Sleep Problems in Patients With Psoriatic Arthritis: A Systematic Literature Review and Metaanalysis

OBJECTIVE

The aim of this systematic review and metaanalysis is to summarize evidence regarding the relationship between psoriatic arthritis (PsA) and sleep problems.

METHODS

We identified 36 eligible studies-26 cross-sectional, 7 cohort, and 3 interventional studies-in PubMed and Embase.

RESULTS

The prevalence of self-reported sleep problems in patients with PsA ranged from 30% to 85%. A metaanalysis of 6 studies that used the Pittsburgh Sleep Quality Index revealed a prevalence of poor sleep quality for patients with PsA of 72.9% (95% CI 63-81.8; I2 = 78%), which was statistically higher than in healthy controls (26.9%, 95% CI 11.7-45.4; I2 = 81%) but not significantly different than in patients with psoriasis (59.8%, 95% CI 46.9-72.1; I2 = 51%). Sleep disturbance was ranked in the top 4 health-related quality of life domains affected by PsA. One study suggested a bidirectional relationship between PsA and obstructive sleep apnea. Predictors of sleep problems included anxiety, pain, erythrocyte sedimentation rate, depression, fatigue, physical function, and tender or swollen joint count. Tumor necrosis factor inhibitors, guselkumab, and filgotinib (a Janus kinase inhibitor) were associated with improved sleep outcomes.

CONCLUSION

Poor sleep quality is prevalent in patients with PsA. Objective sleep measures (ie, actigraphy and polysomnography) have not been used in PsA studies, and evidence on the validity of patient-reported sleep measures in PsA is lacking. Future studies should validate self-reported sleep measures in PsA, explore how sleep quality relates to PsA disease activity and symptoms using both objective and subjective sleep measures, assess the efficacy of strategies to manage sleep problems, and assess the effects of such management on symptoms and disease signs in patients with PsA.

Transit time homogenization in ischemic stroke - A novel biomarker of penumbral microvascular failure?

Cerebral ischemia causes widespread capillary no-flow in animal studies. The extent of microvascular impairment in human stroke, however, is unclear. We examined how acute intra-voxel transit time characteristics and subsequent recanalization affect tissue outcome on follow-up MRI in a historic cohort of 126 acute ischemic stroke patients. Based on perfusion-weighted MRI data, we characterized voxel-wise transit times in terms of their mean transit time (MTT), standard deviation (capillary transit time heterogeneity - CTH), and the CTH:MTT ratio (relative transit time heterogeneity), which is expected to remain constant during changes in perfusion pressure in a microvasculature consisting of passive, compliant vessels. To aid data interpretation, we also developed a computational model that relates graded microvascular failure to changes in these parameters. In perfusion-diffusion mismatch tissue, prolonged mean transit time (>5 seconds) and very low cerebral blood flow (≤6 mL/100 mL/min) was associated with high risk of infarction, largely independent of recanalization status. In the remaining mismatch region, low relative transit time heterogeneity predicted subsequent infarction if recanalization was not achieved. Our model suggested that transit time homogenization represents capillary no-flow. Consistent with this notion, low relative transit time heterogeneity values were associated with lower cerebral blood volume. We speculate that low RTH may represent a novel biomarker of penumbral microvascular failure.

Better Diffusion Segmentation in Acute Ischemic Stroke Through Automatic Tree Learning Anomaly Segmentation

Stroke is the second most common cause of death worldwide, responsible for 6.24 million deaths in 2015 (about 11% of all deaths). Three out of four stroke survivors suffer long term disability, as many cannot return to their prior employment or live independently. Eighty-seven percent of strokes are ischemic. As an increasing volume of ischemic brain tissue proceeds to permanent infarction in the hours following the onset, immediate treatment is pivotal to increase the likelihood of good clinical outcome for the patient. Triaging stroke patients for active therapy requires assessment of the volume of salvageable and irreversible damaged tissue, respectively. With Magnetic Resonance Imaging (MRI), diffusion-weighted imaging is commonly used to assess the extent of permanently damaged tissue, the core lesion. To speed up and standardize decision-making in acute stroke management we present a fully automated algorithm, ATLAS, for delineating the core lesion. We compare performance to widely used threshold based methodology, as well as a recently proposed state-of-the-art algorithm: COMBAT Stroke. ATLAS is a machine learning algorithm trained to match the lesion delineation by human experts. The algorithm utilizes decision trees along with spatial pre- and post-regularization to outline the lesion. As input data the algorithm takes images from 108 patients with acute anterior circulation stroke from the I-Know multicenter study. We divided the data into training and test data using leave-one-out cross validation to assess performance in independent patients. Performance was quantified by the Dice index. The median Dice coefficient of ATLAS algorithm was 0.6122, which was significantly higher than COMBAT Stroke, with a median Dice coefficient of 0.5636 (p < 0.0001) and the best possible performing methods based on thresholding of the diffusion weighted images (median Dice coefficient: 0.3951) or the apparent diffusion coefficient (median Dice coefficeint: 0.2839). Furthermore, the volume of the ATLAS segmentation was compared to the volume of the expert segmentation, yielding a standard deviation of the residuals of 10.25 ml compared to 17.53 ml for COMBAT Stroke. Since accurate quantification of the volume of permanently damaged tissue is essential in acute stroke patients, ATLAS may contribute to more optimal patient triaging for active or supportive therapy.

