Objectives To investigate the prevalence of axial spondyloarthritis (axSpA) in patients with chronic back pain (CBP) of less than 2 years (2y) duration referred to the rheumatologist, the... Show moreObjectives To investigate the prevalence of axial spondyloarthritis (axSpA) in patients with chronic back pain (CBP) of less than 2 years (2y) duration referred to the rheumatologist, the development of diagnosis over time, and patient characteristics of those developing definite (d-)axSpA over 2y. Methods We analysed the 2y data from SPondyloArthritis Caught Early, a European cohort of patients (<45 years) with CBP (>= 3 months, <= 2y) of unknown origin. The diagnostic workup comprised evaluation of clinical SpA features, acute phase reactants, HLA-B27, radiographs and MRI (sacroiliac joints and spine), with repeated assessments. At each visit (baseline, 3 months, 1y and 2y), rheumatologists reported a diagnosis of axSpA or non-axSpA with level of confidence (LoC; 0-not confident at all to 10-very confident). Main outcome: axSpA diagnosis with LoC >= 7 (d-axSpA) at 2y. Results In 552 patients with CBP, d-axSpA was diagnosed in 175 (32%) at baseline and 165 (30%) at 2y. Baseline diagnosis remained rather stable: at 2y, baseline d-axSpA was revised in 5% of patients, while 8% 'gained' d-axSpA. Diagnostic uncertainty persisted in 30%. HLA-B27+ and baseline sacroiliitis imaging discriminated best 2y-d-axSpA versus 2y-d-non-axSpA patients. Good response to non-steroidal anti-inflammatory drugs and MRI-sacroiliitis most frequently developed over follow-up in patients with a new d-axSpA diagnosis. Of the patients who developed MRI-sacroiliitis, 7/8 were HLA-B27+ and 5/8 male. Conclusion A diagnosis of d-axSpA can be reliably made in nearly one-third of patients with CBP referred to the rheumatologist, but diagnostic uncertainty may persist in 5%-30% after 2y. Repeated assessments yield is modest, but repeating MRI may be worthwhile in male HLA-B27+ patients. Show less
Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies... Show moreBackground: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable. Show less
There are long-standing unsolved issues regarding the diagnosis and classification of central disorders of hypersomnolence. These include delineating and identifying phenotypes and unique... Show moreThere are long-standing unsolved issues regarding the diagnosis and classification of central disorders of hypersomnolence. These include delineating and identifying phenotypes and unique conditions (“sui generis”), sleep deprivation’s impact on phenotypes and how to separate sleep deprivation as a trigger from other causes, as well as the association of excessive sleepiness with other disorders. We discuss these issues and present a novel, straightforward classification system with consistent terminology to get out of the impasse and do justice to people with hypersomnolence. Show less
Mastboom, M.J.L.; Verspoor, F.G.M.; Hanff, D.F.; Gademan, M.G.J.; Dijkstra, P.D.S.; Schreuder, H.W.B.; ... ; Sande, M.A.J. van de 2018
