Incidence and Predictors of Thermal Oesophageal and Vagus Nerve Injuries in Ablation Index Guided HPSD Ablation of Atrial Fibrillation: A Prospective Study.

BACKGROUND AND AIMS: High-power-short-duration (HPSD) ablation is an effective treatment for atrial fibrillation but poses risks of thermal injuries to the oesophagus and vagus nerve. This study investigates incidence and predictors of thermal injuries, employing machine learning. METHODS: A prospective observational study was conducted at Leipzig Heart Centre, Germany, excluding patients with multiple prior ablations. All patients received Ablation Index guided HPSD ablation and subsequent oesophagogastroduodenoscopy. A machine learning algorithm categorized ablation points by atrial location and analysed ablation data, including Ablation Index, focusing on the posterior wall. The study is registered in clinicaltrials.gov (NCT05709756). RESULTS: Between February 2021, and August 2023, 238 patients were enrolled, of whom 18 (7.6%; 9 oesophagus, 8 vagus nerve, 1 both) developed thermal injuries, including 8 oesophageal erythemata, two ulcers and no fistula. Higher mean force (15.8±3.9g vs. 13.6±3.9g, p=0.022), ablation point quantity (61.50±20.45 vs. 48.16±19.60, p=0.007), total and maximum Ablation Index (24114±8765 vs. 18894±7863, p=0.008; 499±95 vs. 473±44, p=0.04, respectively) at the posterior wall, but not oesophagus location, correlated significantly with thermal injury occurrence. Patients with thermal injuries had significantly lower distances between left atrium and oesophagus (3.0±1.5mm vs 4.4±2.1mm, p=0.012) and smaller atrial surface areas (24.9±6.5 cm2 vs. 29.5±7.5cm2, p=0.032). CONCLUSION: The low thermal lesion's rate (7.6%) during Ablation Index guided HPSD ablation for atrial fibrillation is noteworthy. Machine learning based ablation data analysis identified several potential predictors of thermal injuries. The correlation between machine learning output and injury development suggests the potential for a clinical tool to enhance procedural safety.

Crossed Andreev Reflection and Elastic Cotunneling in Three Quantum Dots Coupled by Superconductors.

The formation of a topological superconducting phase in a quantum-dot-based Kitaev chain requires nearest neighbor crossed Andreev reflection and elastic cotunneling. Here, we report on a hybrid InSb nanowire in a three-site Kitaev chain geometry-the smallest system with well-defined bulk and edge-where two superconductor-semiconductor hybrids separate three quantum dots. We demonstrate pairwise crossed Andreev reflection and elastic cotunneling between both pairs of neighboring dots and show sequential tunneling processes involving all three quantum dots. These results are the next step toward the realization of topological superconductivity in long Kitaev chain devices with many coupled quantum dots.

Biomechanical performance of dental implants inserted in different mandible locations and at different angles: A finite element study.

STATEMENT OF PROBLEM: Accurate implant placement is essential for the success of dental implants. This placement influences osseointegration and occlusal forces. The freehand technique, despite its cost-effectiveness and time efficiency, may result in significant angular deviations compared with guided implantation, but the effect of angular deviations on the stress-strain state of peri-implant bone is unclear. PURPOSE: The purpose of this finite element analysis (FEA) study was to examine the effects of angular deviations on stress-strain states in peri-implant bone. MATERIAL AND METHODS: Computational modeling was used to investigate 4 different configurations of dental implant positions, each with 3 angles of insertion. The model was developed using computed tomography images, and typical mastication forces were considered. Strains were analyzed using the mechanostat hypothesis. RESULTS: The location of the implant had a significant impact on bone strain intensity. An angular deviation of ±5 degrees from the planned inclination did not significantly affect cancellous bone strains, which primarily support the implant. However, it had a substantial effect on strains in the cortical bone near the implant. Such deviations also significantly influenced implant stresses, especially when the support from the cortical bone was uneven or poorly localized. CONCLUSIONS: In extreme situations, angular deviations can lead to overstraining the cortical bone, risking implant failure from unfavorable interaction with the implant. Accurate implant placement is essential to mitigate these risks.

Dental students' attitudes on cardiopulmonary resuscitation training via virtual reality: an exploratory study.

Purpose Resuscitation guidelines have advocated the use of virtual learning as a form of pre-course e-learning. Virtual reality (VR) has been identified to provide a method of constructive learning with instant feedback. There are increasing publications of VR use in cardiopulmonary resuscitation (CPR) training; however, there is a dearth from the dental profession. Therefore, the aim of this exploratory study was to investigate dental students' opinions in CPR training using VR.Methods In total, 120 dental students undertook both conventional (manikin) and VR CPR training in a cross-over design. The VR scenario was in a hospital setting. Following, students completed a questionnaire evaluating their experiences.Results The majority of students (n = 88) reported that this was the first time that they had utilised VR. The experience of using VR in CPR training was rated as very good. Most students felt that the inclusion of VR in CPR training created a better learning experience and had a high learning potential. However, the hospital setting was not entirely relevant.Conclusion Dental students recommended that VR CPR training should be used as an adjunct to conventional training in dental education, but the VR scenario would benefit being a virtual dental environment.

Subgap spectroscopy along hybrid nanowires by nm-thick tunnel barriers.

Tunneling spectroscopy is widely used to examine the subgap spectra in semiconductor-superconductor nanostructures when searching for Majorana zero modes (MZMs). Typically, semiconductor sections controlled by local gates at the ends of hybrids serve as tunnel barriers. Besides detecting states only at the hybrid ends, such gate-defined tunnel probes can cause the formation of non-topological subgap states that mimic MZMs. Here, we develop an alternative type of tunnel probes to overcome these limitations. After the growth of an InSb-Al hybrid nanowire, a precisely controlled in-situ oxidation of the Al shell is performed to yield a nm-thick AlOx layer. In such thin isolating layer, tunnel probes can be arbitrarily defined at any position along the hybrid nanowire by shadow-wall angle-deposition of metallic leads. In this work, we make multiple tunnel probes along single nanowire hybrids and successfully identify Andreev bound states (ABSs) of various spatial extension residing along the hybrids.

How to measure staff continuity in intensive psychiatric home treatment: a routine data and single case analysis.

Background: Intensive forms of outreach mental health care (IOC) such as crisis resolution or home treatment teams are increasingly implemented as alternatives to inpatient admission, providing recovery-oriented treatment at home at comparable costs and outcomes. However, one issue with IOC is the lack of continuity regarding staff members who provide home visits, complicating relationship building and meaningful therapeutic exchange. The aim of this study is to validate existing primarily qualitative findings using performance data and to explore a possible correlation between the number of staff involved within IOC treatment and the service users' length of stay (LOS). Methods: Routine data from an IOC team in a catchment area in Eastern Germany were analyzed. Basic parameters of service delivery were calculated and an in-depth descriptive analysis regarding staff continuity was performed. Further, an exploratory single case analysis was conducted, presenting the exact sequence of all treatment contacts for one case with low and one with high staff continuity. Results: We analyzed 10.598 face-to-face treatment contacts based on 178 IOC users. The mean LOS was 30.99 days. About 75% of all home visits were conducted by two or more staff members simultaneously. Service users saw an average of 10.24 different staff per treatment episode. On 11% of the care days, only unknown staff, and on 34% of the care days at least one unknown staff member conducted the home visit. 83% of the contacts were performed by the same three staff members and 51% were made by one and the same staff member. A significant positive correlation (p = 0.0007) was found between the number of different practitioners seen by a service user in the first seven days of care and the LOS. Conclusion: Our results suggest that a high number of different staff in the early period of IOC episodes correlates with an extended LOS. Future research must clarify the exact mechanisms of this correlation. Furthermore, it should be investigated how the multiple professions within IOC teams influence the LOS and the quality of treatment and what quality indicators may be suitable to ensure treatment processes.

Equal access to outreach mental health care? Exploring how the place of residence influences the use of intensive home treatment in a rural catchment area in Germany.

