Explainable machine learning prediction of edema adverse events in patients treated with tepotinib.

Tepotinib is approved for the treatment of patients with non-small-cell lung cancer harboring MET exon 14 skipping alterations. While edema is the most prevalent adverse event (AE) and a known class effect of MET inhibitors including tepotinib, there is still limited understanding about the factors contributing to its occurrence. Herein, we apply machine learning (ML)-based approaches to predict the likelihood of occurrence of edema in patients undergoing tepotinib treatment, and to identify factors influencing its development over time.

[Principles of acute pain therapy-An overview taking special features in the patient collective of ophthalmology into consideration].

For many years the quality of perioperative pain management in general has been repeatedly reported as inadequate and there is significant evidence to indicate that this is also true after surgical procedures in ophthalmology. The patient population in ophthalmology is quite challenging due to numerous comorbidities and a high average age resulting in numerous contraindications and organ dysfunctions and requiring special knowledge to ensure high quality acute pain management. The following overview covers basic knowledge of acute pain management, with a particular focus on analgesic approaches and the specifics of the patient population and the associated limitations in terms of analgesic and co-analgesic pharmacological options.