ABSTRACT
Inflammatory bowel disease (IBD) is a chronic, incurable disease involving the gastrointestinal tract. It is characterized by complex, unclear pathogenesis, increased prevalence worldwide, and a wide spectrum of extraintestinal manifestations and comorbidities. Recognition of IBD remains challenging and delays in disease diagnosis still poses a significant clinical problem as it negatively impacts disease outcome. The main diagnostic tool in IBD continues to be invasive endoscopy. We aimed to create an IBD machine learning prediction model based on routinely performed blood, urine, and fecal tests. Based on historical patients' data (702 medical records: 319 records from 180 patients with ulcerative colitis (UC) and 383 records from 192 patients with Crohn's disease (CD)), and using a few simple machine learning classificators, we optimized necessary hyperparameters in order to get reliable few-features prediction models separately for CD and UC. Most robust classificators belonging to the random forest family obtained 97% and 91% mean average precision for CD and UC, respectively. For comparison, the commonly used one-parameter approach based on the C-reactive protein (CRP) level demonstrated only 81% and 61% average precision for CD and UC, respectively. Results of our study suggest that machine learning prediction models based on basic blood, urine, and fecal markers may with high accuracy support the diagnosis of IBD. However, the test requires validation in a prospective cohort.
ABSTRACT
Previous studies reported lower rates of recurrent venous thromboembolism (rVTE) among statin users, but this association could be influenced by concurrent anticoagulation and confounding by statin indication. This study aimed to confirm the beneficial association between statins and rVTE, stratified according to periods with and without anticoagulation, and additionally employ propensity score weighted approach to reduce risk of confounding by indication. The setting was a prospective multicentre cohort study and the outcome was time to first rVTE in statin vs. non-statin users. 980 participants with acute VTE were enrolled (mean age 75.0 years, 47% women), with median follow-up of 2.5 years. Of 241 (24.3%) statin users, 21 (8.7%) suffered rVTE vs. 99 (13.4%) among 739 non-users. The overall adjusted sub-hazard ratio (aSHR) for rVTE comparing statin users to non-users was 0.72 (95%CI 0.44 to 1.19, p = 0.20). This association was only apparent during periods without anticoagulation (aSHR 0.50, 95%CI 0.27 to 0.92, p = 0.03; vs. with anticoagulation: aSHR 1.34, 95%CI 0.54 to 3.35, p = 0.53). Using propensity scores, the rVTE risk during periods without anticoagulation fell further (aSHR 0.20, 95%CI 0.08 to 0.49, p < 0.001). In conclusion, statin use is associated with a more pronounced risk reduction for rVTE than previously estimated, but only during periods without anticoagulation.