Stronger together: The case for multidisciplinary tenure track faculty in academic nursing

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167662/1/Tubbs-Cooley et al 2021.pdfDescription of Tubbs-Cooley et al 2021.pdf : ArticleSEL

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Risk factors for delay in symptomatic presentation of leukaemia, lymphoma and myeloma

Background: UK policy aims to improve cancer outcomes by promoting early diagnosis, which for many haematological malignancies is particularly challenging as the pathways leading to diagnosis can be difficult and prolonged. Methods: A survey about symptoms was sent to patients in England with acute leukaemia, chronic lymphocytic leukaemia (CLL), chronic myeloid leukaemia (CML), myeloma and non-Hodgkin lymphoma (NHL). Symptoms and barriers to first help seeking were examined for each subtype, along with the relative risk of waiting >3 months’ time from symptom onset to first presentation to a doctor, controlling for age, sex and deprivation. Results: Of the 785 respondents, 654 (83.3%) reported symptoms; most commonly for NHL (95%) and least commonly for CLL (67.9%). Some symptoms were frequent across diseases while others were more disease-specific. Overall, 16% of patients (n=114) waited >3 months before presentation; most often in CML (24%) and least in acute leukaemia (9%). Significant risk factors for >3 months to presentation were: night sweats (particularly CLL and NHL), thirst, abdominal pain/discomfort, looking pale (particularly acute leukaemias), and extreme fatigue/tiredness (particularly CML and NHL); and not realising symptom(s) were serious. Conclusions: These findings demonstrate important differences by subtype, which should be considered in strategies promoting early presentation. Not realising the seriousness of some symptoms indicates a worrying lack of public awareness

High performing hospitals: a qualitative systematic review of associated factors and practical strategies for improvement.

BACKGROUND: High performing hospitals attain excellence across multiple measures of performance and multiple departments. Studying high performing hospitals can be valuable if factors associated with high performance can be identified and applied. Factors leading to high performance are complex and an exclusive quantitative approach may fail to identify richly descriptive or relevant contextual factors. The objective of this study was to undertake a systematic review of qualitative literature to identify methods used to identify high performing hospitals, the factors associated with high performers, and practical strategies for improvement. METHODS: Methods used to collect and summarise the evidence contributing to this review followed the 'enhancing transparency in reporting the synthesis of qualitative research' protocol. Peer reviewed studies were identified through Medline, Embase and Cinahl (Jan 2000-Feb 2014) using specified key words, subject terms, and medical subject headings. Eligible studies required the use of a quantitative method to identify high performing hospitals, and qualitative methods or tools to identify factors associated with high performing hospitals or hospital departments. Title, abstract, and full text screening was undertaken by four reviewers, and inter-rater reliability statistics were calculated for each review phase. Risk of bias was assessed. Following data extraction, thematic syntheses identified contextual factors important for explaining success. Practical strategies for achieving high performance were then mapped against the identified themes. RESULTS: A total of 19 studies from a possible 11,428 were included in the review. A range of process, output, outcome and other indicators were used to identify high performing hospitals. Seven themes representing factors associated with high performance (and 25 sub-themes) emerged from the thematic syntheses: positive organisational culture, senior management support, effective performance monitoring, building and maintaining a proficient workforce, effective leaders across the organisation, expertise-driven practice, and interdisciplinary teamwork. Fifty six practical strategies for achieving high performance were catalogued. CONCLUSIONS: This review provides insights into methods used to identify high performing hospitals, and yields ideas about the factors important for success. It highlights the need to advance approaches for understanding what constitutes high performance and how to harness factors associated with high performance

Is increased time to diagnosis and treatment in symptomatic cancer associated with poorer outcomes?:Systematic review

background: It is unclear whether more timely cancer diagnosis brings favourable outcomes, with much of the previous evidence, in some cancers, being equivocal. We set out to determine whether there is an association between time to diagnosis, treatment and clinical outcomes, across all cancers for symptomatic presentations. methods: Systematic review of the literature and narrative synthesis. results: We included 177 articles reporting 209 studies. These studies varied in study design, the time intervals assessed and the outcomes reported. Study quality was variable, with a small number of higher-quality studies. Heterogeneity precluded definitive findings. The cancers with more reports of an association between shorter times to diagnosis and more favourable outcomes were breast, colorectal, head and neck, testicular and melanoma. conclusions: This is the first review encompassing many cancer types, and we have demonstrated those cancers in which more evidence of an association between shorter times to diagnosis and more favourable outcomes exists, and where it is lacking. We believe that it is reasonable to assume that efforts to expedite the diagnosis of symptomatic cancer are likely to have benefits for patients in terms of improved survival, earlier-stage diagnosis and improved quality of life, although these benefits vary between cancers

Multiparametric computer-aided differential diagnosis of Alzheimer's disease and frontotemporal dementia using structural and advanced MRI

textabstractObjectives: To investigate the added diagnostic value of arterial spin labelling (ASL) and diffusion tensor imaging (DTI) to structural MRI for computer-aided classificat