Utility of a Postural Stability/Perceptual Inhibition Dual Task for Identifying Concussion in Adolescents

2021 ◽  
pp. 1-6
Author(s):  
Shawn R. Eagle ◽  
Patrick J. Sparto ◽  
Cynthia L. Holland ◽  
Abdulaziz A. Alkathiry ◽  
Nicholas A. Blaney ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Reaction Time ◽  
Dual Task ◽  
Postural Stability ◽  
Postural Sway ◽  
Area Under The Curve ◽  
Spatial Discrimination ◽  
Discriminative Ability ◽  
Visual Spatial ◽  
Time Accuracy

Context: Research in the area of dual-task paradigms to assess sport-related concussion (SRC) status is growing, but additional assessment of this paradigm in adolescents is warranted. Design: This case-control study compared 49 adolescent athletes aged 12–20 years with diagnosed SRC to 49 age- and sex-matched controls on visual–spatial discrimination and perceptual inhibition (PIT) reaction time tasks performed while balancing on floor/foam pad conditions. Methods: The SRC group completed measures at a single time point between 1 and 10 days postinjury. Primary outcomes were dual-task reaction time, accuracy, and sway. General linear models evaluated differences between groups (P < .05). Logistic regression identified predictors of concussion from outcomes. Area under the curve evaluated discriminative ability of identifying SRC. Results: Results supported significantly higher anterior–posterior (AP) sway values in concussed participants for visual–spatial discrimination and PIT when balancing on the floor (P = .03) and foam pad (P = .03), as well as mediolateral sway values on the floor during visual–spatial discrimination (P = .01). Logistic regression analysis (R2 = .15; P = .001) of all dual-task outcomes identified AP postural sway during the PIT foam dual task as the only significant predictor of concussed status (ß = −2.4; P = .004). Total symptoms (area under the curve = 0.87; P < .001) and AP postural sway on foam (area under the curve = 0.70; P = .001) differentiated concussed from controls. Conclusion: The AP postural sway on foam during a postural stability/PIT dual task can identify concussion in adolescents between 1 and 10 days from injury.

Relationship between Mental Rotation of Body Parts and Postural Stability during Quiet Stance

2014 ◽  
Vol 9 (1) ◽  
Author(s):  
Tsubasa Kawasaki ◽  
Kazuhiro Yasuda ◽  
Kazunobu Fukuhara ◽  
Takahiro Higuchi
Keyword(s):  
Reaction Time ◽  
Mental Rotation ◽  
Postural Stability ◽  
Postural Sway ◽  
Reaction Times ◽  
Body Parts ◽  
Quiet Stance ◽  
Mean Velocity ◽  
Unipedal Standing ◽  
Posterior Direction

AbstractThe present study was designed to investigate a relationship between the ability to quickly perform a mental rotation (MR) task using body (particularly foot) stimuli and postural stability during unipedal and bipedal quiet stance. Twenty-four healthy young adults participated in this study to measure reaction times for the MR (stimuli: foot, hand, and car), postural sway values during unipedal and bipedal standings, and lower extremity functions. Results showed significant correlations between the reaction time for the MR of the foot stimuli (but not for hand and car stimuli) and some of postural sway values (total length of sway and mean velocity in the anterior–posterior direction) during unipedal standing (but not for bipedal standing). Consistently, participants who performed the MR task quickly showed significantly smaller sway values during unipedal standing than those who performed the task slowly. These findings suggest that the ability to mentally imagine the foot movement is likely to relate to postural stability, while involving a challenging postural task, such as unipedal standing. The reaction time for the MR of foot stimuli was also correlated with two-point discrimination (TPD) distance on the plantar skin. Given that the TPD distance not only represents cutaneous acuity but also reflects participants’ body image relating to their feet, MR performance may have been related to postural stability because both involve cognitive processes used for both motor imagery and motor execution of the foot movement.

How Useful is the Power Law of Practice for Recognizing Practice in Concentration Tests?

2007 ◽  
Vol 23 (3) ◽  
pp. 157-165 ◽  
Author(s):  
Carmen Hagemeister
Keyword(s):  
Logistic Regression ◽  
Reaction Time ◽  
Power Law ◽  
Error Rate ◽  
Reaction Times ◽  
Practice Effect ◽  
Practice Effects ◽  
Rate Decrease ◽  
Small Practice

Abstract. When concentration tests are completed repeatedly, reaction time and error rate decrease considerably, but the underlying ability does not improve. In order to overcome this validity problem this study aimed to test if the practice effect between tests and within tests can be useful in determining whether persons have already completed this test. The power law of practice postulates that practice effects are greater in unpracticed than in practiced persons. Two experiments were carried out in which the participants completed the same tests at the beginning and at the end of two test sessions set about 3 days apart. In both experiments, the logistic regression could indeed classify persons according to previous practice through the practice effect between the tests at the beginning and at the end of the session, and, less well but still significantly, through the practice effect within the first test of the session. Further analyses showed that the practice effects correlated more highly with the initial performance than was to be expected for mathematical reasons; typically persons with long reaction times have larger practice effects. Thus, small practice effects alone do not allow one to conclude that a person has worked on the test before.

Preoperative Circulating MiR-210, a Risk Factor for Postoperative Delirium Among Elderly Patients with Gastric Cancer Undergoing Curative Resection

2020 ◽  
Vol 26 (40) ◽  
pp. 5213-5219
Author(s):  
Yun Chen ◽  
Jinwei Zheng ◽  
Junping Chen
Keyword(s):  
Gastric Cancer ◽  
Logistic Regression ◽  
Risk Factor ◽  
Elderly Patients ◽  
Curative Resection ◽  
Postoperative Delirium ◽  
Area Under The Curve ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Clinicopathologic Characteristics

Background: Postoperative delirium (POD) is a very common complication in elderly patients with gastric cancer (GC) and associated with poor prognosis. MicroRNAs (miRNAs) serve as key post-transcriptional regulators of gene expression via targeting mRNAs and play important roles in the nervous system. This study aimed to investigate the potential predictive role of miRNAs for POD. Methods: Elderly GC patients who were scheduled to undergo elective curative resection were consequently enrolled in this study. POD was assessed at 1 day before surgery and 1-7 days after surgery following the guidance of the 5th edition of Diagnostic and Statistical Manual of Mental Disorders (DSM V, 2013). The demographics, clinicopathologic characteristics and preoperative circulating miRNAs by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) were compared between patients with or without POD. Risk factors for POD were assessed via univariate and multivariate logistic regression analyses. Results: A total of 370 participants were enrolled, of which 63 had suffered from POD within postoperative 7 days with an incidence of 17.0%. Preoperative miR-210 was a predictor for POD with an area under the curve (AUC) of 0.921, a cut-off value of 1.67, a sensitivity of 95.11%, and a specificity of 92.06%, (P<0.001). In the multivariate logistic regression model, the relative expression of serum miR-210 was an independent risk factor for POD (OR: 3.37, 95%CI: 1.98–5.87, P=0.003). Conclusions: In conclusion, the present study highlighted that preoperative miR-210 could serve as a potential predictor for POD in elderly GC patients undergoing curative resection.

scholarly journals Prognostic Value of Inflammatory Biomarkers in Patients with Severe COVID-19: A Single-Center Retrospective Study

2021 ◽  
Vol 16 ◽  
pp. 117727192110270
Author(s):  
Gönül Açıksarı ◽  
Mehmet Koçak ◽  
Yasemin Çağ ◽  
Lütfiye Nilsun Altunal ◽  
Adem Atıcı ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Hospital Mortality ◽  
Prognostic Value ◽  
Current Knowledge ◽  
Area Under The Curve ◽  
Prognostic Nutritional Index ◽  
Inflammatory Biomarkers ◽  
Single Center ◽  
Discriminative Ability ◽  
Lymphocyte Ratio

Background: The current knowledge about novel coronavirus-2019 (COVID-19) indicates that the immune system and inflammatory response play a crucial role in the severity and prognosis of the disease. In this study, we aimed to investigate prognostic value of systemic inflammatory biomarkers including C-reactive protein/albumin ratio (CAR), prognostic nutritional index (PNI), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR) in patients with severe COVID-19. Methods: This single-center, retrospective study included a total of 223 patients diagnosed with severe COVID-19. Primary outcome measure was mortality during hospitalization. Multivariate logistic regression analyses were performed to identify independent predictors associated with mortality in patients with severe COVID-19. Receiver operating characteristic (ROC) curve was used to determine cut-offs, and area under the curve (AUC) values were used to demonstrate discriminative ability of biomarkers. Results: Compared to survivors of severe COVID-19, non-survivors had higher CAR, NLR, and PLR, and lower LMR and lower PNI ( P < .05 for all). The optimal CAR, PNI, NLR, PLR, and LMR cut-off values for detecting prognosis were 3.4, 40.2, 6. 27, 312, and 1.54 respectively. The AUC values of CAR, PNI, NLR, PLR, and LMR for predicting hospital mortality in patients with severe COVID-19 were 0.81, 0.91, 0.85, 0.63, and 0.65, respectively. In ROC analysis, comparative discriminative ability of CAR, PNI, and NLR for hospital mortality were superior to PLR and LMR. Multivariate analysis revealed that CAR (⩾0.34, P = .004), NLR (⩾6.27, P = .012), and PNI (⩽40.2, P = .009) were independent predictors associated with mortality in severe COVID-19 patients. Conclusions: The CAR, PNI, and NLR are independent predictors of mortality in hospitalized severe COVID-19 patients and are more closely associated with prognosis than PLR or LMR.

A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multi-centre European project on healthy eating

2021 ◽  
pp. 1-10
Author(s):  
I. Krug ◽  
J. Linardon ◽  
C. Greenwood ◽  
G. Youssef ◽  
J. Treasure ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Prediction Rule ◽  
Predictive Performance ◽  
Individual Risk ◽  
European Project ◽  
Wide Range

Abstract Background Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors. Method Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used. Results All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN). Conclusions Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.

Development of prognostic nomograms using institutional data for patients with triple-negative breast cancer

2021 ◽  
Author(s):  
Jie-Yu Zhou ◽  
Kang-Kang Lu ◽  
Wei-Da Fu ◽  
Hao Shi ◽  
Jun-Wei Gu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Disease Free Survival ◽  
Discriminative Ability ◽  
Free Survival ◽  
Disease Free

Background: Triple-negative breast cancer (TNBC) is an aggressive disease. Nomograms can predict prognosis of patients with TNBC. Methods: A total of 745 eligible TNBC patients were recruited and randomly divided into training and validation groups. Endpoints were disease-free survival and overall survival. Concordance index, area under the curve and calibration curves were used to analyze the predictive accuracy and discriminative ability of nomograms. Results: Based on the training cohort, neutrophil-to-lymphocyte ratio, positive lymph nodes, tumor size and tumor-infiltrating lymphocytes were used to construct a nomogram for disease-free survival. In addition, age was added to the overall survival nomogram. Conclusion: The current study developed and validated well-calibrated nomograms for predicting disease-free survival and overall survival in patients with TNBC.

scholarly journals Using a Guided Machine Learning Ensemble Model to Predict Discharge Disposition following Meningioma Resection

2017 ◽  
Vol 79 (02) ◽  
pp. 123-130 ◽  
Author(s):  
Whitney Muhlestein ◽  
Dallin Akagi ◽  
Justiss Kallos ◽  
Peter Morone ◽  
Kyle Weaver ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Patient Outcomes ◽  
Predictive Power ◽  
Univariate Analysis ◽  
Area Under The Curve ◽  
Motor Deficit ◽  
Ensemble Model ◽  
Algorithm Selection ◽  
Discharge Disposition

Objective Machine learning (ML) algorithms are powerful tools for predicting patient outcomes. This study pilots a novel approach to algorithm selection and model creation using prediction of discharge disposition following meningioma resection as a proof of concept. Materials and Methods A diversity of ML algorithms were trained on a single-institution database of meningioma patients to predict discharge disposition. Algorithms were ranked by predictive power and top performers were combined to create an ensemble model. The final ensemble was internally validated on never-before-seen data to demonstrate generalizability. The predictive power of the ensemble was compared with a logistic regression. Further analyses were performed to identify how important variables impact the ensemble. Results Our ensemble model predicted disposition significantly better than a logistic regression (area under the curve of 0.78 and 0.71, respectively, p = 0.01). Tumor size, presentation at the emergency department, body mass index, convexity location, and preoperative motor deficit most strongly influence the model, though the independent impact of individual variables is nuanced. Conclusion Using a novel ML technique, we built a guided ML ensemble model that predicts discharge destination following meningioma resection with greater predictive power than a logistic regression, and that provides greater clinical insight than a univariate analysis. These techniques can be extended to predict many other patient outcomes of interest.

Dynamic and Static Assessment of Single-Leg Postural Control in Female Soccer Players

2020 ◽  
Vol 29 (2) ◽  
pp. 174-178
Author(s):  
Kelly M. Meiners ◽  
Janice K. Loudon
Keyword(s):  
Lower Extremity ◽  
Postural Stability ◽  
Postural Sway ◽  
Center Of Pressure ◽  
Force Plate ◽  
Soccer Players ◽  
Balance Test ◽  
Left Lower Extremity ◽  
Static Assessment ◽  
Dynamic Postural Stability

Purpose/Background: Various methods are available for assessment of static and dynamic postural stability. The primary purpose of this study was to investigate the relationship between dynamic postural stability as measured by the Star Excursion Balance Test (SEBT) and static postural sway assessment as measured by the TechnoBody™ Pro-Kin in female soccer players. A secondary purpose was to determine side-to-side symmetry in this cohort. Methods: A total of 18 female soccer players completed testing on the SEBT and Technobody™ Pro-Kin balance device. Outcome measures were anterior, posterior medial, and posterior lateral reaches from the SEBT and center of pressure in the x- and y-axes as well as SD of movement in the forward/backward and medial/lateral directions from the force plate on left and right legs. Bivariate correlations were determined between the 8 measures. In addition, paired Wilcoxon signed-rank tests were performed to determine similarity between limb scores. Results: All measures on both the SEBT and postural sway assessment were significantly correlated when comparing dominant with nondominant lower-extremities with the exception of SD of movement in both x- and y-axes. When correlating results of the SEBT with postural sway assessment, a significant correlation was found between the SEBT right lower-extremity posterior lateral reach (r = .567, P < .05) and summed SEBT (r = .486, P < .05) and the center of pressure in the y-axis. A significant correlation was also found on the left lower-extremity, with SD of forward/backward movement and SEBT posterior medial reach (r = −.511, P < .05). Conclusions: Dynamic postural tests and static postural tests provide different information to the overall assessment of balance in female soccer players. Relationship between variables differed based on the subject’s lower-extremity dominance.

scholarly journals Classification of Thyroid Nodules with Stacked Denoising Sparse Autoencoder

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Zexin Li ◽  
Kaiji Yang ◽  
Lili Zhang ◽  
Chiju Wei ◽  
Peixuan Yang ◽  
...  
Keyword(s):  
Likelihood Ratio ◽  
Predictive Value ◽  
Thyroid Nodules ◽  
Area Under The Curve ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Discriminative Ability ◽  
Sparse Autoencoder ◽  
Immune Related Genes

Purpose. Several commercial tests have been used for the classification of indeterminate thyroid nodules in cytology. However, the geographic inconvenience and high cost confine their widespread use. This study aims to develop a classifier for conveniently clinical utility. Methods. Gene expression data of thyroid nodule tissues were collected from three public databases. Immune-related genes were used to construct the classifier with stacked denoising sparse autoencoder. Results. The classifier performed well in discriminating malignant and benign thyroid nodules, with an area under the curve of 0.785 [0.638–0.931], accuracy of 92.9% [92.7–93.0%], sensitivity of 98.6% [95.9–101.3%], specificity of 58.3% [30.4–86.2%], positive likelihood ratio of 2.367 [1.211–4.625], and negative likelihood ratio of 0.024 [0.003–0.177]. In the cancer prevalence range of 20–40% for indeterminate thyroid nodules in cytology, the range of negative predictive value of this classifier was 37–61%, and the range of positive predictive value was 98–99%. Conclusion. The classifier developed in this study has the superb discriminative ability for thyroid nodules. However, it needs validation in cytologically indeterminate thyroid nodules before clinical use.

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