Backgound:Besides the relief of symptoms, the main purpose of any treatment must be to ensure a better quality of life (QOL). Mere recording of symptoms reveals their severity and frequency, but gives scant information on its effect on QOL.Aim:The study was designed to assess QOL in Indian patients with chronic urticaia (CU).Subjects and Methods:We used the validated CU-QOL by Bairadani et al., consisting of five domains and each domain consisting of several items. Each item was scored from 1 to 5 (score 5 = most affected, score 1 = not affected).Criteria for Inclusion:Patients having symptoms of rash and pruritis on most of the days for at least 6 weeks were enrolled.Criteria for Exclusion:If the individuals had CU secondary to any other disease, they were excluded.Results:We enrolled 48 patients. The means of the domains, the items, and the frequency of occurrence of an item were tabulated. The highest mean scores were in the domains of symptoms, followed by sleep disturbances, life activities, looks, and limits. The highest mean scores for the items were for pruritis and wheals followed by sleep disturbances, mood changes, nervousness, embarrassment, fatigue, loss of concentration, reduced work, and social relationships. We also observed a significant relationship among individual items. It is possible that they may have an additive effect.Conclusions:Even though CU significantly affected many items, patients were rarely aware of them. There was a significant inter-item correlation, suggesting that items might be aggravating each other. It is important to address the issues regarding QOL along with symptoms for better management of CU.

Aims and Method. To evaluate the practical utility of off-licence prescribing and clinical outcomes of treatment with atypical antipsychotic Melperone. Method: Prospective data collection on patient's clinical characteristics and outcomes. Results. 17 patients with a diagnosis of refractory schizophrenia were identified as suitable for off-license prescribing of Melperone and commenced treatment (13 were previously treated with Clozapine). Seven of those currently remain on Melperone (41%), and for six patents, the BPRS symptom scores reduced significantly over time (24–61%) additionally patients displayed improvements of their quality of life. Six patients were discontinued due to noncompliance and/or side effects. Melperone was ineffective in the other four patients. Clinical Implications. The example of a small group of patients responding well to a comparably safe and inexpensive atypical antipsychotic with favourable side effect profile should encourage clinicians to use this tool as third-line treatment and to conduct more systematic clinical research.

BackgroundOpioids are strong pain medications that can be essential for acute pain. However, opioids are also commonly used for chronic conditions and illicitly where there are well-recognised concerns about the balance of their benefits and harms. Technologies using artificial intelligence (AI) are being developed to examine and optimise the use of opioids. Yet, this research has not been synthesised to determine the types of AI models being developed and the application of these models.MethodsWe aimed to synthesise studies exploring the use of AI in people taking opioids. We searched three databases: the Cochrane Database of Systematic Reviews, Embase and Medline on 4 January 2021. Studies were included if they were published after 2010, conducted in a real-life community setting involving humans and used AI to understand opioid use. Data on the types and applications of AI models were extracted and descriptively analysed.ResultsEighty-one articles were included in our review, representing over 5.3 million participants and 14.6 million social media posts. Most (93%) studies were conducted in the USA. The types of AI technologies included natural language processing (46%) and a range of machine learning algorithms, the most common being random forest algorithms (36%). AI was predominately applied for the surveillance and monitoring of opioids (46%), followed by risk prediction (42%), pain management (10%) and patient support (2%). Few of the AI models were ready for adoption, with most (62%) being in preliminary stages.ConclusionsMany AI models are being developed and applied to understand opioid use. However, there is a need for these AI technologies to be externally validated and robustly evaluated to determine whether they can improve the use and safety of opioids.

BackgroundThere are concerns about the balance of benefits and harms in people using opioids for chronic non-cancer pain. Technologies using artificial intelligence (AI) are being developed to examine and optimise the use of opioids. Yet, this research has not been synthesised to determine the types of AI models being developed and the application of these models.MethodsWe aimed to synthesise studies exploring the use of AI in people taking opioids. We searched three databases: the Cochrane Database of Systematic Reviews, EMBASE, and Medline, on 4 January 2021. Studies were included if they were published after 2010, conducted in a real-life community setting involving humans, and used AI to understand opioid use. Data on the types and applications of AI models were extracted and descriptively analysed.ResultsEighty-one articles were included in our review, representing over 5.3 million participants and 14.6 million social media posts. Most (93%) studies were conducted in the USA. The types of AI technologies included natural language processing (46%) and a range of machine learning algorithms, the most common being random forest algorithms (36%). AI was predominately applied for the surveillance and monitoring of opioids (46%), followed by risk prediction (42%), pain management (10%), and patient support (2%). Few of the AI models were ready for adoption, with most (62%) being in preliminary stages.ConclusionsA variety of AI models are being developed and applied to understand opioid use. However, there is a need for these AI technologies to be externally validated and robustly evaluated to determine whether they can improve the use and safety of opioids.SUMMARY BOXKey PointsAcross the landscape of opioid research, natural language processing models (46%) and ensemble algorithms, particularly random forest algorithms (36%), were the most common types of AI technologies studied.There were four main domains that AI was applied to assess the use of opioids, including surveillance and monitoring (46%), risk prediction (42%), pain management (10%), and patient support (2%).The AI technologies were at various stages of development, validation, and deployment, with most (62%) models in preliminary stages, 11% required external validation, and few models were openly available to access (6%).