Generalizability of COBRA: A Parsimonious Perioperative Venous Thromboembolism Risk Assessment Model.

INTRODUCTION: Standardized use of venous thromboembolism (VTE) risk assessment models (RAMs) in surgical patients has been limited, in part due to the cumbersome workflow addition required to use available models. The COBRA score-capturing cancer diagnosis, (old) age, body mass index, race, and American Society of Anesthesiologists Physical Status score-has been reported as a potentially automatable VTE RAM that circumvents the cumbersome workflow addition that most RAMs represent. We aimed to test the ability of the COBRA model to effectively risk-stratify patients across various populations. METHODS: Patients were included from the 2014-2019 American College of Surgeons National Surgical Quality Improvement Program (NSQIP) Participant Use Data File for two hospitals, representing colorectal, endocrine, breast, transplant, plastic, and general surgery services. COBRA score was calculated for each patient using preoperative characteristics. We calculated negative predictive value (NPV) for VTE outcomes and compared the COBRA score to NSQIP's expected VTE rate for all patients, between the two hospitals, and between subspecialty service lines. RESULTS: Of the 10,711 patients included, those with COBRA <4 (31%) had projected median VTE rate of 0.21% (interquartile range, 0.09-0.68%; mean, 0.54%). Patients with higher scores (69%) had median rate of 0.88% (0.26-2.07%; 1.46%); relative rate 2.7. The median projected VTE rates for patients identified as low risk were 0.21% and 0.16% and as high risk were 0.87% and 0.89% at hospitals one and 2, respectively. The median projected VTE rates for patients identified as low risk were 0.17%, 0.61%, and 0.08% and as high risk were 0.52%, 1.43%, and 0.18% among general, colorectal, and endocrine surgery patients, respectively. COBRA had NPV of 0.995 and sensitivity of 0.871 as compared to NPV 0.997 and sensitivity 0.857 of the NSQIP model. CONCLUSIONS: The COBRA score is concordant with the traditional gold standard NSQIP VTE RAM and demonstrates interhospital and service-specific generalizability, although performance was limited in especially low-risk patients. The model adequately risk-stratifies surgical patients preoperatively, potentially providing clinical decision support for perioperative workflows.

Utilization of the Caprini risk assessment model(RAM) to predict venous thromboembolism after primary hip and knee arthroplasty: an analysis of the Healthcare Cost and Utilization Project(HCUP).

PURPOSE: This study aims to investigate the potential role of Caprini risk assessment model (RAM) in predicting the risk of venous thromboembolism (VTE) in patients undergoing total hip or knee arthroplasty (THA/TKA). No national study has investigated the role of Caprini RAM after primary THA/TKA. METHODS: Data from The National Sample of Healthcare Cost and Utilization Project (HCUP) in 2019 were utilized for this study. The dataset consisted of 229,134 patients who underwent primary THA/TKA. Deep vein thrombosis (DVT) and pulmonary embolism (PE) were considered as VTE. The incidence of thrombosis was calculated based on different Caprini scores, and the risk of the Caprini indicator for VTE events was evaluated using a forest plot. RESULTS: The prevalence of VTE after primary THA/TKA in the U.S. population in 2019 was found to be 4.7 cases per 1000 patients. Age, body mass index (BMI), and Caprini score showed a positive association with the risk of VTE (P < 0.05). The receiver operating characteristic (ROC) curve analysis indicated that a Caprini score of 9.5 had a sensitivity of 47.2% and a specificity of 82.7%, with an area under the curve (AUC) of 0.693 (95% CI, 0.677-0.710). The highest Youden index was 0.299. Multivariate logistic regression analysis revealed that malignancy, varicose vein, positive blood test for thrombophilia, history of thrombosis, COPD, hip fracture, blood transfusion, and age were significant risk factors for VTE. Based on these findings, a new risk stratification system incorporating the Caprini score was proposed. CONCLUSIONS: Although the Caprini score does not seem to be a good predictive model for VTE after primary THA/TKA, new risk stratification for the Caprini score is proposed to increase its usefulness.

