Predictive value of Caprini risk assessment model, D-dimer, and fibrinogen levels on lower extremity deep vein thrombosis in patients with spontaneous intracerebral hemorrhage.

Introduction: Research indicates that individuals experiencing hemorrhagic stroke face a greater likelihood of developing lower extremity deep vein thrombosis (DVT) compared to those with ischemic stroke. This study aimed to assess the predictive capacity of the Caprini risk assessment model (RAM), D-dimer (D-D) levels, and fibrinogen (FIB) levels for lower extremity DVT in patients with spontaneous intracerebral hemorrhage (sICH). Methodology: This study involved a retrospective analysis of medical records from all sICH patients admitted to Shanghai General Hospital between June 2020 and June 2023. Within 48 h of admission, patients underwent routine screening via color Doppler ultrasonography (CDUS). Patients were categorized into the DVT and control groups based on the occurrence of lower extremity DVT during hospitalization. Differences in Caprini RAM, D-dimer, and FIB levels between the two groups were compared. The sensitivity and specificity of combined Caprini RAM, peripheral blood D-dimer, and FIB levels in predicting lower extremity DVT in sICH patients were analyzed. Receiver operating characteristic (ROC) curves assessed the overall predictive accuracy of Caprini RAM, D-D, and FIB levels. Results: The study involving 842 sICH patients revealed 225 patients with DVT and 617 patients without DVT. Caprini RAM, D-D, and FIB levels were significantly higher in the DVT group compared to the control group (P < 0.05). Sensitivity values for Caprini RAM, D-D, and FIB levels in predicting lower extremity DVT in sICH patients were 0.920, 0.893, and 0.680, respectively, while specificities were 0.840, 0.680, and 0.747, respectively. The ROC curve analysis demonstrated an area under the curve (AUC) of 0.947 for combined DVT prediction, with 97.33% sensitivity and 92.00% specificity, indicating superior predictive value compared to individual applications of Caprini RAM, D-D, and FIB levels. Conclusion: The combined utilization of Caprini RAM, D-D, and FIB levels holds significant clinical relevance in predicting lower extremity DVT in sICH patients.

Transcriptomic profiling and risk assessment in bladder cancer: Insights from copper death-related genes.

BACKGROUND: The study aimed to investigate the role of copper death-related genes (CRGs) in bladder cancer (BC) for improved prognosis assessment. METHODS: Multi-omics techniques were utilized to analyze CRG expression in BC tissues from TCGA and GEO databases. Consensus clustering categorized patients into molecular subtypes based on clinical characteristics and immune cell infiltration. RESULTS: An innovative risk assessment model identified eight critical genes associated with BC risk. In vitro and in vivo experiments validated LIPT1's significant impact on copper-induced cell death, proliferation, migration, and invasion in BC. CONCLUSION: This multi-omics analysis elucidates the pivotal role of CRGs in BC progression, suggesting enhanced risk assessment through molecular subtype categorization and identification of key genes like LIPT1. Insights into these mechanisms offer the potential for improved diagnosis and treatment strategies for BC patients.

External Validation of the Risk Assessment Model of Venous Thromboembolism in Multicenter Internal Medicine Inpatients.

To external validate the risk assessment model (RAM) of venous thromboembolism (VTE) in multicenter internal medicine inpatients. We prospectively collected 595 internal medical patients (310 with VTE patients, 285 non-VTE patients) were from Beijing Shijitan Hospital, Beijing Chaoyang Hospital, and the respiratory department of Beijing Tsinghua Changgeng Hospital from January 2022 to December 2022 for multicenter external validation. The prediction ability of Caprini RAM, Padua RAM, The International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) RAM, and Shijitan (SJT) RAM were compared. This study included a total of 595 internal medicine inpatients, including 242 (40.67%) in the respiratory department, 17 (2.86%) in the respiratory intensive care unit, 49 (8.24%) in the neurology department, 34 (5.71%) in the intensive care unit, 26 (4.37%) in the geriatric department, 22 (3.70%) in the emergency department, 71 (11.93%) in the nephrology department, 63 (10.59%) in the cardiology department, 24 (4.03%) in the hematology department, 6 (1.01%) in the traditional Chinese medicine department, 9 (1.51%) cases in the rheumatology department, 7 (1.18%) in the endocrinology department, 14 (2.35%) in the oncology department, and 11 (1.85%) in the gastroenterology department. Multivariate logistic regression analysis showed that among internal medicine inpatients, age > 60 years old, heart failure, nephrotic syndrome, tumors, history of VTE, and elevated D-dimer were significantly correlated with the occurrence of VTE (P < .05). The incidence of VTE increases with the increase of D-dimer. It was found that the effectiveness of SJT RAM (AUC = 0.80 ± 0.03) was better than Caprini RAM (AUC = 0.74 ± 0.03), Padua RAM (AUC = 0.72 ± 0.03) and IMPROVE RAM (AUC = 0.52 ± 0.03) (P < .05). The sensitivity and Yoden index of SJT RAM were higher than those of Caprini RAM, Pauda RAM, and IMPROVE RAM (P < .05), but specificity was not significantly different between the 4 models (P > .05). The SJT RAM derived from general hospitalized Chinese patients has effective and better predictive ability for internal medicine inpatients at risk of VTE.

