scholarly journals Risk factors, risk assessment, and prognosis in patients with gynecological cancer and thromboembolism

2019 ◽  
Vol 48 (4) ◽  
pp. 030006051989317
Author(s):  
Xindan Wang ◽  
Jing Huang ◽  
Zhao Bingbing ◽  
Shape Li ◽  
Li Li

Objective This study aimed to investigate a suitable risk assessment model to predict deep vein thrombosis (DVT) in patients with gynecological cancer. Methods Data from 212 patients with gynecological cancer in the Affiliated Tumor Hospital of Guangxi Medical University were retrospectively analyzed. Patients were risk-stratified with three different risk assessment models individually, including the Caprini model, Wells DVT model, and Khorana model. Results The difference in risk level evaluated by the Caprini model was not different between the DVT and control groups. However, the DVT group had a significantly higher risk level than the control group with the Wells DVT or Khorana model. The Wells DVT model was more effective for stratifying patients in the DVT group into the higher risk level and for stratifying those in the control group into the lower risk level. Receiver operating curve analysis showed that the area under the curve of the Wells DVT, Khorana, and Caprini models was 0.995 ± 0.002, 0.642 ± 0.038, and 0.567 ± 0.039, respectively. Conclusion The Wells DVT model is the most suitable risk assessment model for predicting DVT. Clinicians could also combine the Caprini and Wells DVT models to effectively identify high-risk patients and eliminate patients without DVT.

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiao-Ying Xie ◽  
Ming-Wei Wang ◽  
Zu-Ying Hu ◽  
Cheng-Jian Cao ◽  
Cong Wang ◽  
...  

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.


Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 452-452
Author(s):  
Sabine Eichinger ◽  
Georg Heinze ◽  
Paul Alexander Kyrle

Abstract Abstract 452 Background: Venous thrombosis is a chronic and potentially fatal disease (case fatality 5-9%). Predicting the likelihood of recurrence is important, as most recurrences can be prevented by antithrombotic therapy, albeit at the price of an increased bleeding risk during anticoagulation. Despite a substantial progress in identifying the determinants of the recurrence risk, predicting recurrence in an individual patient is often not feasible. Venous thromboembolism (VTE) is a multicausal disease and the combined effect of clinical and laboratory factors on the recurrence risk is unknown. It was the aim of our study to develop a simple risk model that improves prediction of the recurrence risk in patients with unprovoked VTE. Methods and Findings: In a prospective multicenter cohort study we followed 929 patients with a first VTE after completion of at least 3 months of anticoagulation. The median observation time was 43.3 months. Patients with VTE provoked by surgery, trauma, cancer, pregnancy or oral contraceptive intake were excluded as were those with a natural inhibitor deficiency or the lupus anticoagulant. The main outcome measure was symptomatic recurrent VTE, which occurred in 176 patients. The probability of recurrence (95% CI) after 2, 5 and 10 years was 13.8% (11.6% to16.5%), 24.6% (21.6% to 28.9%), and 31.8% (27.6% to 37.4%), respectively. To develop a simple and easy to apply risk assessment model, clinical and laboratory variables (age, sex, location of VTE, body mass index, factor V Leiden, prothrombin G20210A mutation, D-Dimer, in vitro thrombin generation) were preselected based on their established relevance for the recurrence risk, simple assessment, and reproducibility. All variables were analyzed in a Cox proportional hazards model, and those significantly associated with recurrence were used to compute risk scores. Only male sex [HR vs. female 1.90 (95% CI 1.31–2.75)], proximal deep vein thrombosis [HR vs. distal 2.08 (95% CI 1.16–3.74)], pulmonary embolism [HR vs. distal thrombosis 2.60 (95% CI 1.49– 4.53)] and elevated levels of D-Dimer [HR per doubling 1.27 (95% CI 1.08–1.51)] or peak thrombin [HR per 100 nM increase 1.38 (95% CI 1.17–1.63)] were related to a higher recurrence risk. We developed a nomogram (Fig. 1) based on sex, location of initial thrombosis, and D-Dimer that can be used to calculate risk scores and to estimate the cumulative probabilities of recurrence in an individual patient. The model has undergone extensive validation by a cross-validation process. The cohort was divided into test and validation samples thereby mimicking independent validation. This process was repeated 1000 times and the results were averaged to avoid dependence of the validation results on a particular partition of our cohort. Patients were assigned to different risk categories according to their risk score, which corresponded well with the recurrence rate as patients with lower scores had lower recurrence rates. Conclusion: By use of a simple scoring system the assessment of the recurrence risk in patients with a first unprovoked VTE can be improved in routine care. Patients with unprovoked VTE in whom the recurrence risk is low enough to consider a limited duration of anticoagulation, can be identified. Disclosures: No relevant conflicts of interest to declare.


2020 ◽  
Author(s):  
Qiao-Ying Xie ◽  
Ming-Wei Wang ◽  
Zu-Ying Hu ◽  
Yan-Ming Chu ◽  
Cheng-Jian Cao ◽  
...  

Abstract Background: Metabolic syndrome (MS) screening is important for the early detection of occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. Finally, the screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. Results: A total of 2844 occupational workers were included, and 10 biomarkers related to MS were screened. The area under the curve (AUC) value for non-Lasso and Lasso regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk factors were basophil absolute count (OR: 3.38), platelet packed volume (OR: 2.63), leukocyte count (OR: 2.01), red blood cell count (OR: 1.99), and alanine aminotransferase level (OR: 1.53). Conclusion: The risk assessment model based on the Lasso regression algorithm helped identify Metabolic syndrome with high accuracy in physically examining an occupational population.


2019 ◽  
Vol 78 ◽  
pp. 03004
Author(s):  
Miaomiao Tian ◽  
Wenzhao Li ◽  
Meijuan Ruan ◽  
Jing Wei ◽  
Weiwei Ma

Drinking water quality has become a great concern to the whole society, especially in heavily polluted rural areas. This paper analyzes the water quality of 100 water supping the US Environmental Protection Agency's (USEPA) recommended health risk assessment model. The results showed that the microbial indicators exceeded the standard in the whole year, and some of the water supply units which lead, nitrated and dissolved solids exceeding the standard. The model recommended by EPA is applied to establish risk assessment model for health risk assessment of adults in wet and dry seasons, respectively. Results of HRA indicated that carcinogenic risk of chromium was 7.61E-05a-1 and the risk value of arsenic was 9.92E-06a-1 which exceed the maximum acceptable risk level recommended by USEPA 5.0×10-5 closely to the ICPR recommendation 1.0×10-6. Meanwhile we conduct health risk assessment (HRA) on relevant non-carcinogenic indicators: nitrate is 2.95E-09a-1, the risk value of fluoride (F) is 2.49E-09a-1, the risk value of lead is 2.39E-09a-1 and copper (Cu) 9.00E-10a-1 exceeds the maximum acceptable risk level risk value recommended by USEPA 1.0×10-9. The above indicators require priority control and management of pollutants that are prioritized and managed.


Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 525 ◽  
Author(s):  
Dejan V. Petrović ◽  
Miloš Tanasijević ◽  
Saša Stojadinović ◽  
Jelena Ivaz ◽  
Pavle Stojković

The main goal of this research was the development of an algorithm for the implementation of negative risk parameters in a synthesis model for a risk level assessment for a specific machine used in the mining industry. Fuzzy sets and fuzzy logic theory, in combination with statistical methods, were applied to analyze the time picture state of the observed machine. Fuzzy logic is presented through fuzzy proposition and a fuzzy composition module. Using these tools, the symmetric position of the fuzzy sets with regard to class was used, and the symmetric fuzzy inference approach was used in an outcome calculation. The main benefit of the proposed model is being able to use numerical and linguistic data in a risk assessment model. The proposed risk assessment model, using fuzzy logic conclusions and min–max composition, was used on a mobile crushing machine. The results indicated that the risk level of the mobile crushing machine was in the “high” category, which means that it is necessary to introduce maintenance policies based on this high risk. The proposed risk assessment model is useful for any engineering system.


2020 ◽  
Vol 26 ◽  
pp. 107602962096145
Author(s):  
Eugene S. Krauss ◽  
MaryAnne Cronin ◽  
Nancy Dengler ◽  
Barry G. Simonson ◽  
Paul Enker ◽  
...  

Two of the more common potential complications after arthroplasty are venous thromboembolism (VTE), which includes deep vein thrombosis (DVT) and pulmonary embolus (PE), and excess bleeding. Appropriate chemoprophylaxis choices are essential to prevent some of these adverse events and from exacerbating others. Risk stratification to prescribe safe and effective medications in the prevention of postoperative VTE has shown benefit in this regard. The Department of Orthopaedic Surgery at Syosset Hospital/Northwell Health, which performs over 1200 arthroplasties annually, has validated and is using the 2013 version of the Caprini Risk Assessment Model (RAM) to stratify each patient for risk of postoperative VTE. This tool results in a culling of information, past and present, personal and familial, that provides a truly thorough evaluation of the patient’s risk for postoperative VTE. The Caprini score then guides the medication choices for thromboprophylaxis. The Caprini score is only valuable if the data is properly collected, and we have learned numerous lessons after applying it for 18 months. Risk stratification requires practice and experience to achieve expertise in perioperative patient evaluation. Having access to pertinent patient information, while gaining proficiency in completing the Caprini RAM, is vital to its efficacy. Ongoing, real time analyses of patient outcomes, with subsequent change in process, is key to improving patient care.


2017 ◽  
Vol 29 (3) ◽  
pp. 331-342 ◽  
Author(s):  
Yanfei Tian ◽  
Xuecheng Sun ◽  
Lijia Chen ◽  
Liwen Huang

In order to set up a mathematical model suitable for nautical navigational environment risk evaluation and systematically master the navigational environment risk characteristics of the Qiongzhou Strait in a quantitative way, a risk assessment model with approach steps is set up based on the grey fixed weight cluster (GFWC). The evaluation index system is structured scientifically through both literature review and expert investigation. The relative weight of each index is designed to be obtained via fuzzy analytic hierarchy process (FAHP); Index membership degree of every grey class is proposed to be achieved by fuzzy statistics (FS) to avoid the difficulty of building whiten weight functions. By using the model, nautical navigational environment risk of the Qiongzhou Strait is determined at a “moderate” level according to the principle of maximum membership degree. The comprehensive risk evaluation of the Qiongzhou Strait nautical navigational environment can provide theoretical reference for implementing targeted risk control measures. It shows that the constructed GFWC risk assessment model as well as the presented steps are workable in case of incomplete information. The proposed strategy can excavate the collected experts’ knowledge mathematically, quantify the weight of each index and risk level, and finally lead to a comprehensive risk evaluation result. Besides, the adoptions of probability and statistic theory, fuzzy theory, aiming at solving the bottlenecks in case of uncertainty, will give the model a better adaptability and executability.


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