Abstract: Introduction The benefits of early thromboprophylaxis in symptomatic COVID-19 outpatients remain unclear. We present the 90-day results from the randomised, open-label, parallel-group, investigator-initiated, multinational OVID phase III trial. Methods Outpatients aged 50 years or older with acute symptomatic COVID-19 were randomised to receive enoxaparin 40 mg for 14 days once daily vs. standard of care (no thromboprophylaxis). The primary outcome was the composite of untoward hospitalisation and all-cause death within 30 days from randomisation. Secondary outcomes included arterial and venous major cardiovascular events, as well as the primary outcome within 90 days from randomisation. The study was prematurely terminated based on statistical criteria after the predefined interim analysis of 30-day data, which has been previously published. In the present analysis, we present the final, 90-day data from OVID and we additionally investigate the impact of thromboprophylaxis on the resolution of symptoms. Results Of the 472 patients included in the intention-to-treat population, 234 were randomised to receive enoxaparin and 238 no thromboprophylaxis. The median age was 57 (Q1-Q3: 53-62) years and 217 (46 %) were women. The 90-day primary outcome occurred in 11 (4.7 %) patients of the enoxaparin arm and in 11 (4.6 %) controls (adjusted relative risk 1.00; 95 % CI: 0.44-2.25): 3 events per group occurred after day 30. The 90-day incidence of cardiovascular events was 0.9 % in the enoxaparin arm vs. 1.7 % in controls (relative risk 0.51; 95 % CI: 0.09-2.75). Individual symptoms improved progressively within 90 days with no difference between groups. At 90 days, 42 (17.9 %) patients in the enoxaparin arm and 40 (16.8 %) controls had persistent respiratory symptoms. Conclusions In adult community patients with COVID-19, early thromboprophylaxis with enoxaparin did not improve the course of COVID-19 neither in terms of hospitalisation and death nor considering COVID-19-related symptoms

Abstract: Aims Identification of high-risk patients and individualized decision support based on objective criteria for rapid discharge after transcatheter aortic valve implantation (TAVI) are key requirements in the context of contemporary TAVI treatment. This study aimed to predict 30-day mortality following TAVI based on machine learning (ML) using data from the German Aortic Valve Registry. Methods and results Mortality risk was determined using a random forest ML model that was condensed in the newly developed TAVI Risk Machine (TRIM) scores, designed to represent clinically meaningful risk modelling before (TRIMpre) and in particular after (TRIMpost) TAVI. Algorithm was trained and cross-validated on data of 22 283 patients (729 died within 30 days post-TAVI) and generalisation was examined on data of 5864 patients (146 died). TRIMpost demonstrated significantly better performance than traditional scores [C-statistics value, 0.79; 95% confidence interval (CI)] [0.74; 0.83] compared to Society of Thoracic Surgeons (STS) with C-statistics value 0.69; 95%-CI [0.65; 0.74]). An abridged (aTRIMpost) score comprising 25 features (calculated using a web interface) exhibited significantly higher performance than traditional scores (C-statistics value, 0.74; 95%-CI [0.70; 0.78]). Validation on external data of 6693 patients (205 died within 30 days post-TAVI) of the Swiss TAVI Registry confirmed significantly better performance for the TRIMpost (C-statistics value 0.75, 95%-CI [0.72; 0.79]) compared to STS (C-statistics value 0.67, CI [0.63; 0.70]). Conclusion TRIM scores demonstrate good performance for risk estimation before and after TAVI. Together with clinical judgement, they may support standardised and objective decision-making before and after TAVI