Development and validation of a nomogram for radiation-induced hepatic toxicity after intensity modulated radiotherapy for hepatocellular carcinoma: a retrospective study.

OBJECTIVE: This study aimed to construct a nomogram to predict radiation-induced hepatic toxicity in patients with hepatocellular carcinoma treated with intensity-modulated radiotherapy. METHODS: This study reviewed the clinical characteristics and dose-volume parameters of 196 patients with hepatocellular carcinoma. Radiation-induced hepatic toxicity was defined as progression of the Child-Pugh score caused by intensity-modulated radiotherapy. Factors relevant to radiation-induced hepatic toxicity were selected using receiver operating characteristic and univariate logistic analysis. A risk assessment model was developed, and its discrimination was validated. RESULTS: Eighty-eight (44.90%) and 28 (14.29%) patients had radiation-induced hepatic toxicity ≥ 1 (Child-Pugh ≥ 1) and radiation-induced hepatic toxicity ≥ 2 (Child-Pugh ≥ 2). Pre-treatment Child-Pugh, body mass index and dose-volume parameters were correlated with radiation-induced hepatic toxicity ≥ 1 using univariate logistic analysis. V15 had the best predictive effectiveness among the dose-volume parameters in both the training (area under the curve: 0.763, 95% confidence interval: 0.683-0.842, P < 0.001) and validation cohorts (area under the curve: 0.759, 95% confidence interval: 0.635-0.883, P < 0.001). The area under the curve values of the model that was constructed by pre-treatment Child-Pugh, body mass index and V15 for radiation-induced hepatic toxicity ≥1 were 0.799 (95% confidence interval: 0.719-0.878, P < 0.001) and 0.775 (95% confidence interval: 0.657-0.894, P < 0.001) in the training and validation cohorts, respectively. Patients with a body mass index ≤ 20.425, Barcelona clinic liver cancer = C, Hepatitis B Virus-positive, Eastern Cooperative Oncology Group = 1-2 and hepatic fibrosis require lower V15 dose limits. CONCLUSIONS: Risk assessment model constructed from Pre-treatment Child-Pugh, V15 and body mass index can guide individualized patient selection of toxicity minimization strategies.

Building a resilient pork supply chain to Salmonella spp.

Salmonella spp. control in pork supply chains has always been a challenging issue and insufficient control can lead to high social and economic consequences. Conventional risk management and risk management approaches and models are not sufficient to address potential food safety shocks caused by Salmonella spp., as they mainly focus on assessing measures to reduce Salmonella spp. risks instead of developing the resilience capability (e.g., flexibility to adapt to sudden changes in the risks). Our study is the first that incorporated the resilience concept to the quantitative modeling of Salmonella spp. spread in the pork supply chain. The objective of this study was to explore the resilience performance of the pork supply chain under different food safety shocks caused by Salmonella spp., and to investigate the effectiveness of interventions on reducing the impact of these shocks on the resilience performance of the chain. Scenario analysis indicated that the effectiveness of the investigated resilience strategies or interventions depended on the risk profile (i.e., default, minimum, maximum level of Salmonella spp. contamination) of the pork supply chain. For pork supply chains with minimum and default risk profiles, more attention should be paid to increasing resilience of pigs towards Salmonella spp. infection. For supply chains with maximum risk profile, the focus should be on improving the performance of the slaughterhouse, such as careful evisceration, logistic slaughtering. To conclude, enhancing resilience performance of the pork supply chain can contribute to a safe pork supply.

Validation of venous thromboembolism predictive model in hematologic malignancies.