Assessment of pesticide exposure to applicators during spraying in orchards with a stretcher-mounted sprayer.

Various health risk assessment models have been developed to evaluate occupational pesticide exposure in China. However, there has been limited investigation into the relationship between health risks and pesticide spraying in orchards. In this study, we analyzed pesticide exposure of applicators while spraying with a stretcher-mounted sprayer in orchards located in four different climatic regions. All garments' unit exposure (UE) demonstrated a right-skewed distribution, with gloves and shins accounting for the highest proportion of dermal pesticide exposure. We observed little difference in dermal and inhalation UE levels between apple and citrus orchards, except for pesticide exposure levels on wipes and faces. While 57% of the inhalation UE distribution variance was attributed to clustering and location effects, no significant differences were observed in dermal exposure levels. We evaluated the impact of different levels of protective clothing on pesticide exposure levels, according to applicators' working habits in China. Our findings revealed that improved levels of protection significantly reduced dermal exposure to pesticides, particularly when wearing gloves during spraying with a stretcher-mounted sprayer. Based on our empirical data, we utilized a simple random sampling model and an intercept-only lognormal mixed model to estimate dermal and inhalation exposure levels. The estimated dermal UE was accurate to within 3-fold with 95% confidence, and half of the estimated inhalation UE was acceptable according to the fold relative accuracy (fRA). Our established and verified statistics for dermal and inhalation UE can be utilized to evaluate the potential pesticide exposure to applicators during spraying in orchards with a stretcher-mounted sprayer.

Comprehensive risk assessment of typhoon disasters in China's coastal areas based on multi-source geographic big data.

Typhoons can bring substantial casualties and economic ramifications, and effective prevention strategies necessitate a comprehensive risk assessment. Nevertheless, existing studies on its comprehensive risk assessment are characterized by coarse spatial scales, limited incorporation of geographic big data, and rarely considering disaster mitigation capacity. To address these problems, this study combined multi-source geographic big data to develop the Comprehensive Risk Assessment Model (CRAM). The model integrated 17 indicators from 4 categories of factors, including exposure, vulnerability, hazard, and mitigation capacity. A subjective-objective combination weighting method was introduced to generate the indicator weights, and comprehensive risk index of typhoon disasters was calculated for 987 counties along China's coastal regions. Results revealed a pronounced spatial heterogeneity of the comprehensive typhoon risk, which exhibited an overall decreasing trend from the southeast coastal areas toward the northwest inland territories. 61.7 % of the counties exhibited a medium-to-high level of comprehensive risk, and counties with very-high risks are predominantly concentrated in the Shandong Peninsula, Yangtze River Delta, Hokkien Golden Triangle, Greater Bay Area, Leizhou Peninsula, and Hainan Province, mainly due to high exposure and hazard factors. The correlation coefficient between the risk assessment results and typhoon-induced direct economic losses reached 0.702, indicating the effectiveness and reliability of the CRAM. Meanwhile, indicators from intrinsic attributes of typhoons and geographic big data had pronounced importance, and regional mitigation capacity should be improved. Our proposed method can help to scientifically understand spatial patterns of comprehensive risk and mitigate the effects of typhoon disasters in China's coastal regions.

Values of Caprini Risk Assessment Scale and D-Dimer for Predicting Venous Thromboembolism During Puerperium.

Purpose: To evaluate the predictive value of the combination of the Caprini risk assessment model (RAM) and D-dimer for venous thromboembolism (VTE) during puerperium. Patients and Methods: This was a retrospective case-control study. Thirty-one puerperium patients with VTE were included as cases, and 279 puerperium women without VTE were matched to cases according to age, number of fetuses, birth day and delivery mode at the ratio of 9:1. Demographic data, clinical data and laboratory parameters within postpartum 24 h were collected. Multivariate analysis, employing the forward stepwise model, was conducted to identify independent factors associated with VTE during puerperium. The predictive values of Caprini RAM, D-dimer and their combination were evaluated using receiver operating characteristic (ROC) curve, and the area under curve (AUC) was compared using Z test. Results: Univariate analysis demonstrated that there were significant differences in D-dimer levels, Caprini score, scarred uterus, adherent placenta, postpartum hemorrhage and intrauterine infection between cases and controls (P<0.05). Multivariate analysis demonstrated that D-dimer levels (OR: 1.754, 95% CI: 1.237-3.182), Caprini score (OR: 1.209, 95% CI: 1.058-2.280), scarred uterus (OR: 1.978, 95% CI: 1.258-3.794), postpartum hemorrhage (OR: 2.276, 95% CI: 1.334-4.347) and intrauterine infection (OR: 2.575, 95% CI: 1.463-4.618) were independently associated with VTE during puerperium with adjustment for adherent placenta and fetal birth weight. The AUCs of D-dimer levels, Caprini score and their combination were 0.748 (SE: 0.030, 95% CI: 0.688-0.807), 0.647 (SE: 0.035, 95% CI: 0.578-0.716) and 0.840 (SE: 0.025, 95% CI: 0.791-0.888). Combination prediction had a higher AUC compared with that of independent prediction (0.840 vs 0.748, Z=2.356, P=0.009; 0.840 vs 0.647, Z=4.487, P<0.001) with a sensitivity of 83.9% and specificity of 80.3%. Conclusion: The combination of the Caprini RAM and D-dimer could significantly elevate the predictive value for VTE during puerperium, and this new tool had the potential in the prediction of VTE during puerperium.

Comprehensive and cumulative risk evaluation of dietary exposure to aflatoxins and ochratoxin A on fermented teas worldwide by a new assessment model.

Recently, mycotoxin risks in fermented tea have received high attention, but mycotoxin transfer rates from tealeaf to infusion during brewing were rarely considered. In addition, the assessment data (i.e., mycotoxin occurrences and tea consumption) in previous assessments were usually limited. Here, a comprehensive and cumulative risk assessment of aflatoxins and ochratoxin A was performed using a tea assessment model, by which mycotoxin transfer rates were included and the assessment data were collected worldwide. By 10 times of brewing, the aflatoxin transfer rate was only 2.94% and OTA was 63.65%. Besides the extreme case, hazard quotients (HQs) from all consumers were lower than the threshold of 1.0, indicating no noncarcinogenic risk; the P95 cumulative margin of exposure (1/MoET) values were 2.52E-04 (30-39 years of age) and 2.42E-04 (≥50 years of age) for two high exposure groups under the upper bound scenario, which a little higher than the carcinogenic risk threshold of 1.00E-04. Notably, the P95 cumulative 1/MoET values (3.24E-03 -7.95E-03) by food assessment model were ten times higher than those of by tea assessment model. The comparative results showed that mycotoxin dietary risks on tea consumption by food assessment model were much overestimated. The result of this study indicated that the contaminants transfer rates should be considered for risk assessment on tea consumption in future work.

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.

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.

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.

Padua prediction score may be inappropriate for VTE risk assessment in hospitalized patients with acute respiratory conditions: A Chinese single-center cohort study.

Background: The Padua Prediction Score (PPS) recommended by the guidelines lacks effective external validation in a Chinese cohort. This study sought to assess the accuracy of the PPS to predict venous thromboembolism (VTE) risk in medical inpatients with acute respiratory conditions. Methods: This consecutive cohort study included 1,574 inpatients from January to August 2019. The occurrence rate of VTE in patients classified at high-risk and low-risk groups according to PPS and Caprini risk assessment model (RAM) was compared. The discriminatory capability of the RAMs was evaluated in all the patients and the subgroup without pharmacological prophylaxis. Reclassification parameters were also used to assess the clinical utility. Results: 170 (10.8%) patients were objectively confirmed as having VTE during hospitalization. The incidence rate of VTE in low-risk patients was 6.3% by PPS, which was significantly higher than that by Caprini RAM (2.6%, p < 0.001). The area under the curve (AUC) for PPS and Caprini RAM was 0.714 (95%CI, 0.672-0.756) and 0.760 (95%CI, 0.724-0.797), respectively (p = 0.003). The AUC of Caprini RAM was larger than PPS even in subgroups without pharmacological prophylaxis (0.774 vs 0.709, p = 0.002). Compared with Caprini RAM, the net reclassification index was estimated at 0.037 (p = 0.436), and integrated discrimination improvement was 0.015 (p = 0.495) by PPS. Conclusions: According to our cohort study, PPS may not be appropriate to predict VTE risk in hospitalized patients with acute respiratory conditions. An accurate, widely applicable, validated RAM needs to be further constructed in Chinese medical inpatients.

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.

Development and validation of machine learning models for venous thromboembolism risk assessment at admission: a retrospective study.

Introduction: Venous thromboembolism (VTE) risk assessment at admission is of great importance for early screening and timely prophylaxis and management during hospitalization. The purpose of this study is to develop and validate novel risk assessment models at admission based on machine learning (ML) methods. Methods: In this retrospective study, a total of 3078 individuals were included with their Caprini variables within 24 hours at admission. Then several ML models were built, including logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB). The prediction performance of ML models and the Caprini risk score (CRS) was then validated and compared through a series of evaluation metrics. Results: The values of AUROC and AUPRC were 0.798 and 0.303 for LR, 0.804 and 0.360 for RF, and 0.796 and 0.352 for XGB, respectively, which outperformed CRS significantly (0.714 and 0.180, P < 0.001). When prediction scores were stratified into three risk levels for application, RF could obtain more reasonable results than CRS, including smaller false positive alerts and larger lower-risk proportions. The boosting results of stratification were further verified by the net-reclassification-improvement (NRI) analysis. Discussion: This study indicated that machine learning models could improve VTE risk prediction at admission compared with CRS. Among the ML models, RF was found to have superior performance and great potential in clinical practice.

Assessment of bleeding risk in cancer patients treated with anticoagulants for venous thromboembolic events.

Introduction: Anticoagulant is the cornerstone of the management of VTE at the cost of a non-negligible risk of bleeding. Reliable and validated clinical tools to predict thromboembolic and hemorrhagic events are crucial for individualized decision-making for the type and duration of anticoagulant treatment. We evaluate the available risk models in real life cancer patients with VTE. The objectives of the study were to describe the bleeding of cancer patients with VTE and to evaluate the performance of the different bleeding models to predict the risk of bleeding during a 6-month follow-up. Materials and Methods: VTE-diagnosed patient's demographic and clinical characteristics, treatment regimens and outcomes for up to 6 months were collected. The primary endpoint was the occurrence of a major bleeding (MB) or a clinically relevant non major bleeding (CRNMB) event, categorized according to the ISTH criteria. Results: During the 6-months follow-up period, 26 out of 110 included patients (26.7%) experienced a bleeding event, with 3 recurrences of bleeding. Out of the 29 bleeding events, 19 events were CRNMB and 10 MB. One patient died because of a MB. Bleeding occurred in 27 % of the patients treated with DOACs and 22% of the patients treated with LMWH. Most of the bleedings were gastrointestinal (9 events, 31%); 26.9% of the bleedings occurred in patient with colorectal cancer and 19.6% in patients with lung cancer. In our cohort, none of the 10 RAMs used in our study were able to distinguish cancer patients with a low risk of bleeding, from all bleeding or non-bleeding patients. The Nieto et al. RAM had the best overall performance (C-statistic = 0.730, 95% CI (0.619-0.840)). However, it classified 1 out of 5 patients with major bleeding in the low risk of bleeding group. The rest of the RAMs showed a suboptimal result, with a range of C-statistic between 0.489, 95%CI (0.360-0.617)) and 0.532, 95%CI (0.406-0.658)). Conclusions: The management of CAT patients is challenging due to a higher risk of both recurrent VTE and bleeding events, as compared with non-cancer patients with VTE. None of the existing RAMs was able to consistently identify patients with risk of anticoagulant associated bleeding events.