Boosted Tree Model Reforms Multimodal Magnetic Resonance Imaging Infarct Prediction in Acute Stroke

BACKGROUND AND PURPOSE

Stroke imaging is pivotal for diagnosis and stratification of patients with acute ischemic stroke to treatment. The potential of combining multimodal information into reliable estimates of outcome learning calls for robust machine learning techniques with high flexibility and accuracy. We applied the novel extreme gradient boosting algorithm for multimodal magnetic resonance imaging-based infarct prediction.

METHODS

In a retrospective analysis of 195 patients with acute ischemic stroke, fluid-attenuated inversion recovery, diffusion-weighted imaging, and 10 perfusion parameters were derived from acute magnetic resonance imaging scans. They were integrated to predict final infarct as seen on follow-up T2-fluid-attenuated inversion recovery using the extreme gradient boosting and compared with a standard generalized linear model approach using cross-validation. Submodels for recanalization and persistent occlusion were calculated and were used to identify the important imaging markers. Performance in infarct prediction was analyzed with receiver operating characteristics. Resulting areas under the curve and accuracy rates were compared using Wilcoxon signed-rank test.

RESULTS

The extreme gradient boosting model demonstrated significantly higher performance in infarct prediction compared with generalized linear model in both cross-validation approaches: 5-folds (P<10e-16) and leave-one-out (P<0.015). The imaging parameters time-to-peak, mean transit time, time-to-maximum, and diffusion-weighted imaging were indicated as most valuable for infarct prediction by the systematic algorithm rating. Notably, the performance improvement was higher with 5-folds cross-validation approach than leave-one-out.

CONCLUSIONS

We demonstrate extreme gradient boosting as a state-of-the-art method for clinically applicable multimodal magnetic resonance imaging infarct prediction in acute ischemic stroke. Our findings emphasize the role of perfusion parameters as important biomarkers for infarct prediction. The effect of cross-validation techniques on performance indicates that the intrapatient variability is expressed in nonlinear dynamics of the imaging modalities.

Spatial distribution of malignant tissue in gliomas: correlations of 11C-L-methionine positron emission tomography and perfusion- and diffusion-weighted magnetic resonance imaging

BACKGROUND

The prognosis of glioma patients is contingent on precise target selection for stereotactic biopsies and the extent of tumor resection. (11)C-L-methionine (MET) positron emission tomography (PET) demonstrates tumor heterogeneity and invasion with high diagnostic accuracy.

PURPOSE

To compare the spatial tumor distribution delineated by MET PET with that by perfusion- and diffusion-weighted magnetic resonance imaging (MRI), in order to understand the diagnostic value of these MRI methods, when PET is not available.

MATERIAL AND METHODS

Presurgical MET PET and MRI, including perfusion- and diffusion-weighted MRI, were acquired in 13 patients (7 high-grade gliomas, 6 low-grade gliomas). A quantitative volume of interest analysis was performed to compare the modalities objectively, supplemented by a qualitative evaluation that assessed the clinical applicability.

RESULTS

The inaccuracy of conventional MRI was confirmed (area under the curve for predicting voxels with high MET uptake = 0.657), whereas cerebral blood volume (CBV) maps calculated from perfusion data improved accuracy (area under the curve = 0.760). We considered CBV maps diagnostically comparable to MET PET in 5/7 cases of high-grade gliomas, but insufficient in all cases of low-grade gliomas when evaluated subjectively. Cerebral blood flow and apparent diffusion coefficient maps did not contribute to further accuracy.

CONCLUSION

Adding perfusion-weighted MRI to the presurgical protocol can increase the diagnostic accuracy of conventional MRI and is a simple and well-established method compared to MET PET. However, the definition of low-grade gliomas with subtle or no alterations on cerebral blood volume maps remains a diagnostic challenge for stand-alone MRI.