Although several scores stratify venous thromboembolism (VTE) risk in solid tumors, hematologic malignancies (HM) are underrepresented. To develop an internal and external validation of a logistic regression model to predict VTE risk in hospitalized HM patients. Validation of the existing VTE predictive model was performed through a prospective case-control study in 496 hospitalized HM patients between December 2010 and 2020 at the Arnaldo Milián University Hospital, Cuba. The predictive model designed with data from 285 patients includes 5 predictive factors: hypercholesterolemia, tumoral activity, use of thrombogenic drugs, diabetes mellitus, and immobilization. The model was internally validated using bootstrap analysis. External validation was realized in a prospective cohort of 211 HM patients. The predictive model had a 76.4% negative predictive value (NPV) and an 81.7% positive predictive value (PPV) in the bootstrapping validation. The area under curve (AUC) in the bootstrapping set was 0.838. Accuracy was 80.1% and 82.9% in the internal and external validation, respectively. In the external validation, the model produced 89.7% of NPV, 67.7% of PPV, 74.6% of sensitivity, and 86.2% of specificity. The AUC in the external validation was 0.900. VTE predictive model is a reproducible and simple tool with good accuracy and discrimination.

Risk factors analysis and risk assessment model construction of systemic lupus erythematosus patients with infection.

OBJECTIVE: To analyze the characteristics of peripheral blood lymphocyte subsets in systemic lupus erythematosus (SLE) patients with infection and non-infection group. Explore the risk factors of infection in SLE patients and establish a risk matrix model to predict the occurrence of co-infection. METHODS: total of 333 SLE patients without infection, 163 patients suffering from infection, and 132 healthy controls (HCs) were recruited. General clinical data and disease activity indicators were collected. The levels of total T, B, CD4+T, CD8+T, NK, Th1, Th2, Th17, and Treg cells in peripheral blood of HCs, SLE patients (including infected and non-infected group) were analyzed by flow cytometry. The risk assessment model was constructed, and the receiver operating characteristic curve was drawn. 39 SLE patients with infection and 20 patients without infection were randomly selected to evaluate the predictive power of the regression model. RESULTS: The levels of T, B, CD4+T, CD8+T, and NK cells in the infected patients were significantly decreased when compared with that of both non-infected patients and HCs (p < .05). The non-infected patients had a higher level of Th17 than that of HCs (p < . 05), but the absolute numbers of Th17 in infected patients was the lowest among the three groups (p < .001). The number of Treg cells in SLE patients was significantly lower than that of HCs (p < .01), and the infected patients had the fewest Treg cells among all these groups (p < . 05). A risk assessment model for SLE with infection was established, p = 1/(1-e-y), Y = 1.763-0.004 × Absolute number of CD4 + T cells-0.005 × Absolute number of NK cells -0.005 × Platelet count(×1012/L) + 1.033 × Absolute number of lymphocytes (×109/L) + 0.023 × C-reactive protein (mg/dL), whose predictive sensitivity is 77.5%, and specificity is 78.3%. CONCLUSION: The new risk assessment model exhibits good predictive ability to assess co-infection risk in SLE patients. T cells, NK cells, and CD4 + T cells along with other parameters help in differentiating Lupus with infection from Lupus alone.

Use of the RCOG risk assessment model and biomarkers to evaluate the risk of postpartum venous thromboembolism.

BACKGROUND: Venous thromboembolism (VTE) is a leading cause of morbidity and mortality during pregnancy and the puerperium. The vast majority of VTE occurs after childbirth. China has not yet established standard risk assessment model for postpartum venous thromboembolism (VTE), the Royal College of Obstetricians and Gynecologists (RCOG) risk assessment model (RAM) is commonly used in clinic at present. Herein, we aimed to evaluate the validity of the RCOG RAM in the Chinese population and try to formulate a local risk assessment model by combining with other biomarkers for VTE prophylaxis. METHODS: The retrospective study was conducted from January 2019 to December 2021at Shanghai First Maternity and Infant Hospital which has approximately 30,000 births annually, and the incidence of VTE, differences between RCOG-recommended risk factors, and other biological indicators from medical records were evaluated. RESULTS: The study included VTE (n = 146) and non-VTE(n = 413) women who examined by imaging for suspicion of postpartum VTE. There was no statistical difference in the incidence rate of postpartum VTE between the low-score group (23.8%) and the high-score group (28%) after stratification by RCOG RAM. However, we found that cesarean section (in the low-score group), white blood cell (WBC) ≥ 8.64*10^9/L (in the high-score group), low-density lipoprotein(LDL) ≥ 2.70 mmol/L, and D-dimer ≥ 3.04 mg/L (in both groups) were highly associated with postpartum VTE. Subsequently, the validity of the RCOG RAM combined with biomarkers as a model for the risk assessment of VTE was estimated and the results showed that this model has good accuracy, sensitivity, and specificity. CONCLUSIONS: Our study indicated that the RCOG RAM was not the best strategy for predicting postpartum VTE. Combined with some biomarkers (including the value of LDL and D-Dimer, and WBC count), the RCOG RAM is more efficient when identifying high-risk groups of postpartum VTE in the Chinese population. TRIAL REGISTRATION: This purely observational study does not require registration based on ICMJE guidelines.

