Pharmacological Treatments and Therapeutic Drug Monitoring in Patients with Chronic Pain.

Pain is an unpleasant sensory and emotional experience that affects every aspect of a patient's life and which may be treated through different pharmacological and non-pharmacological approaches. Analgesics are the drugs most commonly used to treat pain, and in specific situations, the use of opioids may be considered with caution. These drugs, in fact, do not always induce optimal analgesia in patients, and several problems are associated with their use. The purpose of this narrative review is to describe the pharmacological approaches currently used for the management of chronic pain. We review several aspects, from the pain-scale-based methods currently available to assess the type and intensity of pain, to the most frequently administered drugs (non-narcotic analgesics and narcotic analgesics), whose pharmacological characteristics are briefly reported. Overall, we attempt to provide an overview of different pharmacological treatments while also illustrating the relevant guidelines and indications. We then report the strategies that may be used to reduce problems related to opioid use. Specifically, we focus our attention on therapeutic drug monitoring (TDM), a tool that could help clinicians select the most suitable drug and dose to be used for each patient. The actual potential of using TDM to optimize and personalize opioid-based pain treatments is finally discussed based on recent scientific reports.

A LC-MS/MS based methodology for the environmental monitoring of healthcare settings contaminated with antineoplastic agents.

Background: Adverse health events associated with the exposure of healthcare workers to antineoplastic drugs are well documented in literature and are often related to the chemical contamination of work surfaces. It is therefore crucial for healthcare professionals to validate the efficiency of safety procedures by periodic biological and environmental monitoring activities where the main methodological limitations are related to the complexity, in terms of chemical-physical features and chemical-biological stability, of the drugs analyzed. Materials and methods: Here we describe the evaluation and application of a UHPLC-MS/MS based protocol for the environmental monitoring of hospital working areas potentially contaminated with methotrexate, iphosphamide, cyclophosphamide, doxorubicin, irinotecan, and paclitaxel. This methodology was used to evaluate working areas devoted to the preparation of chemotherapeutics and combination regimens at the University Hospital "San Giovanni di Dio e Ruggi d'Aragona" in Salerno (Italy). Results: Our analyses allowed to uncover critical aspects in both working protocols and workspace organization, which highlighted, among others, cyclophosphamide and iphosphamide contamination. Suitable adjustments adopted after our environmental monitoring campaign significantly reduced the exposure risk for healthcare workers employed in the unit analyzed. Conclusion: The use of sensitive analytical approaches such as LC-MS/MS coupled to an accurate wiping procedure in routine environmental monitoring allows to effectively improve chemical safety for exposed workers.

COVID-19 and digital competencies among young physicians: are we (really) ready for the new era? A national survey of the Italian Young Medical Doctors Association.

BACKGROUND: Digital health (DH) is nowadays fundamental for physicians. Despite the improvement of information and communications technology (ICT), Italian medical doctors' (MDs) education system seems inadequate in this area. Moreover, due to the COVID-19 pandemic, societies are waking up to their limitations. The aim of this paper is to analyze the Italian status quo in DH. METHODS: The Italian Young Medical Doctors Association (Segretariato Italiano Giovani Medici - SIGM) proposed a web-based survey to assess DH awareness and previous knowledge among young doctors. Investigated areas were: big data, -omics technology and predictive models, artificial intelligence (AI), internet of things, telemedicine, social media, blockchain and clinical-data storage. RESULTS: A total of 362 participants answered to the survey. Only 13% had experience in big data during clinical or research activities, 13% in -omics technology and predictive models, 13% in AI, 6% had experience in internet of things, 22% experienced at least one telemedicine tool and 23% of the participants declared that during their clinical activities data collection was paper-driven. CONCLUSIONS: Three categories of MDs, high-tech, low-tech and no-tech, can be identified from the survey-based investigation. Our survey's results indicate an urgent need for integration of pre- and post-graduation training in digital health to provide adequate medical education.