Time to recanalisation in patients with cerebral venous thrombosis under anticoagulation therapy
Antonio AraúzJuan Camilo Vargas‐GonzálezNayelli Arguelles-MoralesMiguel A. BarbozaJuan Manuel CallejaElizabeth Martínez-JuradoAngélica Ruíz-FrancoAlejandro Quiroz-CompeánJosé G. Merino
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Few studies have investigated the rates of recanalisation after cerebral venous thrombosis (CVT). Our objective was to investigate the recanalisation rate and to identify predictors of recanalisation in patients with CVT.We included 102 patients with confirmed first-ever, non-septic CVT. All patients received anticoagulation for 12 months or until complete recanalisation. To assess recanalisation, patients underwent MR venography every 3 months until partial or complete recanalisation or for 12 months after diagnosis. We conducted two parallel analyses of complete recanalisation versus partial and no recanalisation versus any recanalisation. As a secondary objective we explored the influence of recanalisation on outcome and recurrent events. We calculated the probability of recanalisation using Kaplan-Meier analysis and conducted multivariate analysis using a Cox model.The mean age of patients was 33.5±11 years (80 (78.4%) women). Survival analysis indicated that 50% of the patients had any recanalisation (grades I, II and III) by 64 days and complete recanalisation (grade III) by 169 days. Adjusted Cox proportional model revealed that age <50 years (HR=11.5 95% CI=1.58 to 84.46, p=0.01) and isolated superior sagittal sinus thrombosis (HR=0.39, 95% CI=0.14 to 1.04, p=0.05) predict complete recanalisation, while age <50 years (HR=4.79; 95% CI=1.69 to 13.5, p=0.003) predicts any recanalisation. Patients with complete recanalisation had a greater chance of good functional outcome (HR=5.17; 95% CI=2.8 to 9.53, p<0.001).We found that recanalisation occurs over time, until month 11. Complete recanalisation may influence functional outcome.Cox proportional hazards regression model (Cox model) is the most commonly used multivariate approach in time-to-event data analysis. A vital issue in fitting Cox model is choosing the appropriate time scale related to the occurrence of the outcome events. However, few domestic studies have focused on selecting and applying time scales for Cox model in the analysis of cohort study data. This study briefly introduced and compared several time scales in the reports from literature; and used data from the Shanghai Women's Health Study to illustrate the impact of different time scales on data analysis results, using the association between central obesity and the risk of liver cancer as an example. On this basis, several suggestions on selecting time scales in Cox model are proposed to provide a reference for the analysis of cohort study data.Cox比例风险回归模型（Cox模型）是时间-事件数据分析中常用的多因素分析方法，拟合Cox模型时一个关键问题是如何选择合适的与结局事件发生相关的时间尺度。目前国内开展的队列研究在资料分析中较少关注Cox模型的时间尺度选择问题。本研究对文献报道中常见的几种时间尺度选择策略进行简要介绍和比较；并利用上海女性健康队列资料，以中心性肥胖与肝癌发病风险的关联为例，说明选择不同时间尺度的Cox模型对数据分析结果的影响；在此基础上提出几点Cox模型时间尺度选择上的建议，以期为队列研究资料的分析提供参考。.
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Postoperatively, 50 oncogynecological patients were examined with J125 fibrinogen, and thrombosis of profound crural veins was noted in 62% of them. The thrombosis mostly frequently occurred on both extremities and in the median crus, there is a tendency to its expansion in 40.7% of cases. Four patients with thrombosis of profound crural veins showed the development of thromboembolism of the pulmonary artery branches. Under consideration is the cause of thrombi formation in oncogynecological patients and the role of the former in the genesis of embolic complications.
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Abstract Cox proportional hazard model is frequently used in survival analysis. Cox proportional hazard model is time independent covariate while many models involve time as a dependent covariate causing incomplete proportional hazard assumption, known as non-proportional hazard. The proposed model in this paper was a non-proportional hazard involving time-independent and time-dependent covariates. The approaching model was carried out by joining a stratified Cox and extended Cox model termed as Stratified-Extended Cox (SE Cox) model. The simulation of the SE Cox model resulted in small MSE for the parameter estimates. In addition, the goodness of value was more appropriate compared to the existing non-proportional hazard model. Hence, the SE Cox model was applied to evaluate student persistence in Universitas Terbuka, Indonesia.
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The paper reviews air travel and venous thrombosis. The risk of venous thrombosis increases with distance flown. The mechanisms that cause thrombosis are unknown, but may be related to hypobaric hypoxia, i.e. reduced air pressure inside the aeroplane, stasis of the lower limbs, often referred to as economy class syndrome (ECS), and dehydration.Symptomless deep venous thrombosis may occur in 10% of healthy long-haul airline travellers over 50 years of age, and up to 4.5% of passengers under 50 at high risk of thrombosis. Wearing of elastic compression stockings during the flight is associated with reduced risk of deep venous thrombosis.Passengers should be encouraged to use the muscle-vein pump regularly, ensure adequate hydration, and in some cases wear elastic compression stockings. A majority of passengers will not need any thromboprophylaxis. Aspirin may have a protective role, but its efficacy in preventing venous thrombosis is much smaller than that of low molecular weight heparins. A high prophylactic dose of a low molecular weight heparin should be considered in passengers at high risk of thrombosis.
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Air travel
Venous stasis
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Though thromboembolic complications in HIV infected patients have been described in literature, recurrent thrombosis is very rare. We present a six-year-old HIV infected boy who presented with recurrent thrombosis. He initially had renal artery thrombosis, then middle cerebral artery thrombosis and finally hepatic vein thrombosis that was fatal.
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The main modelling method used in survival analysis is the Cox regression model that has an assumption of proportional hazards. In the violation of proportional hazards, usage of different survival models is suggested. In this study, the parametric proportional hazards models with time dependent covariates are investigated under nonproportional hazards with a known survival time distribution. The examined models are applied to a real survival data and the results are presented with those obtained with the Cox and parametric proportional hazards models.
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Electrically induced thrombosis is often used for pathophysiologic and therapeutic experiments. The pH value of the vascular wall was measured to approach the features of this thrombosis. After inducing thrombosis by direct current, pH value of the intima of femoral artery was found decreased. The anode was inserted in the lumen. As to the behavior of the pH value, electric thrombosis and clinical thrombosis are similar. Electric thrombosis therefore seems to be valuable for comparing investigations.
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The application of survival analysis on the data of credit motorcycle financing experiencing bad loans after the credit starts early, with sixteen covariates were considered. The model used in survival analysis is the Cox proportional hazard models. Cox models have the assumption that the proportional hazard assumption. Extended Cox models selected to improve cox proportional hazard models when one or more covariates did not meet the assumption of proportional hazards. Extended cox models is an extension of cox models that involve time-dependent variables. Covariates that do not meet the proportional hazards assumption in the Cox models diinteraksikan extended with functions appropriate time, in order to obtain time-dependent covariates. So on the model covariates that are not dependent on time and time dependent covariates. The parameters of these covariates estimated using partial maximum likelihood method. To determine whether the extended Cox model is a suitable model for the data in a particular case, likelihood ratio test was used. The results indicate that extended Cox models with functions time appropriate, provide the best model.Keywords : Credit Risk, Survival Analysis, Cox Proportional Hazard , Extended Cox Model
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