Reasons for hospitalisation and cumulative mortality in people, 75 years or older, at high risk of hospital admission: a prospective study.

BACKGROUND: A small proportion of the older population accounts for a high proportion of healthcare use. For effective use of limited healthcare resources, it is important to identify the group with greatest needs. The aim of this study was to explore frequency and reason for hospitalisation and cumulative mortality, in an older population at predicted high risk of hospital admission, and to assess if a prediction model can be used to identify individuals with the greatest healthcare needs. Furthermore, discharge diagnoses were explored to investigate if they can be used as basis for specific interventions in the high-risk group. METHODS: All residents, 75 years or older, living in Östergötland, Sweden, on January 1st, 2017, were included. Healthcare data from 2016 was gathered and used by a validated prediction model to create risk scores for hospital admission. The population was then divided into groups by percentiles of risk. Using healthcare data from 2017-2018, two-year cumulative incidence of hospitalisation was analysed using Gray´s test. Cumulative mortality was analysed with the Kaplan-Meier method and primary discharge diagnoses were analysed with standardised residuals. RESULTS: Forty thousand six hundred eighteen individuals were identified (mean age 82 years, 57.8% women). The cumulative incidence of hospitalisation increased with increasing risk of hospital admission (24% for percentiles < 60 to 66% for percentiles 95-100). The cumulative mortality also increased with increasing risk (7% for percentiles < 60 to 43% for percentiles 95-100). The most frequent primary discharge diagnoses for the population were heart diseases, respiratory infections, and hip injuries. The incidence was significantly higher for heart diseases and respiratory infections and significantly lower for hip injuries, for the population with the highest risk of hospital admission (percentiles 85-100). CONCLUSIONS: Individuals 75 years or older, with high risk of hospital admission, were demonstrated to have considerable higher cumulative mortality as well as incidence of hospitalisation. The results support the use of the prediction model to direct resources towards individuals with highest risk scores, and thus, likely the greatest care needs. There were only small differences in discharge diagnoses between the risk groups, indicating that interventions to reduce hospitalisations should be personalised. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT03180606, first posted 08/06/2017.

Improving Magnetic STEM-Differential Phase Contrast Imaging using Precession.

Differential phase contrast in scanning transmission electron microscopy (STEM-DPC) is a technique used to image electromagnetic fields in materials. STEM-DPC is based on tracking the minute changes in the position of the bright-field disk, so any effects which cause inhomogeneities in the intensity or geometry of the disk can lead to the contrast from the electromagnetic fields to be obscured. Structural changes, like grain boundaries, thickness variations, or local crystallographic orientation, are a major cause of these inhomogeneities. In this paper, we present how precession of the STEM probe with the objective lens turned off, providing a near field-free environment for magnetic imaging, can average out nonsystematic inhomogeneities in the electron beam. The methodology was tested on a polycrystalline Fe60Al40 thin film with embedded ferromagnetic structures. The effect of precession was assessed on magnetic induction maps created by three different processing algorithms. Results demonstrate that precessed STEM-DPC with the objective lens turned off shows an improvement in the form of smoothing of the variations found in the DPC signal arising from the underlying polycrystalline background.

'Do you have a future when you are 93?' Frail older person's perceptions about the future and end of life - a qualitative interview study in primary care.

OBJECTIVE: To explore frail older persons' perceptions of the future and the end of life. DESIGN: Qualitative content analysis of individual semi-structured interviews. SETTING: Nine primary health care centres in both small and middle-sized municipalities in Sweden that participated in the intervention project Proactive healthcare for frail elderly persons. SUBJECTS/PATIENTS: The study includes 20 older persons (eight women and 12 men, aged 76-93 years). MAIN OUTCOME MEASURES: Frail older persons' perceptions of the future and end of life. RESULTS: The analysis uncovered two main categories: Dealing with the future and Approaching the end of life. Dealing with the future includes two subcategories: Plans and reflections and Distrust and delay. Approaching the end of life includes three subcategories: Practical issues, Worries and realism, and Keeping it away. CONCLUSION: This study highlights the diverse ways older people perceive future and the end of life. The results make it possible to further understand the complex phenomenon of frail older persons' perceptions on the future and the end of life.KEY POINTSThe study found that older persons described their future as contradictory- with a broad spectrum of approaches, where some wanted to deal with these subjects and others wanted to ignore them.•Older persons that consciously planned for the future had tactics that often were related to goals that functioned as motivators to live longer.•Those who adopted a more passive approach did not think about what the future might hold in terms of losing autonomy and deteriorating health.•Older persons that approached end of life in a more proactive way wanted to plan practical arrangements around death but often found it hard to address this issue with relatives.•Those older persons that had a more passive approach to end of life preferred not to think about those issues, and some explicitly stated that they did not want to address the final period of life.

