principles and benefits of three machine learning methods that DeepSurv, Deep-Hit and random survival forest, providing methodological insights for the analysis of complex survival data.

The incidence of intracranial aneurysms is higher in population, and the death and disability rates after rupture are extremely high. Early detection of intracranial aneurysms, prediction of rupture risk, operation program, and evaluation of treatment outcomes are all the clinical concerns. With the continuously deepening integration of artificial intelligence technology and medicine, its application in the diagnosis and treatment of intracranial aneurysms is becoming increasingly widespread. This article reviews the current application status of artificial intelligence technology in the diagnosis and treatment of intracranial aneurysms.