BackgroundPatients who use angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin II receptor blockers (ARBs) are prone to developing side effects like hypotension and even refractory hypotension during anesthesia use, and whether ACEIs/ARBs should be continued or discontinued in such patients remains debatable. The present systematic review and meta-analysis was conducted to clarify the consequences of continuing or withholding these drugs, especially with regards to the incidence of intraoperative hypotension, in patients who continue to use ACEIs/ARBs on the day of their scheduled surgery.MethodsStudies with data pertinent to the incidence of intraoperative hypotension during anesthesia use in patients who continued the use of ACEIs/ARBs on the day of their scheduled surgery were considered for inclusion.ResultsThirteen studies reporting on the incidences of intraoperative hypotension between patients who continued receiving ACEIs/ARBs and those who did not on the day of their surgical procedure were included. The pooled effects showed that hypotension during anesthesia was more likely to develop in patients who continued to take ACEIs/ARBs when compared to those who did not (RR = 1.41, 95% CI: 1.21–1.64). However, there were no significant differences between these groups of patients with regards to postoperative complications including ST-T abnormalities, myocardial injury, myocardial infarction, stroke, major adverse cardiac events, acute kidney injury, or death (RR = 1.25, 95% CI: 0.76–2.04). The differences remained similar in subgroup analyses and sensitivity analyses.ConclusionsNo sufficient available evidence to recommend discontinuing ACEIs/ARBs on the day of surgery was found in this literature review and meta-analysis. However, anesthetists should be cautious about the risk for intraoperative hypotension in patients chronically receiving ACEIs/ARBs, and should know how to treat it effectively.
Deep neural network is a new type of learning algorithm, which has both global and local aspects and performs well in pattern recognition and computational speed. In recent years, deep neural network algorithm has been widely used in scientific research and real life, but its complexity, parallelism and other characteristics lead it to be a very challenging and innovative research area. This study briefly introduces the basic principles and theoretical knowledge of deep neural network algorithms, and mainly discusses their applications and Advancement of feature extraction in the field.
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