A prospective survey was conducted over a one-month period in all surgical patients admitted to the recovery room of a university-affiliated teaching hospital. Complications arising in the recovery room were documented by the nursing staff according to predefined criteria and were critically evaluated. A total of 443 patients were admitted to the recovery room and in 133 (30%) of these, some form of complication was noted. There were 86 patients with complications referable to the central nervous system, 68 with abnormal cardiovascular parameters, 24 with nausea and/or vomiting and 10 with abnormalities referable to the respiratory system. Many patients had more than one complication. The results are discussed, with emphasis on their relevance to current anaesthetic practice. It is concluded that many patients exhibit recovery room complications when they are specifically sought. The recovery period remains a time of great potential danger to patients.
Digital twins, succinctly described as the digital representation of a physical object, is a concept that has emerged relatively recently with increasing application in the manufacturing industry. This article proposes the application of this concept to the healthcare domain to provide enhanced clinical decision support and enable more patient‐centric, and simultaneously more precise and individualized care to ensue. Digital twins combined with advances in Artificial Intelligence (AI) have the potential to facilitate the integration and processing of vast amounts of heterogeneous data stemming from diversified sources. Hence, in healthcare this can provide enhanced diagnosis and treatment decision support. In applying digital twins in combination with AI to complex healthcare contexts to assist clinical decision making, it is also likely that a key current challenge in healthcare; namely, providing better quality care which is of high value and can lead to better clinical outcomes and a higher level of patient satisfaction, can ensue. In this focus article, we address this proposition by focusing on the case study of cancer care and present our conceptualization of a digital twin model combined with AI to address key, current limitations in endometrial cancer treatment. We highlight the role of AI techniques in developing digital twins for cancer care and simultaneously identify key barriers and facilitators of this process from both a healthcare and technology perspective. This article is categorized under: Application Areas > Health Care
Electroconvulsive therapy (ECT) is a safe treatment process in which the risk entailed is said to be generally no greater than that associated with the use of short-acting barbiturates l-4 • Opinions differ as to the relative safety of electroconvulsive therapy in debilitated patients or in those with severe myocardial dysfunction. Gerring and Shields 5 identified a group of patients at high risk for the development of cardiovascular complications, namely myocardial ischaemia and/or arrhythmias following electroconvulsive therapy. In this group, which included patients with a history of angina, myocardial infarction, congestive heart failure, arrhythmias, rheumatic heart disease or an abnormal baseline electrocardiogram, the complication rate was 70%. However, Dec et al. 2 found electroconvulsive therapy to be safe, effective and well tolerated in a group of elderly debilitated patients, one-quarter of whom had severe cardiovascular disease including poor ejection fractions and recent myocardial infarctions.
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