scholarly journals Venous thrombosis following lower-leg cast immobilization and knee arthroscopy: From a population-based approach to individualized therapy

2019 ◽  
Vol 174 ◽  
pp. 62-75 ◽  
Author(s):  
Banne Nemeth ◽  
Suzanne C. Cannegieter
2018 ◽  
Vol 16 (11) ◽  
pp. 2218-2222 ◽  
Author(s):  
B. Nemeth ◽  
J. F. Timp ◽  
A. van Hylckama Vlieg ◽  
F. R. Rosendaal ◽  
S. C. Cannegieter

2014 ◽  
Vol 12 (9) ◽  
pp. 1461-1469 ◽  
Author(s):  
R. A. van Adrichem ◽  
J. Debeij ◽  
R. G. H. H. Nelissen ◽  
I. B. Schipper ◽  
F. R. Rosendaal ◽  
...  

Author(s):  
Yassene Mohammed ◽  
Carolina E. Touw ◽  
Banne Nemeth ◽  
Raymond A. Adrichem ◽  
Christoph H. Borchers ◽  
...  

2018 ◽  
Vol 161 ◽  
pp. 106-110 ◽  
Author(s):  
Kasper Adelborg ◽  
Erzsébet Horváth-Puhó ◽  
Jens Sundbøll ◽  
Paolo Prandoni ◽  
Anne Ording ◽  
...  

2012 ◽  
Vol 130 (1) ◽  
pp. 27-31 ◽  
Author(s):  
Adedayo A. Onitilo ◽  
Suhail A.R. Doi ◽  
Jessica M. Engel ◽  
Ingrid Glurich ◽  
John Johnson ◽  
...  

2022 ◽  
Vol 12 (1) ◽  
pp. 114
Author(s):  
Chao Lu ◽  
Jiayin Song ◽  
Hui Li ◽  
Wenxing Yu ◽  
Yangquan Hao ◽  
...  

Osteoarthritis (OA) is the most common joint disease associated with pain and disability. OA patients are at a high risk for venous thrombosis (VTE). Here, we developed an interpretable machine learning (ML)-based model to predict VTE risk in patients with OA. To establish a prediction model, we used six ML algorithms, of which 35 variables were employed. Recursive feature elimination (RFE) was used to screen the most related clinical variables associated with VTE. SHapley additive exPlanations (SHAP) were applied to interpret the ML mode and determine the importance of the selected features. Overall, 3169 patients with OA (average age: 66.52 ± 7.28 years) were recruited from Xi’an Honghui Hospital. Of these, 352 and 2817 patients were diagnosed with and without VTE, respectively. The XGBoost algorithm showed the best performance. According to the RFE algorithms, 15 variables were retained for further modeling with the XGBoost algorithm. The top three predictors were Kellgren–Lawrence grade, age, and hypertension. Our study showed that the XGBoost model with 15 variables has a high potential to predict VTE risk in patients with OA.


2014 ◽  
Vol 44 (6) ◽  
pp. 537-545 ◽  
Author(s):  
M. Bohensky ◽  
A. Barker ◽  
R. Morello ◽  
R. N. De Steiger ◽  
A. Gorelik ◽  
...  

Blood ◽  
1996 ◽  
Vol 88 (10) ◽  
pp. 3698-3703 ◽  
Author(s):  
SR Poort ◽  
FR Rosendaal ◽  
PH Reitsma ◽  
RM Bertina

We have examined the prothrombin gene as a candidate gene for venous thrombosis in selected patients with a documented familial history of venous thrombophilia. All the exons and the 5′- and 3′-UT region of the prothrombin gene were analyzed by polymerase chain reaction and direct sequencing in 28 probands. Except for known polymorphic sites, no deviations were found in the coding regions and the 5′-UT region. Only one nucleotide change (a G to A transition) at position 20210 was identified in the sequence of the 3′-UT region. Eighteen percent of the patients had the 20210 AG genotype, as compared with 1% of a group of healthy controls (100 subjects). In a population-based case-control study, the 20210 A allele was identified as a common allele (allele frequency, 1.2%; 95% confidence interval, 0.5% to 1.8%), which increased the risk of venous thrombosis almost threefold odds ratio, 2.8; 95% confidence interval, 1.4 to 5.6. The risk of thrombosis increased for all ages and both sexes. An association was found between the presence of the 20210 A allele and elevated prothrombin levels. Most individuals (87%) with the 20210 A allele are in the highest quartile of plasma prothrombin levels (> 1.15 U/mL). Elevated prothrombin itself also was found to be a risk factor for venous thrombosis.


Blood ◽  
1995 ◽  
Vol 85 (10) ◽  
pp. 2756-2761 ◽  
Author(s):  
T Koster ◽  
FR Rosendaal ◽  
E Briet ◽  
FJ van der Meer ◽  
LP Colly ◽  
...  

A deficiency of protein C (PC), antithrombin, or protein S is strongly associated with deep-vein thrombosis in selected patients and their families. However, the strength of the association with venous thrombosis in the general population is unknown. This study was a population-based, patient-control study of 474 consecutive outpatients, aged less than 70 years, with a first, objectively diagnosed, episode of venous thrombosis and without an underlying malignant disease, and 474 healthy controls who matched for age and sex. Relative risks were estimated as matched odds ratios. Based on a single measurement, there were 22 (4.6%) patients with a PC deficiency (PC activity, less than 0.67 U/mL or PC antigen, less than 0.33 U/mL when using coumarins). Among the controls, the frequency was 1.5% (seven subjects). Thus, there is a threefold increase in risk of thrombosis in subjects with PC levels below 0.67 or 0.33 U/mL [matched odds ratio, 3.1; 95% confidence interval (CI), 1.4 to 7.0]. When a PC deficiency was based on two repeated measurements, the relative risk for thrombosis increased to 3.8 (95% CI, 1.3 to 10); when it was based on DNA-confirmation, the relative risk increased further to 6.5 (95% CI, 1.8 to 24). In addition, there was a gradient in thrombosis risk, according to PC levels. The results for antithrombin are similar to those for PC, although less pronounced (relative risk, 2.2; 95% CI, 1.0 to 4.7). We could not find an association between reduced total protein S (relative risk, 0.7; 95% CI, 0.3 to 1.8) or free protein S levels (relative risk, 1.6; 95% CI, 0.6 to 4.0) and thrombosis risk. Although not very frequent, PC and antithrombin deficiency are clearly associated with an increase in thrombosis risk.


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