Malignant hyperthermia is a rare but life-threatening pharmacogenetic disorder triggered by exposure to specific anesthetic agents. Although this occurrence could affect virtually any patient during the perioperative time, the pediatric population is particularly vulnerable, and it has a five-fold higher incidence in children compared to adults. In the last few decades, synergistic efforts among leading anesthesiology, pediatrics, and neurology associations have produced new evidence concerning the diagnostic pathway, avoiding unnecessary testing and limiting false diagnoses. However, a personalized approach and an effective prevention policy focused on clearly recognizing the high-risk population, defining perioperative trigger-free hospitalization, and rapid activation of supportive therapy should be improved. Based on epidemiological data, many national scientific societies have produced consistent guidelines, but many misconceptions are common among physicians and healthcare workers. This review shall consider all these aspects and summarize the most recent updates.

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Machine learning and cluster analysis applied to the clinical setting of an intensive care unit can be a valuable aid for clinical management, especially with the increasing complexity of clinical monitoring. Providing a method to measure clinical experience, a proxy for that automatic gestalt evaluation that an experienced clinician sometimes effortlessly, but often only after long, hard consideration and consultation with colleagues, relies upon for decision making, is what we wanted to achieve with the application of machine learning to antibiotic therapy and clinical monitoring in the present work. This is a single-center retrospective analysis proposing methods for evaluation of vitals and antimicrobial therapy in intensive care patients. For each patient included in the present study, duration of antibiotic therapy, consecutive days of treatment and type and combination of antimicrobial agents have been assessed and considered as single unique daily record for analysis. Each parameter, composing a record was normalized using a fuzzy logic approach and assigned to five descriptive categories (fuzzy domain sub-sets ranging from “very low” to “very high”). Clustering of these normalized therapy records was performed, and each patient/day was considered to be a pertaining cluster. The same methodology was used for hourly bed-side monitoring. Changes in patient conditions (monitoring) can lead to a shift of clusters. This can provide an additional tool for assessing progress of complex patients. We used Fuzzy logic normalization to descriptive categories of parameters as a form nearer to human language than raw numbers.