Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective:To construct classification models of traumatic brain injury (TBI) based on the injury data collected at the scene of the accidents and validate its efficacy.Methods:A retrospective cohort study was conducted to analyze the pre-hospital treatment data of 368 TBI patients admitted to the Second Affiliated Hospital of Army Military Medical University from January 2019 to December 2023, including 243 males and 125 females, aged 18-82 years [(48.1±20.8)years]. The patients′ Glasgow coma scale (GCS) scores were 3-15 points [11.0(3.0, 15.0)points] at emergency medical service arrival. The patients were randomly assigned to the training set ( n=257) and test set ( n=111) at a ratio of 7∶3. According to the admission diagnosis, the patients fell into the mild TBI group ( n=62), medium TBI group ( n=137), severe TBI group ( n=120), and extremely severe TBI group ( n=49). In the training set, 44 patients fell into mild TBI group, 98 into medium TBI group, 82 into severe TBI group and 33 into extremely severe TBI group, while in the test set, 18 patients fell into mild TBI group, 39 into medium TBI group, 38 into severe TBI group and 16 into extremely severe TBI group. The following 12 kinds of injury data, including MARCH [massive hemorrhage (M), airway obstruction (A), respiratory failure (R), circulatory failure (C) and hypothermia (H)], GCS, pre-hospital index (PHI), shock index (SI), reverse SI multiplied by GCS (rSIG), optic nerve sheath diameter (ONSD) measured by ultrasound, scalp and skull injuries were collected at the scene of the accidents. Three machine algorithm including random forest (RF), support vector machine (SVM) and logistic regression (LR) were used to construct scene data-based TBI classification models. The accuracy rate, precision rate, recall rate, F1 value and area under receiver operating characteristic (ROC) curve (AUC) of the 3 models were used to verify the efficiency of the models for TBI classification. Shapley additive explanations (SHAP) method was used to interpret the results of the optimal model. The 12 kinds of injury data in the models were sorted according to their contribution to the TBI classification and the injury data with greater contribution were selected. Results:In the test set, the accuracy rate of the RF, SVM and LR models was 0.93, 0.92 and 0.87, respectively; the precision rate was 0.93, 0.92 and 0.89, respectively; the recall rate was 0.93, 0.92 and 0.87, respectively; the F1 value was 0.93, 0.92 and 0.87, respectively. In the mild, medium, severe and extremely severe TBI groups in the test set, the AUC of the RF model was 0.96 (95% CI 0.92, 0.98), 0.98 (95% CI 0.94, 0.99), 0.97 (95% CI 0.95, 0.98), and 0.97 (95% CI 0.96, 0.98), respectively; the AUC of the SVM model was 0.90 (95% CI 0.88, 0.94), 0.95 (95% CI 0.92, 0.97), 0.96 (95% CI 0.94, 0.98), and 0.95 (95% CI 0.92, 0.99), respectively; the AUC of the LR model was 0.90 (95% CI 0.83, 0.96), 0.90 (95% CI 0.84, 0.95), 0.96 (95% CI 0.95, 0.98), and 0.95 (95% CI 0.94, 0.97), respectively. The RF model demonstrated optimal discriminative performance for TBI classification. As the SHAP′s interpretation of the RF model indicated, among the 12 kinds of injury data, those with greater contributions to the TBI classification were GCS, rSIG, SI, PHI, respiratory failure, ONSD, and circulatory failure in sequence. Conclusions:Of the scene data-based TBI classification models, the RF model achieves good predictive performance for TBI classification when compared with the SVM model and LR model. Besides, GCS, rSIG, SI, PHI, respiratory failure, ONSD and circulatory failure contribute significantly to the classification of TBI in the RF model, which may assist emergency medical personnel in field triage and management of TBI at accident scenes.

Objective:To construct classification models of traumatic brain injury (TBI) based on the injury data collected at the scene of the accidents and validate its efficacy.Methods:A retrospective cohort study was conducted to analyze the pre-hospital treatment data of 368 TBI patients admitted to the Second Affiliated Hospital of Army Military Medical University from January 2019 to December 2023, including 243 males and 125 females, aged 18-82 years [(48.1±20.8)years]. The patients′ Glasgow coma scale (GCS) scores were 3-15 points [11.0(3.0, 15.0)points] at emergency medical service arrival. The patients were randomly assigned to the training set ( n=257) and test set ( n=111) at a ratio of 7∶3. According to the admission diagnosis, the patients fell into the mild TBI group ( n=62), medium TBI group ( n=137), severe TBI group ( n=120), and extremely severe TBI group ( n=49). In the training set, 44 patients fell into mild TBI group, 98 into medium TBI group, 82 into severe TBI group and 33 into extremely severe TBI group, while in the test set, 18 patients fell into mild TBI group, 39 into medium TBI group, 38 into severe TBI group and 16 into extremely severe TBI group. The following 12 kinds of injury data, including MARCH [massive hemorrhage (M), airway obstruction (A), respiratory failure (R), circulatory failure (C) and hypothermia (H)], GCS, pre-hospital index (PHI), shock index (SI), reverse SI multiplied by GCS (rSIG), optic nerve sheath diameter (ONSD) measured by ultrasound, scalp and skull injuries were collected at the scene of the accidents. Three machine algorithm including random forest (RF), support vector machine (SVM) and logistic regression (LR) were used to construct scene data-based TBI classification models. The accuracy rate, precision rate, recall rate, F1 value and area under receiver operating characteristic (ROC) curve (AUC) of the 3 models were used to verify the efficiency of the models for TBI classification. Shapley additive explanations (SHAP) method was used to interpret the results of the optimal model. The 12 kinds of injury data in the models were sorted according to their contribution to the TBI classification and the injury data with greater contribution were selected. Results:In the test set, the accuracy rate of the RF, SVM and LR models was 0.93, 0.92 and 0.87, respectively; the precision rate was 0.93, 0.92 and 0.89, respectively; the recall rate was 0.93, 0.92 and 0.87, respectively; the F1 value was 0.93, 0.92 and 0.87, respectively. In the mild, medium, severe and extremely severe TBI groups in the test set, the AUC of the RF model was 0.96 (95% CI 0.92, 0.98), 0.98 (95% CI 0.94, 0.99), 0.97 (95% CI 0.95, 0.98), and 0.97 (95% CI 0.96, 0.98), respectively; the AUC of the SVM model was 0.90 (95% CI 0.88, 0.94), 0.95 (95% CI 0.92, 0.97), 0.96 (95% CI 0.94, 0.98), and 0.95 (95% CI 0.92, 0.99), respectively; the AUC of the LR model was 0.90 (95% CI 0.83, 0.96), 0.90 (95% CI 0.84, 0.95), 0.96 (95% CI 0.95, 0.98), and 0.95 (95% CI 0.94, 0.97), respectively. The RF model demonstrated optimal discriminative performance for TBI classification. As the SHAP′s interpretation of the RF model indicated, among the 12 kinds of injury data, those with greater contributions to the TBI classification were GCS, rSIG, SI, PHI, respiratory failure, ONSD, and circulatory failure in sequence. Conclusions:Of the scene data-based TBI classification models, the RF model achieves good predictive performance for TBI classification when compared with the SVM model and LR model. Besides, GCS, rSIG, SI, PHI, respiratory failure, ONSD and circulatory failure contribute significantly to the classification of TBI in the RF model, which may assist emergency medical personnel in field triage and management of TBI at accident scenes.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

The condition of patients with severe traumatic brain injury (sTBI) complicated by corona virus 2019 disease (COVID-19) is complex. sTBI can significantly increase the probability of COVID-19 developing into severe or critical stage, while COVID-19 can also increase the surgical risk of sTBI and the severity of postoperative lung lesions. There are many contradictions in the treatment process, which brings difficulties to the clinical treatment of such patients. Up to now, there are few clinical studies and therapeutic norms relevant to sTBI complicated by COVID-19. In order to standardize the clinical treatment of such patients, Critical Care Medicine Branch of China International Exchange and Promotive Association for Medical and Healthcare and Editorial Board of Chinese Journal of Trauma organized relevant experts to formulate the Chinese expert consensus on clinical treatment of adult patients with severe traumatic brain injury complicated by corona virus infection 2019 ( version 2023) based on the joint prevention and control mechanism scheme of the State Council and domestic and foreign literatures on sTBI and COVID-19 in the past 3 years of the international epidemic. Fifteen recommendations focused on emergency treatment, emergency surgery and comprehensive management were put forward to provide a guidance for the diagnosis and treatment of sTBI complicated by COVID-19.

Objective:To investigate the changes of plasma prothrombin fragment 1+ 2 (F 1+2), tissue factor positive microparticle (TF+ MP) and thrombin antithrombin complex (TAT) level before and after the treatment of low molecular weight heparin combined with reteplase in patients with malignant tumor and lower extremity venous thrombosis. Methods:From July 2016 to October 2019, 64 patients with malignant tumors and lower extremity venous thrombosis in the Third Hospital of Changsha were selected, they were divided into observation group ( n=32) and control group ( n=32) by simple randomization. The control group was treated with low molecular heparin, and the observation group was treated with low molecular heparin combined with reteplase. The efficacy, clinical symptom improvement time, incidence of adverse reactions, difference in lower limb circumference, blood flow velocity, activated partial thromboplastin time (APTT), prothrombin time (PT), plasma F 1+2, TF+ MP, TAT level before and after treatment were compared between the two groups; the correlations of plasma F 1+2, TF+ MP, and TAT level with clinical symptom improvement time, peripheral diameter difference of lower extremity, blood flow velocity, APTT, and PT were analyzed. Results:The total effective rate of the observation group (87.50%) was higher than that of the control group (65.63%) ( P<0.05); The improvement time of clinical symptoms in the observation group was shorter than that in the control group ( P<0.05); After treatment, the peripheral limb diameter difference of the observation group was lower than that of the control group, and the blood flow velocity was higher than that of the control group ( P<0.05); The APTT and PT in the observation group were higher than those in the control group after treatment ( P<0.05); The plasma F 1+2, TF+ MP, and TAT level in the observation group were lower than those in the control group after treatment ( P<0.05); The levels of plasma F 1+2, TF+ MP, and TAT were positively correlated with symptom improvement time and lower limb circumference difference, and negatively correlated with blood flow velocity, APTT, and PT ( P<0.05); There was no significant difference in the incidence of adverse reactions (18.75%) between the observation group and the control group (12.50%) during the treatment period ( P>0.05). Conclusions:Plasma F 1+2, TF+ MP, and TAT expression in patients with malignant tumors and venous thrombosis of the lower extremity can be used as biological indicators to evaluate the patient's condition and treatment effect. Low molecular weight heparin combined with reteplase can significantly reduce the plasma F 1+2, TF+ MP and TAT level, promote the improvement of symptoms, effectively reduce the peripheral diameter difference of lower extremity, improve blood flow velocity and coagulation function, and has a significant effect.

OBJECTIVE: To investigate the relationship between the sleep duration on peak melatonin secretion and renal function injury in steel workers. METHODS: A judgment sampling method was used to select 7 655 steel workers in a steel production department for health examination and questionnaire survey, and the relationship between renal function injury and non-peak sleep time(equivalent to the reduction of sleep duration on peak melatonin secretion) was analyzed using restriction cubic spline(RCS) model and multi-factor logistic regression. RESULTS: The detection rate of renal injury in the study subjects was 14.5%(1 110/7 655). RCS model analysis results showed that there was a linear dose-response relationship between non-peak sleep time and renal injury(overall correlation test χ~2=16.85, P<0.01, nonlinear test χ~2=3.70, P>0.05). Multivariate logistic regression analysis results showed that the workers of non-peak sleep time more than or equal to 4 515-day had higher risk of renal function injury than the workers of non-peak sleep time less than 4 515-day(P<0.05), with odds ratio and 95% confidence interval of 1.4(1.2-1.7), after adjustment for the potential influencing factors such as individual factors and occupational factors. CONCLUSION: Reduction of peak melatonin secretion sleep duration is correlated with renal function injury in steel workers.

OBJECTIVE: To explore the relationship between shift work and type 2 diabetes in oil workers. METHODS: A total of 2 666 oil workers in an oil group were selected as the study subjects using the typical sampling method. Questionnaire survey was conducted by a self-designed Questionnaire of Health Assessment for Oil Workers, and blood glucose level was measure. RESULTS: The prevalence of type 2 diabetes in the study subjects was 10.1%(268/2 666). The prevalence of type 2 diabetes in shift workers was higher than that in non-shift workers(13.1% vs 6.0%, P<0.01). After adjusting for the influence of confounding factors such as gender, body mass index, family history of diabetes, history of hypertension, history of hyperlipidemia, and physical exercise, multivariate logistic regression analysis results show that the longer the shift work length, the higher the risk of developing type 2 diabetes(P<0.01), workers with shift work(3 shifts in a day, 2 shifts operating) had a higher risk of type 2 diabetes than that in non-shift workers(P<0.05). CONCLUSION: The shift work length and shift workers with 3 shifts in a day, 2 shifts operating can increase the risk of type 2 diabetes in oil workers.

