Predictors of von Willebrand disease diagnosis in individuals with borderline von Willebrand factor plasma levels. (9th January 2015) - Bucciarelli, P.; Siboni, S. M.; Stufano, F.; Biguzzi, E.; Canciani, M. T.; Baronciani, L.; Pagliari, M. T.; La Marca, S.; Mistretta, C.; Rosendaal, F. R.; Peyvandi, F.
Summary Background In individuals with borderline von Willebrand factor (VWF) plasma levels, second‐level tests are required to confirm or exclude von Willebrand disease (VWD). These tests are time‐consuming and expensive. Objective To assess which parameters can predict VWD diagnosis in individuals with borderline VWF levels (30–60 IU dL−1). Methods Nine hundred and fifty individuals with bleeding episodes or abnormal coagulation test results were investigated with first‐level tests (blood count, prothrombin time, activated partial thromboplastin time, blood clotting factor VIII, VWF ristocetin cofactor activity [VWF:RCo], and VWF antigen), and 93 (62 females and 31 males; median age, 28 years; interquartile range 15–44) had borderline VWF:RCo levels. All underwent second‐level investigations to confirm or exclude VWD. A multivariable logistic regression model was fitted with sex, age, bleeding score, family history, VWF:RCo and ABO blood group as predictors, and used to predict VWD diagnosis. Results Forty‐five of the 93 individuals (48%) had VWD (84% type 1). A negative linear relationship between VWF:RCo levels and risk of VWD diagnosis was present, and was particularly evident with blood group non‐O [adjusted odds ratio 7.00 (95% confidence interval [CI] 1.48–33.11) for every 5 IU dL−1 decrease in VWF:RCo]. The other variable clearly associated with VWD diagnosis was female sex (adjusted odds ratio 5.76 [95% CI 1.47–22.53]). The area under the receiver operating characteristic curve of the full logistic model was 0.89 (95% CI 0.82–0.95). Conclusions In individuals with borderline VWF, the two strongest predictors of VWD diagnosis are low VWF:RCo levels (particularly in those with blood group non‐O) and female sex. This predictive model has a promising discriminative ability to identify patients with borderline VWF levels who are likely to have VWD.