Pacientes com esquizofrenia polimedicados usuários de clozapina : alterações no hemograma e principais interações medicamentosas envolvendo clozapina

Os pacientes com esquizofrenia geralmente são polimedicados e as alterações no hemograma e as interações medicamentosas são prevalentes neste grupo de pacientes. A clozapina demonstrou ser eficaz em pacientes resistentes ao tratamento. A agranulocitose e a neutropenia são os principais efeitos adversos da clozapina. Avaliamos as alterações no hemograma nos últimos cinco anos e as interações medicamentosas em pacientes estáveis com esquizofrenia, usuários de clozapina. Foram recrutados cento e vinte e um pacientes ambulatoriais com esquizofrenia. O diagnóstico foi feito por exame clínico utilizando a Lista de verificação de critérios operacionais para doença psicótica (OPCRIT). Os dados foram coletados através de revisão de registros médicos e entrevistas por três pesquisadores devidamente treinados. Todos os participantes assinaram um termo de consentimento antes da coleta de dados. Apenas 18 pacientes (14,90%) usaram clozapina isoladamente, todos os outros eram polimedicados (mediana, 3 medicamentos, amplitude entre 1-11). 58,7% dos pacientes apresentaram alterações no hemograma nos últimos 5 anos. Quatorze tipos de interações medicamentosas de gravidade moderada ou maior foram identificados nas amostras do estudo. O controle hematológico e o conhecimento das interações medicamentosas são fundamentais para o sucesso do tratamento. Este conhecimento também é importante para melhorar o aconselhamento aos pacientes sobre o uso correto de medicamentos.Patients with schizophrenia are usually polymedicated and changes in blood counts and drug interactions are prevalent in this group of patients. Clozapine has been shown to be efficacious in treatment-resistant patients. Agranulocytosis and neutropenia are the main adverse effects of clozapine We evaluated changes in blood count I the last five years and drug interactions in stable schizophrenic patients using clozapine. One hundred and twenty one outpatients with schizophrenia were recruited. The diagnosis was made by clinical examination using the Operational Criteria Checklist for Psychotic Illness (OPCRIT). Data were collected through medical record review and interviews by three properly trained researchers. All participants signed a consent form before data collection. Only 18 patients (14.90%) used clozapine alone, all others were polymedicated (median, 3 drugs; range, 1-11 drugs). 58.7% of patients had changes in the blood count during the last 5 years. Fourteen types of drug interactions of moderate or major severity were identified in the study samples. Hematological control and knowledge of drug interactions are critical to the success of treatment. This knowledge is also important for improved advice to patients on the correct use of medication

Cold-start problems in data-driven prediction of drug-drug interaction effects

Combining drugs, a phenomenon often referred to as polypharmacy, can induce additional adverse effects. The identification of adverse combinations is a key task in pharmacovigilance. In this context, in silico approaches based on machine learning are promising as they can learn from a limited number of combinations to predict for all. In this work, we identify various subtasks in predicting effects caused by drug–drug interaction. Predicting drug–drug interaction effects for drugs that already exist is very different from predicting outcomes for newly developed drugs, commonly called a cold-start problem. We propose suitable validation schemes for the different subtasks that emerge. These validation schemes are critical to correctly assess the performance. We develop a new model that obtains AUC-ROC =0.843 for the hardest cold-start task up to AUC-ROC =0.957 for the easiest one on the benchmark dataset of Zitnik et al. Finally, we illustrate how our predictions can be used to improve post-market surveillance systems or detect drug–drug interaction effects earlier during drug development

An application of machine learning to explore relationships between factors of organisational silence and culture, with specific focus on predicting silence behaviours

