Evolutionary algorithms and decision trees for predicting poor outcome after endovascular treatment for acute ischemic stroke.

Despite the large overall beneficial effects of endovascular treatment in patients with acute ischemic stroke, severe disability or death still occurs in almost one-third of patients. These patients, who might not benefit from treatment, have been previously identified with traditional logistic regression models, which may oversimplify relations between characteristics and outcome, or machine learning techniques, which may be difficult to interpret. We developed and evaluated a novel evolutionary algorithm for fuzzy decision trees to accurately identify patients with poor outcome after endovascular treatment, which was defined as having a modified Rankin Scale score (mRS) higher or equal to 5. The created decision trees have the benefit of being comprehensible, easily interpretable models, making its predictions easy to explain to patients and practitioners. Insights in the reason for the predicted outcome can encourage acceptance and adaptation in practice and help manage expectations after treatment. We compared our proposed method to CART, the benchmark decision tree algorithm, on classification accuracy and interpretability. The fuzzy decision tree significantly outperformed CART: using 5-fold cross-validation with on average 1090 patients in the training set and 273 patients in the test set, the fuzzy decision tree misclassified on average 77 (standard deviation of 7) patients compared to 83 (±7) using CART. The mean number of nodes (decision and leaf nodes) in the fuzzy decision tree was 11 (±2) compared to 26 (±1) for CART decision trees. With an average accuracy of 72% and much fewer nodes than CART, the developed evolutionary algorithm for fuzzy decision trees might be used to gain insights into the predictive value of patient characteristics and can contribute to the development of more accurate medical outcome prediction methods with improved clarity for practitioners and patients.

Venous thrombosis and coagulation parameters in patients with pure venous malformations.

INTRODUCTION: Venous malformations (VMs) are ubiquitous, low-flow vascular anomalies known to be occasionally painful due to thrombotic episodes within the lesion. The prevalence of superficial or deep vein thrombosis is unclear. METHODS: A cross-sectional study among outpatients aged ≥ 12 years with pure VMs was performed, quantifying the prevalence of thrombosis by screening all patients with compression ultrasonography (CUS). Additionally, we evaluated whether coagulation alterations were related to thrombosis observed with CUS. RESULTS: In total, 69 patients with pure VMs were eligible, median age was 30 years (range 12-63) and 52% were female. A total of 68 patients underwent CUS. Superficial vein thrombosis was observed in 10 (15%) cases; 1 patient had a current asymptomatic deep venous thrombosis. Residual superficial or deep thrombosis was observed in 25 patients (36%). In total, 49% had either a history or current signs of a thrombotic event and overall 10% had venous thromboembolism. In approximately 50% of the patients the D-dimer level was above 0.5 mg/l. Median P-selectin and Von Willebrand factor levels were 29 ng/ml (interquartile range (IQR) 21-34) and 108% (IQR 83-132), respectively. No differences were observed in the coagulation parameters between the patients with and without current clots in their VM. CONCLUSION: This study shows that superficial or deep vein thrombosis is common among patients with a pure VM. Physicians should be aware of this high incidence, especially if other risk factors for thrombosis are present.