Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes

The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model-SuStaIn, to be utilised for short-term infectious disease like COVID-19, based on 11 commonly recorded clinical measures. We used 1344 patients from the National COVID-19 Chest Imaging Database (NCCID), hospitalised for RT-PCR confirmed COVID-19 disease, splitting them equally into a training and an independent validation cohort. We discovered three COVID-19 subtypes (General Haemodynamic, Renal and Immunological) and introduced disease severity stages, both of which were predictive of distinct risks of in-hospital mortality or escalation of treatment, when analysed using Cox Proportional Hazards models. A low-risk Normal-appearing subtype was also discovered. The model and our full pipeline are available online and can be adapted for future outbreaks of COVID-19 or other infectious disease

Functional, Volumetric, and Textural Analysis of Malignant Pleural Mesothelioma Using Computed Tomography and Deep Convolutional Neural Networks

Malignant pleural mesothelioma (mesothelioma) is a malignancy of the pleura, a membrane lining the outside of the lungs and the inside of the chest wall. This thoracic cancer is primarily caused by asbestos exposure and carries a poor prognosis. Computed tomography (CT) is the main imaging modality used for the diagnosis and treatment evaluation of mesothelioma in the United States. Mesothelioma is typically aspherical and presents with different morphology and growth patterns from most other cancers. These aspects of the disease, combined with recent advances in personalized therapies and molecularly targeted therapies, have motivated investigations into alternative and potentially more sensitive measures of mesothelioma treatment response. Image-based measurement of mesothelioma tumor volume has seen interest from researchers in the staging of this disease and as a potential marker for patient prognosis and tumor response to treatment. The histologic subtype of mesothelioma is the most significant prognostic factor for this disease. The first study presented in this work investigated the use of functional CT imaging for the assessment of mesothelioma response to treatment. This prospective study showed differing trends in hemodynamic change between scans for patients on treatment and patients on observation, which suggests a potential role of this imaging method for the assessment of tumor response to treatment. A future study of a larger patient population on more coherent treatment regimens is needed to confirm the results of this pilot study. In another investigation of this work, deep convolutional neural networks (CNNs) were implemented for the automated segmentation of mesothelioma tumor on CT scans, showing a significantly improved segmentation performance when compared with a previously published non-deep learning-based method for the automated segmentation of this disease. This study demonstrated the suitability and state-of-the-art performance of deep CNNs for the task of mesothelioma tumor segmentation on CT scans. Next, a study investigated the performance of a deep CNN-based tumor segmentation method on CT scans that exhibited both mesothelioma tumor and pleural effusions, a common presentation on radiologic scans of mesothelioma patients. This study showed significantly improved tumor segmentation performance when compared with the previous deep CNN-based method on scans of mesothelioma patients exhibiting tumor and pleural effusions. The last study presented in this work explored the differentiation of histologic subtypes of mesothelioma on CT scans using texture analysis through a retrospective investigation. CT scans of patients with pathologically confirmed epithelioid and ``non-epithelioid'' mesothelioma (i.e., biphasic and sarcomatoid tumors), acquired prior to talc pleurodesis, were included in the final analysis. The database of this study included in-house scans and outside scans acquired on a variety of CT scanners, and data heterogeneity precluded the robust classification of tumors as epithelioid and non-epithelioid; however, the method warrants further investigation as more data become available. This dissertation investigated methods for the quantitative image-based analysis of mesothelioma tumor through standard contrast-enhanced and functional imaging and through the acquisition of image-based tumor volume using advanced deep learning-based algorithms. In this work, the novel investigation of deep CNNs for the segmentation of mesothelioma on CT scans demonstrated the state-of-the-art performance of this technique for the automated acquisition of mesothelioma tumor volumetrics, the first pilot study dedicated to the investigation of DCE-CT for mesothelioma treatment response assessment indicated that this method should be explored in a larger patient cohort, and texture feature analysis was explored for the first time for the differentiation of mesothelioma tumor subtype in a retrospective study. The research presented in this work will hopefully advance the methods available to researchers and clinicians for the quantitative analysis of mesothelioma in the image-based management of mesothelioma patients

