6 research outputs found

    Incidence of Free of Charge Physiotherapy in a Danish National Cohort of Stroke, Parkinson’s Disease, Multiple Sclerosis and Rheumatoid Arthritis Patients

    Get PDF
    Background: Denmark is a welfare state with a publically funded healthcare system that includes the right to free of charge physiotherapy (FCP) for patients with chronic or progressive disease who fulfill strict criteria. The aim of this study was to investigate the incidence of referral to FCP in patients with a hospital diagnosis of stroke, multiple sclerosis (MS), Parkinson’s disease (PD) and rheumatoid arthritis (RA) between 2007 and 2016. Methods: The study was register-based and included data from The Danish National Patient Registry and The National Health Service Registry. The study population included the four largest disease groups receiving FCP in Denmark. The incidence of receiving FCP was reported as the cumulated incidence proportion (CIP). Results: The study showed that FCP was mainly initiated within the first 2 years after diagnosis. The 2-year CIP was 8% for stroke patients, 53% for PD patients, 49% for MS patients, and 16% for RA patients. The proportion of patients referred to FCP generally increased over the period of the study due to more patients being referred from medical specialists in primary care. Conclusion: This study found substantial differences in the incidence of referral to FCP in a Danish population of stroke, PD, MS and RA patients

    Incidence of Free of Charge Physiotherapy in a Danish National Cohort of Stroke, Parkinson’s Disease, Multiple Sclerosis and Rheumatoid Arthritis Patients

    No full text
    Background: Denmark is a welfare state with a publically funded healthcare system that includes the right to free of charge physiotherapy (FCP) for patients with chronic or progressive disease who fulfill strict criteria. The aim of this study was to investigate the incidence of referral to FCP in patients with a hospital diagnosis of stroke, multiple sclerosis (MS), Parkinson’s disease (PD) and rheumatoid arthritis (RA) between 2007 and 2016. Methods: The study was register-based and included data from The Danish National Patient Registry and The National Health Service Registry. The study population included the four largest disease groups receiving FCP in Denmark. The incidence of receiving FCP was reported as the cumulated incidence proportion (CIP). Results: The study showed that FCP was mainly initiated within the first 2 years after diagnosis. The 2-year CIP was 8% for stroke patients, 53% for PD patients, 49% for MS patients, and 16% for RA patients. The proportion of patients referred to FCP generally increased over the period of the study due to more patients being referred from medical specialists in primary care. Conclusion: This study found substantial differences in the incidence of referral to FCP in a Danish population of stroke, PD, MS and RA patients

    Interdisciplinary intervention (GAIN) for adults with post-concussion symptoms: a study protocol for a stepped-wedge cluster randomised trial

    No full text
    Background Persistent post-concussion symptoms (PCS) are associated with prolonged disability, reduced health-related quality of life and reduced workability. At present, no strong evidence for treatments for people with persistent PCS exists. Our research group developed a novel intervention, “Get going After concussIoN (GAIN)”, that incorporates multiple evidence-based strategies including prescribed exercise, cognitive behavioural therapy, and gradual return to activity advice. In a previous randomised trial, GAIN provided in a hospital setting was effective in reducing symptoms in 15–30-year-olds with PCS 2–6 months post-injury. In the current study, we describe the protocol for a trial designed to test the effectiveness of GAIN in a larger municipality setting. Additionally, we test the intervention within a broader age group and evaluate a broader range of outcomes. The primary hypothesis is that participants allocated to enhanced usual care plus GAIN report a higher reduction in PCS 3 months post-intervention compared to participants allocated to enhanced usual care only. Methods The study is a stepped-wedge cluster-randomised trial with five clusters. The 8-week interdisciplinary GAIN program will be rolled out to clusters in 3-month intervals. Power calculation yield at least 180 participants to be enrolled. Primary outcome is mean change in PCS measured by the Rivermead Post-Concussion Symptoms Questionnaire from enrolment to 3 months after end of treatment. Secondary outcomes include participation in and satisfaction with everyday activities, labour market attachment and other behavioural measures. Self-reported outcomes are measured at baseline, by end of treatment and at 3, 6, and 18 months after end of treatment. Registry-based outcomes are measured up to 36 months after concussion. Discussion The trial will provide important information concerning the effectiveness of the GAIN intervention in a municipality setting. Furthermore, it will provide knowledge of possible barriers and facilitators that may be relevant for future implementation of GAIN in different settings. Trial registration The current GAIN trial is registered in ClinicalTrials.gov (study identifier: NCT04798885 ) on 20 October 2020.Arts, Faculty ofNon UBCPsychology, Department ofReviewedFacultyResearche

    Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification

    Get PDF
    Purpose In both structural and functional MRI, there is a need for accurate and reliable automatic segmentation of brain regions. Inconsistent segmentation reduces sensitivity and may bias results in clinical studies. The current study compares the performance of publicly available segmentation tools and their impact on diffusion quantification, emphasizing the importance of using recently developed segmentation algorithms and imaging techniques. Methods Four publicly available, automatic segmentation methods (volBrain, FSL, FreeSurfer and SPM) are compared to manual segmentation of the thalamus and hippocampus imaged with a recently proposed T1-weighted MRI sequence (MP2RAGE). We evaluate morphometric accuracy on 22 healthy subjects and impact on diffusivity measurements obtained from aligned diffusion-weighted images on a subset of 10 subjects. Results Compared to manual segmentation, the highest Dice similarity index of the thalamus is obtained with volBrain using a local library (M = 0.913, SD = 0.014) followed by volBrain using an external library (M = 0.868, SD = 0.024), FSL (M = 0.806, SD = 0.034), FreeSurfer (M = 0.798, SD = 0.049) and SPM (M = 0.787, SD = 0.031). The same order is found for hippocampus with volBrain local (M = 0.892, SD = 0.016), volBrain external (M = 0.859, SD = 0.014), FSL (M = 0.808, SD = 0.017), FreeSurfer (M = 0.771, SD = 0.023) and SPM (M = 0.735, SD = 0.038). For diffusivity measurements, volBrain provides values closest to those obtained from manual segmentations. volBrain is the only method where FA values do not differ significantly from manual segmentation of the thalamus. Conclusions Overall we find that volBrain is superior in thalamus and hippocampus segmentation compared to FSL, FreeSurfer and SPM. Furthermore, the choice of segmentation technique and training library affects quantitative results from diffusivity measures in thalamus and hippocampus.This work was funded in part by MINDLab UNIK initiative at Aarhus University, funded by the Danish Ministry of Science, Technology and Innovation, Grant Agreement Number 09065250, partly by the Spanish grant TIN2013-43457-R from the Ministerio de Economia competitividad and with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Programme IdEx Bordeaux (ANR-10-IDEX-03-02) by funding HL-DTI grant, Cluster of excellence CPU, LaBEX TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project "Defi ImagIn".Naess-Schmidt, E.; Tietze, A.; Blicher, JU.; Petersen, M.; Mikkelsen, IK.; Coupe, P.; Manjón Herrera, JV.... (2016). Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification. International Journal of Computer Assisted Radiology and Surgery. 11(11):1979-1991. https://doi.org/10.1007/s11548-016-1433-0S197919911111Mulder ER, de Jong RA, Knol DL, van Schijndel RA, Cover KS, Visser PJ, Barkhof F, Vrenken H (2014) Hippocampal volume change measurement: quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST. Neuroimage 92:169–181Heckemann RA, Hajnal JV, Aljabar P, Rueckert D, Hammers A (2006) Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33(1):115–126Rohlfing T, Brandt R, Menzel R, Maurer CR (2004) Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage 21(4):1428–1442Aljabar P, Heckemann RA, Hammers A, Hajnal JV, Rueckert D (2009) Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage 46(3):726–738Coupé P, Manjón JV, Fonov V, Pruessner J, Robles M, Collins DL (2011) Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. Neuroimage 54(2):940–954Tong T, Wolz R, Coupé P, Hajnal JV, Rueckert D (2013) Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling. Neuroimage 76:11–23Eskildsen SF, Coupé P, Fonov V, Manjón JV, Leung KK, Guizard N, Wassef SN, Østergaard LR, Collins DL (2012) BEaST: brain extraction based on nonlocal segmentation technique. Neuroimage 59(3):2362–2373Falangola MF, Jensen JH, Tabesh A, Hu C, Deardorff RL, Babb JS, Ferris S, Helpern JA (2013) Non-Gaussian diffusion MRI assessment of brain microstructure in mild cognitive impairment and Alzheimer’s disease. Magn Reson Imaging 31(6):840–846Mitchell AS, Sherman SM, Sommer MA, Mair RG, Vertes RP, Chudasama Y (2014) Advances in understanding mechanisms of thalamic relays in cognition and behavior. J Neurosci 34(46):15340–15346Vestergaard-Poulsen P, Wegener G, Hansen B, Bjarkam CR, Blackband SJ, Nielsen