Influence of preoperative frailty on quality of life after cardiac surgery: Protocol for a systematic review and meta-analysis.

BackgroundFrailty has emerged as an important prognostic marker of adverse outcomes after cardiac surgery, but evidence regarding its ability to predict quality of life after cardiac surgery is currently lacking. Whether frail patients derive the same quality of life benefit after cardiac surgery as patients without frailty remains unclear.MethodsThis systematic review will include interventional studies (RCT and others) and observational studies evaluating the effect of preoperative frailty on quality-of-life outcomes after cardiac surgery amongst patients 65 years and older. Studies will be retrieved from major databases including the Cochrane Central Register of Controlled Trials, Embase, and Medline. The primary exposure will be frailty status, independent of the tool used. The primary outcome will be change in quality of life, independent of the tool used. Secondary outcomes will include readmission during the year following the index intervention, discharge to a long-term care facility and living in a long-term care facility at one year. Screening, inclusion, data extraction and quality assessment will be performed independently by two reviewers. Meta-analysis based on the random-effects model will be conducted to compare the outcomes between frail and non-frail patients. The evidential quality of the findings will be assessed with the GRADE profiler.ConclusionThe findings of this systematic review will be important to clinicians, patients and health policy-makers regarding the use of preoperative frailty as a screening and assessment tool before cardiac surgery.Study registrationOSF registries (https://osf.io/vm2p8)

The internal thoracic artery skeletonization study: A paired, within-patient comparison [NCT00265499]

BACKGROUND: Traditional harvesting of the internal thoracic artery (ITA) for use as a conduit in coronary bypass surgery involves the dissection of a rim of tissue surrounding the artery on either side. Recent studies, primarily observational, have suggested that skeletonization of the ITA can improve conduit flow, increase length, and reduce the risk of deep sternal infection in high risk patients. Furthermore, skeletonization of the ITA can potentially preserve intercostal nerves and reduce post-operative pain and dysesthesias associated with ITA harvesting. In order to assess the effects of ITA skeletonization, we report a prospective, randomized, within-patient study design that shares many features of a cross-over study. METHODS: Patients undergoing bilateral internal thoracic artery harvest will be randomized to having one side skeletonized and the other harvested in a non-skeletonized manner. Outcome measures include ITA flow and length measured intra-operatively, post-operative pain and dysesthesia, evaluated at discharge, four weeks, and three months post-operatively, and sternal perfusion assessed using single photon emission computed tomography. Harvest times as well as safety endpoints of ITA injury will be recorded. DISCUSSION: This study design, using within-patient comparisons and paired analyses, minimizes the variability of the outcome measures, which is seldom possible in the evaluation of surgical techniques, with minimal chance of carryover effects that can hamper the interpretation of traditional cross-over studies. This study will provide a valid evaluation of clinically relevant effects of internal thoracic artery skeletonization in improving outcomes following coronary artery bypass surgery

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery