Objective:To investigate the risk factors of deep vein thrombosis in patients with malignant tumors and to construct the predication model.Methods:A retrospective case-control study was conducted. The patients with malignant tumors in the Medical Information Mart for Intensive Care (MIMIC)-Ⅳ database from 2008-2019 were enrolled. The basic information, laboratory examination indexes, complications, disease score and survival time of patients were collected. Patients were classified into thrombotic group and non-thrombotic group based on whether deep vein thrombosis occurred. The differences in the baseline data, laboratory examination indexes and survival status of the 2 groups were compared. The variables with statistically significant differences ( P < 0.05) were included in univariate logistic regression model for the development of deep vein thrombosis, and multivariate logistic regression analysis was further performed by backward method to screen out independent factors influencing the development of deep vein thrombosis, and a prediction model for deep vein thrombosis in patients with malignant tumors was built based on the factors. Taking actual development of DVT as the gold standard, receiver operating characteristic (ROC) curve analysis was used to analyze the constructed model in predicting the efficacy of deep vein thrombosis in patients with malignant tumors. Results:A total of 6 699 patients with malignant tumors were finally included, including 3 803 males (56.8%) and 2 896 females (43.2%). The age of admission was (67±13) years old, ranging from 19 to 101 years old; there were 213 cases (3.2%) in the thrombotic group and 6 486 cases (96.8%) in the non-thrombotic group. There were significant differences in body mass index (BMI), proportion of patients with tumor metastasis, simplified acute physiological score Ⅱ (SAPSⅡ), stay length in intensive care unit, 28-day mortality and 1-year mortality between the 2 groups (all P < 0.01). Neutrophil count, platelet count, the ratio of neutrophil to lymphocyte, the ratio of platelet to lymphocyte (PLR), and systemic immune inflammatory index in the thrombotic group were higher than those in non-thrombotic group; while lymphocyte count, prothrombin time (PT), and partial thromboplastin time in the thrombotic group were lower than those in non-thrombotic group, and the differences were statistically significant (all P < 0.01). Multivariate logistic regression analysis showed that increased BMI ( OR = 1.106, 95% CI: 1.070-1.154, P < 0.001) and the decreased PT ( OR = 0.861, 95% CI: 0.781-0.951, P = 0.003), the increased PLR ( OR = 1.001, 95% CI: 1.000-1.002, P < 0.001), the increased SAPSⅡ( OR = 1.043, 95% CI: 1.025-1.062, P < 0.001) were independent risk factors for deep vein thrombosis in patients with malignant tumors. The constructed prediction model: Logit (P) =-5.566+0.101×BMI-0.149×PT+0.001×PLR+0.042×SAPSⅡ. ROC curve analysis showed that the area under the curve of the constructed model for predicting deep vein thrombosis in patients with malignant tumors was 0.801 (95% CI: 0.743-0.859), which was better than that of a single indicator (the range of area under the curve of the four indexes: 0.654-0.728). The sensitivity and specificity of the constructed model at the optimal cut-off value (-2.86) was 69.8% and 81.7%, respectively. Conclusions:The BMI, PT, PLR and SAPSⅡ are independent influencing factors of deep vein thrombosis in patients with malignant tumors. The model constructed by the above four variables shows a good predictive value in the development of deep vein thrombosis of patients with malignant tumors.

