Clinical measures of hip and foot–ankle mechanics as predictors of rearfoot motion and posture

Background: Limited studies concern the influence of obesity-induced dysregulation of adipokines in functional recovery after stroke neurorehabilitation. Objective: To investigate the relationship between serum leptin, resistin, and adiponectin and functional recovery before and after neurorehabilitation of obese stroke patients. The adipokine potential significance as prognostic markers of rehabilitation outcomes was also verified. Methods: Twenty obese post-acute stroke patients before and after neurorehabilitation and thirteen obese volunteers without-stroke, as controls, were examined. Adipokines were determined by commercially available enzyme-linked immunosorbent assay (ELISA) kits. Functional deficits were assessed before and after neurorehabilitation with the Barthel Index (BI), modified Rankin Scale (mRS), and Functional Independence Measure (FIM). Results: Compared to controls, higher leptin and resistin values and lower adiponectin values were observed in stroke patients before neurorehabilitation and no correlations were found between adipokines and clinical outcome measures. Neurorehabilitation was associated with improved scores of BI, mRS, and FIM. After neurorehabilitation, decreased values of Body Mass Index (BMI) and resistin together increased adiponectin were detected in stroke patients, while leptin decreased but not statistically. Comparing adipokine values assessed before neurorehabilitation with the outcome measures after neurorehabilitation, correlations were observed for leptin with BI-score, mRS-score, and FIM-score. No other adipokine levels nor BMI assessed before neurorehabilitation correlated with the clinical measures after neurorehabilitation. The forward stepwise regression analysis identified leptin as prognostic factor for BI, mRS, and FIM. Conclusions: Our data show the effectiveness of neurorehabilitation in modulating adipokines levels and suggest that leptin could assume the significance of biomarker of functional recovery.

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AB1294 Psoriatic arthritis - a follow-up study applying dynamic MRI, conventional MRI and clinical measures:

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2012-eular.1290 ◽

2013 ◽

Vol 71 (Suppl 3) ◽

pp. 711.14-711

Author(s):

R.P. Poggenborg ◽

P. Bøyesen ◽

C. Wiell ◽

S.J. Pedersen ◽

I.J. Sørensen ◽

...

Keyword(s):

Psoriatic Arthritis ◽

Dynamic Mri ◽

Follow Up Study ◽

Conventional Mri ◽

Clinical Measures

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HOW DO LEARNING DISORDERS IMPACT CLINICAL MEASURES FOLLOWING CONCUSSION?

Orthopaedic Journal of Sports Medicine ◽

10.1177/2325967120s00183 ◽

2020 ◽

Vol 8 (4_suppl3) ◽

pp. 2325967120S0018

Author(s):

Aaron J. Zynda ◽

Mathew A. Stokes ◽

Jane S. Chung ◽

C. Munro Cullum ◽

Shane M. Miller

Keyword(s):

Symptom Score ◽

Learning Disorders ◽

Total Symptom Score ◽

Adhd Group ◽

Motor Speed ◽

The North ◽

History Of ◽

Visual Motor ◽

Clinical Measures ◽

The Impact

Background: There is limited evidence examining the impact of learning disorders on testing and screening scores used in evaluation following concussion in adolescents. Purpose: To examine differences in clinical measures between adolescents with a history of dyslexia or ADD/ADHD and those without a history of learning disorder (LD) following concussion. Methods: Data were collected from participants enrolled in the North Texas Concussion Network Prospective Registry (ConTex). Participants ages 10-18 who had been diagnosed with a concussion sustained within 30 days of enrollment were included. Participants were separated into three groups based on self-reported prior diagnosis: dyslexia, ADD/ADHD, and no history of LD. Clinical measures from initial presentation were examined, including ImPACT®, King-Devick (KD), SCAT-5 symptom log, Patient Health Questionnaire (PHQ-8), and Generalized Anxiety Disorder (GAD-7) scale. Independent t-test analysis was performed to compare scores between groups. Results: A total of 993 participants were included; 68 with dyslexia, 141 with ADD/ADHD, and 784 with no history of LD. There was no difference in age, sex, time since injury, or history of concussion between the dyslexia group and no LD group. In the ADD/ADHD group, there were significantly more male participants (64.5% and 50.3% respectively, p=0.002). Participants with a history of dyslexia had a significant increase in KD time (63.7 sec vs 56.5 sec, p=0.019). Additionally, ImPACT® testing showed a decrease in visual motor speed (28.87 vs 32.99, p= 0.010). Total symptom score was higher in this group as well (36.22 vs 28.27, p=0.013). In those with a history of ADD/ADHD, multiple domains were found to be significantly different on ImPACT® testing including visual motor speed (30.05), reaction time (0.75), and cognitive efficiency (0.23) when compared to those with no LD (32.99, 0.71, and 0.27 respectively, p=0.004, 0.047, 0.027). KD time was also significantly higher in this group (62.1 sec vs 56.5 sec, p=0.008), as was the total symptom score (32.99 vs 28.27, p=0.043). PHQ-8 and GAD-7 were both significantly higher in the group with ADD/ADHD (5.79 and 5.06 respectively, p=0.001) than those with no LD (4.32 and 3.56, p=0.001). Conclusion: Differences were seen in participants with a history of dyslexia and ADD/ADHD on clinical concussion measures, including ImPACT® and KD testing, SCAT-5 symptom log, and screenings for depression and anxiety. A better understanding of the unique profiles seen in these patients will aid providers in their evaluation and assist as they counsel families regarding their child’s injury.

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Validation of the adjusted multi-biomarker disease activity score as a prognostic test for radiographic progression in rheumatoid arthritis: a combined analysis of multiple studies

Arthritis Research & Therapy ◽

10.1186/s13075-020-02389-4 ◽

2021 ◽

Vol 23 (1) ◽

Author(s):

Jeffrey R. Curtis ◽

Michael E. Weinblatt ◽

Nancy A. Shadick ◽

Cecilie H. Brahe ◽

Mikkel Østergaard ◽

...

Keyword(s):

Risk Factors ◽

Disease Activity ◽

Radiographic Progression ◽

Protein Biomarkers ◽

Significant Information ◽

Disease Activity Measure ◽

Conventional Dmards ◽

Risk Curve ◽

Progression Risk ◽

Clinical Measures

Abstract Background The multi-biomarker disease activity (MBDA) test measures 12 serum protein biomarkers to quantify disease activity in RA patients. A newer version of the MBDA score, adjusted for age, sex, and adiposity, has been validated in two cohorts (OPERA and BRASS) for predicting risk for radiographic progression. We now extend these findings with additional cohorts to further validate the adjusted MBDA score as a predictor of radiographic progression risk and compare its performance with that of other risk factors. Methods Four cohorts were analyzed: the BRASS and Leiden registries and the OPERA and SWEFOT studies (total N = 953). Treatments included conventional DMARDs and anti-TNFs. Associations of radiographic progression (ΔTSS) per year with the adjusted MBDA score, seropositivity, and clinical measures were evaluated using linear and logistic regression. The adjusted MBDA score was (1) validated in Leiden and SWEFOT, (2) compared with other measures in all four cohorts, and (3) used to generate curves for predicting risk of radiographic progression. Results Univariable and bivariable analyses validated the adjusted MBDA score and found it to be the strongest, independent predicator of radiographic progression (ΔTSS > 5) compared with seropositivity (rheumatoid factor and/or anti-CCP), baseline TSS, DAS28-CRP, CRP SJC, or CDAI. Neither DAS28-CRP, CDAI, SJC, nor CRP added significant information to the adjusted MBDA score as a predictor, and the frequency of radiographic progression agreed with the adjusted MBDA score when it was discordant with these measures. The rate of progression (ΔTSS > 5) increased from < 2% in the low (1–29) adjusted MBDA category to 16% in the high (45–100) category. A modeled risk curve indicated that risk increased continuously, exceeding 40% for the highest adjusted MBDA scores. Conclusion The adjusted MBDA score was validated as an RA disease activity measure that is prognostic for radiographic progression. The adjusted MBDA score was a stronger predictor of radiographic progression than conventional risk factors, including seropositivity, and its prognostic ability was not significantly improved by the addition of DAS28-CRP, CRP, SJC, or CDAI.

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A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

Scientific Reports ◽

10.1038/s41598-021-82098-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Shaker El-Sappagh ◽

Jose M. Alonso ◽

S. M. Riazul Islam ◽

Ahmad M. Sultan ◽

Kyung Sup Kwak

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cross Validation ◽

Disease Risk ◽

Binary Classification ◽

Fuzzy Rule ◽

Large Set ◽

Detection Model ◽

Multi Class Classification ◽

Clinical Measures

AbstractAlzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.

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