Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
National insights on Malignant Hyperthermia: a SIAARTI Survey on clinical practices, preparedness, and future directions
1
Zitationen
7
Autoren
2025
Jahr
Abstract
<title>Abstract</title> <bold>Background</bold> Malignant Hyperthermia Syndrome is a rare pharmacogenetic disorder, highly life-threatening if diagnosis and treatment is delayed. The purpose of this study is to assess the knowledge and current practices of Italian anesthesiogists in managing Malignant Hypertermia episodes. <bold>Methods</bold> We conducted a national survey. Data were collected via an online questionnaire distributed by the Italian Society of Anaesthesia, Analgesia, Resuscitation and Intensive Care (SIAARTI). Responses were collected over 15 weeks between July 15 and October 15, 2024, using an online GDPR-compliant platform <bold>Results</bold> A total of 395 anesthetists completed the survey. The majority are employed in public (35%) and university hospitals (26%), with an average of 20 years of professional experience. MH had been managed at least once by 31% of respondents, and 70% of them declared they always report adverse reactions In over 90% of cases, preventive measures (removal of trigger drugs, ventilator wash-out, perioperative care) are indentified, although only 49% reported having an internal protocol in place at their institution. In most centers (89%) non anesthesiologists are responsible for the storgae and supply of dantrolene and only 66% of respondents correctly identifying sterile water as its appropriate solvent. <bold>Discussion </bold>Our results highlight the need for broader standardization of MH management. Despite limitation in sample size and difference in geographical and hospital setting, the survey reveales a discrepancy between clinical practice and recommended strategies. While preventive measures are widely recognized, they are often not strandardized, and a correct treatment remains an area for significant improvement.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.245 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.102 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.468 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.429 Zit.