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Background: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.
Methods: Addressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.
Findings Training and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.
Interpretation: Although multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.
Introduction: Acute stroke care delivered by interdisciplinary teams is time-sensitive. Simulation-based team training is a promising tool to improve team performance in medical operations. It has the potential to improve process times, team communication, patient safety, and staff satisfaction. We aim to assess whether a multi-level approach consisting of a stringent workflow revision based on peer-to-peer review and 2–3 one-day in situ simulation trainings can improve acute stroke care processing times in high volume neurocenters within a 6 months period.
Methods and Analysis: The trial is being carried out in a pre-test-post-test design at 7 tertiary care university hospital neurocenters in Germany. The intervention is directed at the interdisciplinary multiprofessional stroke teams. Before and after the intervention, process times of all direct-to-center stroke patients receiving IV thrombolysis (IVT) and/or endovascular therapy (EVT) will be recorded. The primary outcome measure will be the “door-to-needle” time of all consecutive stroke patients directly admitted to the neurocenters who receive IVT. Secondary outcome measures will be intervention-related process times of the fraction of patients undergoing EVT and effects on team communication, perceived patient safety, and staff satisfaction via a staff questionnaire.
Interventions: We are applying a multi-level intervention in cooperation with three “STREAM multipliers” from each center. First step is a central meeting of the multipliers at the sponsor's institution with the purposes of algorithm review in a peer-to-peer process that is recorded in a protocol and an introduction to the principles of simulation training and debriefing as well as crew resource management and team communication. Thereafter, the multipliers cooperate with the stroke team trainers from the sponsor's institution to plan and execute 2–3 one-day simulation courses in situ in the emergency department and CT room of the trial centers whereupon they receive teaching materials to perpetuate the trainings.
Clinical Trial Registration: STREAM is a registered trial at https://clinicaltrials.gov/ct2/show/NCT03228251.
Background: The objective of the STREAM Trial was to evaluate the effect of simulation training on process times in acute stroke care.
Methods: The multicenter prospective interventional STREAM Trial was conducted between 10/2017 and 04/2019 at seven tertiary care neurocenters in Germany with a pre- and post-interventional observation phase. We recorded patient characteristics, acute stroke care process times, stroke team composition and simulation experience for consecutive direct-to-center patients receiving intravenous thrombolysis (IVT) and/or endovascular therapy (EVT). The intervention consisted of a composite intervention centered around stroke-specific in situ simulation training. Primary outcome measure was the ‘door-to-needle’ time (DTN) for IVT. Secondary outcome measures included process times of EVT and measures taken to streamline the pre-existing treatment algorithm.
Results: The effect of the STREAM intervention on the process times of all acute stroke operations was neutral. However, secondary analyses showed a DTN reduction of 5 min from 38 min pre-intervention (interquartile range [IQR] 25–43 min) to 33 min (IQR 23–39 min, p = 0.03) post-intervention achieved by simulation-experienced stroke teams. Concerning EVT, we found significantly shorter door-to-groin times in patients who were treated by teams with simulation experience as compared to simulation-naive teams in the post-interventional phase (−21 min, simulation-naive: 95 min, IQR 69–111 vs. simulation-experienced: 74 min, IQR 51–92, p = 0.04).
Conclusion: An intervention combining workflow refinement and simulation-based stroke team training has the potential to improve process times in acute stroke care.
Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA Consortium
(2017)
Background: Treatment of acute stroke is highly time-dependent and performed by a multiprofessional, interdisciplinary team. Interface problems are expectable and issues relevant to patient safety are omnipresent. The Safety Attitudes Questionnaire (SAQ) is a validated and widely used instrument to measure patient safety climate. The objective of this study was to evaluate the SAQ for the first time in the context of acute stroke care. Methods: A survey was carried out during the STREAM trial (NCT 032282) at seven university hospitals in Germany from October 2017 to October 2018. The anonymous survey included 33 questions (5-point Likert scale, 1 = disagree to 5 = agree) and addressed the entire multiprofessional stroke team. Statistical analyses were used to examine psychometric properties as well as descriptive findings. Results: 164 questionnaires were completed yielding a response rate of 66.4%. 67.7% of respondents were physicians and 25.0% were nurses. Confirmatory Factor Analysis revealed that the original 6-factor structure fits the data adequately. The SAQ for acute stroke care showed strong internal consistency (α = 0.88). Exploratory analysis revealed differences in scores on the SAQ dimensions when comparing physicians to nurses and when comparing physicians according to their duration of professional experience. Conclusion: The SAQ is a helpful and well-applicable tool to measure patient safety in acute stroke care. In comparison to other high-risk fields in medicine, patient safety climate in acute stroke care seems to be on a similar level with the potential for further improvements. Trial registration: www.ClinicalTrials.gov Identifier: NCT032282.
