Infraestructura de Medicina de Precisión asociada a la Ciencia y la Tecnología
We are a multidisciplinary team of researchers from several hospitals and research centers throughout Spain, with the common goal of developing a predictive model that helps determine how patients experiencing their first psychotic episode will respond to antipsychotic medication. To achieve this, we follow up with individuals who experience their first psychotic episode over the course of a year. In this longitudinal study, we collected clinical, neuropsychological, neuroimaging, and biological markers, and applied advanced mathematical models to identify key predictors of treatment response. In addition, we have data from two cohorts of patients with a first psychotic episode with longitudinal follow-up derived from two national projects: the PEPs project (N=335) and the PAFIP cohort (N=350). In total we hope to be able to include a total of 1000 patients with a first psychotic episode with a minimum follow-up of one year. Our ultimate goal is to create a predictive tool of clinical evolution, capable of predicting whether a specific treatment will be effective or cause side effects. This could lead to more personalized and effective healthcare, allowing doctors to choose the best medication for each patient based on the model’s predictions. In short, we intend to generate and validate the first antipsychotic response model, which will serve as the basis for the development of a novel computer application that will be integrated into the Spanish Health System.
1-Impact on the health of the population and the NHS. Patients who present with a first psychotic episode experience a reduction in psychotic depression, which includes homelessness, unemployment and poverty, with unemployment rates of 80% to 90%. Approximately 20% have chronic symptoms and disability. Patients with a first psychotic episode who do not respond to treatment face a greater reduction in their quality of life with a greater presence of adverse effects and an increase in rates of serious comorbidities and increased risk of suicide. The annual costs associated with treatment resistance are 3 to 11 times higher than the annual cost of patients who do respond to treatment. Treatment resistance accounts for more than €34 billion in annual direct medical costs.
The development of predictors of phenotypes of response to treatment with PA opens the door to the application of personalized medicine. Each group could benefit from differences in the efficacy and toxicity of antipsychotic treatments and other therapeutic approaches, such as the use of psychoeducational interventions, cognitive behavioural therapy, or individual and family interventions. Overall, the application of personalized medicine in the treatment of the first psychotic episode would avoid adverse effects and improve adherence, both factors leading to an improvement in the prognosis. This would prevent relapses and re-hospitalizations.
2-Economic impact. Psychotic disorders represent a significant economic burden on patients, taxpayers, and society, and the total costs related to the illness appear to be disproportionate to the prevalence of the illness. Of the factors driving direct health care costs, hospital visits and medications contribute the most to spending, accounting for 10% and 6% of the total cost, respectively. Indirect costs include high unemployment rates and caregiver burden, which account for 38% and 34% of the total cost, respectively. The total cost per patient with a first psychotic episode is more than 4 times the average total cost for a demographically adjusted population.
Antipsychotics are considered effective but produce numerous adverse events, including weight gain, metabolic alterations, hyperprolactinemia, cognitive alterations, extrapyramidal symptoms, sedation, and sexual dysfunction, among others. These adverse effects, together with lack of awareness about the disease, social isolation, stigma of the disease and substance abuse, are the factors with the greatest effect on non-adherence to pharmacological treatment. In the context of non-adherence, patients who were previously in remission may experience relapse and those with existing symptoms may experience persistence of symptoms. Non-adherence leads to increases in patient and service costs. The cost of rehospitalization due to non-adherence to antipsychotic medication at approximately 1.5 billion euros per year.
The development of predictors of phenotypes of response to treatment with antipsychotics would have an economic impact on health systems, reducing the economic costs associated with the disease, and would also improve the quality of life of the patient and their caregivers, reducing the stigmatization of the disease and facilitating the social integration of patients.
3-Social impact. The results of these studies will be disseminated through the appropriate platforms to make them known to the general population, and in particular to patients affected by this type of pathology. To this end, institutions such as CIBERSAM, the Spanish Society of Psychiatry (SEP) and the Spanish Society of Biological Psychiatry (SEPB) promote meetings and encounters with the associations of those affected. In this sense, the Institutional Relations and Communication Area of CIBERSAM aims to facilitate the connection of all the research groups in the consortium with the environment and the community.
This project, following the challenges proposed by the ICPerMed Vision paper, aims to advance in the management of genomic and health data, both in the treatment of its confidentiality and access by citizens and researchers; the involvement of health authorities in the promotion of personalized medicine, ensuring and facilitating its implementation in health systems; the education and involvement of health professionals to ensure knowledge, access and application of personalised and precision medicine strategies, the multidisciplinary integration of researchers and clinicians to develop personalised medicine, data collection and its use for a more efficient patient-centred health system.