Veterans Health Administration

About: Veterans Health Administration is a government organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Population & Veterans Affairs. The organization has 63820 authors who have published 98417 publications receiving 4835425 citations. The organization is also known as: VHA.

Coronary-Artery Revascularization before Elective Major Vascular Surgery

Abstract: Background The benefit of coronary-artery revascularization before elective major vascular surgery is unclear. Methods We randomly assigned patients at increased risk for perioperative cardiac complications and clinically significant coronary artery disease to undergo either revascularization or no revascularization before elective major vascular surgery. The primary end point was long-term mortality. Results Of 5859 patients scheduled for vascular operations at 18 Veterans Affairs medical centers, 510 (9 percent) were eligible for the study and were randomly assigned to either coronary-artery revascularization before surgery or no revascularization before surgery. The indications for a vascular operation were an expanding abdominal aortic aneurysm (33 percent) or arterial occlusive disease of the legs (67 percent). Among the patients assigned to preoperative coronary-artery revascularization, percutaneous coronary intervention was performed in 59 percent, and bypass surgery was performed in 41 percent. T...

Hypoglycemia in Diabetes

Abstract: Iatrogenic hypoglycemia causes recurrent morbidity in most people with type 1 diabetes and many with type 2 diabetes, and it is sometimes fatal. The barrier of hypoglycemia generally precludes maintenance of euglycemia over a lifetime of diabetes and thus precludes full realization of euglycemia’s long-term benefits. While the clinical presentation is often characteristic, particularly for the experienced individual with diabetes, the neurogenic and neuroglycopenic symptoms of hypoglycemia are nonspecific and relatively insensitive; therefore, many episodes are not recognized. Hypoglycemia can result from exogenous or endogenous insulin excess alone. However, iatrogenic hypoglycemia is typically the result of the interplay of absolute or relative insulin excess and compromised glucose counterregulation in type 1 and advanced type 2 diabetes. Decrements in insulin, increments in glucagon, and, absent the latter, increments in epinephrine stand high in the hierarchy of redundant glucose counterregulatory factors that normally prevent or rapidly correct hypoglycemia. In insulin-deficient diabetes (exogenous) insulin levels do not decrease as glucose levels fall, and the combination of deficient glucagon and epinephrine responses causes defective glucose counterregulation. Reduced sympathoadrenal responses cause hypoglycemia unawareness. The concept of hypoglycemia-associated autonomic failure in diabetes posits that recent antecedent hypoglycemia causes both defective glucose counterregulation and hypoglycemia unawareness. By shifting glycemic thresholds for the sympathoadrenal (including epinephrine) and the resulting neurogenic responses to lower plasma glucose concentrations, antecedent hypoglycemia leads to a vicious cycle of recurrent hypoglycemia and further impairment of glucose counterregulation. Thus, short-term avoidance of hypoglycemia reverses hypoglycemia unawareness in most affected patients. The clinical approach to minimizing hypoglycemia while improving glycemic control includes 1 ) addressing the issue, 2 ) applying the principles of aggressive glycemic therapy, including flexible and individualized drug regimens, and 3 ) considering the risk factors for iatrogenic hypoglycemia. The latter include factors that result in absolute or relative insulin excess: drug dose, timing, and type; patterns of food ingestion and exercise; interactions with alcohol and other drugs; and altered sensitivity to or clearance of insulin. They also include factors that are clinical surrogates of compromised glucose counterregulation: endogenous insulin deficiency; history of severe hypoglycemia, hypoglycemia unawareness, or both; and aggressive glycemic therapy per se, as evidenced by lower HbA 1c levels, lower glycemic goals, or both. In a patient with hypoglycemia unawareness (which implies recurrent hypoglycemia) a 2- to 3-week period of scrupulous avoidance of hypoglycemia is advisable. Pending the prevention and cure of diabetes or the development of methods that provide glucose-regulated insulin replacement or secretion, we need to learn to replace insulin in a much more physiological fashion, to prevent, correct, or compensate for compromised glucose counterregulation, or both if we are to achieve near-euglycemia safely in most people with diabetes.

Applications of machine learning in drug discovery and development.

Abstract: Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development. Machine learning has been applied to numerous stages in the drug discovery pipeline. Here, Vamathevan and colleagues discuss the most useful techniques and how machine learning can promote data-driven decision making in drug discovery and development. They highlight major hurdles in the field, such as the required data characteristics for applying machine learning, which will need to be solved as machine learning matures.