Diffusion-Weighted Imaging for Skin Pathologies of the Breast-A Feasibility Study.

Several breast pathologies can affect the skin, and clinical pathways might differ significantly depending on the underlying diagnosis. This study investigates the feasibility of using diffusion-weighted imaging (DWI) to differentiate skin pathologies in breast MRIs. This retrospective study included 88 female patients who underwent diagnostic breast MRI (1.5 or 3T), including DWI. Skin areas were manually segmented, and the apparent diffusion coefficients (ADCs) were compared between different pathologies: inflammatory breast cancer (IBC; n = 5), benign skin inflammation (BSI; n = 11), Paget's disease (PD; n = 3), and skin-involved breast cancer (SIBC; n = 11). Fifty-eight women had healthy skin (H; n = 58). The SIBC group had a significantly lower mean ADC than the BSI and IBC groups. These differences persisted for the first-order features of the ADC (mean, median, maximum, and minimum) only between the SIBC and BSI groups. The mean ADC did not differ significantly between the BSI and IBC groups. Quantitative DWI assessments demonstrated differences between various skin-affecting pathologies, but did not distinguish clearly between all of them. More extensive studies are needed to assess the utility of quantitative DWI in supplementing the diagnostic assessment of skin pathologies in breast imaging.

Lesion-conditioning of synthetic MRI-derived subtraction-MIPs of the breast using a latent diffusion model.

The purpose of this feasibility study is to investigate if latent diffusion models (LDMs) are capable to generate contrast enhanced (CE) MRI-derived subtraction maximum intensity projections (MIPs) of the breast, which are conditioned by lesions. We trained an LDM with n = 2832 CE-MIPs of breast MRI examinations of n = 1966 patients (median age: 50 years) acquired between the years 2015 and 2020. The LDM was subsequently conditioned with n = 756 segmented lesions from n = 407 examinations, indicating their location and BI-RADS scores. By applying the LDM, synthetic images were generated from the segmentations of an independent validation dataset. Lesions, anatomical correctness, and realistic impression of synthetic and real MIP images were further assessed in a multi-rater study with five independent raters, each evaluating n = 204 MIPs (50% real/50% synthetic images). The detection of synthetic MIPs by the raters was akin to random guessing with an AUC of 0.58. Interrater reliability of the lesion assessment was high both for real (Kendall's W = 0.77) and synthetic images (W = 0.85). A higher AUC was observed for the detection of suspicious lesions (BI-RADS ≥ 4) in synthetic MIPs (0.88 vs. 0.77; p = 0.051). Our results show that LDMs can generate lesion-conditioned MRI-derived CE subtraction MIPs of the breast, however, they also indicate that the LDM tended to generate rather typical or 'textbook representations' of lesions.

Smart forecasting of artifacts in contrast-enhanced breast MRI before contrast agent administration.

OBJECTIVES: To evaluate whether artifacts on contrast-enhanced (CE) breast MRI maximum intensity projections (MIPs) might already be forecast before gadolinium-based contrast agent (GBCA) administration during an ongoing examination by analyzing the unenhanced T1-weighted images acquired before the GBCA injection. MATERIALS AND METHODS: This IRB-approved retrospective analysis consisted of n = 2884 breast CE MRI examinations after intravenous administration of GBCA, acquired with n = 4 different MRI devices at different field strengths (1.5 T/3 T) during clinical routine. CE-derived subtraction MIPs were used to conduct a multi-class multi-reader evaluation of the presence and severity of artifacts with three independent readers. An ensemble classifier (EC) of five DenseNet models was used to predict artifacts for the post-contrast subtraction MIPs, giving as the input source only the pre-contrast T1-weighted sequence. Thus, the acquisition directly preceded the GBCA injection. The area under ROC (AuROC) and diagnostics accuracy scores were used to assess the performance of the neural network in an independent holdout test set (n = 285). RESULTS: After majority voting, potentially significant artifacts were detected in 53.6% (n = 1521) of all breast MRI examinations (age 49.6 ± 12.6 years). In the holdout test set (mean age 49.7 ± 11.8 years), at a specificity level of 89%, the EC could forecast around one-third of artifacts (sensitivity 31%) before GBCA administration, with an AuROC = 0.66. CONCLUSION: This study demonstrates the capability of a neural network to forecast the occurrence of artifacts on CE subtraction data before the GBCA administration. If confirmed in larger studies, this might enable a workflow-blended approach to prevent breast MRI artifacts by implementing in-scan personalized predictive algorithms. CLINICAL RELEVANCE STATEMENT: Some artifacts in contrast-enhanced breast MRI maximum intensity projections might be predictable before gadolinium-based contrast agent injection using a neural network. KEY POINTS: • Potentially significant artifacts can be observed in a relevant proportion of breast MRI subtraction sequences after gadolinium-based contrast agent administration (GBCA). • Forecasting the occurrence of such artifacts in subtraction maximum intensity projections before GBCA administration for individual patients was feasible at 89% specificity, which allowed correctly predicting one in three future artifacts. • Further research is necessary to investigate the clinical value of such smart personalized imaging approaches.

