Embodied meaning-making in the experiences and behaviours of persons with dementia

Background: The aim of the study was to explore and articulate how meaning-making appears and how meaningfulness is experienced in persons with severe dementia. Although there is little knowledge about meaning-making and experience of meaningfulness for this group, this article assumes that persons with dementia are as much in need of meaningfulness in life as any others, and hence, that they are involved in the process of meaning-making. Methods: The study was conducted using a qualitative method with exploratory design. Ten patients with severe dementia at a specialized dementia ward at an old age psychiatric department in hospital were observed through participant observation performed over four months. The field-notes from the observation contained narratives carrying with them a dimension of meaning played out in an everyday setting and thus named Meaning-making dramas. The narratives were analyzed looking for expressions where experiences of meaning-making and meaningfulness could be identified. Results: The narratives demonstrate that persons with severe dementia are involved in processes of meaning-making. The narratives include expressions of meaning-making, and of interactions that include apparent crises of meaning, but also transitions into what may be interpreted as meaningfulness based on experiences of significance, orientation and belonging. The role of the body and the senses has proved significant in these processes. The findings also suggest that experiences of meaning contribute to experience of personhood. Conclusions: The relevance to clinical practice indicates that working from a person-centred approach in dementia care also includes paying attention to the dimension of meaning. This dimension is important both for the person living with dementia and for the people caring for them. Acknowledging meaning as a central human concern, it is crucial to seek understanding and knowledge about the significance of meaning in vulnerable groups such as persons with dementia.Sykehuset Innlandet HF 150332publishedVersio

Multiple psychiatric polygenic risk scores predict associations between childhood adversity and bipolar disorder

BACKGROUND: It remains unclear how adverse childhood experiences (ACE) and increased genetic risk for bipolar disorder (BD) interact to influence BD symptom outcomes. Here we calculated multiple psychiatric polygenic risk scores (PRS) and used the measures of ACE to understand these gene-environment interactions. METHOD: 885 BD subjects were included for analyses. BD, ADHD, MDD and SCZ PRSs were calculated using the PRS-CS-auto method. ACEs were evaluated using the Children Life Event Questionnaire (CLEQ). Participants were divided into groups based on the presence of ACE and the total number of ACEs. The associations between total ACE number, PRSs and their interactions were evaluated using multiple linear and logistic regressions. Secondary analyses were performed to evaluate the influence of ACE and PRS on sub-phenotypes of BD. RESULTS: The number of ACEs increased with the ADHD PRS. BD participants who had ACEs showed an earlier age of BD onset and higher odds of having rapid cycling. Increased BD PRS was associated with increased odds of developing psychotic symptoms. Higher ADHD PRS was associated with increased odds of having rapid cycling. No prediction effect was observed from MDD and SCZ PRS. And, we found no significant interaction between ACE numbers and any of the PRSs in predicting any selected BD sub-phenotypes. LIMITATIONS: The study was limited by sample size, ACE definition, and cross-sectional data collection method. CONCLUSIONS: The findings consolidate the importance of considering multiple psychiatric PRSs in predicting symptom outcomes among BD patients

Antipsychotic Polypharmacy and Adverse Drug Reactions Among Adults in a London Mental Health Service, 2008-2018

Background: Antipsychotic polypharmacy (APP) occurs commonly but it is unclear whether it is associated with an increased risk of adverse drug reactions. Electronic health records (EHRs) offer an opportunity to examine APP using real-world data. In this study, we use EHR data to identify periods when patients were prescribed 2+ antipsychotics and compare these with periods of antipsychotic monotherapy. To determine the relationship between APP and subsequent instances of adverse drug reactions: QT interval prolongation, hyperprolactinaemia, and increased body weight (body mass index [BMI] ≥ 25). / Methods: We extracted anonymised EHR data. Patients aged 16+ receiving antipsychotic medication at Camden & Islington NHS Foundation Trust between 1 January 2008 and 31 December 2018 were included. Multilevel mixed-effects logistic regression models were used to elucidate the relationship between APP and the subsequent presence of QT interval prolongation, hyperprolactinaemia, and/or increased BMI following a period of APP within 7, 30, or 180 days respectively. / Results: We identified 35,409 observations of antipsychotic prescribing among 13,391 patients. APP was associated with a subsequent increased risk of hyperprolactinaemia (adjusted odds ratio 2.46; 95% C.I. 1.87-3.24) and of having a BMI > 25 (adjusted odds ratio 1.75; 95% C.I. 1.33-2.31) in the period following the APP prescribing. / Conclusions: Our observations suggest that APP should be carefully managed with attention to hyperprolactinaemia and obesity