Vocational education in retail. Stadus, needs, policy and implementation

Ritgerðin er meistaraprófsritgerð í opinberri stjórnsýslu við félagsvísindasvið Háskóla Íslands. Viðfangsefni hennar er starfsnám í smásöluverslun sem er skoðað út frá stöðu þess, þörfum, stefnu og framkvæmd. Staðan er skoðuð út frá atvinnugreininni og menntun innan hennar. Rýnt er í þarfir einkum út frá mati hagsmunaaðila á þeim. Greiningin á stefnunni er aðallega út frá stefnu stjórnvalda en aðkoma hagsmunaaðila og framkvæmdaaðila er einnig skoðuð. Hvað framkvæmdina varðar er áherslan á skóla og fræðsluaðila en þáttur hagsmunaaðila er einnig greindur. Rannsóknaraðferðin er blönduð aðferð sem byggir bæði á eigindlegum og megindlegum hefðum. Rannsóknin er einnig undir áhrifum af tilviksrannsóknaraðferðinni en uppfyllir þó ekki öll skilyrði til að geta talist tilviksrannsókn. Í efnisöflun er leitað víða fanga. Tekin eru viðtöl, unnin könnun á verslun í Sveitarfélaginu Hornafirði, fengin sérunnin tölfræðigögn og rýnt í tölfræðigögn, skýrslur og skjöl sem þegar voru til. Helstu niðurstöður eru þær að starfsmenntun í smásöluverslunum á Íslandi er lítil og það er helst innan stærri verslunarkeðja sem einhver starfsmenntun á sér stað. Þarfir fyrir meiri menntun eru til staðar en þær birtast ekki með skýrum hætti. Stefna stjórnvalda er ómarkviss og það sem þó hefur verið unnið á því sviði hefur í litlum mæli komist í framkvæmd.This is master dissertation in public administration from the School of Social Sciences in University of Iceland. The research is about vocational education in retail in relation to status, needs, policy and implementation. The status in the industry is examined and the education within it. The study of the needs stresses the perspective of companies and the employees. The analyse of the policy in mainly considering the public policy but the view of the industry and those who implement it is also taken in to account. In the implementation the main focus is on schools and the educators, but also to some extent the view of the industry. The research method is a mixed and based on both qualitative and quantitative method. The research is also under influence of the case study method though it does not satisfy all the conditions in that method. The research material is from different sources. There are interviews, observation on retail in the municipality Hornafjordur, statistics, documents and reports. The main results are that vocational education in retail in Iceland is very little and mainly in the large retail chains. There is a need for more education only it does not merge clearly. The public policy is not focused or clear and a small part of it is implemented

Epidemiological study on cognitive abilities in the elderly in two separate rural areas in Iceland

Neðst á síðunni er hægt að nálgast greinina í heild sinni með því að smella á hlekkinn View/OpenObjectives: This study was undertaken to estimate the cognitive abilities in an elderly population in rural areas in Iceland and to get an idea of the prevalence of dementia. By examining inhabitants in two different areas it was further possible to detect any possible difference in these areas. Material and methods: All persons aged 70 and over, living independently in the community and in institutions in two geographically separate areas were contacted. The areas were an agricultural (area A) and a fishing (area F) one. Four simple neuropsychological tests where used, the MMSE (Mini Mental State Examination), WAIS (Wechsler Adult Intelligence Scale)-Similarities, Trail making test A and Trail making test B. Two students in psychology and a teacher were trained in applying the tests but the results were scored and interpreted by the authors. Results: In area A, 280 of 353 (79.3%) participated and in area F, 190 of 238 (79.8%). Participation was thus similar in both areas. There was a highly significant difference in all the tests with p<0.01 in Trail making test B but p<0.001 in the other three tests. In all the tests the results were better among the population in area A. The prevalence of dementia as estimated by the MMSE showed a prevalence of 14.4% in area A and 35.7% in area F. Conclusion: A significant difference in cognitive abilities was found between the elderly inhabitants of two separate rural areas in Iceland. There is substantial evidence to suggest that this difference is real but it is however not clear if the prevalence of dementia is higher in this study than in others. It is postulated that the difference found is due to cultural differences.Markmið: Markmið rannsóknarinnar var annars vegar að fá vitneskju um vitræna hæfileika aldraðra Íslendinga í afmörkuðu dreifbýli og hins vegar að fá hugmynd um algengi heilabilunar. Með því að skoða tvö aðskilin landsvæði var einnig unnt að meta hvort munur gæti verið á milli þeirra í þessu tilliti. Efniviður og aðferðir: Valin voru tvö aðskilin landsvæði. Í öðru þeirra hefur aðalatvinnuvegur verið landbúnaður (svæði L) en í hinu sjávarútvegur (svæði S). Notast var við fjögur einföld próf á vitræna getu, MMS-próf (Mini Mental State Examination), líkingar úr WAIS greindarprófi Wechslers (Wechsler Adult Intelligence Scale) fyrir fullorðna, slóðarpróf A og slóðarpróf B. Þjálfaðir voru tveir sálfræðinemar og kennari í fyrirlagningu prófanna, en höfundar fóru yfir niðurstöður og túlkuðu. Reynt var að ná til allra, innan sem utan stofnunar, sem náð höfðu 70 ára aldri hinn 1. desember 1991. Niðurstöður: Af svæði L var rætt við 280 af 353 (79,3%) og af svæði S 190 af 238 (79,8%), voru heimtur því ámóta af báðum svæðum. Fram kom mjög marktækur munur á frammistöðu hópanna eftir svæðum og var útkoman betri á svæði L á öllum prófunum. Í þremur prófanna náði munurinn marktækni með p-gildi <0,001, en í einu þeirra (slóðarprófi B) var marktæknin p<0,01, en fæstir gátu leyst það próf af hendi. Ef MMS-prófið er notaö til að meta algengi heilabilunar á þessum svæðum virðast 14,4% á svæði L og 35,7% á svæði S vera undir viðmiðunarmörkum sem voru við 22/23 stig í þessari rannsókn. Ályktun: Verulegur munur reyndist vera á frammistöðu aldraðra íbúa þessara tveggja landsvæða í úrlausn einfaldra prófa á vitræna getu. Rök eru færð að því að þessi munur sé raunverulegur, en vegna aðferðarfræðinnar sem notast var við er ekki ljóst hvort algengi heilabilunar er hærri hér á landi en annars staðar

