Relationships Across Clinical Measures of Vocal Quality and Functioning and Their Relationship With Patient Perception

2021 ◽  
pp. 1-13
Author(s):  
Nichole Houle ◽  
Aaron M. Johnson
Keyword(s):  
Principal Components ◽  
Clinical Decision Making ◽  
Principal Component ◽  
Retrospective Chart Review ◽  
Voice Disorder ◽  
Initial Assessment ◽  
Linear Regression Models ◽  
Perceived Severity ◽  
Vocal Quality ◽  
Clinical Measures

Purpose The purpose of this study was to investigate the relationships among subjective auditory-perceptual ratings of vocal quality, objective acoustic and aerodynamic measures of vocal function, and patient-perceived severity of their vocal complaint. Method This study was a retrospective chart review of adult patients evaluated at a single outpatient center over a 1.5-year time period. Twenty-two clinical objective and subjective measures of voice were extracted from 676 charts (310 males, 366 females). To identify the underlying concepts addressed in an initial voice assessment, principal component analyses were conducted for males and females to account for sex differences. Linear regression models were conducted to examine the relationship between the principal components and patient perceived severity. Results Seven principal components were identified for both sexes and accounted for 75% and 71% of the variance in the clinical measures, respectively. Of these seven principal components, only two predicted male patient perceived severity, which accounted for 22% of the variance. In contrast, four principal components predicted female patient perceived severity of their voice disorder and accounted for 19% of the variance. Conclusions The results highlight the underlying aspects of vocal quality and functioning that are evaluated during an initial assessment. Male and female patients differ in which of these components may contribute self-perceived severity of a voice disorder. Identifying these underlying components may support clinical decision making when developing a clinical protocol and highlights the overlap between patient concerns and clinical measures. Supplemental Material https://doi.org/10.23641/asha.16879603

scholarly journals Relationships Between Stepping-Reaction Movement Patterns and Clinical Measures of Balance, Motor Impairment, and Step Characteristics After Stroke

2021 ◽  
Author(s):  
Courtney L Pollock ◽  
Michael A Hunt ◽  
S Jayne Garland ◽  
Tanya D Ivanova ◽  
James M Wakeling
Keyword(s):  
Principal Components ◽  
Sagittal Plane ◽  
Motor Impairment ◽  
Movement Patterns ◽  
Principal Component ◽  
Control Group ◽  
Leg Extension ◽  
Step Initiation ◽  
Balance Ability ◽  
Clinical Measures

Abstract Objective Successful stepping reactions, led by either the paretic or nonparetic leg, in response to a loss of balance are critical to safe mobility poststroke. The purpose of this study was to measure sagittal plane hip, knee, ankle, and trunk kinematics during 2-step stepping reactions initiated by paretic and nonparetic legs of people who had stroke and a control group. Methods Principal component analysis (PCA) was used to reduce the data into movement patterns explaining interlimb coordination of the stepping and stance legs. Correlations among principal components loading scores and clinical measures of balance ability (as measured on the Community Balance and Mobility scale), motor impairment (as measured on the foot and leg sections of the Chedoke-McMaster Stroke Assessment), and step characteristics (length and velocity) were used to examine the effect of stroke on stepping reaction movement patterns. Results The first 5 principal components explained 95.9% of the movement pattern of stepping reactions and differentiated between stepping reactions initiated by paretic legs, nonparetic legs, or the legs of controls. Moderate-strong associations (ρ/r > 0.50) between specific principal component loading scores and clinical measures and step characteristics were dependent on the initiating leg. Lower levels of motor impairment, higher levels of balance ability, and faster and longer steps were associated with stepping reactions initiated by the paretic leg that comprised paretic leg flexion and nonparetic leg extension. Step initiation with the nonparetic leg showed associations between higher scores on clinical measures and movement patterns of flexion in both paretic and nonparetic legs. Conclusions Movement patterns of stepping reactions poststroke were influenced by the initiating leg. After stroke, specific movement patterns showed associations with clinical measures depending on the initiating leg, suggesting that these movement patterns are important to retraining of stepping reactions. Specifically, use of flexion patterning and assessment of between-leg pattern differentiation may be important aspects to consider during retraining of stepping reactions poststroke. Impact Evidence-based interventions targeting balance reactions are still in their infancy. This investigation of stepping reactions poststroke addresses a major gap in research.

Cardiorespiratory fitness is positively associated with both healthy and western dietary pattern in Iranian middle-aged

2020 ◽  
pp. 1-10
Author(s):  
Hossein Shahinfar ◽  
Farhang Djafari ◽  
Nadia Babaei ◽  
Samira Davarzani ◽  
Mojdeh Ebaditabar ◽  
...  
Keyword(s):  
Cardiorespiratory Fitness ◽  
Dietary Patterns ◽  
Dietary Pattern ◽  
Principal Component ◽  
Total Sample ◽  
Linear Regression Models ◽  
Middle Aged ◽  
Anthropometric Measures ◽  
Western Dietary Pattern ◽  
Healthy Dietary Pattern

Abstract. Background: The association between dietary patterns and cardiorespiratory fitness (CRF) is not well established. Objective: We sought to investigate association between a posteriori dietary pattern and CRF in middle-aged adults. Design: Adults (n = 276), aged 20–74 years, who were residents of Tehran, Iran were recruited. Diet was assessed by using a validated 168-item semi-quantitative food frequency questionnaire. Principal component analysis was used to derive dietary patterns. Socio-economic status, anthropometric measures, body composition, and blood pressure were recorded. CRF was assessed by using a graded exercise treadmill test. Analysis of variance and linear regression models were used to discern the association between dietary patterns and CRF. Results: Higher scores of the healthy dietary pattern had no association with VO2max (p = 0.13 ). After controlling for potential confounders, VO2max was positively associated across tertiles of healthy dietary patterns (p < 0.001). Higher adherence to the “mixed” dietary pattern was inversely related to VO2max (p < 0.01). After adjusting for confounders, the significant association disappeared (p = 0.14). Higher scores of the “Western” dietary pattern was not associated with VO2max (p = 0.06). However, after controlling for potential confounders, VO2max was positively associated with the “Western” dietary pattern (p = 0.01). A positive linear association between the “healthy” dietary pattern and CRF for the total sample (R2 = 0.02; p < 0.01) were presented. Conclusions: Overall, our findings suggest that higher adherence to a “healthy” and “Western” dietary pattern was positively associated with CRF. However, further studies are required to examine and clarify the causal relationship between dietary patterns and CRF.

Structuring Assessments of Psychopathology

2006 ◽  
Vol 27 (2) ◽  
pp. 87-92 ◽  
Author(s):  
Willem K.B. Hofstee ◽  
Dick P.H. Barelds ◽  
Jos M.F. Ten Berge
Keyword(s):  
Principal Components ◽  
Personality Assessment ◽  
Clinical Sample ◽  
Principal Component ◽  
Normal Sample ◽  
Normative Sample ◽  
Assessment Data ◽  
Obsessive Compulsive ◽  
Oblique Rotation ◽  
Two Samples

Hofstee and Ten Berge (2004a) have proposed a new look at personality assessment data, based on a bipolar proportional (-1, .. . 0, .. . +1) scale, a corresponding coefficient of raw-scores likeness L = ΢XY/N, and raw-scores principal component analysis. In a normal sample, the approach resulted in a structure dominated by a first principal component, according to which most people are faintly to mildly socially desirable. We hypothesized that a more differentiated structure would arise in a clinical sample. We analyzed the scores of 775 psychiatric clients on the 132 items of the Dutch Personality Questionnaire (NPV). In comparison to a normative sample (N = 3140), the eigenvalue for the first principal component appeared to be 1.7 times as small, indicating that such clients have less personality (social desirability) in common. Still, the match between the structures in the two samples was excellent after oblique rotation of the loadings. We applied the abridged m-dimensional circumplex design, by which persons are typed by their two highest scores on the principal components, to the scores on the first four principal components. We identified five types: Indignant (1-), Resilient (1-2+), Nervous (1-2-), Obsessive-Compulsive (1-3-), and Introverted (1-4-), covering 40% of the psychiatric sample. Some 26% of the individuals had negligible scores on all type vectors. We discuss the potential and the limitations of our approach in a clinical context.

Comparison of Principal Component Solutions in Two Populations

Methodology ◽  
2016 ◽  
Vol 12 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Gregor Sočan
Keyword(s):  
Simulation Study ◽  
Principal Components ◽  
Common Factor ◽  
Principal Component ◽  
Component Model ◽  
Bootstrap Procedure ◽  
Factor Loadings ◽  
Component Loadings ◽  
Principal Component Model ◽  
Two Populations

Abstract. When principal component solutions are compared across two groups, a question arises whether the extracted components have the same interpretation in both populations. The problem can be approached by testing null hypotheses stating that the congruence coefficients between pairs of vectors of component loadings are equal to 1. Chan, Leung, Chan, Ho, and Yung (1999) proposed a bootstrap procedure for testing the hypothesis of perfect congruence between vectors of common factor loadings. We demonstrate that the procedure by Chan et al. is both theoretically and empirically inadequate for the application on principal components. We propose a modification of their procedure, which constructs the resampling space according to the characteristics of the principal component model. The results of a simulation study show satisfactory empirical properties of the modified procedure.

Features of glomerular filtration rate evaluation for kidney function assessment in patients with rheumatoid arthritis

2020 ◽  
pp. 44-48
Author(s):  
V. A. Aleksandrov ◽  
L. N. Shilova ◽  
A. V. Aleksandrov
Keyword(s):  
Rheumatoid Arthritis ◽  
Glomerular Filtration Rate ◽  
Glomerular Filtration ◽  
Cystatin C ◽  
Filtration Rate ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Initial Assessment ◽  
Serum Cystatin C ◽  
Creatinine Concentration

The development of renal dysfunction in patients with rheumatoid arthritis (RA) is due to the presence and severity of autoimmune disorders, chronic systemic inflammation, a multiplicity of comorbid conditions, and pharmacotherapy features. The most important parameter that describes the general condition of the kidneys is glomerular filtration rate (GFR). This review presents the data on the possibilities of modern methods for determining estimated GFR (e-GFR) and the specificity of their use in various clinical situations that accompany the course of RA. For the initial assessment of GFR in patients with RA it is advisable to use the measurement of e-GFR based on serum creatinine concentration using the CKD-EPI equation (2009) (with or without indexing by body surface area). In cases where the e-GFR equations are not reliable enough or the results of this test are insufficient for clinical decision making, the serum cystatin C level should be measured and the combined GFR calculation based on creatinine and cystatin C should be used.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

Evaluation functions for integral mapping

2017 ◽  
Vol 921 (3) ◽  
pp. 24-29 ◽  
Author(s):  
S.I. Lesnykh ◽  
A.K. Cherkashin
Keyword(s):  
Principal Component Analysis ◽  
Environmental Factors ◽  
Principal Components ◽  
Principal Component ◽  
Evaluation Function ◽  
Final Value ◽  
Evaluation Functions ◽  
Geographical Environment ◽  
Environmental Background ◽  
Integral Mapping

The proposed procedure of integral mapping is based on calculation of evaluation functions on the integral indicators (II) taking into account the feature of the local geographical environment, when geosystems in the same states in the different environs have various estimates. Calculation of II is realized with application of a Principal Component Analysis for processing of the forest database, allowing to consider in II the weight of each indicator (attribute). The final value of II is equal to a difference of the first (condition of geosystem) and the second (condition of environmental background) principal components. The evaluation functions are calculated on this value for various problems of integral mapping. The environmental factors of variability is excluded from final value of II, therefore there is an opportunity to find the invariant evaluation function and to determine coefficients of this function. Concepts and functions of the theory of reliability for making the evaluation maps of the hazard of functioning and stability of geosystems are used.

scholarly journals EXPRESS: Exploration of Principal Component Analysis: Deriving PCA Visually Using Spectra

2021 ◽  
pp. 000370282098784
Author(s):  
James Renwick Beattie ◽  
Francis Esmonde-White
Keyword(s):  
Principal Components ◽  
Principal Component ◽  
Original Data ◽  
Specific Chemical ◽  
Successive Refinement ◽  
Minimal Loss ◽  
Components Analysis ◽  
The Mathematical Model ◽  
Application Specific ◽  
Reconstructed Data

Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal Components Analysis (PCA) is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning PCA is not well understood by many applied analytical scientists and spectroscopists who use PCA. The meaning of features identified through PCA are often unclear. This manuscript traces the journey of the spectra themselves through the operations behind PCA, with each step illustrated by simulated spectra. PCA relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of PCA, such the scores representing ‘concentration’ or ‘weights’. The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a PCA model shows how to interpret application specific chemical meaning of the PCA loadings and how to analyze scores. A critical benefit of PCA is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.

scholarly journals A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽  
2021 ◽  
Vol 26 (6) ◽  
pp. 1546
Author(s):  
Ioanna Dagla ◽  
Anthony Tsarbopoulos ◽  
Evagelos Gikas
Keyword(s):  
Design Of Experiments ◽  
High Performance ◽  
Similarity Index ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Distance Measures ◽  
Linear Regression Models ◽  
Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

The factor structure of the GHQ-60 in a community sample

1988 ◽  
Vol 18 (1) ◽  
pp. 211-218 ◽  
Author(s):  
J. L. Vazquez-Barquero ◽  
P. Williams ◽  
J. F. Diez-Manrique ◽  
J. Lequerica ◽  
A. Arenal
Keyword(s):  
Factor Structure ◽  
Principal Components ◽  
Good Description ◽  
Social Performance ◽  
Community Sample ◽  
General Health Questionnaire ◽  
Principal Component ◽  
Northern Spain ◽  
Relative Operating Characteristic ◽  
Component Structure

SynopsisThe factor structure of the 60-item version of the General Health Questionnaire was explored, using data collected in a community study in a rural area of northern Spain. Six principal components, similar to those previously reported with this instrument, were found to provide a good description of the data structure.The 30-item and 12-item versions of the GHQ were then disembedded from the parent version, and further principal components analyses carried out. Again, the results were similar to previous studies: in each of the three versions analysed here, the two most important components represented a disturbance of mood (‘general dysphoria’)– including aspects of anxiety, depression and irritability– and a disturbance of social performance (‘social function/optimism’).The principal component structure of the GHQ-60 was then utilized to calculate factor scores, and these were compared with PSE ratings using Relative Operating Characteristic (ROC) analysis. While four of the six factors discriminated well (area under the ROC curve 0–75 or more) between PSE ‘cases’ and ‘non-cases’, only one, depressive thoughts, was a good discriminator between depressed and non-depressed PSE ‘cases’.

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