Context: Despite overwhelming evidence for endovascular therapy in anterior circulation ischemic stroke due to large-vessel occlusion, data regarding the treatment of acute basilar artery occlusion (BAO) are still equivocal. The BASICS trial failed to show an advantage of endovascular therapy (EVT) over best medical treatment (BMT). In contrast, data from the recently published BASILAR registry showed a better outcome in patients receiving EVT.
Objective: The aim of the study was to investigate the safety and efficacy of EVT plus BMT vs. BMT alone in acute BAO.
Methods: We analyzed the clinical course and short-term outcomes of patients with radiologically confirmed BAO dichotomized by BMT plus EVT or BMT only as documented in a state-wide prospective registry of consecutive patients hospitalized due to acute stroke. The primary endpoint was a favorable functional outcome (mRS 0–3) at hospital discharge assessed as common odds ratio using binary logistic regression. Secondary subgroup analyses and propensity score matching were added. Safety outcomes included mortality, the rate of intracerebral hemorrhages, and complications during hospitalization.
Results: We included 403 patients with acute BAO (2017–2019). A total of 270 patients (67%) were treated with BMT plus EVT and 133 patients (33%) were treated with BMT only. A favorable outcome (mRS 0–3) was observed in 33.8% of the BMT and 26.7% of the BMT plus EVT group [OR.770, CI (0.50–1.2)]. Subgroup analyses for patients with a NIHSS score > 10 at admission to the hospital revealed a benefit from EVT [OR 3.05, CI (1.03–9.01)].
Conclusions: In this prospective, quasi population-based registry of patients hospitalized with acute BAO, BMT plus EVT was not superior to BMT alone. Nevertheless, our results suggest that severely affected BAO patients are more likely to benefit from EVT.
Rationale and objectives: The objective of this study was to analyze the role of dynamic magnetic resonance imaging (MRI) in patients who suffered from groin pain and whose physical examination and ultrasound returned inconclusive/indefinite results, as well as in patients receiving an ongoing assessment for a previous herniotomy.
Material and methods: For this study, 25 patients 14 women and 11 men were selected with a mean age of 41.6 years, including clinical complaints, such as groin pain and or a previous herniotomies. These patients underwent dynamic MRI. Reports were created by a radiology resident and a radiology consultant. Clinical and ultrasound documentation were compared to with imaging results from the MRI.
Results: The results of the dynamic MRI were negative for 23 patients (92%) and positive for two patients (8%). One patient suffered from an indirect hernia and one from a femoral hernia. A repeated hernia was an excluding for the preoperated patients with pain and ongoing assessment.
Conclusions: Dynamic MRI shows substantially higher diagnostic performance in exclusion of inguinal hernia, when compared to a physical examination and ultrasound. The examination can also be used in assessments to analyze the operation’s results.
Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.
Identifying co-expression of lipid species is challenging, but indispensable to identify novel therapeutic targets for breast cancer treatment. Lipid metabolism is often dysregulated in cancer cells, and changes in lipid metabolism affect cellular processes such as proliferation, autophagy, and tumor development. In addition to mRNA analysis of sphingolipid metabolizing enzymes, we performed liquid chromatography time-of-flight mass spectrometry analysis in three breast cancer cell lines. These breast cancer cell lines differ in estrogen receptor and G-protein coupled estrogen receptor 1 status. Our data show that sphingolipids and non-sphingolipids are strongly increased in SKBr3 cells. SKBr3 cells are estrogen receptor negative and G-protein coupled estrogen receptor 1 positive. Treatment with G15, a G-protein coupled estrogen receptor 1 antagonist, abolishes the effect of increased sphingolipid and non-sphingolipid levels in SKBr3 cells. In particular, ether lipids are expressed at much higher levels in cancer compared to normal cells and are strongly increased in SKBr3 cells. Our analysis reveals that this is accompanied by increased sphingolipid levels such as ceramide, sphingadiene-ceramide and sphingomyelin. This shows the importance of focusing on more than one lipid class when investigating molecular mechanisms in breast cancer cells. Our analysis allows unbiased screening for different lipid classes leading to identification of co-expression patterns of lipids in the context of breast cancer. Co-expression of different lipid classes could influence tumorigenic potential of breast cancer cells. Identification of co-regulated lipid species is important to achieve improved breast cancer treatment outcome.