Prevalence and influencing factors for artifact development in breast MRI-derived maximum intensity projections.

BACKGROUND: Magnetic resonance imaging (MRI) provides high diagnostic sensitivity for breast cancer. However, MRI artifacts may impede the diagnostic assessment. This is particularly important when evaluating maximum intensity projections (MIPs), such as in abbreviated MRI (AB-MRI) protocols, because high image quality is desired as a result of fewer sequences being available to compensate for problems. PURPOSE: To describe the prevalence of artifacts on dynamic contrast enhanced (DCE) MRI-derived MIPs and to investigate potentially associated attributes. MATERIAL AND METHODS: For this institutional review board approved retrospective analysis, MIPs were generated from subtraction series and cropped to represent the left and right breasts as regions of interest. These images were labeled by three independent raters regarding the presence of MRI artifacts. MRI artifact prevalence and associations with patient characteristics and technical attributes were analyzed using descriptive statistics and generalized linear models (GLMMs). RESULTS: The study included 2524 examinations from 1794 patients (median age 50 years), performed on 1.5 and 3.0 Tesla MRI systems. Overall inter-rater agreement was kappa = 0.54. Prevalence of significant unilateral artifacts was 29.2% (736/2524), whereas bilateral artifacts were present in 37.8% (953/2524) of all examinations. According to the GLMM, artifacts were significantly positive associated with age (odds ratio [OR] = 1.52) and magnetic field strength (OR = 1.55), whereas a negative effect could be shown for body mass index (OR = 0.95). CONCLUSION: MRI artifacts on DCE subtraction MIPs of the breast, as used in AB-MRI, are a relevant topic. Our results show that, besides the magnetic field strength, further associated attributes are patient age and body mass index, which can provide possible targets for artifact reduction.

Image quality assessment using deep learning in high b-value diffusion-weighted breast MRI.

The objective of this IRB approved retrospective study was to apply deep learning to identify magnetic resonance imaging (MRI) artifacts on maximum intensity projections (MIP) of the breast, which were derived from diffusion weighted imaging (DWI) protocols. The dataset consisted of 1309 clinically indicated breast MRI examinations of 1158 individuals (median age [IQR]: 50 years [16.75 years]) acquired between March 2017 and June 2020, in which a DWI sequence with a high b-value equal to 1500 s/mm2 was acquired. From these, 2D MIP images were computed and the left and right breast were cropped out as regions of interest (ROI). The presence of MRI image artifacts on the ROIs was rated by three independent observers. Artifact prevalence in the dataset was 37% (961 out of 2618 images). A DenseNet was trained with a fivefold cross-validation to identify artifacts on these images. In an independent holdout test dataset (n = 350 images) artifacts were detected by the neural network with an area under the precision-recall curve of 0.921 and a positive predictive value of 0.981. Our results show that a deep learning algorithm is capable to identify MRI artifacts in breast DWI-derived MIPs, which could help to improve quality assurance approaches for DWI sequences of breast examinations in the future.

Reproducibility in 2023 - An End-to-End Template for Analysis and Manuscript Writing.

