PubMed Health⌕ Search

PubMed · 17024603

Postthrombotic syndrome.

Abstract

Despite considerable progress in the diagnosis and treatment of deep vein thrombosis (DVT) of the lower extremities, one of every three patients will develop postthrombotic sequelae within 2 years; these sequelae are severe in approximately 20% of cases and produce considerable socioeconomic consequences. Among factors potentially related to the development of the postthrombotic syndrome (PTS) are older age, obesity, insufficient oral anticoagulant therapy, and recurrent ipsilateral thrombosis. Whether the extent and location of the initial thrombosis are associated with the development of PTS is controversial. Based on recent findings, the lack of vein recanalization within the first 6 months appears to be an important predictor of PTS, whereas the development of transpopliteal venous reflux is not. The diagnosis of PTS can be made on clinical grounds for patients with a history of DVT. The combination of a standardized clinical evaluation with the results of compression ultrasonography and Doppler ultrasound helps diagnose or exclude a previous proximal vein thrombosis. According to the results of recent clinical studies, the prompt administration of adequate compression elastic stockings in patients with symptomatic DVT has the potential to reduce the frequency of late PTS development by half. The management of this condition is demanding and often frustrating. However, when carefully supervised and instructed to wear proper elastic stockings, more than 50% of patients will either remain stable or improve during long-term follow-up. Clinical presentation helps predict the prognosis; the outcome of patients who refer with initially severe manifestations is more favorable than that of patients whose symptoms deteriorate progressively over time.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Raffaele Pesavento, Enrico Bernardi, Alessia Concolato, Fabio Dalla Valle, Antonio Pagnan, Paolo Prandoni. 2006. Postthrombotic syndrome.. https://doi.org/10.1055/s-2006-951460

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

On the exact interval estimation for the difference in paired areas under the ROC curves.

An important measure for comparison of accuracy between two diagnostic procedures is the difference in paired areas under the receiver operating characteristic (ROC) curves. Non-parametric and maximum likelihood methods have been proposed for interval estimation for the difference in paired areas under ROC curves. However, these two methods are asymptotic procedures and their performance in finite sample sizes has not been thoroughly investigated. We propose to use the concept of generalized pivotal quantities (GPQs) to construct an exact confidence interval for the difference in paired areas under ROC curves. A simulation study is conducted to empirically investigate the probability coverage and expected length of the three methods for various combinations of sample sizes, values of the area under the ROC curve and correlations. Simulation results demonstrate that the exact confidence interval based on the concept of GPQs provides not only sufficient probability coverage but also reasonable expected length. Numerical examples using published data sets illustrate the proposed method.

Clinical Trials as Topic↗

An efficient test for the analysis of dichotomized variables when the reliability is known.

A difference in an outcome variable between the treatment groups in a trial does not necessarily mean that there is a difference in the number of patients who experience relevant improvement on that variable. When the relevant improvement corresponds with an outcome or change in outcome that exceeds a certain threshold, the outcome variable can be dichotomized. A responder is a patient whose outcome exceeds the threshold. Comparisons can be made between the number of responders in the two treatment groups using logistic regression, or some other method to evaluate binary outcomes. An important disadvantage of this approach is the loss of power. In general, it is more efficient to test the difference between the mean values. We developed a statistical test that compares response rates for a dichotomized variable. It requires that an estimate of the reliability of the outcome variable is available. Simulations showed that the test was valid and robust over a wide range of distributions and sample sizes. The power was greater than the power of a chi(2) test, which would enable substantial reduction in the sample size.

Clinical Trials as Topic↗

Sample size determination for logistic regression revisited.

There is no consensus on the approach to compute the power and sample size with logistic regression. Some authors use the likelihood ratio test; some use the test on proportions; some suggest various approximations to handle the multivariate case. We advocate the use of the Wald test since the Z-score is routinely used for statistical significance testing of regression coefficients. The null-variance formula became popular from early studies, which contradicts modern software, which utilizes the method of maximum likelihood estimation (MLE), when the variance of the MLE is estimated at the MLE, not at the null. We derive general Wald-based power and sample size formulas for logistic regression and then apply them to binary exposure and confounder to obtain a closed-form expression. These formulas are applied to minimize the total sample size in a case-control study to achieve a given power by optimizing the ratio of controls to cases. Approximately, the optimal number of controls to cases is equal to the square root of the alternative odds ratio. Our sample size and power calculations can be carried out online at www.dartmouth.edu/ approximately eugened.

Clinical Trials as Topic↗