NC, Jespersen SN (2011) Diffusion-weighted MRI and quantitative biophysical modeling of hippocampal neurite loss in chronic stress. PLoS ONE 6(7):e20653Granziera C, Daducci A, Romascano D, Roche A, Helms G, Krueger G, Hadjikhani N (2014) Structural abnormalities in the thalamus of migraineurs with aura: a multiparametric study at 3 T. Hum Brain Mapp 35(4):1461–1468Coupé P, Eskildsen SF, Manjón JV, Fonov VS, Collins DL (2012) Simultaneous segmentation and grading of anatomical structures for patient’s classification: application to Alzheimer’s disease. Neuroimage 59(4):3736–3747Marques JP, Kober T, Krueger G, van der Zwaag W, Van de Moortele PFF, Gruetter R (2010) MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage 49(2):1271–1281Fujimoto K, Polimeni JR, van der Kouwe AJW, Reuter M, Kober T, Benner T, Fischl B, Wald LL (2014) Quantitative comparison of cortical surface reconstructions from MP2RAGE and multi-echo MPRAGE data at 3 and 7 T. Neuroimage 90:60–73Dudo RO, Hart PE, Stork D (2001) Pattern classification, 2nd edn. Wiley, HobokenLeemans A, Jeurissen B, Sijbers J, Jones D (2009) ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. In: Proceedings 17th scientific meeting, international society for magnetic resonance in medicine, vol 17, no 2, p 3537Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128Power BD, Wilkes FA, Hunter-Dickson M, van Westen D, Santillo AF, Walterfang M, Nilsson C, Velakoulis D, Looi JCL (2015) Validation of a protocol for manual segmentation of the thalamus on magnetic resonance imaging scans. Psychiatry Res 232(1):98–105Boccardi M, Bocchetta M, Apostolova LG, Barnes J, Bartzokis G, Corbetta G,DeCarliC, deToledo-Morrell L, Firbank M, Ganzola R, Gerritsen L, Henneman W, Killiany RJ, Malykhin N, Pasqualetti P, Pruessner JC, Redolfi A, Robitaille N, Soininen H, Tolomeo D, Wang L, Watson C, Wolf H, Duvernoy H, Duchesne S, Jack CR, Frisoni GB (2014) Delphi definition of the EADC-ADNI harmonized protocol for hippocampal segmentation on magnetic resonance. Alzheimers Dement 11(2):126–138Manjón JV, Coupé P (2015) volBrain: an online MRI brain volumetry system. Hum Brain Mapp 15:2015Patenaude B, Smith SM, Kennedy DN, Jenkinson M (2011) A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 56(3):907–922Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, Van Der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM (2002) Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33(3):341–355Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26(3):839–851Frisoni GB, Jack CR, Bocchetta M, Bauer C, Frederiksen KS, Liu Y, Preboske G, Swihart T, Blair M, Cavedo E, Grothe MJ, Lanfredi M, Martinez O, Nishikawa M, Portegies M, Stoub T, Ward C, Apostolova LG, Ganzola R, Wolf D, Barkhof F, Bartzokis G, DeCarli C, Csernansky JG, Detoledo-Morrell L, Geerlings MI, Kaye J, Killiany RJ, Lehericy S, Matsuda H, O’Brien J, Silbert LC, Scheltens P, Soininen H, Teipel S, Waldemar G, Fellgiebel A, Barnes J, Firbank M, Gerritsen L, Henneman W, Malykhin N, Pruessner JC, Wang L, Watsonl C, Wolf H, Deleon M, Pantel J, Ferrari C, Bosco P, Pasqualetti P, Duchesne S, Duvernoy H, Boccardi M, Albert MS, Bennet D, Camicioli R, Collins DL, Dubois B, Hampel H, Denheijer T, Hock C, Jagust W, Launer L, Maller JJ, Mueller S, Sachdev P, Simmons A, Thompson PM, Visser PJ, Wahlund LO, Weiner MW, Winblad B (2015) The EADC-ADNI harmonized protocol for manual hippocampal segmentation on magnetic resonance: evidence of validity. Alzheimer’s Dement 11(2):111–125Næss-Schmidt ET, Tietze A, Mikkelsen IK, Petersen M, Blicher JU, Coupé P, Manjón JV, Eskildsen SF (2015) Patch-based segmentation from MP2RAGE images: comparison to conventional techniques. In: Wu G, Coupé P, Zhan Y, Munsell B, Rueckert D (eds) First international workshop, patch-techniques in medical imaging. Lecture notes in computer science, held in conjunction with MICCAI 2015, vol 9467. Munich, Germany, pp.180–187Barbagallo G, Nicoletti G, Cherubini A, Trotta M, Tallarico T, Chiriaco C, Nisticò R, Salvino D, Bono F, Valentino P, Quattrone A (2014) Diffusion tensor MRI changes in gray structures of the frontal-subcortical circuits in amyotrophic lateral sclerosis. Neurol Sci 35(6):911–918Okubo G, Okada T, Yamamoto A, KanagakiM, Fushimi Y, Okada T, Murata K, Togashi K (2015) MP2RAGE for deep gray matter measurement of the brain: a comparative study with MPRAGE. J Magn Reson Imaging 43(1):55–6
    corecore