Costs and effects of comprehensive geriatric assessment in primary care for older adults with high risk for hospitalisation.

BACKGROUND: The healthcare system needs effective strategies to identify the most vulnerable group of older patients, assess their needs and plan their care proactively. To evaluate the effectiveness of comprehensive geriatric assessment (CGA) of older adults with a high risk of hospitalisation we conducted a prospective, pragmatic, matched-control multicentre trial at 19 primary care practices in Sweden. METHODS: We identified 1604 individuals aged 75 years and older using a new, validated algorithm that calculates a risk score for hospitalisation from electronic medical records. After a nine-month run-in period for CGA in the intervention group, 74% of the available 646 participants had accepted and received CGA, and 662 participants remained in the control group. Participants at intervention practices were invited to CGA performed by a nurse together with a physician. The CGA was adapted to the primary care context. The participants thereafter received actions according to individual needs during a two-year follow-up period. Participants at control practices received care as usual. The primary outcome was hospital care days. Secondary outcomes were number of hospital care episodes, number of outpatient visits, health care costs and mortality. Outcomes were analysed according to intention to treat and adjusted for age, gender and risk score. We used generalised linear mixed models to compare the intervention group and control group regarding all outcomes. RESULTS: Mean age was 83.2 years, 51% of the 1308 participants were female. Relative risk reduction for hospital care days was - 22% (- 35% to - 4%, p = 0.02) during the two-year follow-up. Relative risk reduction for hospital care episodes was - 17% (- 30% to - 2%, p = 0.03). There were no significant differences in outpatient visits or mortality. Health care costs were significantly lower in the intervention group, adjusted mean difference was € - 4324 (€ - 7962 to - 686, p = 0.02). CONCLUSIONS AND RELEVANCE: Our findings indicate that CGA in primary care can reduce the need for hospital care days in a high-risk population of older adults. This could be of great importance in order to manage increasing prevalence of frailty and multimorbidity. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT03180606 , first posted 08/06/2017.

Unravelling stacking order in epitaxial bilayer MX2 using 4D-STEM with unsupervised learning.

Following an extensive investigation of various monolayer transition metal dichalcogenides (MX2), research interest has expanded to include multilayer systems. In bilayer MX2, the stacking order strongly impacts the local band structure as it dictates the local confinement and symmetry. Determination of stacking order in multilayer MX2 domains usually relies on prior knowledge of in-plane orientations of constituent layers. This is only feasible in case of growth resulting in well-defined triangular domains and not useful in-case of closed layers with hexagonal or irregularly shaped islands. Stacking order can be discerned in the reciprocal space by measuring changes in diffraction peak intensities. Advances in detector technology allow fast acquisition of high-quality four-dimensional datasets which can later be processed to extract useful information such as thickness, orientation, twist and strain. Here, we use 4D scanning transmission electron microscopy combined with multislice diffraction simulations to unravel stacking order in epitaxially grown bilayer MoS2. Machine learning based data segmentation is employed to obtain useful statistics on grain orientation of monolayer and stacking in bilayer MoS2.

Clinically useful prediction of hospital admissions in an older population.

BACKGROUND: The healthcare for older adults is insufficient in many countries, not designed to meet their needs and is often described as disorganized and reactive. Prediction of older persons at risk of admission to hospital may be one important way for the future healthcare system to act proactively when meeting increasing needs for care. Therefore, we wanted to develop and test a clinically useful model for predicting hospital admissions of older persons based on routine healthcare data. METHODS: We used the healthcare data on 40,728 persons, 75-109 years of age to predict hospital in-ward care in a prospective cohort. Multivariable logistic regression was used to identify significant factors predictive of unplanned hospital admission. Model fitting was accomplished using forward selection. The accuracy of the prediction model was expressed as area under the receiver operating characteristic (ROC) curve, AUC. RESULTS: The prediction model consisting of 38 variables exhibited a good discriminative accuracy for unplanned hospital admissions over the following 12 months (AUC 0.69 [95% confidence interval, CI 0.68-0.70]) and was validated on external datasets. Clinically relevant proportions of predicted cases of 40 or 45% resulted in sensitivities of 62 and 66%, respectively. The corresponding positive predicted values (PPV) was 31 and 29%, respectively. CONCLUSION: A prediction model based on routine administrative healthcare data from older persons can be used to find patients at risk of admission to hospital. Identifying the risk population can enable proactive intervention for older patients with as-yet unknown needs for healthcare.