Antipsychotic polypharmacy and adverse drug reactions among adults in a London mental health service, 2008-2018

BACKGROUND: Antipsychotic polypharmacy (APP) occurs commonly but it is unclear whether it is associated with an increased risk of adverse drug reactions (ADRs). Electronic health records (EHRs) offer an opportunity to examine APP using real-world data. In this study, we use EHR data to identify periods when patients were prescribed 2 + antipsychotics and compare these with periods of antipsychotic monotherapy. To determine the relationship between APP and subsequent instances of ADRs: QT interval prolongation, hyperprolactinaemia, and increased body weight [body mass index (BMI) ⩾ 25]. METHODS: We extracted anonymised EHR data. Patients aged 16 + receiving antipsychotic medication at Camden & Islington NHS Foundation Trust between 1 January 2008 and 31 December 2018 were included. Multilevel mixed-effects logistic regression models were used to elucidate the relationship between APP and the subsequent presence of QT interval prolongation, hyperprolactinaemia, and/or increased BMI following a period of APP within 7, 30, or 180 days respectively. RESULTS: We identified 35 409 observations of antipsychotic prescribing among 13 391 patients. Compared with antipsychotic monotherapy, APP was associated with a subsequent increased risk of hyperprolactinaemia (adjusted odds ratio 2.46; 95% CI 1.87-3.24) and of registering a BMI > 25 (adjusted odds ratio 1.75; 95% CI 1.33-2.31) in the period following the APP prescribing. CONCLUSIONS: Our observations suggest that APP should be carefully managed with attention to hyperprolactinaemia and obesity

Prediction of disease comorbidity using explainable artificial intelligence and machine learning techniques: A systematic review

OBJECTIVE: Disease comorbidity is a major challenge in healthcare affecting the patient's quality of life and costs. AI-based prediction of comorbidities can overcome this issue by improving precision medicine and providing holistic care. The objective of this systematic literature review was to identify and summarise existing machine learning (ML) methods for comorbidity prediction and evaluate the interpretability and explainability of the models. MATERIALS AND METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was used to identify articles in three databases: Ovid Medline, Web of Science and PubMed. The literature search covered a broad range of terms for the prediction of disease comorbidity and ML, including traditional predictive modelling. RESULTS: Of 829 unique articles, 58 full-text papers were assessed for eligibility. A final set of 22 articles with 61 ML models was included in this review. Of the identified ML models, 33 models achieved relatively high accuracy (80-95%) and AUC (0.80-0.89). Overall, 72% of studies had high or unclear concerns regarding the risk of bias. DISCUSSION: This systematic review is the first to examine the use of ML and explainable artificial intelligence (XAI) methods for comorbidity prediction. The chosen studies focused on a limited scope of comorbidities ranging from 1 to 34 (mean = 6), and no novel comorbidities were found due to limited phenotypic and genetic data. The lack of standard evaluation for XAI hinders fair comparisons. CONCLUSION: A broad range of ML methods has been used to predict the comorbidities of various disorders. With further development of explainable ML capacity in the field of comorbidity prediction, there is a significant possibility of identifying unmet health needs by highlighting comorbidities in patient groups that were not previously recognised to be at risk for particular comorbidities

Meaning in Life for Patients With Severe Dementia: A Qualitative Study of Healthcare Professionals' Interpretations

The need for meaning in life is a key aspect of being human, and a central issue in the psychology of religion. Understanding experience of meaning for persons with severe dementia is challenging due to the impairments associated with the illness. Despite these challenges, this article argues that meaning in life is as important for a person with severe dementia as it is for everyone else. This study was conducted in a Norwegian hospital and nursing home context and was part of a research project on meaning in life for persons with severe dementia. The study builds on two other studies which focused on how meaning-making and experience of meaningfulness appeared in patients with severe dementia. By presenting the findings from these two studies for a group of healthcare professionals and introducing them to research on meaning in life, the aim of this study was to explore how healthcare professionals interpret the patients' experience of meaning in life in practise for patients with severe dementia in a hospital and nursing home context, and to highlight its clinical implications. The study was conducted using a qualitative method with exploratory design. The data were collected at a round table conference, a method inspired by a mode of action research called “co-operative inquiry.” Altogether 27 professional healthcarers, from a variety of professions, with high competence in dementia care participated together with six researchers authoring this article. This study revealed that healthcare professionals were constantly dealing with different forms of meaning in their everyday care for people with dementia. The findings also showed clear connexions between understanding of meaning and fundamental aspects of good dementia care. Meaning corresponded well with the principles of person-centred care, and this compatibility allowed the healthcare professionals to associate meaning in life as a perspective into their work without having much prior knowledge or being familiar with the use of this perspective. The study points out that awareness of meaning in life as an integrated perspective in clinical practise will contribute to a broader and enhanced repertoire, and hence to improved dementia care. Facilitating experience of meaning calls for increased resources in personnel and competence in future dementia care.publishedVersio

Increased COVID-19 mortality rate in rare disease patients: a retrospective cohort study in participants of the Genomics England 100,000 Genomes project

BACKGROUND: Several common conditions have been widely recognised as risk factors for COVID-19 related death, but risks borne by people with rare diseases are largely unknown. Therefore, we aim to estimate the difference of risk for people with rare diseases comparing to the unaffected. METHOD: To estimate the correlation between rare diseases and COVID-19 related death, we performed a retrospective cohort study in Genomics England 100k Genomes participants, who tested positive for Sars-Cov-2 during the first wave (16-03-2020 until 31-July-2020) of COVID-19 pandemic in the UK (n = 283). COVID-19 related mortality rates were calculated in two groups: rare disease patients (n = 158) and unaffected relatives (n = 125). Fisher's exact test and logistic regression was used for univariable and multivariable analysis, respectively. RESULTS: People with rare diseases had increased risk of COVID19-related deaths compared to the unaffected relatives (OR [95% CI] = 3.47 [1.21- 12.2]). Although, the effect was insignificant after adjusting for age and number of comorbidities (OR [95% CI] = 1.94 [0.65-5.80]). Neurology and neurodevelopmental diseases was significantly associated with COVID19-related death in both univariable (OR [95% CI] = 4.07 [1.61-10.38]) and multivariable analysis (OR [95% CI] = 4.22 [1.60-11.08]). CONCLUSIONS: Our results showed that rare disease patients, especially ones affected by neurology and neurodevelopmental disorders, in the Genomics England cohort had increased risk of COVID-19 related death during the first wave of the pandemic in UK. The high risk is likely associated with rare diseases themselves, while we cannot rule out possible mediators due to the small sample size. We would like to raise the awareness that rare disease patients may face increased risk for COVID-19 related death. Proper considerations for rare disease patients should be taken when relevant policies (e.g., returning to workplace) are made

Towards mitigating health inequity via machine learning: a nationwide cohort study to develop and validate ethnicity-specific models for prediction of cardiovascular disease risk in COVID-19 patients

Background Emerging data-driven technologies in healthcare, such as risk prediction models, hold great promise but also pose challenges regarding potential bias and exacerbation of existing health inequalities, which have been observed across diseases such as cardiovascular disease (CVD) and COVID-19. This study addresses the impact of ethnicity in risk prediction modelling for cardiovascular events following SARS-CoV-2 infection and explores the potential of ethnicity-specific models to mitigate disparities. Methods This retrospective cohort study utilises six linked datasets accessed through National Health Service (NHS) England’s Secure Data Environment (SDE) service for England, via the BHF Data Science Centre’s CVD-COVID-UK/COVID-IMPACT Consortium. Inclusion criteria were established, and demographic information, risk factors, and ethnicity categories were defined. Four feature selection methods (LASSO, Random Forest, XGBoost, QRISK) were employed and ethnicity-specific prediction models were trained and tested using logistic regression. Discrimination (AUROC) and calibration performance were assessed for different populations and ethnicity groups. Findings Several differences were observed in the models trained on the whole study cohort vs ethnicity-specific groups. At the feature selection stage, ethnicity-specific models yielded different selected features. AUROC discrimination measures showed consistent performance across most ethnicity groups, with QRISK-based models performing relatively poorly. Calibration performance exhibited variation across ethnicity groups and age categories. Ethnicity-specific models demonstrated the potential to enhance calibration performance for certain ethnic groups. Interpretation This research highlights the importance of considering ethnicity in risk prediction modelling to ensure equitable healthcare outcomes. Differences in selected features and asymmetric calibration across ethnicities underscore the necessity of tailored approaches. Ethnicity-specific models offer a pathway to addressing disparities and improving model performance. The study emphasises the role of data-driven technologies in either alleviating or exacerbating existing health inequalities. Evidence before this study Research has suggested that SARS-CoV-2 infections may have prognostic value in predicting later cardiovascular disease outcomes, two diseases where ethnicity-based health inequalities have been observed. Existing health inequalities are at risk of being exacerbated by bias in emerging data-driven technologies such as risk prediction models, and there currently exists no recommended practice to mitigate this issue. Model performances are not typically stratified by ethnic groups and, if reported, ethnic groups are often only included in higher-level categories that have been criticised for simplicity of definition and for missing key ethnic heterogeneity

