Machine Learning-based Cerebral Venous Thrombosis Diagnosis with Clinical Data.

OBJECTIVES: Cerebral Venous Thrombosis (CVT) poses diagnostic challenges due to the variability in disease course and symptoms. The prognosis of CVT relies on early diagnosis. Our study focuses on developing a machine learning-based screening algorithm using clinical data from a large neurology referral center in southern Iran. METHODS: The Iran Cerebral Venous Thrombosis Registry (ICVTR code: 9001013381) provided data on 382 CVT cases from Namazi Hospital. The control group comprised of adult headache patients without CVT as confirmed by neuroimaging and was retrospectively selected from those admitted to the same hospital. We collected 60 clinical and demographic features for model development and validation. Our modeling pipeline involved imputing missing values and evaluating four machine learning algorithms: generalized linear model, random forest, support vector machine, and extreme gradient boosting. RESULTS: A total of 314 CVT cases and 575 controls were included. The highest AUROC was reached when imputation was used to estimate missing values for all the variables, combined with the support vector machine model (AUROC=0.910, Recall=0.73, Precision=0.88). The best recall was achieved also by the support vector machine model when only variables with less than 50% missing rate were included (AUROC=0.887, Recall=0.77, Precision=0.86). The random forest model yielded the best precision by using variables with less than 50% missing rate (AUROC=0.882, Recall=0.61, Precision=0.94). CONCLUSION: The application of machine learning techniques using clinical data showed promising results in accurately diagnosing CVT within our study population. This approach offers a valuable complementary assistive tool or an alternative to resource-intensive imaging methods.

ABO blood group and Rh factor distributions in patients with epilepsy.

OBJECTIVE: We investigated the ABO blood group and Rh factor distributions in patients with epilepsy (PWE) in comparison with a comparator population. METHODS: We recruited patients who were admitted to the epilepsy ward at Namazi hospital in Shiraz, Iran, in 2021. We classified epilepsies into two categories: focal vs. generalized. We also used the anonymous data from Fars Blood Transfusion Organization from 15th June to 30th June, 2021, as the comparator population (to estimate the frequencies of various blood types in the cohort from which PWE were recruited). RESULTS: Overall, 390 PWE were included [131 (33.6%) with generalized and 259 (66.4%) with focal epilepsy]. We also included 7672 blood donors [from Fars Blood Transfusion Organization data]. The O phenotype had the highest frequencies in both PWE and the comparator population, followed by A, B, and AB blood groups. Similar patterns were observed in patients with focal and generalized epilepsy. With regard to Rh blood group, the Rh-positive phenotype was more prevalent in all groups. The differences between the groups were not significant in any of the comparisons. CONCLUSION: While we did not observe any significant associations between blood group and epilepsy in the current study, previous studies have demonstrated compelling evidence that risks of some neuropsychiatric disorders are related to the chemistry of blood, including blood group classification. The issue of the association between epilepsy and blood group should be investigated in large and well-designed studies in the future.