Effectiveness of an intervention for improving drug prescription in primary care patients with multimorbidity and polypharmacy:Study protocol of a cluster randomized clinical trial (Multi-PAP project)

This study was funded by the Fondo de Investigaciones Sanitarias ISCIII (Grant Numbers PI15/00276, PI15/00572, PI15/00996), REDISSEC (Project Numbers RD12/0001/0012, RD16/0001/0005), and the European Regional Development Fund ("A way to build Europe").Background: Multimorbidity is associated with negative effects both on people's health and on healthcare systems. A key problem linked to multimorbidity is polypharmacy, which in turn is associated with increased risk of partly preventable adverse effects, including mortality. The Ariadne principles describe a model of care based on a thorough assessment of diseases, treatments (and potential interactions), clinical status, context and preferences of patients with multimorbidity, with the aim of prioritizing and sharing realistic treatment goals that guide an individualized management. The aim of this study is to evaluate the effectiveness of a complex intervention that implements the Ariadne principles in a population of young-old patients with multimorbidity and polypharmacy. The intervention seeks to improve the appropriateness of prescribing in primary care (PC), as measured by the medication appropriateness index (MAI) score at 6 and 12months, as compared with usual care. Methods/Design: Design:pragmatic cluster randomized clinical trial. Unit of randomization: family physician (FP). Unit of analysis: patient. Scope: PC health centres in three autonomous communities: Aragon, Madrid, and Andalusia (Spain). Population: patients aged 65-74years with multimorbidity (≥3 chronic diseases) and polypharmacy (≥5 drugs prescribed in ≥3months). Sample size: n=400 (200 per study arm). Intervention: complex intervention based on the implementation of the Ariadne principles with two components: (1) FP training and (2) FP-patient interview. Outcomes: MAI score, health services use, quality of life (Euroqol 5D-5L), pharmacotherapy and adherence to treatment (Morisky-Green, Haynes-Sackett), and clinical and socio-demographic variables. Statistical analysis: primary outcome is the difference in MAI score between T0 and T1 and corresponding 95% confidence interval. Adjustment for confounding factors will be performed by multilevel analysis. All analyses will be carried out in accordance with the intention-to-treat principle. Discussion: It is essential to provide evidence concerning interventions on PC patients with polypharmacy and multimorbidity, conducted in the context of routine clinical practice, and involving young-old patients with significant potential for preventing negative health outcomes. Trial registration: Clinicaltrials.gov, NCT02866799Publisher PDFPeer reviewe

Severe immunosuppression is related to poorer immunogenicity to SARS-CoV-2 vaccines among people living with HIV.

The aim of this study was to assess the immunogenicity of SARS-CoV-2 available vaccines among people living with HIV (PLWH) after a complete vaccination scheme, and determine predictors of seroconversion. This multicentre prospective cohort study included 420 PLWH who had received a standard immunization, either with mRNA or adenoviral-vectored COVID-19 vaccines. Antibody response was evaluated within 1 to 2 months after the last dose of the vaccine with a quantitative determination of antitrimeric spike protein-specific IgG antibodies and IgG neutralizing antibodies. Overall, 384 of 420 PLWH (91%) showed antibody response to vaccination. Seroconversion was observed in 308 of 326 individuals with cluster of differentiation 4 (CD4) counts ≥350 cells/mm3 (95%), 55 of 61 PLWH with 200 to 349 cells/mm3 (90%), and 21 of 33 PLWH with CD4 counts HIV-related immunosuppression impairs the antibody response to SARS-CoV-2 vaccines. Specific vaccination schemes should be urgently tailored in this setting, particularly in patients with CD4 cell count

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts