Anaesthetic depth and delirium: a challenging balancing act.

This editorial highlights the findings of the Balanced Anaesthesia Delirium study, a 515-patient substudy of the 6644 patient Balanced Anaesthesia trial, which found that targeting deep anaesthesia in patients undergoing major noncardiac surgery was not associated with significantly increased postoperative death or major morbidity. The substudy found that using bispectral index (BIS) guidance with the intention of deliberately achieving deep volatile agent-based anaesthesia (target BIS reading 35 vs 50) significantly increased delirium incidence (28% vs 19%), although not subsyndromal delirium incidence (45% vs 49%). We discuss the implications of these findings for anaesthetic practice, and address whether the BIS should be used as a guide to deliver precision anaesthesia for delirium prevention. We posit that subpopulation-based differences within this multicentre substudy could have affected delirium occurrence, since the findings appeared to rest on outcomes in patients from East Asia. We conclude that questions of whether and for whom deep anaesthesia is deliriogenic remain unanswered.

Observed to expected 30-day mortality as a benchmark for transcatheter aortic valve replacement.

OBJECTIVE: The observed-to-expected 30-day mortality ratio (O:E ratio) is a standard metric by which transcatheter aortic valve replacement (TAVR) trials have been evaluated. Early TAVR trials consistently demonstrated O:E ratio less than 0.6 after TAVR when based on the Society for Thoracic Surgery Predicted Risk of Mortality (STS-PROM) for surgical aortic valve replacement. Recent published results from the Transcatheter Valve Therapy (TVT) Registry have demonstrated O:E ratios of 1.0. We evaluated our own O:E ratios for TAVR to investigate this discordance. METHODS: Data were collected prospectively for TAVR patients from 2008 through 2015 (N = 546) and were reviewed retrospectively. The observed mortality and STS-PROM were calculated to formulate O:E ratios and were compared over a variety of subgroups. RESULTS: Overall, the O:E ratio for 30-day mortality was 0.4 and significantly less than 1 (P < .001; 95% confidence interval, 0.25-0.63). The O:E ratio relationship remained less than 0.5 for patients with low (STS-PROM < 4), moderate (STS-PROM = 4-8) and high risk (STS-PROM > 8). The O:E ratio was significantly higher for transapical patients (O:E ratio = 0.8) when compared with transfemoral patients (O:E ratio = 0.2). Lastly, O:E ratios for both commercial (O:E ratio = 0.5) and research (O:E ratio = 0.3) patients were similar (P = .337), and both were significantly less than 1 (P = .007 and P < .001, respectively). CONCLUSIONS: The STS-PROM consistently overestimated 30-day mortality after TAVR. Achieving an O:E ratio less than 0.6 may be a realistic goal for all TAVR programs. While an accurate and specific risk calculator for 30-day mortality after TAVR remains to be established, our data suggest that current TVT results are not acceptable for commercial TAVR and that programs with an O:E ratio greater than 0.6, based on the STS-PROM, should reevaluate internal processes to improve their results.

An evolutionary framework for association testing in resequencing studies.

Sequencing technologies are becoming cheap enough to apply to large numbers of study participants and promise to provide new insights into human phenotypes by bringing to light rare and previously unknown genetic variants. We develop a new framework for the analysis of sequence data that incorporates all of the major features of previously proposed approaches, including those focused on allele counts and allele burden, but is both more general and more powerful. We harness population genetic theory to provide prior information on effect sizes and to create a pooling strategy for information from rare variants. Our method, EMMPAT (Evolutionary Mixed Model for Pooled Association Testing), generates a single test per gene (substantially reducing multiple testing concerns), facilitates graphical summaries, and improves the interpretation of results by allowing calculation of attributable variance. Simulations show that, relative to previously used approaches, our method increases the power to detect genes that affect phenotype when natural selection has kept alleles with large effect sizes rare. We demonstrate our approach on a population-based re-sequencing study of association between serum triglycerides and variation in ANGPTL4.