The Core Elements of Psychiatric and Mental Health Nursing: Time, Honest Engagement, Therapeutic Relations, Professional Nursing and Lifetime-Perspective.

Defining psychiatric and mental health nursing has been a challenge for decades, and it is still difficult to find a comprehensive definition. We have identified a possibility to clarify psychiatric and mental health nursing based on humanistic philosophy in a general psychiatric care context. The aim was therefore to identify and synthesize the theoretical frameworks from which psychiatric and mental health nursing models are developed. We systematically collected and evaluated articles based on Grounded Theory (GT) methodology regarding psychiatric or mental health nursing. The PRISMA statement for systematic reviews was used and the formal process of synthesis, as a three-step process of identifying first -, second - and third-order themes following the examples of Howell Major and Savin-Baden. The synthesis resulted in a model describing five core elements of psychiatric and mental health nursing: 'professional nursing', 'therapeutic relationships' and 'honest engagement', with time as the all-encompassing theme, including the patients' 'lifetime perspective'. Psychiatric and mental health nursing is a caring support towards recovery, where the patient's lifetime perspective must be in focus during the caring process with a relationship built on an honest engagement. Time is therefore essential for psychiatric and mental health nursing.

Clinically feasible automated MRI volumetry of the brain as a prognostic marker in subjective and mild cognitive impairment.

Background/aims: The number of patients suffering from cognitive decline and dementia increases, and new possible treatments are being developed. Thus, the need for time efficient and cost-effective methods to facilitate an early diagnosis and prediction of future cognitive decline in patients with early cognitive symptoms is becoming increasingly important. The aim of this study was to evaluate whether an MRI based software, NeuroQuant® (NQ), producing volumetry of the hippocampus and whole brain volume (WBV) could predict: (1) conversion from subjective cognitive decline (SCD) at baseline to mild cognitive impairment (MCI) or dementia at follow-up, and from MCI at baseline to dementia at follow-up and (2) progression of cognitive and functional decline defined as an annual increase in the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) score. Methods: MRI was performed in 156 patients with SCD or MCI from the memory clinic at Oslo University Hospital (OUH) that had been assessed with NQ and had a clinical follow-up examination. Logistic and linear regression analyses were performed with hippocampus volume and WBV as independent variables, and conversion or progression as dependent variables, adjusting for demographic and other relevant covariates including Mini-Mental State Examination-Norwegian Revised Version score (MMSE-NR) and Apolipoprotein E ɛ4 (APOE ɛ4) carrier status. Results: Hippocampus volume, but not WBV, was associated with conversion to MCI or dementia, but neither were associated with conversion when adjusting for MMSE-NR. Both hippocampus volume and WBV were associated with progression as measured by the annual change in CDR-SB score in both unadjusted and adjusted analyses. Conclusion: The results indicate that automated regional MRI volumetry of the hippocampus and WBV can be useful in predicting further cognitive decline in patients with early cognitive symptoms.

Regional MRI volumetry using NeuroQuant versus visual rating scales in patients with cognitive impairment and dementia.

BACKGROUND AND PURPOSE: The aims were to compare the novel regional brain volumetric measures derived by the automatic software NeuroQuant (NQ) with clinically used visual rating scales of medial temporal lobe atrophy (MTA), global cortical atrophy-frontal (GCA-f), and posterior atrophy (PA) brain regions, assessing their diagnostic validity, and to explore if combining automatic and visual methods would increase diagnostic prediction accuracy. METHODS: Brain magnetic resonance imaging (MRI) examinations from 86 patients with subjective and mild cognitive impairment (i.e., non-dementia, n = 41) and dementia (n = 45) from the Memory Clinic at Oslo University Hospital were assessed using NQ volumetry and with visual rating scales. Correlations, receiver operating characteristic analyses calculating area under the curves (AUCs) for diagnostic accuracy, and logistic regression analyses were performed. RESULTS: The correlations between NQ volumetrics and visual ratings of corresponding regions were generally high between NQ hippocampi/temporal volumes and MTA (r = -0.72/-0.65) and between NQ frontal volume and GCA-f (r = -0.62) but lower between NQ parietal/occipital volumes and PA (r = -0.49/-0.37). AUCs of each region, separating non-dementia from dementia, were generally comparable between the two methods, except that NQ hippocampi volume did substantially better than visual MTA (AUC = 0.80 vs. 0.69). Combining both MRI methods increased only the explained variance of the diagnostic prediction substantially regarding the posterior brain region. CONCLUSIONS: The findings of this study encourage the use of regional automatic volumetry in locations lacking neuroradiologists with experience in the rating of atrophy typical of neurodegenerative diseases, and in primary care settings.

Constructing personalized characterizations of structural brain aberrations in patients with dementia using explainable artificial intelligence.

Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine.

Differences in metalloproteinases and their tissue inhibitors in the cerebrospinal fluid are associated with delirium.

BACKGROUND: The aetiology of delirium is not known, but pre-existing cognitive impairment is a predisposing factor. Here we explore the associations between delirium and cerebrospinal fluid (CSF) levels of matrix metalloproteinases (MMPs) and their tissue inhibitors (TIMPs), proteins with important roles in both acute injury and chronic neurodegeneration. METHODS: Using a 13-plex Discovery Assay®, we quantified CSF levels of 9 MMPs and 4 TIMPs in 280 hip fracture patients (140 with delirium), 107 cognitively unimpaired individuals, and 111 patients with Alzheimer's disease dementia. The two delirium-free control groups without acute trauma were included to unravel the effects of acute trauma (hip fracture), dementia, and delirium. RESULTS: Here we show that delirium is associated with higher levels of MMP-2, MMP-3, MMP-10, TIMP-1, and TIMP-2; a trend suggests lower levels of TIMP-4 are also associated with delirium. Most delirium patients had pre-existing dementia and low TIMP-4 is the only marker associated with delirium in adjusted analyses. MMP-2, MMP-12, and TIMP-1 levels are clearly higher in the hip fracture patients than in both control groups and several other MMP/TIMPs are impacted by acute trauma or dementia status. CONCLUSIONS: Several CSF MMP/TIMPs are significantly associated with delirium in hip fracture patients, but alterations in most of these MMP/TIMPs could likely be explained by acute trauma and/or pre-fracture dementia. Low levels of TIMP-4 appear to be directly associated with delirium, and the role of this marker in delirium pathophysiology should be further explored.

Delirium is a syndrome in which there are substantial changes in a person's ability to focus, understand, or pay attention to events. Delirium often occurs in response to sudden trauma and is more common in persons with pre-existing cognitive impairment. What happens in the brain during delirium is not well understood. To learn more, we have studied whether markers in the cerebrospinal fluid were altered in people with delirium compared to people without delirium. To understand differences specifically caused by delirium, we included two control groups without acute trauma, one with cognitively healthy participants and one with dementia patients. We found several markers altered in people with delirium, with most of the markers similarly altered in people with cognitive impairment due to dementia. One marker was directly linked to delirium and could potentially shed light on the brain processes that cause the syndrome.