Antipsychotics for the Treatment of Behavioral and Psychological Symptoms of Dementia (BPSD)

Behavioral and psychological symptoms of dementia (BPSD), i.e. verbal and physical aggression, agitation, psychotic symptoms (hallucinations and delusions), sleep disturbances, oppositional behavior, and wandering, are a common and potentially severe problem complicating dementia. Their prevalence is very high and it is estimated that up to 90% of patients with Alzheimer’s disease (AD) may present at least one BPSD. Beside the obvious impact on the quality of life of people with dementia, BPSD are responsible for increased risk of patient institutionalization and increased costs. Furthermore, they are associated with caregivers’ stress and depression. Drugs used include antipsychotics, antidepressants, anticonvulsivants, anxiolytics, cholinesterase inhibitors and N-methyl-D-aspartate receptor modulators. Among these, the most commonly used are anti-psychotics. These drugs have been used for many decades, but in the last years new compounds have been marketed with the promise of comparable efficacy but less frequent adverse effects (especially extra-pyramidal side effects). Their safety, however, has been challenged by data showing a potential increase in adverse cerebrovascular side effects and mortality. This review will summarize the pathophysiology and neuropharmacology of BPSD, it will describe the characteristics of the anti-psychotics most commonly used focusing on their efficacy and safety in BPSD

Models of hospital care during COVID-19 pandemic in Italy: results from a survey of physicians

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Inappropriate Drugs in Elderly Patients with Severe Cognitive Impairment: Results from the Shelter Study

It has been estimated that Nursing Home (NH) residents with impaired cognitive status receive an average of seven to eight drugs daily. The aim of this study was to determine prevalence and factors associated with use of inappropriate drugs in elderly patients with severe cognitive impairment living in NH in Europe

Assessment of nursing home residents in Europe: the Services and Health for ELderly in Long TERm care (SHELTER) study

Kaisu Pitkälä ryhmän SHELTER project jäsen. Jäseniä yhteensä 32. WOS:000300767900001Peer reviewe

Prevalence of dyslipidemia and hypercholesterolemia awareness: results from the Lookup 7+ online project

Background: Cardiovascular disease still represents the leading cause of death worldwide. Management of risk factors remains crucial; despite this, hypercholesterolemia, which is one of the most important modifiable cardiovascular risk factor, is still high prevalent in general population. The aim of this study is to determine the prevalence of dyslipidemia and hypercholesterolemia awareness in a very large population. Methods: More than 65 000 users completed the online, self-administered survey. It was structured like a 'journey' where each stage corresponded to a cardiovascular risk factor: blood pressure, body mass index, cholesterol, diet, physical exercise, smoke and blood sugar. At the end, the user received a final evaluation of his health status. Results: The mean age was 52.5 years (SD 13.9, range 18-98), with 35 402 (53.7%) men. About 56% of all participants believed to have normal cholesterol values, when only 40% of them really showed values <200 mg/dl. Only about 30% of all participants self-predicted to have abnormal cholesterol values whereas we found high cholesterol levels in about 60% of people. Conclusions: Dyslipidemia is very prevalent and half of the people with high cholesterol is not aware of having high values

Applying the FAIR4Health Solution to Identify Multimorbidity Patterns and Their Association with Mortality through a Frequent Pattern Growth Association Algorithm

This article belongs to the Special Issue Addressing the Growing Burden of Chronic Diseases and Multimorbidity: Characterization and InterventionsThe current availability of electronic health records represents an excellent research opportunity on multimorbidity, one of the most relevant public health problems nowadays. However, it also poses a methodological challenge due to the current lack of tools to access, harmonize and reuse research datasets. In FAIR4Health, a European Horizon 2020 project, a workflow to implement the FAIR (findability, accessibility, interoperability and reusability) principles on health datasets was developed, as well as two tools aimed at facilitating the transformation of raw datasets into FAIR ones and the preservation of data privacy. As part of this project, we conducted a multicentric retrospective observational study to apply the aforementioned FAIR implementation workflow and tools to five European health datasets for research on multimorbidity. We applied a federated frequent pattern growth association algorithm to identify the most frequent combinations of chronic diseases and their association with mortality risk. We identified several multimorbidity patterns clinically plausible and consistent with the bibliography, some of which were strongly associated with mortality. Our results show the usefulness of the solution developed in FAIR4Health to overcome the difficulties in data management and highlight the importance of implementing a FAIR data policy to accelerate responsible health research.This study was performed in the framework of FAIR4Health, a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824666. Also, this research has been co-supported by the Carlos III National Institute of Health, through the IMPaCT Data project (code IMP/00019), and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both co-funded by European Regional Development Fund (FEDER) ‘A way of making Europe’, and by REDISSEC (RD16/0001/0005) and RICAPPS (RD21/0016/0019) from Carlos III National Institute of Health. This work was also supported by Instituto de Investigación Sanitaria Aragón and Carlos III National Institute of Health [Río Hortega Program, grant number CM19/00164].Peer reviewe

FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research

Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators' performance.This research was financially supported by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 824666 (project FAIR4Health). Also, this research has been co-supported by the Carlos III National Institute of Health, through the IMPaCT Data project (code IMP/00019), and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both co-funded by European Regional Development Fund (FEDER) ‘A way of making Europe’.Peer reviewe

Prognostic Interplay of Functional Status and Multimorbidity Among Older Patients Discharged From Hospital

Objectives: The purpose of this study was to investigate the prognostic weight of multimorbidity and functional impairment over long-term mortality among older patients discharged from acute care hospitals.Design: A prospective multicenter observational study.Setting and Participants: Our series consisted of 1967 adults aged >= 65 years consecutively admitted to acute care wards in Italy, in the context of the Report-AGE project.Methods: After signing a written informed consent, all patients underwent comprehensive geriatric assessment by Inter-RAI Minimum Data Set acute care. The primary endpoint of the present study was long-term mortality. Patients were grouped into 3 functional clusters and 3 disease clusters using the K-medians cluster analysis. The association of functional clusters, disease clusters, and Charlson score categories with long-term mortality was investigated through Cox regression analysis and the inter-cluster classification agreement was further estimated. Finally, the additive effect of either disease clusters or Charlson score on predictive ability of functional clusters was assessed by using changes in Harrell's C-index and categorical Net Reclassification Index (NRI).Results: Functional clusters, disease clusters, and Charlson score were significant predictors of long-term mortality, but the interclassification agreement was poor. Functional clusters predicted mortality with greater accuracy [C-index 0.66, 95% confidence interval (CI) 0.65-0.68] compared with disease clusters (C-index 0.54, 95% CI 0.53-0.56), and Charlson score (C-index 0.58, 95% CI 0.56-0.59). Adding multi-morbidity (NRI 0.23, 95% CI 0.14-0.31) or Charlson score (NRI 0.13, 95% CI 0.03-0.20) to functional cluster model slightly improved the accuracy of prediction.Conclusions and Implications: Functional impairment may better predict prognosis compared with multimorbidity, which may be relevant to optimally address individuals' needs and to design tailored preventive interventions. (C) 2021 AMDA - The Society for Post-Acute and Long-Term Care Medicine