Objective:To discuss the relationships between the expressions of silence information regulator 6 (SIRT6) and survivin and clinicopathological features of gastric cancer, and to investigate their effects in gastric cancer.Methods:The tumor tissues of 110 gastric cancer patients admitted to the First Affiliated Hospital of Xi′an Jiaotong University from March 2013 to October 2014, as well as 40 adjacent tissues and 20 normal tissues, were selected to detect the expressions of SIRT6 and survivin by immunohistochemistry. The correlations between the expression levels of SIRT6 and survivin and the clinicopathological features and prognosis of gastric cancer patients were analyzed.Results:The positive rates of SIRT6 were 41.8% (46/110), 77.5% (31/40) and 85.0% (17/20) in gastric cancer, adjacent tissues and normal gastric tissues, respectively, and the difference among the three groups was statistically significant ( χ2=23.200, P<0.001). The positive rate of SIRT6 in gastric cancer tissue was lower than that in adjacent and normal tissues ( χ2=14.949, P<0.001; χ2=12.634, P<0.001). The expression of SIRT6 was correlated with tumor differentiation ( χ2=19.654, P<0.001). The positive rates of survivin were 58.2% (64/110), 15.0% (6/40) and 0 (0/20) in gastric cancer, adjacent tissues and normal gastric tissues, respectively, and the difference among the three groups was statistically significant ( χ2=38.449, P<0.001). The positive rate of survivin in gastric cancer tissue was higher than that in adjacent and normal tissues ( χ2=21.976, P<0.001; χ2=22.920, P<0.001). The expression of survivin was correlated with the depth of infiltration ( χ2=20.853, P<0.001). The expression of SIRT6 was correlated with survivin in gastric cancer tissues ( C=0.211, P=0.024). Survival analysis showed that 3-year survival rate was 53.1% in the SIRT6 negative patients, lower than 78.3% in the positive patients, and the difference was statistically significant ( χ2=4.004, P=0.045), while the 3-year survival rates of the survivin positive and negative patients were 53.1% and 78.3%, and the difference was not significant ( χ2=3.717, P=0.054). Cox multivariate regression analysis showed that lymph node metastasis ( RR=6.618, 95% CI: 2.152-20.358, P=0.001) and SIRT6 negative expression ( RR=0.228, 95% CI: 0.081-0.644, P=0.005) were the risk factors for poor prognosis of gastric cancer. Conclusion:SIRT6 is poorly expressed in gastric cancer tissues and is related to the prognosis of gastric cancer, while survivin is highly expressed in gastric cancer tissues. The expression of SIRT6 and survivin is negatively correlated, suggesting that the expression imbalance of SIRT6 and survivin may play an important role in the occurrence and development of gastric cancer.

OBJECTIVE: To explore the relationship between Effort-Reward Imbalance(ERI) and insomnia using structural equation modeling. METHODS: A total of 5 769 steel workers from an iron and steel company were selected as study objects by convenient sampling method. The Effort-Reward Imbalance Scale and Five-Item Athens Insomnia Scale were used to investigate their ERI and insomnia respectively. A structural equation modeling was constructed to analyze the relationship between ERI and insomnia. RESULTS: The scores of work effort and internal investment were positively correlated with the score of insomnia [the Spearman correlation coefficient(r_S) were 0.127 and 0.122 respectively, P<0.01]. Work reward scores were negatively correlated with the score of insomnia(r_S=-0.126, P<0.01). We successfully construct a structural equation model between ERI and insomnia in steel workers. According to this model, work effort, work reward and internal investment had direct effect on insomnia [the standardized path coefficient(β) were 0.065,-0.067 and 0.091 respectively, P<0.05]. Work effort and work reward have direct effect on insomnia(the β were 0.048 and-0.010 respectively, P<0.05). CONCLUSION: ERI increases the risk of insomnia. Both effort and internal investment have positive effect on insomnia, while reward has negative effect on insomnia.