BACKGROUND: Internationally, intensive psychiatric home treatment has been increasingly implemented as a community-based alternative to inpatient admission. Since 2018, the so-called Inpatient Equivalent Home Treatment (IEHT; German: "Stationsäquivalente Behandlung", short: "StäB") has been introduced as a particularly intensive form of home treatment that provides at least one daily treatment contact in the service users' (SU) home environment. Prior research shows that this can be challenging in rural catchment areas. Our paper investigates to which extent the location of the SU home location within the catchment area as well as the distance between the home and the clinic influence the utilisation of inpatient treatment compared to IEHT. METHOD: Routine data of one psychiatric hospital in the federal state of Brandenburg in Germany were analysed for the observational period 07/2018-06/2021. Two comparison groups were formed: SU receiving inpatient treatment and SU receiving IEHT. The SU places of residence were respectively anonymised and converted into geo-coordinates. A geographic information system (GIS) was used to visualise the places of residence, and car travel distances as well as travel times to the clinic were determined. Spatial analyses were performed to show the differences between comparison groups. In a more in-depth analysis, the proximity of SU residences to each other was examined as an indicator of possible clustering. RESULTS: During the observational period, the location of 687 inpatient and 140 IEHT unique SU were mapped using the GIS. SU receiving treatment resided predominantly within the catchment area, and this proportion was slightly higher for SU receiving IEHT than for those treated in inpatient setting (95.3% vs. 84.7%). In the catchment area, the geographical distribution of SU place of residence was similar in the two groups. There was a general higher service provision in the more densely populated communities close to Berlin. SU with residence in peripheral communities were mainly treated within the inpatient setting. The mean travel times and distances to the place of residence only differed minimally between the two groups of SU (p > 0.05). The places of residence of SU treated with IEHT were located in greater proximity to each other than those of SU treated in inpatient setting (p < 0.1). CONCLUSION: In especially peripheral parts of the examined catchment area, it may be more difficult to have access to IEHT rather than to inpatient services. The results raise questions regarding health equity and the planning of health care services and have important implications for the further development of intensive home treatment. Telehealth interventions such as blended-care approaches and an increase of flexibility in treatment intensity, e.g. eliminating the daily visit requirement, could ease the implementation of intensive home treatment especially in rural areas.

Off-label use of antidepressants, antipsychotics, and mood-stabilizers in psychiatry.

Off-label drug prescribing in psychiatry is increasing. Many psychotropic drugs are approved for psychopathologic syndromes rather than based on international standard diagnostic classification systems which might facilitate the clinical decision for off-label prescriptions. The objective of this study was to analyze the prevalence and category of off-label use of psychotropic drugs. The study was conducted in 10 psychiatric hospitals in Germany over a period of 2 years. Prescription data of all patients were retrospectively analyzed after identification of antidepressants, antipsychotics, and mood-stabilizers, which were classified as off-label according to the German prescribing information and diagnostic classification according to ICD-10. In total, 53,909 patient cases (46% female) with a mean age of 46.8 (SD: 18) years were included in the study. 30.2% of the cases received at least one off-label prescription of a psychotropic drug during hospital stay. Off-label prevalence rates differed markedly between different diagnostic groups (ICD-10 F0/G3: 47%, F1: 33%, F2: 25%, F3: 21%, F4: 27%, F6: 46%, F7: 84%). The most often off-label prescribed drugs were quetiapine and mirtazapine for organic mental disorders (F0/G3), valproate and quetiapine in patients with disorders due to psychoactive substance use (F1), valproate in patients with psychotic disorders (F2), and risperidone and olanzapine in patients with affective disorders (F3). The prevalence rate of psychotropic off-label prescriptions is high if restricted to product description and ICD-10 diagnosis. Therefore, current psychiatric guidelines should drug-specifically issue this problem by defining psychiatric off-label indications based on a clear benefit-risk assessment.

Influence of head positioning during cone-beam CT imaging on the accuracy of virtual 3D models.