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.

Risk assessment for postoperative venous thromboembolism using the modified Caprini risk assessment model in lung cancer.

Background: Postoperative venous thromboembolism (VTE) is a well-documented cause of morbidity and mortality in lung cancer patients. However, risk identification remains limited. In this study, we sought to analyze the risk factors for VTE and verify the predictive value of the modified Caprini risk assessment model (RAM). Methods: This prospective single-center study included patients with resectable lung cancer who underwent resection between October 2019 and March 2021. The incidence of VTE was estimated. Logistic regression was used to analyze the risk factors for VTE. Receiver operating characteristic (ROC) curve analysis was performed to test the ability of the modified Caprini RAM to predict VTE. Results: The VTE incidence was 10.5%. Several variables, including age, D-dimer, hemoglobin (Hb), bleeding, and patient confinement to bed were significantly associated with VTE after surgery. The difference between the VTE and non-VTE groups in the high-risk levels was statistically significant (P<0.001), while the low and moderate risk levels showed no significant difference. The combined use of the modified Caprini score and the Hb and D-dimer levels showed an area under the curve (AUC) was 0.822 [95% confidence interval (CI): 0.760-0.855. P<0.001]. Conclusions: The risk-stratification approach of the modified Caprini RAM is not particularly valid after lung resection in our population. The use of the modified Caprini RAM combined with Hb and D-dimer levels shows a good diagnostic performance for VTE prediction in patients with lung cancer undergoing resection.

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.

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.

An impact-based environmental risk assessment model toolbox for offshore produced water discharges.

We present a novel approach to environmental risk assessment of produced water discharges based on explicit impact and probability, using a combination of transport, fate and toxicokinetic-toxicodynamic models within a super-individual framework, with a probabilistic element obtained from ensemble simulations. Our approach is motivated by a need for location and species specific tools which also accounts for the dynamic nature of exposure and uptake of produced water components in the sea. Our approach is based on the well-established fate model DREAM, and accounts for time-variable exposure, considers body burden and effects for specific species and stressors, and assesses the probability of impact. Using a produced water discharge in the Barents Sea, with early life stages of spawning haddock, we demonstrate that it is possible to conduct a model-based risk assessment that highlights the effect of natural variations in environmental conditions. The benefits, limitations and potential for further improvements are discussed.

A Novel Risk Assessment Model Predicts Major Bleeding Risk at Admission in Medical Inpatients.

Background: Venous thromboembolism (VTE) is the leading cause of preventable hospital death in the US. Guidelines from the American College of Chest Physicians and American Society for Hematology recommend providing pharmacological VTE prophylaxis to acutely or critically ill medical patients at acceptable bleeding risk, but there is currently only one validated risk assessment model (RAM) for estimating bleeding risk. We developed a RAM using risk factors at admission and compared it with the International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) model. Methods: A total of 46,314 medical patients admitted to a Cleveland Clinic Health System hospital from 2017-2020 were included. Data were split into training (70%) and validation (30%) sets with equivalent bleeding event rates in each set. Potential risk factors for major bleeding were identified from the IMPROVE model and literature review. Penalized logistic regression using LASSO was performed on the training set to select and regularize important risk factors for the final model. The validation set was used to assess model calibration and discrimination and compare performance with IMPROVE. Bleeding events and risk factors were confirmed through chart review. Results: The incidence of major in-hospital bleeding was 0.58%. Active peptic ulcer (OR = 5.90), prior bleeding (OR = 4.24), and history of sepsis (OR = 3.29) were the strongest independent risk factors. Other risk factors included age, male sex, decreased platelet count, increased INR, increased PTT, decreased GFR, ICU admission, CVC or PICC placement, active cancer, coagulopathy, and in-hospital antiplatelet drug, steroid, or SSRI use. In the validation set, the Cleveland Clinic Bleeding Model (CCBM) had better discrimination than IMPROVE (0.86 vs. 0.72, p < .001) and, at equivalent sensitivity (54%), categorized fewer patients as high-risk (6.8% vs. 12.1%, p < .001). Conclusions: From a large population of medical inpatients, we developed and validated a RAM to accurately predict bleeding risk at admission. The CCBM may be used in conjunction with VTE risk calculators to decide between mechanical and pharmacological prophylaxis for at-risk patients.