Reproducibility imposes some special requirements at different stages of each project, including reproducible workflows for the analysis including to follow best practices regarding code style and to make the creation of the manuscript reproducible as well. Available tools therefore include version control systems such as Git and document creation tools such as Quarto or R Markdown. However, a re-usable project template mapping the entire process from performing the data analysis to finally writing the manuscript in a reproducible manner is yet lacking. This work aims to fill this gap by presenting an open source template for conducting reproducible research projects utilizing a containerized framework for both developing and conducting the analysis and summarizing the results in a manuscript. This template can be used instantly without any customization.

Interhemispheric competition during sleep.

Our understanding of the functions and mechanisms of sleep remains incomplete, reflecting their increasingly evident complexity1-3. Likewise, studies of interhemispheric coordination during sleep4-6 are often hard to connect precisely to known sleep circuits and mechanisms. Here, by recording from the claustra of sleeping bearded dragons (Pogona vitticeps), we show that, although the onsets and offsets of Pogona rapid-eye-movement (REMP) and slow-wave sleep are coordinated bilaterally, these two sleep states differ markedly in their inter-claustral coordination. During slow-wave sleep, the claustra produce sharp-wave ripples independently of one another, showing no coordination. By contrast, during REMP sleep, the potentials produced by the two claustra are precisely coordinated in amplitude and time. These signals, however, are not synchronous: one side leads the other by about 20 ms, with the leading side switching typically once per REMP episode or in between successive episodes. The leading claustrum expresses the stronger activity, suggesting bilateral competition. This competition does not occur directly between the two claustra or telencephalic hemispheres. Rather, it occurs in the midbrain and depends on the integrity of a GABAergic (γ-aminobutyric-acid-producing) nucleus of the isthmic complex, which exists in all vertebrates and is known in birds to underlie bottom-up attention and gaze control. These results reveal that a winner-take-all-type competition exists between the two sides of the brain of Pogona, which originates in the midbrain and has precise consequences for claustrum activity and coordination during REMP sleep.

COL10A1 allows stratification of invasiveness of colon cancer and associates to extracellular matrix and immune cell enrichment in the tumor parenchyma.

Background: Treatment options for metastatic colorectal cancer (CRC) are mostly ineffective. We present new evidence that tumor tissue collagen type X alpha 1 (COL10A1) is a relevant candidate biomarker to improve this dilemma. Methods: Several public databases had been screened to observe COL10A1 expression in transcriptome levels with cell lines and tissues. Protein interactions and alignment to changes in clinical parameters and immune cell invasion were performed, too. We also used algorithms to build a novel COL10A1-related immunomodulator signature. Various wet-lab experiments were conducted to quantify COL10A1 protein and transcript expression levels in disease and control cell models. Results: COL10A1 mRNA levels in tumor material is clinical and molecular prognostic, featuring upregulation compared to non-cancer tissue, increase with histomorphological malignancy grading of the tumor, elevation in tumors that invade perineural areas, or lymph node invasion. Transcriptomic alignment noted a strong positive correlation of COL10A1 with transcriptomic signature of cancer-associated fibroblasts (CAFs) and populations of the immune compartment, namely, B cells and macrophages. We verified those findings in functional assays showing that COL10A1 are decreased in CRC cells compared to fibroblasts, with strongest signal in the cell supernatant of the cells. Conclusion: COL10A1 abundance in CRC tissue predicts metastatic and immunogenic properties of the disease. COL10A1 transcription may mediate tumor cell interaction with its stromal microenvironment.

DQAgui: a graphical user interface for the MIRACUM data quality assessment tool.

