In-hospital versus after-discharge complete revascularization in patients with ST segment elevation myocardial infarction and multivessel disease. REVIVA-ST trial.

INTRODUCTION: Complete revascularization (CR) in patients with ST-segment elevation myocardial infarction (STEMI) and multivessel disease (MVD), is associated with a reduction in major adverse cardiovascular events (MACE). However, there is uncertainty about whether nonculprit-lesion revascularization should be performed, during index hospitalization or delayed, especially regarding health care resources utilization. In this study, we aimed to evaluate the impact of in-hospital nonculprit-lesion revascularization vs. delayed (after discharge) revascularization on the length of index hospitalization. METHODS: In this single-center study, we randomly assigned patients with STEMI and MVD who underwent successful culprit-lesion PCI to a strategy of either CR during in-hospital admission or a delayed CR after discharge. The first primary endpoint was the length of hospital stay. The second endpoint was the composite of cardiovascular death, myocardial infarction or ischemia-driven revascularization at 12 months (MACE). RESULTS: From January 2018 to December 2022, we enrolled 258 patients (131 allocated to CR during in-hospital admission and 127 to an after-discharge CR). We found a significant reduction in the length of hospital stay in those assigned to after-discharge CR strategy [4 days (3-5) versus 7 days (5-9); p = 0.001]. At 12-month of follow-up, no differences were found in the occurrence of MACE, 7 (5.34%) patients in in-hospital CR and 4 (3.15%) in after-discharge CR strategy; (hazard ratio, 0.59; 95% confidence interval, 0.17 to 2.02; p = 0.397). CONCLUSIONS: In STEMI patients with MVD, an after-discharge CR strategy reduces the length of index hospitalization without an increased risk of MACE after 12 months of follow-up. TRIAL REGISTRATION: ClinicalTrials.gov number: NCT04743154.

Using Large Language Models to Enhance the Reusability of Sensor Data.

The Internet of Things generates vast data volumes via diverse sensors, yet its potential remains unexploited for innovative data-driven products and services. Limitations arise from sensor-dependent data handling by manufacturers and user companies, hindering third-party access and comprehension. Initiatives like the European Data Act aim to enable high-quality access to sensor-generated data by regulating accuracy, completeness, and relevance while respecting intellectual property rights. Despite data availability, interoperability challenges impede sensor data reusability. For instance, sensor data shared in HTML formats requires an intricate, time-consuming processing to attain reusable formats like JSON or XML. This study introduces a methodology aimed at converting raw sensor data extracted from web portals into structured formats, thereby enhancing data reusability. The approach utilises large language models to derive structured formats from sensor data initially presented in non-interoperable formats. The effectiveness of these language models was assessed through quantitative and qualitative evaluations in a use case involving meteorological data. In the proposed experiments, GPT-4, the best performing LLM tested, demonstrated the feasibility of this methodology, achieving a precision of 93.51% and a recall of 85.33% in converting HTML to JSON/XML, thus confirming its potential in obtaining reusable sensor data.