Development and validation of a new risk assessment model for immunomodulatory drug-associated venous thrombosis among Chinese patients with multiple myeloma.

BACKGROUND: Individuals with multiple myeloma (MM) receiving immunomodulatory drugs (IMiDs) are at risk of developing venous thromboembolism (VTE), a serious complication. There is no established clinical model for predicting VTE in the Chinese population. We develop a new risk assessment model (RAM) for IMiD-associated VTE in Chinese MM patients. METHODS: We retrospectively selected 1334 consecutive MM patients receiving IMiDs from 16 medical centers in China and classified them randomly into the derivation and validation cohorts. A multivariate Cox regression model was used for analysis. RESULTS: The overall incidence of IMiD-related VTE in Chinese MM patients was 6.1%. Independent predictive factors of VTE (diabetes, ECOG performance status, erythropoietin-stimulating agent use, dexamethasone use, and VTE history or family history of thrombosis) were identified and merged to develop the RAM. The model identified approximately 30% of the patients in each cohort at high risk for VTE. The hazard ratios (HRs) were 6.08 (P < 0.001) and 6.23 (P < 0.001) for the high-risk subcohort and the low-risk subcohort, respectively, within both the derivation and validation cohorts. The RAM achieved satisfactory discrimination with a C statistic of 0.64. The stratification approach of the IMWG guidelines yielded respective HRs of 1.77 (P = 0.053) and 1.81 (P = 0.063). The stratification approach of the SAVED score resulted in HRs of 3.23 (P = 0.248) and 1.65 (P = 0.622), respectively. The IMWG guideline and the SAVED score-based method yielded C statistics of 0.58 and 0.51, respectively. CONCLUSIONS: The new RAM outperformed the IMWG guidelines and the SAVED score and could potentially guide the VTE prophylaxis strategy for Chinese MM patients.

The association of FGF-21 with the risk of newly diagnosed type-2 diabetes mellitus: a cross-sectional study in Southern China.

BACKGROUND: This study investigated the relationship between fibroblast growth factor 21 (FGF-21) and newly diagnosed type-2 diabetes mellitus (T2DM). METHODS: In this cross-sectional study, FGF-21 and T2DM risk were analyzed using restricted cubic splines with univariate or multivariate logistic regression analysis. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated via logistic regression analysis. Cluster and subgroup analyses were conducted to evaluate the associations between FGF-21 and diabetes in different subpopulations. Nomograms and ROC curves were used to explore the clinical utility of FGF-21 in the diabetes assessment model. RESULTS: High levels of FGF-21 were significantly associated with a high risk of T2DM after adjusting for confounding factors in both the total population and subpopulations (P for trend < 0.001). In the total population, the ORs of diabetes with increasing FGF-21 quartiles were 1.00 (reference), 1.24 (95% CI 0.56-2.80; quartile 2), 2.47 (95% CI 1.18-5.33; quartile 3), and 3.24 (95% CI 1.53-7.14; quartile 4) in Model 4 (P < 0.001), and the trend was consistent in different subpopulations. In addition, compared with the model constructed with conventional noninvasive indicators, the AUC of the model constructed by adding FGF-21 was increased from 0.668 (95% CI: 0.602-0.733) to 0.715 (95% CI: 0.654-0.777), indicating that FGF-21 could significantly improve the risk-assessment efficiency of type-2 diabetes. CONCLUSION: This study demonstrated that a high level of circulating FGF-21 was positively correlated with diabetes, and levels of FGF-21 could be an important biomarker for the assessment of diabetes risk.