OBJECTIVE: Cone beam computed tomography (CBCT) images are being increasingly used to acquire three-dimensional (3D) models of the skull for additive manufacturing purposes. However, the accuracy of such models remains a challenge, especially in the orbital area. The aim of this study is to assess the impact of four different CBCT imaging positions on the accuracy of the resulting 3D models in the orbital area. METHODS: An anthropomorphic head phantom was manufactured by submerging a dry human skull in silicon to mimic the soft tissue attenuation and scattering properties of the human head. The phantom was scanned on a ProMax 3D MAX CBCT scanner using 90 and 120 kV for four different field of view positions: standard; elevated; backwards tilted; and forward tilted. All CBCT images were subsequently converted into 3D models and geometrically compared with a "gold-standard" optical scan of the dry skull. RESULTS: Mean absolute deviations of the 3D models ranged between 0.15 ± 0.11 mm and 0.56 ± 0.28 mm. The elevated imaging position in combination with 120 kV tube voltage resulted in an improved representation of the orbital walls in the resulting 3D model without compromising the accuracy. CONCLUSIONS: Head positioning during CBCT imaging can influence the accuracy of the resulting 3D model. The accuracy of such models may be improved by positioning the region of interest (e.g. the orbital area) in the focal plane (Figure 2a) of the CBCT X-ray beam.

A review on the application of deep learning for CT reconstruction, bone segmentation and surgical planning in oral and maxillofacial surgery.

Computer-assisted surgery (CAS) allows clinicians to personalize treatments and surgical interventions and has therefore become an increasingly popular treatment modality in maxillofacial surgery. The current maxillofacial CAS consists of three main steps: (1) CT image reconstruction, (2) bone segmentation, and (3) surgical planning. However, each of these three steps can introduce errors that can heavily affect the treatment outcome. As a consequence, tedious and time-consuming manual post-processing is often necessary to ensure that each step is performed adequately. One way to overcome this issue is by developing and implementing neural networks (NNs) within the maxillofacial CAS workflow. These learning algorithms can be trained to perform specific tasks without the need for explicitly defined rules. In recent years, an extremely large number of novel NN approaches have been proposed for a wide variety of applications, which makes it a difficult task to keep up with all relevant developments. This study therefore aimed to summarize and review all relevant NN approaches applied for CT image reconstruction, bone segmentation, and surgical planning. After full text screening, 76 publications were identified: 32 focusing on CT image reconstruction, 33 focusing on bone segmentation and 11 focusing on surgical planning. Generally, convolutional NNs were most widely used in the identified studies, although the multilayer perceptron was most commonly applied in surgical planning tasks. Moreover, the drawbacks of current approaches and promising research avenues are discussed.

Soil phosphorus status and P nutrition strategies of European beech forests on carbonate compared to silicate parent material.

Sustainable forest management requires understanding of ecosystem phosphorus (P) cycling. Lang et al. (2017) [Biogeochemistry, https://doi.org/10.1007/s10533-017-0375-0] introduced the concept of P-acquiring vs. P-recycling nutrition strategies for European beech (Fagus sylvatica L.) forests on silicate parent material, and demonstrated a change from P-acquiring to P-recycling nutrition from P-rich to P-poor sites. The present study extends this silicate rock-based assessment to forest sites with soils formed from carbonate bedrock. For all sites, it presents a large set of general soil and bedrock chemistry data. It thoroughly describes the soil P status and generates a comprehensive concept on forest ecosystem P nutrition covering the majority of Central European forest soils. For this purpose, an Ecosystem P Nutrition Index (ENI P ) was developed, which enabled the comparison of forest P nutrition strategies at the carbonate sites in our study among each other and also with those of the silicate sites investigated by Lang et al. (2017). The P status of forest soils on carbonate substrates was characterized by low soil P stocks and a large fraction of organic Ca-bound P (probably largely Ca phytate) during early stages of pedogenesis. Soil P stocks, particularly those in the mineral soil and of inorganic P forms, including Al- and Fe-bound P, became more abundant with progressing pedogenesis and accumulation of carbonate rock dissolution residue. Phosphorus-rich impure, silicate-enriched carbonate bedrock promoted the accumulation of dissolution residue and supported larger soil P stocks, mainly bound to Fe and Al minerals. In carbonate-derived soils, only low P amounts were bioavailable during early stages of pedogenesis, and, similar to P-poor silicate sites, P nutrition of beech forests depended on tight (re)cycling of P bound in forest floor soil organic matter (SOM). In contrast to P-poor silicate sites, where the ecosystem P nutrition strategy is direct biotic recycling of SOM-bound organic P, recycling during early stages of pedogenesis on carbonate substrates also involves the dissolution of stable Ca-Porg precipitates formed from phosphate released during SOM decomposition. In contrast to silicate sites, progressing pedogenesis and accumulation of P-enriched carbonate bedrock dissolution residue at the carbonate sites promote again P-acquiring mechanisms for ecosystem P nutrition. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10533-021-00884-7.

