Comparison of 1-year outcome in patients with severe aorta stenosis treated conservatively or by aortic valve replacement or by percutaneous transcatheter aortic valve implantation (data from a multicenter Spanish registry)

The factors that influence decision making in severe aortic stenosis (AS) are unknown. Our aim was to assess, in patients with severe AS, the determinants of management and prognosis in a multicenter registry that enrolled all consecutive adults with severe AS during a 1-month period. One-year follow-up was obtained in all patients and included vital status and aortic valve intervention (aortic valve replacement [AVR] and transcatheter aortic valve implantation [TAVI]). A total of 726 patients were included, mean age was 77.3 ± 10.6 years, and 377 were women (51.8%). The most common management was conservative therapy in 468 (64.5%) followed by AVR in 199 (27.4%) and TAVI in 59 (8.1%). The strongest association with aortic valve intervention was patient management in a tertiary hospital with cardiac surgery (odds ratio 2.7, 95% confidence interval 1.8 to 4.1, p <0.001). The 2 main reasons to choose conservative management were the absence of significant symptoms (136% to 29.1%) and the presence of co-morbidity (128% to 27.4%). During 1-year follow-up, 132 patients died (18.2%). The main causes of death were heart failure (60% to 45.5%) and noncardiac diseases (46% to 34.9%). One-year survival for patients treated conservatively, with TAVI, and with AVR was 76.3%, 94.9%, and 92.5%, respectively, p <0.001. One-year survival of patients treated conservatively in the absence of significant symptoms was 97.1%. In conclusion, most patients with severe AS are treated conservatively. The outcome in asymptomatic patients managed conservatively was acceptable. Management in tertiary hospitals is associated with valve intervention. One-year survival was similar with both interventional strategies.

Using multivariate sequential patterns to improve survival prediction in intensive care burn unit

Resuscitation and stabilization are key issues in Intensive Care Burn Units and early survival predictions help to decide the best clinical action during these phases. Current survival scores of burns focus on clinical variables such as age or the body surface area. However, the evolution of other parameters (e.g. diuresis or fluid balance) during the first days is also valuable knowledge. In this work we suggest a methodology and we propose a Temporal Data Mining algorithm to estimate the survival condition from the patient’s evolution. Experiments conducted on 480 patients show the improvement of survival prediction.