Caprini risk assessment model combined with D-dimer to predict the occurrence of deep vein thrombosis and guide intervention after laparoscopic radical resection of colorectal cancer.

BACKGROUND: To explore the diagnostic value of Caprini risk assessment model (2005) combined with D-dimer for deep vein thrombosis, and to exclude patients with low incidence of thrombosis who might not need anticoagulation after surgery. METHODS: A total of 171 colorectal cancer patients who underwent surgery from January 2022 to August 2022 were enrolled in this study. Caprini risk assessment model was used to evaluate patients the day before surgery, and full-length venous ultrasonography of lower extremity was used to assess whether patients had thrombosis one day before surgery and the sixth day after surgery. The value of D-dimer was measured by enzyme-linked immunosorbent assays on the first day after surgery, and clinical data of patients were collected during hospitalization. RESULTS: A total of 171 patients were divided into IPC Group and IPC + LMWH Group according to whether low molecular weight heparin (LMWH) were used to prevent thrombus after surgery. Eventually, 17.6% (15/85) patients in IPC Group and 7% (6/86) patients in IPC + LMWH Group developed DVT. Through separate analysis of IPC Group, it is found that Caprini score and D-dimer were independent risk factors for DVT (Caprini OR 3.39 [95% CI 1.38-8.32]; P = 0.008, D-Dimer OR 6.142 [95% CI 1.209-31.187]; P = 0.029). The area under ROC curve of Caprini risk assessment model is 0.792 (95% CI 0.69-0.945, P < 0.01), the cut-off value is 9.5, and the area under ROC curve of D-dimer is 0.738 (95%CI 0.555-0.921, P < 0.01), the cut-off value is 0.835 µg/mL, and the area under the ROC curve was 0.865 (95% CI 0.754-0.976, P < 0.01) when both of them were combined. Based on decision curve analysis, it is found that Caprini risk assessment model combined with D-dimer can benefit patients more. All patients are divided into four groups. When Caprini score < 10 and D-dimer < 0.835 µg/mL, only 1.23% (1/81) of patients have thrombosis and LMWH has little significance. When Caprini score > 10 and D-dimer > 0.835 µg/mL, the incidence of DVT is 38.7% (12/31) and LMWH should be considered. CONCLUSIONS: The Caprini risk assessment model and D-dimer can provide more accurate risk stratification for patients after laparoscopic radical resection of colorectal cancer.

A narrative review of the application of machine learning in venous thromboembolism.

OBJECTIVE: To summarize the current research progress of machine learning and venous thromboembolism. METHODS: The literature on risk factors, diagnosis, prevention and prognosis of machine learning and venous thromboembolism in recent years was reviewed. RESULTS: Machine learning is the future of biomedical research, personalized medicine, and computer-aided diagnosis, and will significantly promote the development of biomedical research and healthcare. However, many medical professionals are not familiar with it. In this review, we will introduce several commonly used machine learning algorithms in medicine, discuss the application of machine learning in venous thromboembolism, and reveal the challenges and opportunities of machine learning in medicine. CONCLUSION: The incidence of venous thromboembolism is high, the diagnostic measures are diverse, and it is necessary to classify and treat machine learning, and machine learning as a research tool, it is more necessary to strengthen the special research of venous thromboembolism and machine learning.