[Outsourcing of nursing staff costs in psychiatry? : A secondary data analysis of possible effects on the remuneration system in psychiatry]. / Ausgliederung der Pflegekosten in der Psychiatrie? : Eine Sekundärdatenanalyse möglicher Auswirkungen auf das PEPP-System.

BACKGROUND: Nursing staff were excluded from the German DRG system for somatic hospital treatment and will be funded separately in the future. In psychiatry and psychosomatic medicine, binding personnel requirements have been defined but there has been no regulation of how these personnel requirements are adequately financed. OBJECTIVE: The objective of this study was to analyze the costs of inpatient psychiatry and psychosomatic medicine and to evaluate possible effects of funding nursing staff separately. MATERIAL AND METHODS: This analysis is based on aggregated daily treatment costs of selected hospitals (data year 2018), which annually submit their performance and cost data to the Institute for the Hospital Remuneration System (InEK) for the empirical further development of the remuneration system. RESULTS: Nursing staff represent the largest cost factor in inpatient psychiatry and psychosomatic medicine. Excluding nursing staff drastically reduces the variance of psychiatric DRG renumeration and even exceeds its proportion of the total costs. After outsourcing nursing costs, psychiatric DRGs achieve only a very limited cost separation. CONCLUSION: The binding personnel requirements necessitate adequate financing of nursing staff. This raises the debate about the further development of psychiatric remuneration. The question arises as to whether the effort associated with using the psychiatric DRG system justifies its usefulness as an instrument for budgeting when core functions such as cost separation are only given to a limited extent. Alternative approaches to budgeting should also be examined for putting costs and benefits in a better ratio.

Pharmacotherapy, drug-drug interactions and potentially inappropriate medication in depressive disorders.

INTRODUCTION: The aim of this study was to describe the number and type of drugs used to treat depressive disorders in inpatient psychiatry and to analyse the determinants of potential drug-drug interactions (pDDI) and potentially inappropriate medication (PIM). METHODS: Our study was part of a larger pharmacovigilance project funded by the German Innovation Funds. It included all inpatients with a main diagnosis in the group of depressive episodes (F32, ICD-10) or recurrent depressive disorders (F33) discharged from eight psychiatric hospitals in Germany between 1 October 2017 and 30 September 2018 or between 1 January and 31 December 2019. RESULTS: The study included 14,418 inpatient cases. The mean number of drugs per day was 3.7 (psychotropic drugs = 1.7; others = 2.0). Thirty-one percent of cases received at least five drugs simultaneously (polypharmacy). Almost one half of all cases received a combination of multiple antidepressant drugs (24.8%, 95% CI 24.1%-25.5%) or a treatment with antidepressant drugs augmented by antipsychotic drugs (21.9%, 95% CI 21.3%-22.6%). The most frequently used antidepressants were selective serotonin reuptake inhibitors, followed by serotonin and norepinephrine reuptake inhibitors and tetracyclic antidepressants. In multivariate analyses, cases with recurrent depressive disorders and cases with severe depression were more likely to receive a combination of multiple antidepressant drugs (Odds ratio recurrent depressive disorder: 1.56, 95% CI 1.41-1.70, severe depression 1.33, 95% CI 1.18-1.48). The risk of any pDDI and PIM in elderly patients increased substantially with each additional drug (Odds Ratio: pDDI 1.32, 95% CI: 1.27-1.38, PIM 1.18, 95% CI: 1.14-1.22) and severity of disease (Odds Ratio per point on CGI-Scale: pDDI 1.29, 95% CI: 1.11-1.46, PIM 1.27, 95% CI: 1.11-1.44), respectively. CONCLUSION: This study identified potential sources and determinants of safety risks in pharmacotherapy of depressive disorders and provided additional data which were previously unavailable. Most inpatients with depressive disorders receive multiple psychotropic and non-psychotropic drugs and pDDI and PIM are relatively frequent. Patients with a high number of different drugs must be intensively monitored in the management of their individual drug-related risk-benefit profiles.

