| 1. |
- Israelsson, Johan, et al.
(författare)
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Health status and psychological distress among in-hospital cardiac arrest survivors in relation to gender
- 2017
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Ingår i: Resuscitation. - : Elsevier. - 0300-9572 .- 1873-1570. ; 114, s. 27-33
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Tidskriftsartikel (refereegranskat)abstract
- AIM: To describe health status and psychological distress among in-hospital cardiac arrest (IHCA) survivors in relation to gender.METHODS: This national register study consists of data from follow-up registration of IHCA survivors 3-6 months post cardiac arrest (CA) in Sweden. A questionnaire was sent to the survivors, including measurements of health status (EQ-5D-5L) and psychological distress (HADS).RESULTS: Between 2013 and 2015, 594 IHCA survivors were included in the study. The median values for EQ-5D-5L index and EQ VAS among survivors were 0.78 (q1-q3=0.67-0.86) and 70 (q1-q3=50-80) respectively. The values were significantly lower (p<0.001) in women compared to men. In addition, women reported more problems than men in all dimensions of EQ-5D-5L, except self-care. A majority of the respondents reported no problems with anxiety (85.4%) and/or symptoms of depression (87.0%). Women reported significantly more problems with anxiety (p<0.001) and symptoms of depression (p<0.001) compared to men. Gender was significantly associated with poorer health status and more psychological distress. No interaction effects for gender and age were found.CONCLUSIONS: Although the majority of survivors reported acceptable health status and no psychological distress, a substantial proportion reported severe problems. Women reported worse health status and more psychological distress compared to men. Therefore, a higher proportion of women may be in need of support. Health care professionals should make efforts to identify health problems among survivors and offer individualised support when needed.
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| 2. |
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| 3. |
- Gerdle, Björn, et al.
(författare)
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Who benefits from multimodal rehabilitation - an exploration of pain, psychological distress, and life impacts in over 35,000 chronic pain patients identified in the Swedish Quality Registry for Pain Rehabilitation
- 2019
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Ingår i: Journal of Pain Research. - : DOVE Medical Press Ltd.. - 1178-7090. ; 12, s. 891-908
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Tidskriftsartikel (refereegranskat)abstract
- Background: Chronic pain patients frequently suffer from psychological symptoms. There is no consensus concerning the prevalence of severe anxiety and depressive symptoms and the strength of the associations between pain intensity and psychological distress. Although an important aspect of the clinical picture is understanding how the pain condition impacts life, little is known about the relative importance of pain and psychological symptoms for individual's life impact. The aims of this study were to identify subgroups of pain patients; to analyze if pain, psychological distress, and life impact variables influence subgrouping; and to investigate how patients in the subgroups benefit from treatments.Methods: Background variables, pain aspects (intensity/severity and spreading), psychological distress (depressive and anxiety symptoms), and two life impact variables (pain interference and perceived life control) were obtained from the Swedish Quality Registry for Pain Rehabilitation for chronic pain patients and analyzed mainly using advanced multivariate methods.Results: Based on >35,000 patients, 35%-40% had severe anxiety or depressive symptoms. Severe psychological distress was associated with being born outside Europe (21%-24% vs 6%-8% in the category without psychological distress) and low education level (20.7%-20.8% vs 26%-27% in the category without psychological distress). Dose relationships existed between the two psychological distress variables and pain aspects, but the explained variances were generally low. Pain intensity/severity and the two psychological distress variables were significantly associated (R2=0.40-0.48; P>0.001) with the two life impact variables (pain interference and life control). Two subgroups of patients were identified at baseline (subgroup 1: n=15,901-16,119; subgroup 2: n=20,690-20,981) and the subgroup with the worst situation regarding all variables participated less in an MMRP (51% vs 58%, P<0.001) but showed the largest improvements in outcomes.Conclusion: The results emphasize the need to assess both pain and psychological distress and not take for granted that pain involves high psychological stress in the individual case. Not all patients benefit from MMRP. A better matching between common clinical pictures and the content of MMRPs may help improve results. We only partly found support for treatment resistance in patients with psychological distress burden.
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| 4. |
- Palmqvist, Sebastian, et al.
(författare)
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Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease
- 2015
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Ingår i: Neurology. - : Lippincott Williams & Wilkins. - 1526-632X .- 0028-3878. ; 85:14, s. 1240-1249
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Tidskriftsartikel (refereegranskat)abstract
- Objective:To compare the diagnostic accuracy of CSF biomarkers and amyloid PET for diagnosing early-stage Alzheimer disease (AD).Methods:From the prospective, longitudinal BioFINDER study, we included 122 healthy elderly and 34 patients with mild cognitive impairment who developed AD dementia within 3 years (MCI-AD). -Amyloid (A) deposition in 9 brain regions was examined with [F-18]-flutemetamol PET. CSF was analyzed with INNOTEST and EUROIMMUN ELISAs. The results were replicated in 146 controls and 64 patients with MCI-AD from the Alzheimer's Disease Neuroimaging Initiative study.Results:The best CSF measures for identifying MCI-AD were A42/total tau (t-tau) and A42/hyperphosphorylated tau (p-tau) (area under the curve [AUC] 0.93-0.94). The best PET measures performed similarly (AUC 0.92-0.93; anterior cingulate, posterior cingulate/precuneus, and global neocortical uptake). CSF A42/t-tau and A42/p-tau performed better than CSF A42 and A42/40 (AUC difference 0.03-0.12, p < 0.05). Using nonoptimized cutoffs, CSF A42/t-tau had the highest accuracy of all CSF/PET biomarkers (sensitivity 97%, specificity 83%). The combination of CSF and PET was not better than using either biomarker separately.Conclusions:Amyloid PET and CSF biomarkers can identify early AD with high accuracy. There were no differences between the best CSF and PET measures and no improvement when combining them. Regional PET measures were not better than assessing the global A deposition. The results were replicated in an independent cohort using another CSF assay and PET tracer. The choice between CSF and amyloid PET biomarkers for identifying early AD can be based on availability, costs, and doctor/patient preferences since both have equally high diagnostic accuracy.Classification of evidence:This study provides Class III evidence that amyloid PET and CSF biomarkers identify early-stage AD equally accurately.
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| 5. |
- Ge, Chenjie, 1991, et al.
(författare)
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Multiscale Deep Convolutional Networks for Characterization and Detection of Alzheimer's Disease using MR Images
- 2019
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Ingår i: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. ; 2019-September, s. 789-793
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the issues of Alzheimer's disease (AD) characterization and detection from Magnetic Resonance Images (MRIs). Many existing AD detection methods use single-scale feature learning from brain scans. In this paper, we propose a multiscale deep learning architecture for learning AD features. The main contributions of the paper include: (a) propose a novel 3D multiscale CNN architecture for the dedicated task of AD detection; (b) propose a feature fusion and enhancement strategy for multiscale features; (c) empirical study on the impact of several settings, including two dataset partitioning approaches, and the use of multiscale and feature enhancement. Experiments were conducted on an open ADNI dataset (1198 brain scans from 337 subjects), test results have shown the effectiveness of the proposed method with test accuracy of 93.53%, 87.24% (best, average) on subject separated dataset, and 99.44%, 98.80% (best, average) on random brain scan-partitioned dataset. Comparison with eight existing methods has provided further support to the proposed method.
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| 6. |
- Ge, Chenjie, 1991, et al.
(författare)
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3D Multi-Scale Convolutional Networks for Glioma Grading Using MR Images
- 2018
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Ingår i: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. - 9781479970612 ; , s. 141-145
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Konferensbidrag (refereegranskat)abstract
- This paper addresses issues of grading brain tumor, glioma, from Magnetic Resonance Images (MRIs). Although feature pyramid is shown to be useful to extract multi-scale features for object recognition, it is rarely explored in MRI images for glioma classification/grading. For glioma grading, existing deep learning methods often use convolutional neural networks (CNNs) to extract single-scale features without considering that the scales of brain tumor features vary depending on structure/shape, size, tissue smoothness, and locations. In this paper, we propose to incorporate the multi-scale feature learning into a deep convolutional network architecture, which extracts multi-scale semantic as well as fine features for glioma tumor grading. The main contributions of the paper are: (a) propose a novel 3D multi-scale convolutional network architecture for the dedicated task of glioma grading; (b) propose a novel feature fusion scheme that further refines multi-scale features generated from multi-scale convolutional layers; (c) propose a saliency-aware strategy to enhance tumor regions of MRIs. Experiments were conducted on an open dataset for classifying high/low grade gliomas. Performance on the test set using the proposed scheme has shown good results (with accuracy of 89.47%).
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| 7. |
- Schöll, Michael, 1980, et al.
(författare)
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Biomarkers for tau pathology.
- 2019
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Ingår i: Molecular and cellular neurosciences. - : Elsevier BV. - 1095-9327 .- 1044-7431. ; 97, s. 18-33
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Forskningsöversikt (refereegranskat)abstract
- The aggregation of fibrils of hyperphosphorylated and C-terminally truncated microtubule-associated tau protein characterizes 80% of all dementia disorders, the most common neurodegenerative disorders. These so-called tauopathies are hitherto not curable and their diagnosis, especially at early disease stages, has traditionally proven difficult. A keystone in the diagnosis of tauopathies was the development of methods to assess levels of tau protein in vivo in cerebrospinal fluid, which has significantly improved our knowledge about these conditions. Tau proteins have also been measured in blood, but the importance of tau-related changes in blood is still unclear. The recent addition of positron emission tomography ligands to visualize, map and quantify tau pathology has further contributed with information about the temporal and spatial characteristics of tau accumulation in the living brain. Together, the measurement of tau with fluid biomarkers and positron emission tomography constitutes the basis for a highly active field of research. This review describes the current state of biomarkers for tau biomarkers derived from neuroimaging and from the analysis of bodily fluids and their roles in the detection, diagnosis and prognosis of tau-associated neurodegenerative disorders, as well as their associations with neuropathological findings, and aims to provide a perspective on how these biomarkers might be employed prospectively in research and clinical settings.