Objective:To investigate the risk factors of deep vein thrombosis in patients with malignant tumors and to construct the predication model.Methods:A retrospective case-control study was conducted. The patients with malignant tumors in the Medical Information Mart for Intensive Care (MIMIC)-Ⅳ database from 2008-2019 were enrolled. The basic information, laboratory examination indexes, complications, disease score and survival time of patients were collected. Patients were classified into thrombotic group and non-thrombotic group based on whether deep vein thrombosis occurred. The differences in the baseline data, laboratory examination indexes and survival status of the 2 groups were compared. The variables with statistically significant differences ( P < 0.05) were included in univariate logistic regression model for the development of deep vein thrombosis, and multivariate logistic regression analysis was further performed by backward method to screen out independent factors influencing the development of deep vein thrombosis, and a prediction model for deep vein thrombosis in patients with malignant tumors was built based on the factors. Taking actual development of DVT as the gold standard, receiver operating characteristic (ROC) curve analysis was used to analyze the constructed model in predicting the efficacy of deep vein thrombosis in patients with malignant tumors. Results:A total of 6 699 patients with malignant tumors were finally included, including 3 803 males (56.8%) and 2 896 females (43.2%). The age of admission was (67±13) years old, ranging from 19 to 101 years old; there were 213 cases (3.2%) in the thrombotic group and 6 486 cases (96.8%) in the non-thrombotic group. There were significant differences in body mass index (BMI), proportion of patients with tumor metastasis, simplified acute physiological score Ⅱ (SAPSⅡ), stay length in intensive care unit, 28-day mortality and 1-year mortality between the 2 groups (all P < 0.01). Neutrophil count, platelet count, the ratio of neutrophil to lymphocyte, the ratio of platelet to lymphocyte (PLR), and systemic immune inflammatory index in the thrombotic group were higher than those in non-thrombotic group; while lymphocyte count, prothrombin time (PT), and partial thromboplastin time in the thrombotic group were lower than those in non-thrombotic group, and the differences were statistically significant (all P < 0.01). Multivariate logistic regression analysis showed that increased BMI ( OR = 1.106, 95% CI: 1.070-1.154, P < 0.001) and the decreased PT ( OR = 0.861, 95% CI: 0.781-0.951, P = 0.003), the increased PLR ( OR = 1.001, 95% CI: 1.000-1.002, P < 0.001), the increased SAPSⅡ( OR = 1.043, 95% CI: 1.025-1.062, P < 0.001) were independent risk factors for deep vein thrombosis in patients with malignant tumors. The constructed prediction model: Logit (P) =-5.566+0.101×BMI-0.149×PT+0.001×PLR+0.042×SAPSⅡ. ROC curve analysis showed that the area under the curve of the constructed model for predicting deep vein thrombosis in patients with malignant tumors was 0.801 (95% CI: 0.743-0.859), which was better than that of a single indicator (the range of area under the curve of the four indexes: 0.654-0.728). The sensitivity and specificity of the constructed model at the optimal cut-off value (-2.86) was 69.8% and 81.7%, respectively. Conclusions:The BMI, PT, PLR and SAPSⅡ are independent influencing factors of deep vein thrombosis in patients with malignant tumors. The model constructed by the above four variables shows a good predictive value in the development of deep vein thrombosis of patients with malignant tumors.

OBJECTIVETo evaluate possible inactivating effect of recombined decoction in on mumps virus.

METHODSBy adopting tissue cell culturing technology, a group of viruses including the mumps virus, herpes simplex virus (type I, II), rubella virus, cytomegalovirus (CMV), herpes zoster virus, influenza virus, parainfluenza virus, adeno viruses, respiratory syneytial virus (RSV) were cultured. The cells infected with the viruses were treated with the decoction.

RESULTSThe decoction showed remarkable inhibitory and killing effects on the mumps virus while had no obvious inhibitory and killing effects on host's cells, herpes simplex virus (type I, II), rubella virus, cytomegalovirus (CMV), herpes zoster virus, influenza virus, parainfluenza virus, adeno viruses, respiratory syneytial virus (RSV).

CONCLUSIONSThe decoction had obvious inhibitory and killing effects on mumps virus during single layer cells culture.

Cells, Cultured ; Cytomegalovirus ; drug effects ; Drugs, Chinese Herbal ; pharmacology ; Mumps virus ; drug effects ; Respiratory Syncytial Viruses ; drug effects ; Respirovirus ; drug effects ; Rubella virus ; drug effects ; Simplexvirus ; drug effects