Comparison of convolutional neural network training strategies for cone-beam CT image segmentation.

BACKGROUND AND OBJECTIVE: Over the past decade, convolutional neural networks (CNNs) have revolutionized the field of medical image segmentation. Prompted by the developments in computational resources and the availability of large datasets, a wide variety of different two-dimensional (2D) and three-dimensional (3D) CNN training strategies have been proposed. However, a systematic comparison of the impact of these strategies on the image segmentation performance is still lacking. Therefore, this study aimed to compare eight different CNN training strategies, namely 2D (axial, sagittal and coronal slices), 2.5D (3 and 5 adjacent slices), majority voting, randomly oriented 2D cross-sections and 3D patches. METHODS: These eight strategies were used to train a U-Net and an MS-D network for the segmentation of simulated cone-beam computed tomography (CBCT) images comprising randomly-placed non-overlapping cylinders and experimental CBCT images of anthropomorphic phantom heads. The resulting segmentation performances were quantitatively compared by calculating Dice similarity coefficients. In addition, all segmented and gold standard experimental CBCT images were converted into virtual 3D models and compared using orientation-based surface comparisons. RESULTS: The CNN training strategy that generally resulted in the best performances on both simulated and experimental CBCT images was majority voting. When employing 2D training strategies, the segmentation performance can be optimized by training on image slices that are perpendicular to the predominant orientation of the anatomical structure of interest. Such spatial features should be taken into account when choosing or developing novel CNN training strategies for medical image segmentation. CONCLUSIONS: The results of this study will help clinicians and engineers to choose the most-suited CNN training strategy for CBCT image segmentation.

Polypharmacy and the risk of drug-drug interactions and potentially inappropriate medications in hospital psychiatry.

PURPOSE: The aim of this study was to analyze the epidemiology of polypharmacy in hospital psychiatry. Another aim was to investigate predictors of the number of drugs taken and the associated risks of drug-drug interactions and potentially inappropriate medications in the elderly. METHODS: Daily prescription data were obtained from a pharmacovigilance project sponsored by the Innovations Funds of the German Federal Joint Committee. RESULTS: The study included 47 071 inpatient hospital cases from eight different study centers. The mean number of different drugs during the entire stay was 6.1 (psychotropic drugs = 2.7; others = 3.4). The mean number of drugs per day was 3.8 (psychotropic drugs = 1.6; others = 2.2). One third of cases received at least five different drugs per day on average during their hospital stay (polypharmacy). Fifty-one percent of patients received more than one psychotropic drug simultaneously. Hospital cases with polypharmacy were 18 years older (p < 0.001), more likely to be female (52% vs. 40%, p < 0.001) and had more comorbidities (5 vs. 2, p < 0.001) than hospital cases without polypharmacy. The risks of drug-drug interactions (OR = 3.7; 95% CI = 3.5-3.9) and potentially inappropriate medication use in the elderly (OR = 2.2; CI = 1.9-2.5) substantially increased in patients that received polypharmacy. CONCLUSION: Polypharmacy is frequent in clinical care. The number of used drugs is a proven risk factor of adverse drug reactions due to drug-drug interactions and potentially inappropriate medication use in the elderly. The potential interactions and the specific pharmacokinetics and -dynamics of older patients should always be considered when multiple drugs are used.

Efficient high cone-angle artifact reduction in circular cone-beam CT using deep learning with geometry-aware dimension reduction.