BACKGROUND: With the growing impact of observational research studies, there is also a growing focus on data quality (DQ). As opposed to experimental study designs, observational research studies are performed using data mostly collected in a non-research context (secondary use). Depending on the number of data elements to be analyzed, DQ reports of data stored within research networks can grow very large. They might be cumbersome to read and important information could be overseen quickly. To address this issue, a DQ assessment (DQA) tool with a graphical user interface (GUI) was developed and provided as a web application. METHODS: The aim was to provide an easy-to-use interface for users without prior programming knowledge to carry out DQ checks and to present the results in a clearly structured way. This interface serves as a starting point for a more detailed investigation of possible DQ irregularities. A user-centered development process ensured the practical feasibility of the interactive GUI. The interface was implemented in the R programming language and aligned to Kahn et al.'s DQ categories conformance, completeness and plausibility. RESULTS: With DQAgui, an R package with a web-app frontend for DQ assessment was developed. The GUI allows users to perform DQ analyses of tabular data sets and to systematically evaluate the results. During the development of the GUI, additional features were implemented, such as analyzing a subset of the data by defining time periods and restricting the analyses to certain data elements. CONCLUSIONS: As part of the MIRACUM project, DQAgui is now being used at ten German university hospitals for DQ assessment and to provide a central overview of the availability of important data elements in a datamap over 2 years. Future development efforts should focus on design optimization and include a usability evaluation.

The Preperitoneal Space in Hernia Repair.

The preperitoneal spaces relevant for incisional hernia repair and minimally invasive groin hernia repair are described in terms of surgical anatomy. Emphasis is put on the transversalis fascia and the urogenital fascia and its extensions, the vesicoumbilical fascia, and the spermatic sheath of Stoppa procedure. Steps in hernia surgery where these structures are relevant are reviewed.

ROS and cGMP signaling modulate persistent escape from hypoxia in Caenorhabditis elegans.

The ability to detect and respond to acute oxygen (O2) shortages is indispensable to aerobic life. The molecular mechanisms and circuits underlying this capacity are poorly understood. Here, we characterize the behavioral responses of feeding Caenorhabditis elegans to approximately 1% O2. Acute hypoxia triggers a bout of turning maneuvers followed by a persistent switch to rapid forward movement as animals seek to avoid and escape hypoxia. While the behavioral responses to 1% O2 closely resemble those evoked by 21% O2, they have distinct molecular and circuit underpinnings. Disrupting phosphodiesterases (PDEs), specific G proteins, or BBSome function inhibits escape from 1% O2 due to increased cGMP signaling. A primary source of cGMP is GCY-28, the ortholog of the atrial natriuretic peptide (ANP) receptor. cGMP activates the protein kinase G EGL-4 and enhances neuroendocrine secretion to inhibit acute responses to 1% O2. Triggering a rise in cGMP optogenetically in multiple neurons, including AIA interneurons, rapidly and reversibly inhibits escape from 1% O2. Ca2+ imaging reveals that a 7% to 1% O2 stimulus evokes a Ca2+ decrease in several neurons. Defects in mitochondrial complex I (MCI) and mitochondrial complex I (MCIII), which lead to persistently high reactive oxygen species (ROS), abrogate acute hypoxia responses. In particular, repressing the expression of isp-1, which encodes the iron sulfur protein of MCIII, inhibits escape from 1% O2 without affecting responses to 21% O2. Both genetic and pharmacological up-regulation of mitochondrial ROS increase cGMP levels, which contribute to the reduced hypoxia responses. Our results implicate ROS and precise regulation of intracellular cGMP in the modulation of acute responses to hypoxia by C. elegans.

The COVID-19 Data Exchange Platform of the German University Medicine.

COVID-19 has challenged the healthcare systems worldwide. To quickly identify successful diagnostic and therapeutic approaches large data sharing approaches are inevitable. Though organizational clinical data are abundant, many of them are available only in isolated silos and largely inaccessible to external researchers. To overcome and tackle this challenge the university medicine network (comprising all 36 German university hospitals) has been founded in April 2020 to coordinate COVID-19 action plans, diagnostic and therapeutic strategies and collaborative research activities. 13 projects were initiated from which the CODEX project, aiming at the development of a Germany-wide Covid-19 Data Exchange Platform, is presented in this publication. We illustrate the conceptual design, the stepwise development and deployment, first results and the current status.

[Possibility of determining «Paradox¼ and «Lancaster¼ barrel boring type by human body gunshot wounds morphology and clothing defects]. / O vozmozhnosti sudebno-meditsinskoi diagnostiki tipa sverlovki kanala stvola «Paradox¼ i «Lancaster¼ po morfologii ognestrel'nykh ranenii tela cheloveka i povrezhdenii odezhdy.

In the specialized forensic medical literature there is no data that allow to reliably and objectively confirm application «Paradox¼ or «Lancaster¼ boring firearms to cause injury. This study analyzes the literature in order to determine the possibility of diagnosing these types of weapon's barrel boring by the morphology of injury. The results of the analysis demonstrate a high probability of barrel boring type distinctive features existing. In addition, has been found the opportunity to determine the type of bullet in the cartridge used to damage. Concluded that it is necessary to do an additional experimental research of the specified conditions for causing firearm injuries.

Automated artifact detection in abbreviated dynamic contrast-enhanced (DCE) MRI-derived maximum intensity projections (MIPs) of the breast.

OBJECTIVES: To automatically detect MRI artifacts on dynamic contrast-enhanced (DCE) maximum intensity projections (MIPs) of the breast using deep learning. METHODS: Women who underwent clinically indicated breast MRI between October 2015 and December 2019 were included in this IRB-approved retrospective study. We employed two convolutional neural network architectures (ResNet and DenseNet) to detect the presence of artifacts on DCE MIPs of the left and right breasts. Networks were trained on images acquired up to and including the year 2018 using a 5-fold cross-validation (CV). Ensemble classifiers were built with the resulting CV models and applied to an independent holdout test dataset, which was formed by images acquired in 2019. RESULTS: Our study sample contained 2265 examinations from 1794 patients (median age at first acquisition: 50 years [IQR: 17 years]), corresponding to 1827 examinations of 1378 individuals in the training dataset and 438 examinations of 416 individuals in the holdout test dataset with a prevalence of image-level artifacts of 53% (1951/3654 images) and 43% (381/876 images), respectively. On the holdout test dataset, the ResNet and DenseNet ensembles demonstrated an area under the ROC curve of 0.92 and 0.94, respectively. CONCLUSION: Neural networks are able to reliably detect artifacts that may impede the diagnostic assessment of MIPs derived from DCE subtraction series in breast MRI. Future studies need to further explore the potential of such neural networks to complement quality assurance and improve the application of DCE MIPs in a clinical setting, such as abbreviated protocols. KEY POINTS: • Deep learning classifiers are able to reliably detect MRI artifacts in dynamic contrast-enhanced protocol-derived maximum intensity projections of the breast. • Automated quality assurance of maximum intensity projections of the breast may be of special relevance for abbreviated breast MRI, e.g., in high-throughput settings, such as cancer screening programs.

Development of a controlled-environment assay to induce iron deficiency chlorosis in soybean by adjusting calcium carbonates, pH, and nodulation.

BACKGROUND: Soybean iron deficiency chlorosis (IDC) is an important nutrient stress frequently found in high pH and/or soils high in calcium carbonates. To advance the understanding of IDC resistance in soybean, a rapid (21-day) controlled-environment assay was developed to investigate the effects of nodulation, pH, and calcium carbonate levels on soybean iron deficiency traits. This system was tested on four genotypes known to exhibit differences in iron efficiency, including two standard IDC check cultivars and a pair of near-isogenic lines exhibiting variation at an IDC resistance quantitative trait locus. Visual score, chlorophyll content, plant height, root dry mass, and shoot dry mass were measured to quantify iron stress. RESULTS: Calcium carbonate levels and nodulation were found to have the greatest effects on IDC severity. Increasing calcium carbonate levels worsened IDC symptoms, while nodulation reduced symptoms in all genotypes. Higher pH levels increased iron deficiency symptoms in check genotypes 'Corsoy 79' and 'Dawson', but did not induce iron deficiency symptoms in near-isogenic lines. A significant interaction was observed between genotype, nodulation, and calcium carbonate level, indicating that a specific treatment level could discern IDC symptoms between genotypes differing in resistance to IDC. CONCLUSIONS: IDC symptoms were successfully induced in the Check Genotypes Experiment as well as the NIL Experiment, indicating the success of using this assay for inducing IDC in controlled environments. However, our results suggest that treatment levels that best differentiate genotypes for their IDC resistance may need to be determined for each experiment because of the unique way in which different genotypes display symptoms and respond to iron deficiency conditions.

