41 research outputs found

    Another step closer to measuring the ghosts in the nursery: preliminary validation of the Trauma Reflective Functioning Scale

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    The aim of this study was to examine preliminary evidence of the validity of the Trauma Reflective Functioning Scale and to investigate reflective functioning (RF) and attachment in pregnant women with histories of trauma, with a particular focus on the capacity to mentalize regarding trauma and its implications for adaptation to pregnancy and couple functioning. The Adult Attachment Interview was used to assess attachment, unresolved trauma and mentalization (measured as RF) regarding relationships with attachment figures (RF-G) and trauma (RF-T) in 100 pregnant women with histories of abuse and neglect. The majority (63%) of women had insecure attachment states of mind and approximately half were unresolved regarding trauma. Furthermore, the majority of women manifested deficits specific to RF-T. Their RF-T was significantly lower than their RF-G; the findings indicate that women with histories of childhood abuse and neglect do not manifest a generic inhibition of reflectiveness, but a collapse of mentalization specific to trauma. Low RF-T, indicative of difficulty in considering traumatic experiences in mental state terms, was associated with difficulty in investment in the pregnancy and lack of positive feelings about the baby and motherhood. In addition, low RF-T was also associated with difficulties in intimate relationships. Results of a regression analysis with RF indicated that RF-T was the best predictor of investment in pregnancy and couple functioning. In sum, the study provides preliminary evidence that RF-T can be reliably measured and is a valid construct that has potential usefulness for research and clinical practice. It highlights the importance of mentalization specifically about trauma and suggests that it is not the experience of trauma per se, but the absence of mentalization regarding trauma that is associated with difficulties in close relationships and in making the transition to parenthood

    Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

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    IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

    Considerations on Baseline Generation for Imaging AI Studies Illustrated on the CT-Based Prediction of Empyema and Outcome Assessment

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    For AI-based classification tasks in computed tomography (CT), a reference standard for evaluating the clinical diagnostic accuracy of individual classes is essential. To enable the implementation of an AI tool in clinical practice, the raw data should be drawn from clinical routine data using state-of-the-art scanners, evaluated in a blinded manner and verified with a reference test. Three hundred and thirty-five consecutive CTs, performed between 1 January 2016 and 1 January 2021 with reported pleural effusion and pathology reports from thoracocentesis or biopsy within 7 days of the CT were retrospectively included. Two radiologists (4 and 10 PGY) blindly assessed the chest CTs for pleural CT features. If needed, consensus was achieved using an experienced radiologist’s opinion (29 PGY). In addition, diagnoses were extracted from written radiological reports. We analyzed these findings for a possible correlation with the following patient outcomes: mortality and median hospital stay. For AI prediction, we used an approach consisting of nnU-Net segmentation, PyRadiomics features and a random forest model. Specificity and sensitivity for CT-based detection of empyema (n = 81 of n = 335 patients) were 90.94 (95%-CI: 86.55–94.05) and 72.84 (95%-CI: 61.63–81.85%) in all effusions, with moderate to almost perfect interrater agreement for all pleural findings associated with empyema (Cohen’s kappa = 0.41–0.82). Highest accuracies were found for pleural enhancement or thickening with 87.02% and 81.49%, respectively. For empyema prediction, AI achieved a specificity and sensitivity of 74.41% (95% CI: 68.50–79.57) and 77.78% (95% CI: 66.91–85.96), respectively. Empyema was associated with a longer hospital stay (median = 20 versus 14 days), and findings consistent with pleural carcinomatosis impacted mortality

    Towards More Structure: Comparing TNM Staging Completeness and Processing Time of Text-Based Reports versus Fully Segmented and Annotated PET/CT Data of Non-Small-Cell Lung Cancer

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    Results of PET/CT examinations are communicated as text-based reports which are frequently not fully structured. Incomplete or missing staging information can be a significant source of staging and treatment errors. We compared standard text-based reports to a manual full 3D-segmentation-based approach with respect to TNM completeness and processing time. TNM information was extracted retrospectively from 395 reports. Moreover, the RIS time stamps of these reports were analyzed. 2995 lesions using a set of 41 classification labels (TNM features + location) were manually segmented on the corresponding image data. Information content and processing time of reports and segmentations were compared using descriptive statistics and modelling. The TNM/UICC stage was mentioned explicitly in only 6% (n=22) of the text-based reports. In 22% (n=86), information was incomplete, most frequently affecting T stage (19%, n=74), followed by N stage (6%, n=22) and M stage (2%, n=9). Full NSCLC-lesion segmentation required a median time of 13.3 min, while the median of the shortest estimator of the text-based reporting time (R1) was 18.1 min (p=0.01). Tumor stage (UICC I/II: 5.2 min, UICC III/IV: 20.3 min, p<0.001), lesion size (p<0.001), and lesion count (n=1: 4.4 min, n=12: 37.2 min, p<0.001) correlated significantly with the segmentation time, but not with the estimators of text-based reporting time. Numerous text-based reports are lacking staging information. A segmentation-based reporting approach tailored to the staging task improves report quality with manageable processing time and helps to avoid erroneous therapy decisions based on incomplete reports. Furthermore, segmented data may be used for multimedia enhancement and automatization

    Metal-Supported Solid Oxide Fuel Cells with Exceptionally High Power Density for Range Extender Systems

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    Solid oxide fuel cells (SOFCs) exhibit potential to become a key technology for future clean energy systems. The metal-supported SOFC exhibits decisive strengths like fast start-up capability, mechanical robustness, and acceptable cost, making it the concept of choice for mobile applications. As a promising example, SOFC-powered range extenders for electric vehicles offer fast refueling and significantly increased driving range, while lowering size, weight, and the cost of the vehicle’s battery. Here, we report the development of a metal-supported SOFC aiming at exceptionally high power density. A knowledge-based improvement of all electrochemically active cell components enables a performance increase up to a factor of 10 and demonstrates the effectiveness of target-oriented optimization of processing and microstructure. Ultimately, enhanced cells meet the industrial performance target by providing a current density of 2.8 A × cm−2 at 650°C and 0.7 V, setting a benchmark for SOFC performance
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