Particle filter-based parameter estimation algorithm for prognostic risk assessment of progression in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is a malignant tumor that threatens human life and health. The development of a new NSCLC risk assessment model based on electronic medical records has great potential for reducing the risk of cancer recurrence. In this process, machine learning is a powerful method for automatically extracting risk factors and indicating impact weights for NSCLC deaths. However, when the number of samples reaches a certain value, it is difficult for machine learning to improve the prediction accuracy, and it is also challenging to use the characteristic data of subsequent patients effectively. Therefore, this study aimed to build a postoperative survival risk assessment model for patients with NSCLC that updates the model parameters and improves model accuracy based on new patient data. The model perspective was a combination of particle filtering and parameter estimation. To demonstrate the feasibility and further evaluate the performance of our approach, we performed an empirical analysis experiment. The study showed that our method achieved an overall accuracy of 92% and a recall of 71% for deceased patients. Compared with traditional machine learning models, the accuracy of the model estimated by particle filter parameters has been improved by 2%, and the recall rate for dead patients has been improved by 11%. Additionally, this study outcome shows that this method can better utilize subsequent patients' characteristic data, be more relevant to different patients, and help achieve precision medicine.

Identification of immune-associated prognostic biomarkers in lung adenocarcinoma on the basis of gene co-expression network.

Objective: We aimed to explore immune-related prognosis genes of lung adenocarcinoma (LUAD).Materials and methods: TCGA-LUAD and GSE31210 data sets were accessed from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) respectively. By using "WGCNA" R package, we established a gene co-expression network and clustered genes into various modules. The correlation between immune scores and module eigengenes by using Pearson analysis. Screened hub genes and constructed prognostic model by using LASSO and Cox regression analysis. Evaluated model by survival analysis and receiver operating characteristic (ROC) curves. Hub genes expression in clinical tissues of LUAD patients by qRT-PCR analysis. ssGSEA and TIMER (a website tool for examination of different immune cells in different cancers) analyzed immune correlation of hub genes. Gene set variation analysis (GSVA) uncovered difference of signal pathway between high- and low-risk score group.Results: We found that brown module significantly correlated with the immune scores of immune cells. Therefore, we constructed a 7-gene prognostic model based on brown module genes, and indicated that this model possessed good predictive performance. Patients in training and validation sets were stratified into the high- and low-risk group using this model. Also, hub genes CDCP1, PLSCR1 and CD79A were highly expressed in clinical tissues of LUAD patients, while ID1, CLEC7A, KIAA1324 and CMTM7 were lowly expressed. Both ssGSEA and TIMER revealed a significant negative correlation between risk score and B cell infiltration. Additionally, some signal pathways were suppressed in the high-risk group.Conclusion: We identified 7 immune-associated prognostic markers, which may play vital roles in LUAD and could be used as hopeful targets for immunotherapy of LUAD.

Stratifying the risks of venous thromboembolism in the lateral skull base surgery using Caprini risk assessment model.

PURPOSE: To analyze and stratify the possible risk factors of venous thromboembolism (VTE) in lateral skull base surgery (LSBS) using the Caprini risk assessment model. METHODS: In a single center, a retrospective study was conducted with patients who underwent LSBS from June 2016 to August 2021. The clinical characteristics and blood chemistry tests were collected. The incidence of VTE within 30 days of surgery was recorded. The Caprini risk score was calculated to assess the postoperative VTE risk. RESULTS: Among the 123 patients in this study, the VTE incidence within 30 postoperative days was 8.9%. The total Caprini risk score in VTE patients (5.6 ± 1.9 points) was significantly higher than that of non-VTE patients (4.6 ± 1.4 points; p = 0.028). The binary logistic regression showed the total Caprini score as the only independent indicator of postoperative VTE. The receiver operating characteristic curve analysis showed that the Caprini score at 6.5 points had low sensitivity (36.4%) but high specificity (91.1%), with the largest area under the curve being 0.659. The VTE rate was significantly higher in patients with a total Caprini score ≥ 7 points (28.6%) compared to those with a total Caprini score ≤ 6 points (7.3%; p = 0.022). CONCLUSION: LSBS patients have a high risk of developing postoperative VTE. Patients with a Caprini score ≥ 7 points had a significantly higher risk of developing VTE after LSBS. The Caprini risk system was useful in assessing the VTE risk in LSBS. However, more data, calibration, and validation are necessary to establish an exclusive Caprini risk system for LSBS.

Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums.

Machine learning methods can establish complex nonlinear relationships between input and response variables for stadium fire risk assessment. However, the output of machine learning models is considered very difficult due to their complex "black box" structure, which hinders their application in stadium fire risk assessment. The SHapley Additive exPlanations (SHAP) method makes a local approximation to the predictions of any regression or classification model so as to be faithful and interpretable, and assigns significant values (SHAP value) to each input variable for a given prediction. In this study, we designed an indicator attribute threshold interval to classify and quantify different fire risk category data, and then used a random forest model combined with SHAP strategy in order to establish a stadium fire risk assessment model. The main objective is to analyze the impact analysis of each risk characteristic on four different risk assessment models, so as to find the complex nonlinear relationship between risk characteristics and stadium fire risk. This helps managers to be able to make appropriate fire safety management and smart decisions before an incident occurs and in a targeted manner to reduce the incidence of fires. The experimental results show that the established interpretable random forest model provides 83% accuracy, 86% precision, and 85% recall for the stadium fire risk test dataset. The study also shows that the low level of data makes it difficult to identify the range of decision boundaries for Critical mode and Hazardous mode.

Risk assessment scales to predict risk of lower extremity deep vein thrombosis among multiple trauma patients: a prospective cohort study.

BACKGROUND: Deep vein thrombosis (DVT) is a common complication in orthopedic patients. Previous studies have focused on major orthopedic surgery.There are few studies with multiple trauma. We aimed to describe the prevalence of DVT and compare the predictive power of the different risk assessment scales in patients with multiple trauma. METHODS: This prospective cohort study involved multiple trauma patients admitted to our hospital between October 2021 and December 2022. Data were prospectively collected for thrombotic risk assessments using the Risk Assessment Profile for thromboembolism(RAPT), the DVT risk assessment score (DRAS), and the Trauma Embolic Scoring System (TESS), respectively. The receiver operation characteristic (ROC) curve and the area under the curve (AUC) were evaluated to compare the predictive power. The whole leg duplex ultrasound of both lower extremities Doppler ultrasound was used to determine DVT incidence. RESULTS: A total of 210 patients were included, and the incidence of DVT was 26.19%. Distal DVT accounted for 87.27%; postoperative DVT, 72.73%; and bilateral lower extremity thrombosis, 30.91%. There were significant differences in age, education degree, pelvic fracture, surgery, ISS, D-dimer level, length of hospital stay and ICU stay between the thrombosis group and the non-thrombosis group. The AUCs for RAPT, DRAS, and TESS were 0.737, 0.710, and 0.683, respectively. There were no significant differences between the three ROC curves. CONCLUSIONS: The incidence of DVT was relatively high during hospitalization. We prospectively validated the tests to predict risk of DVT among patients with multiple trauma to help trauma surgeons in the clinical administration of DVT prophylaxis.

Herbaceous ornamental plants with conspicuous aesthetic traits contribute to plant invasion risk in subtropical urban parks.

Global ornamental horticulture is a major pathway for plant invasions, while urban parks are key areas for introducing non-native ornamental plants. To react appropriately to the challenges (e.g., biological invasion issues) and opportunities (e.g., urban ecosystem services) of herbaceous ornamentals in urban parks, we conducted a comprehensive invasive risk assessment in 363 urban parks in Chongqing, a subtropical city in China. The results found more than 1/3 of the 119 non-native species recorded in urban parks had a high invasion risk, and more than five species had potential invasion risk in 96.29% of the study area, indicating herbaceous ornamentals in urban parks are potentially a pool of invasive species that deserves attention. Moreover, humans have chosen herbaceous ornamentals with more aesthetic characteristics in urban parks, where exotic species were more prominent than native species in floral traits, such as more conspicuous flowers and longer flowering periods. The findings can inform urban plant management, provide an integrated approach to assessing herbaceous ornamentals' invasion risk, and offer insights into understanding the filtering effects of human aesthetic preferences.