Staff experiences of a new tool for comprehensive geriatric assessment in primary care (PASTEL): a focus group study.

Objective: Comprehensive geriatric assessment (CGA) is recommended for the management of frailty. Little is known about professionals' experiences of CGA; therefore we wanted to investigate the experiences of staff in primary care using a new CGA tool: the Primary care Assessment Tool for Elderly (PASTEL).Design: Focus group interviews. Manifest qualitative content analysis.Setting: Nine primary health care centres in Sweden that participated in a CGA intervention. These centres represent urban as well as rural areas.Subjects: Nine nurses, five GPs and one pharmacist were divided into three focus groups.Main outcome measures: Participants' experiences of conducting CGA with PASTEL.Results: The analysis resulted in four main categories. A valuable tool for selected patients: The participants considered the assessment tool to be feasible and valuable. They stated that having enough time for the assessment interview was essential but views about the ideal patient for assessment were divided. Creating conditions for dialogue: The process of adapting the assessment to the individual and create conditions for dialogue was recognised as important. Managing in-depth conversations: In-depth conversations turned out to be an important component of the assessment. Patients were eager to share their stories, but talking about the future or the end of life was demanding. The winding road of actions and teamwork: PASTEL was regarded as a good preparation tool for care planning and a means of support for identifying appropriate actions to manage frailty but there were challenges to implement these actions and to obtain good teamwork.Conclusion: The participants reported that PASTEL, a tool for CGA, gave a holistic picture of the older person and was helpful in care planning.Key pointsTo manage frailty using comprehensive geriatric assessment (CGA) in primary care, there is a need for tools that are efficient, user-friendly and which support patient involvement and teamwork•This study found that the Primary care Assessment tool for Elderly (PASTEL) is regarded as both valuable and feasible by primary care professionals•Use of carefully selected items in the tool and allowing enough time for dialogue may enhance patient-centeredness•The PASTEL tool supports the process of identifying actions to manage frailty in older adults. Teamwork related to the tool and CGA in primary care needs to be further investigated and developed.

Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part I: Data Acquisition, Live Processing, and Storage.

The use of fast pixelated detectors and direct electron detection technology is revolutionizing many aspects of scanning transmission electron microscopy (STEM). The widespread adoption of these new technologies is impeded by the technical challenges associated with them. These include issues related to hardware control, and the acquisition, real-time processing and visualization, and storage of data from such detectors. We discuss these problems and present software solutions for them, with a view to making the benefits of new detectors in the context of STEM more accessible. Throughout, we provide examples of the application of the technologies presented, using data from a Medipix3 direct electron detector. Most of our software are available under an open source licence, permitting transparency of the implemented algorithms, and allowing the community to freely use and further improve upon them.

Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post-Acquisition Data Processing, Visualization, and Structural Characterization.

Fast pixelated detectors incorporating direct electron detection (DED) technology are increasingly being regarded as universal detectors for scanning transmission electron microscopy (STEM), capable of imaging under multiple modes of operation. However, several issues remain around the post-acquisition processing and visualization of the often very large multidimensional STEM datasets produced by them. We discuss these issues and present open source software libraries to enable efficient processing and visualization of such datasets. Throughout, we provide examples of the analysis methodologies presented, utilizing data from a 256 × 256 pixel Medipix3 hybrid DED detector, with a particular focus on the STEM characterization of the structural properties of materials. These include the techniques of virtual detector imaging; higher-order Laue zone analysis; nanobeam electron diffraction; and scanning precession electron diffraction. In the latter, we demonstrate a nanoscale lattice parameter mapping with a fractional precision ≤6 × 10−4 (0.06%).

Strain Anisotropy and Magnetic Domains in Embedded Nanomagnets.