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| 8. |
- Memedi, Mevludin, et al.
(författare)
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Validity and responsiveness of at-home touch-screen assessments in advanced Parkinson's disease
- 2015
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Ingår i: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 19:6, s. 1829-1834
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this study was to investigate if a telemetry test battery can be used to measure effects of Parkinson’s disease (PD) treatment intervention and disease progression in patients with fluctuations. Sixty-five patients diagnosed with advanced PD were recruited in an open longitudinal 36-month study; 35 treated with levodopa-carbidopa intestinal gel (LCIG) and 30 were candidates for switching from oral PD treatment to LCIG. They utilized a test battery, consisting of self-assessments of symptoms and fine motor tests (tapping and spiral drawings), four times per day in their homes during week-long test periods. The repeated measurements were summarized into an overall test score (OTS) to represent the global condition of the patient during a test period. Clinical assessments included ratings on Unified PD Rating Scale (UPDRS) and 39-item PD Questionnaire (PDQ-39) scales. In LCIG-naïve patients, mean OTS compared to baseline was significantly improved from the first test period on LCIG treatment until month 24. In LCIG-non-naïve patients, there were no significant changes in mean OTS until month 36. The OTS correlated adequately with total UPDRS (rho = 0.59) and total PDQ-39 (0.59). Responsiveness measured as effect size was 0.696 and 0.536 for OTS and UPDRS respectively. The trends of the test scores were similar to the trends of clinical rating scores but dropout rate was high. Correlations between OTS and clinical rating scales were adequate indicating that the test battery contains important elements of the information of well-established scales. The responsiveness and reproducibility were better for OTS than for total UPDRS.
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| 9. |
- Hemmingsson, Helena, 1957-, et al.
(författare)
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Eye-Gaze Control Technology as Early Intervention for a Non-Verbal Young Child with High Spinal Cord Injury : A Case Report
- 2018
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Ingår i: Technologies. - : MDPI AG. - 2227-7080. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Assistive technology (AT) can be used as early intervention in order to reduce activity limitations in play and communication. This longitudinal case study examines eye-gaze control technology as early intervention for a young child with high spinal cord injury without the ability to make sounds. The young child was followed by repeated measures concerning performance and communication from baseline at 9 months to 26 months, and finalized at 36 months by field observations in the home setting. The results showed eye-gaze performance and frequency of use of eye-gaze control technology increased over time. Goals set at 15 months concerning learning and using the AT; naming objects and interactions with family was successfully completed at 26 months. Communicative functions regarding obtaining objects and social interaction increased from unintentional actions to purposeful choices and interactions. At 36 months, the toddler was partly independent in eye gazing, used all activities provided, and made independent choices. In conclusion, the results show that a 9-month-old child with profound motor disabilities can benefit from eye-gaze control technology in order to gradually perform activities, socially interact with family members, and make choices.
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| 10. |
- Ge, Chenjie, 1991, et al.
(författare)
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Multi-Stream Multi-Scale Deep Convolutional Networks for Alzheimer's Disease Detection using MR Images
- 2019
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Ingår i: Neurocomputing. - : Elsevier BV. - 0925-2312 .- 1872-8286. ; 350, s. 60-69
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the issue of Alzheimer's disease (AD) detection from Magnetic Resonance Images (MRIs). Existing AD detection methods rely on global feature learning from the whole brain scans, while depending on the tissue types, AD related features in dierent tissue regions, e.g. grey matter (GM), white matter (WM), and cerebrospinal uid (CSF), show different characteristics. In this paper, we propose a deep learning method for multi-scale feature learning based on segmented tissue areas. A novel deep 3D multi-scale convolutional network scheme is proposed to generate multi-resolution features for AD detection. The proposed scheme employs several parallel 3D multi-scale convolutional networks, each applying to individual tissue regions (GM, WM and CSF) followed by feature fusions. The proposed fusion is applied in two separate levels: the rst level fusion is applied on different scales within the same tissue region, and the second level is on dierent tissue regions. To further reduce the dimensions of features and mitigate overtting, a feature boosting and dimension reduction method, XGBoost, is utilized before the classication. The proposed deep learning scheme has been tested on a moderate open dataset of ADNI (1198 scans from 337 subjects), with excellent test performance on randomly partitioned datasets (best 99.67%, average 98.29%), and good test performance on subject-separated partitioned datasets (best 94.74%, average 89.51%). Comparisons with state-of-the-art methods are also included.
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