Research indicates that there are many individual reasons why people do not speak up when confronted with situations that may concern them within their working environment. One of the areas that requires more focused research is the role culture plays in why a person may remain silent when such situations arise. The purpose of this study is to use data science techniques to explore the patterns in a data set that would lead a person to engage in organisational silence. The main research question the thesis asks is: Is Machine Learning a tool that Social Scientists can use with respect to Organisational Silence and Culture, that augments commonly used statistical analysis approaches in this domain. This study forms part of a larger study being run by the third supervisor of this thesis. A questionnaire was developed by organisational psychologists within this group to collect data covering six traits of silence as well as cultural and individual attributes that could be used to determine if someone would engage in silence or not. This thesis explores three of those cultures to find main effects and interactions between variables that could influence silence behaviours. Data analysis was carried out on data collected in three European countries, Italy, Germany and Poland (n=774). The data analysis comprised of (1) exploring the characteristics of the data and determining the validity and reliability of the questionnaire; (2) identifying a suitable classification algorithm which displayed good predictive accuracy and modelled the data well based on eight already confirmed hypotheses from the organisational silence literature and (3) investigate newly discovered patterns and interactions within the data, that were previously not documented in the Silence literature on how culture plays a role in predicting silence. It was found that all the silence constructs showed good validity with the exception of Opportunistic Silence and Disengaged Silence. Validation of the cultural dimensions was found to be poor for all constructs when aggregated to individual level with the exception of Humane Orientation Organisational Practices, Power Distance Organisational Practices, Humane Orientation Societal Practices and Power Distance Societal Practices. In addition, not all constructs were invariant across countries. For example, a number of constructs showed invariance across the Poland and Germany samples, but failed for the Italian sample. Ten models were trained to identify predictors of a binary variable, engaged in Organisational Silence. Two of the most accurate models were chosen for further analysis of the main effects and interactions within the dataset, namely Random Forest (AUC = 0.655) and Conditional Inference Forests (AUC = 0.647). Models confirmed 9 out of 16 of the known relationships, and identified three additional potential interactions within the data that were previously not documented in the silence literature on how culture plays a role in predicting silence. For example, Climate for Authenticity was discovered to moderate the effect of both Power Distance Societal Practices and Diffident Silence in reducing the probability of someone engaging in silence. This is the first time this instrument was validated via statistical techniques for suitability to be used across cultures. The techniques of modelling the silence data using classification algorithms with Partial Dependency Plots is a novel and previously unexplored method of exploring organisational silence. In addition, the results identified new information on how culture plays a role in silence behaviours. The results also highlighted that models such as ensembles that identify non-linear relationships without making assumptions about the data, and visualisations depicting interactions identified by such models, can offer new insights over and above the current toolbox of analysis techniques prevalent in social science research

Using spontaneously generated online patient experiences to improve healthcare : A case study using Modafinil

Background Acknowledged issues with the RCT focus of EBM and recognition of the value of patient input have created a need for new methods of knowledge generation that can give the depth of qualitative studies but on a much larger scale. Almost half of the global population uses social media regularly, with increasing numbers of people using online spaces as either a first- or second-line health information and exchange resource. Estimates suggest the volume of online health related data grew by 300% between 2017 and 2020. As a data source, this unstructured freeform textual data is a form of patient generated health data, containing a mass of patient centred, contextually grounded detail about the perceptions and health concerns of those who post online. Methods for analysing it are at an early stage of development, but it is seen as having potential to add to clinical understanding, either by augmenting existing knowledge, or in aiding understanding of real-world usage of healthcare interventions and services. Objectives To explore how large-scale analysis of SGOPE can help with understanding patient perspectives of their conditions, symptoms, and self-management behaviours, assess the effectiveness of interventions, contribute to the process of knowledge and evidence creation, and consequently help healthcare systems improve outcomes in the most efficient manner. A secondary aim is to contribute to the development of methods that can be generalised across other interventions or services. Methods Using Modafinil as a case study, a multistage approach was taken. First, an exploratory study, comparing both qualitative and basic NLP techniques was undertaken on a small sample of 260 posts to identify topics, evaluate effectiveness and identify perceived causal text. An umbrella scoping review was then undertaken exploring how and for what purposes SGOPE data is currently being used within healthcare research. Findings from both then guided the main study, which used a variety of unsupervised NLP tools to explore the main dataset of over 69k posts. Individual methods were compared against each other. Results from both studies were compared and for evaluation. Results In contrast to the existing inconclusive systematic review evidence for Modafinil for anything other than narcolepsy, both studies found that Modafinil is seen as by posters as effective in treating fatigue and cognition symptoms in a wide range of conditions. Both identified the topics mentioned in the data, although more work needs to be done to develop the NLP methods to achieve a greater depth of understanding. The first study identified eight themes within the posts: reason for taking, impact of symptoms, acquisition, dosage, side-effects, comparison with other interventions, effectiveness, and quality of life outcomes. Effectiveness of Modafinil was found to be 68% positive, 12% mixed and 18% negative. Expressions of causal belief were identified. In the main study, effectiveness was measured with sentiment analysis, with all methods showing strong positive sentiment. Topic modelling identified groups of themes. Linguistic techniques extracted phrases indicating causality. Various analysis methods were compared to develop a method that could be generalised across other health topics

Managementul utilizării raționale a medicamentului: Manual

Manualul „Managementul utilizării raționale a medicamentuluI” a fost discutat și aprobat la ședința Școala de Management în Sănătate Publică (proces-verbal nr. 12 din 10.05.2022), la Comisia științifico-metodico de profil Medicina comunitară a USMF „Nicolae Testemițanu” (proces-verbal nr. 4 din 28.06.2022) și la Consiliul de Management al Calității al USMF „Nicolae Testemițanu” (proces-verbal nr. 7 din 30.06.2022) și recomandat pentru editare. Acest manual este destinat tuturor managerilor din domeniul sănătății, precum și celor implicați în procesul decizional din sfera sănătății publice. Manualul a fost elaborat și publicat în parteneriat cu Crucea Roșie din Elveția cu suportul Agenției Elvețiene pentru Dezvoltare și Cooperare (SDC)