Nanoscale modifications of strain and magnetic anisotropy can open pathways to engineering magnetic domains for device applications. A periodic magnetic domain structure can be stabilized in sub-200 nm wide linear as well as curved magnets, embedded within a flat non-ferromagnetic thin film. The nanomagnets are produced within a non-ferromagnetic B2-ordered Fe60 Al40 thin film, where local irradiation by a focused ion beam causes the formation of disordered and strongly ferromagnetic regions of A2 Fe60 Al40 . An anisotropic lattice relaxation is observed, such that the in-plane lattice parameter is larger when measured parallel to the magnet short-axis as compared to its length. This in-plane structural anisotropy manifests a magnetic anisotropy contribution, generating an easy-axis parallel to the short axis. The competing effect of the strain and shape anisotropies stabilizes a periodic domain pattern in linear as well as spiral nanomagnets, providing a versatile and geometrically controllable path to engineering the strain and thereby the magnetic anisotropy at the nanoscale.

Liftout of High-Quality Thin Sections of a Perovskite Oxide Thin Film Using a Xenon Plasma Focused Ion Beam Microscope.

It is shown that a xenon plasma focused ion beam (FIB) microscope is an excellent tool for high-quality preparation of functional oxide thin films for atomic resolution electron microscopy. Samples may be prepared rapidly, at least as fast as those prepared using conventional gallium FIB. Moreover, the surface quality after 2 kV final polishing with the Xe beam is exceptional with only about 3 nm of amorphized surface present. The sample quality was of a suitably high quality to allow atomic resolution high-angle annular dark field imaging and integrated differential phase contrast without any further preparation, and the resulting images were good enough for quantitative evaluation of atomic positions to reveal the oxygen octahedral tilt pattern. This suggests that such xenon plasma FIB instruments may find widespread application in transmission electron microscope and scanning transmission electron microscope specimen preparation.

Expression of MATE1, P-gp, OCTN1 and OCTN2, in epithelial and immune cells in the lung of COPD and healthy individuals.

BACKGROUND: Several inhaled drugs are dependent on organic cation transporters to cross cell membranes. To further evaluate their potential to impact on inhaled drug disposition, the localization of MATE1, P-gp, OCTN1 and OCTN2 were investigated in human lung. METHODS: Transporter proteins were analysed by immunohistochemistry in lung tissue from healthy subjects and COPD patients. Transporter mRNA was analysed by qPCR in lung tissue and in bronchoalveolar lavage (BAL) cells from smokers and non-smokers. RESULTS: We demonstrate for the first time MATE1 protein expression in the lung with localization to the apical side of bronchial and bronchiolar epithelial cells. Interestingly, MATE1 was strongly expressed in alveolar macrophages as demonstrated both in lung tissue and in BAL cells, and in inflammatory cells including CD3 positive T cells. P-gp, OCTN1 and OCTN2 were also expressed in the alveolar epithelial cells and in inflammatory cells including alveolar macrophages. In BAL cells from smokers, MATE1 and P-gp mRNA expression was significantly lower compared to cells from non-smokers whereas no difference was observed between COPD patients and healthy subjects. THP-1 cells were evaluated as a model for alveolar macrophages but did not reflect the transporter expression observed in BAL cells. CONCLUSIONS: We conclude that MATE1, P-gp, OCTN1 and OCTN2 are expressed in pulmonary lung epithelium, in alveolar macrophages and in other inflammatory cells. This is important to consider in the development of drugs treating pulmonary disease as the transporters may impact drug disposition in the lung and consequently affect pharmacological efficacy and toxicity.

Exploring deep learning models for 4D-STEM-DPC data processing.

For the study of magnetic materials at the nanoscale, differential phase contrast (DPC) imaging is a potent tool. With the advancements in direct detector technology, and consequent popularity gain for four-dimensional scanning transmission electron microscopy (4D-STEM), there has been an ongoing development of new and enhanced ways for STEM-DPC big data processing. Conventional algorithms are experimentally tailored, and so in this article we explore how supervised learning with convolutional neural networks (CNN) can be utilized for automated and consistent processing of STEM-DPC data. Two different approaches are investigated, one with direct tracking of the beam with regression analysis, and one where a modified U-net is used for direct beam segmentation as a pre-processing step. The CNNs are trained on experimentally obtained 4D-STEM data, enabling them to effectively handle data collected under similar instrument acquisition parameters. The model outputs are compared to conventional algorithms, particularly in how they process data in the presence of strong diffraction contrast, and how they affect domain wall profiles and width measurement.