[A model for assessing and correcting the risk of developing pressure ulcers in the system of long-term care for geriatric patients with limited mobility.]

The article is devoted to the results of the third stage of the scientific and practical project on the introduction of advanced social practices based on innovative gerontotechnologies into the system of long-term care for geriatric patients. It has been established that senile xerosis or skin «frailty¼ occurs in 75% of the population over 65 years old, when increased dryness of the skin leads to peeling, the formation of microcracks, the appearance of senile itching, sleep disturbances, the development of pressure ulcers and, as a result, a decrease in motor activity. During the project, a tactic was developed for the care of «frail¼ skin in patients with senile xerosis and the risk of pressure ulcers. The article presents information about the current problems of diagnosis, correction and prevention of pressure ulcers in patients with limited mobility of elderly and senile age. The results of a comparative analysis of the severity of pressure ulcers and their impact on the quality of life in the system of long-term care for geriatric patients with limited mobility before and after the use of Cicalfate+ Eau Thermale Avène («Pierre Fabre¼, France) emollient were presented. A model was built and practical recommendations were given on the use of the most informative and adequate scales for predicting and assessing the risk of developing pressure ulcers in patients of older age groups.

Psychological, Structural, Social and Economic Determinants of Suicide Attempt: Risk Assessment and Decision Making Strategies.

The aim of this research was to conduct a risk assessment and management of psychological, structural, social and economic determinants (PSSED) in a suicide attempt. The sample consisted of 353 individuals who had a recorded history of suicidal attempt; and 20 professional individuals by purposive sampling method within a descriptive cross-sectional design. Worksheets for RAM and AHP were used for data collection in this study. The rate of suicide attempt was 7.21 per 100,000 population in this study. Analysis showed that depression and mental disorders; personality disorders; family problems; socio-cultural and economic problems; lack of awareness; and low level of education have a high level of risk for suicide attempts. Psychiatric and psychological services; awareness and knowledge of life skills; medical services to dysfunctional families; development of community-based planning for PSSED of suicide; and employment and entrepreneurship services may lower suicide attempt risk.

Prognostic risk assessment model and drug sensitivity analysis of colon adenocarcinoma (COAD) based on immune-related lncRNA pairs.

PURPOSE: The aim of this study was to identify and screen long non-coding RNA (lncRNA) associated with immune genes in colon cancer, construct immune-related lncRNA pairs, establish a prognostic risk assessment model for colon adenocarcinoma (COAD), and explore prognostic factors and drug sensitivity. METHOD: Our method was based on data from The Cancer Genome Atlas (TCGA). To begin, we obtained all pertinent demographic and clinical information on 385 patients with COAD. All lncRNAs significantly related to immune genes and with differential expression were identified to construct immune lncRNA pairs. Subsequently, least absolute shrinkage and selection operator and Cox models were used to screen out prognostic-related immune lncRNAs for the establishment of a prognostic risk scoring formula. Finally, We analysed the functional differences between subgroups and screened the drugs, and establish an individual prediction nomogram model. RESULTS: Our final analysis confirmed eight lncRNA pairs to construct prognostic risk assessment model. Results showed that the high-risk and low-risk groups had significant differences (training (n = 249): p < 0.001, validation (n = 114): p = 0.022). The prognostic model was certified as an independent prognosis model. Compared with the common clinicopathological indicators, the prognostic model had better predictive efficiency (area under the curve (AUC) = 0.805). Finally, We have analysed highly differentiated cellular pathways such as mucosal immune response, identified 9 differential immune cells, 10 sensitive drugs, and establish an individual prediction nomogram model (C-index = 0.820). CONCLUSION: Our study verified that the eight lncRNA pairs mentioned can be used as biomarkers to predict the prognosis of COAD patients. Identified cells, drugs may have an positive effect on colon cancer prognosis.