Increased COVID-19 mortality rate in rare disease patients:a retrospective cohort study in participants of the Genomics England 100,000 Genomes project

BACKGROUND: Several common conditions have been widely recognised as risk factors for COVID-19 related death, but risks borne by people with rare diseases are largely unknown. Therefore, we aim to estimate the difference of risk for people with rare diseases comparing to the unaffected. METHOD: To estimate the correlation between rare diseases and COVID-19 related death, we performed a retrospective cohort study in Genomics England 100k Genomes participants, who tested positive for Sars-Cov-2 during the first wave (16-03-2020 until 31-July-2020) of COVID-19 pandemic in the UK (n = 283). COVID-19 related mortality rates were calculated in two groups: rare disease patients (n = 158) and unaffected relatives (n = 125). Fisher's exact test and logistic regression was used for univariable and multivariable analysis, respectively. RESULTS: People with rare diseases had increased risk of COVID19-related deaths compared to the unaffected relatives (OR [95% CI] = 3.47 [1.21- 12.2]). Although, the effect was insignificant after adjusting for age and number of comorbidities (OR [95% CI] = 1.94 [0.65-5.80]). Neurology and neurodevelopmental diseases was significantly associated with COVID19-related death in both univariable (OR [95% CI] = 4.07 [1.61-10.38]) and multivariable analysis (OR [95% CI] = 4.22 [1.60-11.08]). CONCLUSIONS: Our results showed that rare disease patients, especially ones affected by neurology and neurodevelopmental disorders, in the Genomics England cohort had increased risk of COVID-19 related death during the first wave of the pandemic in UK. The high risk is likely associated with rare diseases themselves, while we cannot rule out possible mediators due to the small sample size. We would like to raise the awareness that rare disease patients may face increased risk for COVID-19 related death. Proper considerations for rare disease patients should be taken when relevant policies (e.g., returning to workplace) are made

Genome-wide association study of antisocial personality disorder diagnostic criteria provides evidence for shared risk factors across disorders

INTRODUCTION: While progress has been made in determining the genetic basis of antisocial behaviour, little progress has been made for antisocial personality disorder (ASPD), a condition that often co-occurs with other psychiatric conditions including substance use disorders, attention deficit hyperactivity disorder (ADHD), and anxiety disorders. This study aims to improve the understanding of the genetic risk for ASPD and its relationship with other disorders and traits. METHODS: We conducted a genome-wide association study (GWAS) of the number of ASPD diagnostic criteria data from 3217 alcohol-dependent participants recruited in the UK (UCL, N = 644) and the USA (Yale-Penn, N = 2573). RESULTS: We identified rs9806493, a chromosome 15 variant, that showed a genome-wide significant association (Z-score = -5.501, P = 3.77 × 10-8) with ASPD criteria. rs9806493 is an eQTL for SLCO3A1 (Solute Carrier Organic Anion Transporter Family Member 3A1), a ubiquitously expressed gene with strong expression in brain regions that include the anterior cingulate and frontal cortices. Polygenic risk score analysis identified positive correlations between ASPD and smoking, ADHD, depression traits, and posttraumatic stress disorder. Negative correlations were observed between ASPD PRS and alcohol intake frequency, reproductive traits, and level of educational attainment. CONCLUSION: This study provides evidence for an association between ASPD risk and SLCO3A1 and provides insight into the genetic architecture and pleiotropic associations of ASPD