High cone-angle artifacts (HCAAs) appear frequently in circular cone-beam computed tomography (CBCT) images and can heavily affect diagnosis and treatment planning. To reduce HCAAs in CBCT scans, we propose a novel deep learning approach that reduces the three-dimensional (3D) nature of HCAAs to two-dimensional (2D) problems in an efficient way. Specifically, we exploit the relationship between HCAAs and the rotational scanning geometry by training a convolutional neural network (CNN) using image slices that were radially sampled from CBCT scans. We evaluated this novel approach using a dataset of input CBCT scans affected by HCAAs and high-quality artifact-free target CBCT scans. Two different CNN architectures were employed, namely U-Net and a mixed-scale dense CNN (MS-D Net). The artifact reduction performance of the proposed approach was compared to that of a Cartesian slice-based artifact reduction deep learning approach in which a CNN was trained to remove the HCAAs from Cartesian slices. In addition, all processed CBCT scans were segmented to investigate the impact of HCAAs reduction on the quality of CBCT image segmentation. We demonstrate that the proposed deep learning approach with geometry-aware dimension reduction greatly reduces HCAAs in CBCT scans and outperforms the Cartesian slice-based deep learning approach. Moreover, the proposed artifact reduction approach markedly improves the accuracy of the subsequent segmentation task compared to the Cartesian slice-based workflow.

Comparative dosimetry of radiography device, MSCT device and two CBCT devices in the elbow region.

The aim of the study was to estimate and to compare effective doses in the elbow region resulting from four different x-ray imaging modalities. Absorbed organ doses were measured using 11 metal oxide field effect transistor (MOSFET) dosimeters that were placed in a custom-made anthropomorphic elbow RANDO phantom. Examinations were performed using Shimadzu FH-21 HR radiography device, Siemens Sensation Open 24-slice MSCT-device, NewTom 5G CBCT device, and Planmed Verity CBCT device, and the effective doses were calculated according to ICRP 103 recommendations. The effective dose for the conventional radiographic device was 1.5 µSv. The effective dose for the NewTom 5G CBCT ranged between 2.0 and 6.7 µSv, for the Planmed Verity CBCT device 2.6 µSv and for the Siemens Sensation MSCT device 37.4 µSv. Compared with conventional 2D radiography, this study demonstrated a 1.4-4.6 fold increase in effective dose for CBCT and 25-fold dose for standard MSCT protocols. When compared with 3D CBCT protocols, the study showed a 6-19 fold increase in effective dose using a standard MSCT protocol. CBCT devices offer a feasible low-dose alternative for elbow 3D imaging when compared to MSCT.

Predicting the risk of drug-drug interactions in psychiatric hospitals: a retrospective longitudinal pharmacovigilance study.

OBJECTIVES: The aim was to use routine data available at a patient's admission to the hospital to predict polypharmacy and drug-drug interactions (DDI) and to evaluate the prediction performance with regard to its usefulness to support the efficient management of benefits and risks of drug prescriptions. DESIGN: Retrospective, longitudinal study. SETTING: We used data from a large multicentred pharmacovigilance project carried out in eight psychiatric hospitals in Hesse, Germany. PARTICIPANTS: Inpatient episodes consecutively discharged between 1 October 2017 and 30 September 2018 (year 1) or 1 January 2019 and 31 December 2019 (year 2). OUTCOME MEASURES: The proportion of rightly classified hospital episodes. METHODS: We used gradient boosting to predict respective outcomes. We tested the performance of our final models in unseen patients from another calendar year and separated the study sites used for training from the study sites used for performance testing. RESULTS: A total of 53 909 episodes were included in the study. The models' performance, as measured by the area under the receiver operating characteristic, was 'excellent' (0.83) and 'acceptable' (0.72) compared with common benchmarks for the prediction of polypharmacy and DDI, respectively. Both models were substantially better than a naive prediction based solely on basic diagnostic grouping. CONCLUSION: This study has shown that polypharmacy and DDI can be predicted from routine data at patient admission. These predictions could support an efficient management of benefits and risks of hospital prescriptions, for instance by including pharmaceutical supervision early after admission for patients at risk before pharmacological treatment is established.