Advanced neural networks for classification of MRI in psoriatic arthritis, seronegative, and seropositive rheumatoid arthritis.

OBJECTIVES: To evaluate whether neural networks can distinguish between seropositive RA, seronegative RA, and PsA based on inflammatory patterns from hand MRIs and to test how psoriasis patients with subclinical inflammation fit into such patterns. METHODS: ResNet neural networks were utilized to compare seropositive RA vs PsA, seronegative RA vs PsA, and seropositive vs seronegative RA with respect to hand MRI data. Results from T1 coronal, T2 coronal, T1 coronal and axial fat-suppressed contrast-enhanced (CE), and T2 fat-suppressed axial sequences were used. The performance of such trained networks was analysed by the area under the receiver operating characteristics curve (AUROC) with and without presentation of demographic and clinical parameters. Additionally, the trained networks were applied to psoriasis patients without clinical arthritis. RESULTS: MRI scans from 649 patients (135 seronegative RA, 190 seropositive RA, 177 PsA, 147 psoriasis) were fed into ResNet neural networks. The AUROC was 75% for seropositive RA vs PsA, 74% for seronegative RA vs PsA, and 67% for seropositive vs seronegative RA. All MRI sequences were relevant for classification, however, when deleting contrast agent-based sequences the loss of performance was only marginal. The addition of demographic and clinical data to the networks did not provide significant improvements for classification. Psoriasis patients were mostly assigned to PsA by the neural networks, suggesting that a PsA-like MRI pattern may be present early in the course of psoriatic disease. CONCLUSION: Neural networks can be successfully trained to distinguish MRI inflammation related to seropositive RA, seronegative RA, and PsA.

Shift of radiotherapy use during the first wave of the COVID-19 pandemic? An analysis of German inpatient data.

OBJECTIVE: To assess the change in inpatient radiotherapy related to COVID-19 lockdown measures during the first wave of the pandemic in 2020. METHODS: We included cases hospitalized between January 1 and August 31, 2018-2020, with a primary ICD-10 diagnosis of C00-C13, C32 (head and neck cancer, HNC) and C53 (cervical cancer, CC). Data collection was conducted within the Medical Informatics Initiative. Outcomes were fractions and admissions. Controlling for decreasing hospital admissions during holidays, calendar weeks of 2018/2019 were aligned to Easter 2020. A lockdown period (LP; 16/03/2020-02/08/2020) and a return-to-normal period (RNP; 04/05/2020-02/08/2020) were defined. The study sample comprised a control (admission 2018/19) and study cohort (admission 2020). We computed weekly incidence and IR ratios from generalized linear mixed models. RESULTS: We included 9365 (CC: 2040, HNC: 7325) inpatient hospital admissions from 14 German university hospitals. For CC, fractions decreased by 19.97% in 2020 compared to 2018/19 in the LP. In the RNP the reduction was 28.57% (p < 0.001 for both periods). LP fractions for HNC increased by 10.38% (RNP: 9.27%; p < 0.001 for both periods). Admissions for CC decreased in both periods (LP: 10.2%, RNP: 22.14%), whereas for HNC, admissions increased (LP: 2.25%, RNP: 1.96%) in 2020. Within LP, for CC, radiotherapy admissions without brachytherapy were reduced by 23.92%, whereas surgery-related admissions increased by 20.48%. For HNC, admissions with radiotherapy increased by 13.84%, while surgery-related admissions decreased by 11.28% in the same period. CONCLUSION: Related to the COVID-19 lockdown in an inpatient setting, radiotherapy for HNC treatment became a more frequently applied modality, while admissions of CC cases decreased.

Age of Red Cells for Transfusion and Outcomes in Patients with ARDS.

Packed red blood cells (PRBCs), stored for prolonged intervals, might contribute to adverse clinical outcomes in critically ill patients. In this study, short-term outcome after transfusion of PRBCs of two storage duration periods was analyzed in patients with Acute Respiratory Distress Syndrome (ARDS). Patients who received transfusions of PRBCs were identified from a cohort of 1044 ARDS patients. Patients were grouped according to the mean storage age of all transfused units. Patients transfused with PRBCs of a mean storage age ≤ 28 days were compared to patients transfused with PRBCs of a mean storage age > 28 days. The primary endpoint was 28-day mortality. Secondary endpoints included failure-free days composites. Two hundred and eighty-three patients were eligible for analysis. Patients in the short-term storage group had similar baseline characteristics and received a similar amount of PRBC units compared with patients in the long-term storage group (five units (IQR, 3-10) vs. four units (2-8), p = 0.14). The mean storage age in the short-term storage group was 20 (±5.4) days compared with 32 (±3.1) days in the long-term storage group (mean difference 12 days (95%-CI, 11-13)). There was no difference in 28-day mortality between the short-term storage group compared with the long-term storage group (hazard ratio, 1.36 (95%-CI, 0.84-2.21), p = 0.21). While there were no differences in ventilator-free, sedation-free, and vasopressor-free days composites, patients in the long-term storage group compared with patients in the short-term storage group had a 75% lower chance for successful weaning from renal replacement therapy (RRT) within 28 days after ARDS onset (subdistribution hazard ratio, 0.24 (95%-CI, 0.1-0.55), p < 0.001). Further analysis indicated that even a single PRBC unit stored for more than 28 days decreased the chance for successful weaning from RRT. Prolonged storage of PRBCs was not associated with a higher mortality in adults with ARDS. However, transfusion of long-term stored PRBCs was associated with prolonged dependence of RRT in critically ill patients with an ARDS.

[Singing Voice Handicap Index-12 : Development and validation of a German version]. / Der Singing Voice Handicap Index-12 : Entwicklung und Validierung einer deutschen Fassung.

BACKGROUND: The aim of this study was to develop a shortened German version of the Singing Voice Handicap Index (SVHI). The SVHI is a one-dimensional instrument for self-assessment of a voice disorder in singers. The questionnaire developed in the USA comprises 36 items and has been available in a validated German version since 2013. METHODS: Bicentric data from a total of 200 patients formed the basis for item analysis and selection. Using corrected item-total correlations, 12 items were selected for the abridged version. The internal consistency was calculated. The SVHI-12 was subsequently validated in 97 vocal patients and 105 vocally healthy singers (control group) using the test-retest procedure. RESULTS: The SVHI-12 achieved a good internal consistency (Cronbach's alpha = 0.93) and a good test-retest reliability (intra-class correlation r = 0.88 ; p < 0.001). The patients had significantly higher overall scores (18 ± 13 vs. 7 ± 6) compared to the healthy control group. The SVHI-12 overall score correlated significantly positively with the severity of the voice disorder as reported by the patient (r = 0.68; p < 0.001). As a threshold value above which a voice can be described as disturbed, a total score > 7 points was calculated using receiver operating curve analysis. As an indication of a voice disorder, a sensitivity of 81% and a specificity of 71% is thus achieved (Youden index 0.523, area under the curve 0.827, 95% confidence interval 0.769-0.885). CONCLUSION: The shortened SVHI has similarly good psychometric characteristics to the original SVHI. With the SVHI-12, a valid and effective instrument for the detection of singing voice disorders is available for German-speaking countries.

Design of an Interactive Web Application for Teaching Uncertainty Interpretations of Clinical Tests.

BACKGROUND: Assessing the uncertainty of diagnostic findings is essential for advising patients. Previous research has demonstrated the difficulty of computing the expected correctness of positive or negative results, although clinical decision support (CDS) tools promise to facilitate adequate interpretations. OBJECTIVES: To teach the potential utility of CDS tools to medical students, we designed an interactive software module that computes and visualizes relevant probabilities from typical inputs. METHODS: We reviewed the literature on recommended graphical approaches and decided to support contingency tables, plain table formats, tree diagrams, and icon arrays. RESULTS: We implemented these functions in a single-page web application, which was configured to complement our local learning management system where students also access interpretation tasks. CONCLUSION: Our technical choices promoted a rapid implementation. We intend to explore the utility of the tool during some upcoming courses. Future developments could also model a more complex clinical reality where the likelihood of alternative diagnoses is estimated from sets of clinical investigations.