Chirurg最新文献

Geriatric patients often have cardiovascular diseases that require differentiated perioperative management of hemostasis. The operation-related bleeding risk and the individual thromboembolism risk mutually influence each other, so that a differentiated preoperative assessment of the further prescription of coagulation-modulating medication is required. In many cases the active coagulation medication can be interrupted without replacement or continued unchanged. In cardiovascular diseases with antiplatelet medication, the preoperative risk-benefit assessment for most operations leads to the continuation of previous platelet aggregation inhibitor monotherapy; however, if there is a high risk of cardiovascular thromboembolism with dual platelet inhibition, the individual perioperative medication should be closely coordinated with a geriatrician or cardiologist.In most cases, the intake of vitamin K antagonists (VKA) can be preoperatively interrupted. In cases of high risk of thromboembolism, a temporary bridging with heparin must be carried out. The introduction of the four new direct oral antagonists (DOAC) has made the perioperative management of anticoagulation much easier. Bridging with heparin is not necessary. Perioperatively, only the dosage and timing of interruption of the DOACs have to be determined individually depending on the operative bleeding risk as well as the age, body weight and kidney function of the patient. If bleeding complications arise under the influence of the DOACs, antidotes are available for three of the four DOACs, which in acute cases can be used in addition to prothrombin complex concentrates and fresh frozen plasma to normalize coagulation.

The growing influence of artificial intelligence on radiology not only leads to a fundamental change in the way diagnoses are made but also creates a wealth of additional information. Many programs correlate the parameters of image evaluation with the results of histological, molecular biological and genetic examinations and from these they derive therapeutic and prognostic statements that are intended to serve the planning of individual precision medicine. This information is included in the findings report and is therefore also fully available to the patient; however, the information takes no account of influencing factors, such as the time lag between diagnosis and start of treatment, comorbidities as well as the availability and tolerability of drugs. It is foreseeable that the supplementary statements of the expert systems will considerably influence the discourse between doctor and patient.

Background: The economic pressure in the healthcare system has noticeably increased in the past few years. The manifestation of an "economization in medicine" development raises questions about the compatibility of physicians' duties and economic incentives in the healthcare system.

Objective: Against this background the article analyzes areas of conflict in the German healthcare system and surgery in particular. The main questions focus on: what lines of conflict can arise between ethical duties and economic requirements and what possibilities for conflict resolution can provide orientation on the macrolevel and microlevel?

Material and methods: The article is based on the analysis of normative regulations, guidelines and statements from the self-administrative institutions and multidisciplinary literature from medicine, medical ethics and health economics. Core issues in the conflict area between "humanity-ethics-economics" are structured and recommendations for action are derived.

Results and discussion: Superordinate regulatory framework conditions and their subsequent incentives must not conflict with the ethical principles of medical care, especially the primary orientation to patient welfare. Institutional and individual healthcare providers have a responsibility towards patients first and only secondarily for an economically appropriate spending of public resources. The provision of medical care for people must enable an adequate livelihood. Institutional maximization of profits is to be avoided, especially concerning financial investors. In the corona pandemic, economic disincentives are becoming apparent and necessitate readjustments. Possible recommendations for action are the empowerment of the medical profession and management to engage in a qualified exchange.

Background: Digital systems have increasingly become integrated into the modern operating room in the last few decades. This has brought about a massive change, especially in minimally invasive surgery.

Objective: The article provides an overview of the current technical innovations and the perspectives of digitalization and artificial intelligence (AI) in surgery.

Material and methods: The article is based on a literature search via PubMed and research work by the participating coauthors.

Results: Current research is increasingly looking at machine learning techniques that take advantage of the complex data in surgery; however, the integration of artificial intelligence systems into the operating room and clinical practice has only just begun.

Discussion: Translational research of artificial intelligence in surgery is still in its infancy but has great potential to improve patient care; however, to accelerate the incorporation of intelligent systems into the clinical practice, the creation of interdisciplinary research groups led by surgeons is necessary.

Oncological surgery is a discipline which closely interacts with other clinical partners and remains in many cases the cornerstone of a curative treatment of solid tumors. Due to the progress in the field of systemic tumor treatment as well as innovations in surgical techniques, the indications in oncological surgery are also changing, such as extended indications for patients with oligometastatic disease. Surgery of metastases has long been established for colorectal cancer and is being further tested for other entities, such as pancreatic and gastric cancer, within randomized controlled clinical trials (e.g. RENAISSANCE and METAPANC). A new challenge is the handling of a clinical complete remission after total neoadjuvant therapy, for example in locally advanced rectal cancer or in esophageal cancer. Here, organ and function preservation are increasingly propagated but should only be performed within clinical trials until stratification enables the identification of patients in whom this concept is oncologically safe. The personalized use of oncological surgery is dependent on the patient, the tumor and on the total multimodal concept.

Ethical, legal and social aspects are gaining increasingly more attention in the development and during the initial clinical application of medical devices. The introduction of elements of artificial intelligence (AI) and systems which are using AI makes this already complex topic even more challenging. The introduction of so-called dynamic AI or dynamic machine learning (ML) algorithms in this respect represents a turning point. Unlike conventional medical devices, the development of systems using dynamic AI is not yet complete at the beginning of the clinical application. The aim of a dynamic AI system is to continuously improve through practical use and by the processing of usage data. This continuous evolution, along with the lack of transparency regarding internal work processes, could make it difficult to understand the underlying rationale for the assessments made by the algorithms. This aspect affects the acceptance of the technology both by clinicians and patients and furthermore questions the autonomy of patients and clinicians in the course of the treatment process. A way out of this ethical and regulatory dilemma must urgently be found and will require extreme efforts from all stakeholders. At present, no consensual solution is apparent. What is quite certain, however, is that users, i.e. in concrete terms surgeons, must play a much more active role than they have done in the past when dealing with AI-based medical devices and should prepare themselves to actively accompany the software life cycle of AI technologies.