Epidemiological study on cognitive abilities in the elderly in two separate rural areas in Iceland

Neðst á síðunni er hægt að nálgast greinina í heild sinni með því að smella á hlekkinn View/OpenObjectives: This study was undertaken to estimate the cognitive abilities in an elderly population in rural areas in Iceland and to get an idea of the prevalence of dementia. By examining inhabitants in two different areas it was further possible to detect any possible difference in these areas. Material and methods: All persons aged 70 and over, living independently in the community and in institutions in two geographically separate areas were contacted. The areas were an agricultural (area A) and a fishing (area F) one. Four simple neuropsychological tests where used, the MMSE (Mini Mental State Examination), WAIS (Wechsler Adult Intelligence Scale)-Similarities, Trail making test A and Trail making test B. Two students in psychology and a teacher were trained in applying the tests but the results were scored and interpreted by the authors. Results: In area A, 280 of 353 (79.3%) participated and in area F, 190 of 238 (79.8%). Participation was thus similar in both areas. There was a highly significant difference in all the tests with p<0.01 in Trail making test B but p<0.001 in the other three tests. In all the tests the results were better among the population in area A. The prevalence of dementia as estimated by the MMSE showed a prevalence of 14.4% in area A and 35.7% in area F. Conclusion: A significant difference in cognitive abilities was found between the elderly inhabitants of two separate rural areas in Iceland. There is substantial evidence to suggest that this difference is real but it is however not clear if the prevalence of dementia is higher in this study than in others. It is postulated that the difference found is due to cultural differences.Markmið: Markmið rannsóknarinnar var annars vegar að fá vitneskju um vitræna hæfileika aldraðra Íslendinga í afmörkuðu dreifbýli og hins vegar að fá hugmynd um algengi heilabilunar. Með því að skoða tvö aðskilin landsvæði var einnig unnt að meta hvort munur gæti verið á milli þeirra í þessu tilliti. Efniviður og aðferðir: Valin voru tvö aðskilin landsvæði. Í öðru þeirra hefur aðalatvinnuvegur verið landbúnaður (svæði L) en í hinu sjávarútvegur (svæði S). Notast var við fjögur einföld próf á vitræna getu, MMS-próf (Mini Mental State Examination), líkingar úr WAIS greindarprófi Wechslers (Wechsler Adult Intelligence Scale) fyrir fullorðna, slóðarpróf A og slóðarpróf B. Þjálfaðir voru tveir sálfræðinemar og kennari í fyrirlagningu prófanna, en höfundar fóru yfir niðurstöður og túlkuðu. Reynt var að ná til allra, innan sem utan stofnunar, sem náð höfðu 70 ára aldri hinn 1. desember 1991. Niðurstöður: Af svæði L var rætt við 280 af 353 (79,3%) og af svæði S 190 af 238 (79,8%), voru heimtur því ámóta af báðum svæðum. Fram kom mjög marktækur munur á frammistöðu hópanna eftir svæðum og var útkoman betri á svæði L á öllum prófunum. Í þremur prófanna náði munurinn marktækni með p-gildi <0,001, en í einu þeirra (slóðarprófi B) var marktæknin p<0,01, en fæstir gátu leyst það próf af hendi. Ef MMS-prófið er notaö til að meta algengi heilabilunar á þessum svæðum virðast 14,4% á svæði L og 35,7% á svæði S vera undir viðmiðunarmörkum sem voru við 22/23 stig í þessari rannsókn. Ályktun: Verulegur munur reyndist vera á frammistöðu aldraðra íbúa þessara tveggja landsvæða í úrlausn einfaldra prófa á vitræna getu. Rök eru færð að því að þessi munur sé raunverulegur, en vegna aðferðarfræðinnar sem notast var við er ekki ljóst hvort algengi heilabilunar er hærri hér á landi en annars staðar

Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes

Abstract The COVID-19 pandemic has been a great challenge to healthcare systems worldwide. It highlighted the need for robust predictive models which can be readily deployed to uncover heterogeneities in disease course, aid decision-making and prioritise treatment. We adapted an unsupervised data-driven model—SuStaIn, to be utilised for short-term infectious disease like COVID-19, based on 11 commonly recorded clinical measures. We used 1344 patients from the National COVID-19 Chest Imaging Database (NCCID), hospitalised for RT-PCR confirmed COVID-19 disease, splitting them equally into a training and an independent validation cohort. We discovered three COVID-19 subtypes (General Haemodynamic, Renal and Immunological) and introduced disease severity stages, both of which were predictive of distinct risks of in-hospital mortality or escalation of treatment, when analysed using Cox Proportional Hazards models. A low-risk Normal-appearing subtype was also discovered. The model and our full pipeline are available online and can be adapted for future outbreaks of COVID-19 or other infectious disease