RESUMO
PURPOSE: Most data regarding infective endocarditis (IE) after transcatheter aortic valve implantation (TAVI) comes from TAVI registries, rather than IE dedicated cohorts. The objective of our study was to compare the clinical and microbiological profile, imaging features and outcomes of patients with IE after SAVR with a biological prosthetic valve (IE-SAVR) and IE after TAVI (IE-TAVI) from 6 centres with an Endocarditis Team (ET) and broad experience in IE. METHODS: Retrospective analysis of prospectively collected data. From the time of first TAVI implantation in each centre to March 2021, all consecutive patients admitted for IE-SAVR or IE-TAVI were prospectively enrolled. Follow-up was monitored during admission and at 12 months after discharge. RESULTS: 169 patients with IE-SAVR and 41 with IE-TAVI were analysed. Early episodes were more frequent among IE-TAVI. Clinical course during hospitalization was similar in both groups, except for a higher incidence of atrioventricular block in IE-SAVR. The most frequently causative microorganisms were S. epidermidis, Enterococcus spp. and S. aureus in both groups. Periannular complications were more frequent in IE-SAVR. Cardiac surgery was performed in 53.6% of IE-SAVR and 7.3% of IE-TAVI (p=0.001), despite up to 54.8% of IE-TAVI patients had an indication. No differences were observed about death during hospitalization (32.7% vs 35.0%), and at 1-year follow-up (41.8% vs 37.5%), regardless of whether the patient underwent surgery or not. CONCLUSION: Patients with IE-TAVI had a higher incidence of early prosthetic valve IE. Compared to IE-SAVR, IE-TAVI patients underwent cardiac surgery much less frequently, despite having surgical indications. However, in-hospital and 1-year mortality rate was similar between both groups.
RESUMO
This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers' expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team's workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers' results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings.