scholarly journals Exploratory classification of clinical phenotypes in Japanese patients with antineutrophil cytoplasmic antibody-associated vasculitis using cluster analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Haruki Watanabe ◽  
◽  
Ken-ei Sada ◽  
Masayoshi Harigai ◽  
Koichi Amano ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Disease Model ◽  
Survival Rates ◽  
Japanese Patients ◽  
Antineutrophil Cytoplasmic Antibody ◽  
Eosinophilic Granulomatosis With Polyangiitis ◽  
Reactive Protein ◽  
And Cluster Analysis

AbstractA novel patient cluster in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) may be identified in Japan. We performed multiple correspondence and cluster analysis regarding 427 clinically diagnosed AAV patients excluding eosinophilic granulomatosis with polyangiitis. Model 1 included the ANCA phenotype, items of the Birmingham Vasculitis Activity Score, and interstitial lung disease; model 2 included serum creatinine (s-Cr) and C-reactive protein (CRP) levels with model 1 components. In seven clusters determined in model 1, the ANCA-negative (n = 8) and proteinase 3-ANCA-positive (n = 41) groups emerged as two distinct clusters. The other five myeloperoxidase-ANCA-positive clusters were characterized by ear, nose, and throat (ENT) (n = 47); cutaneous (n = 36); renal (n = 256), non-renal (n = 33); and both ENT and cutaneous symptoms (n = 6). Four clusters in model 2 were characterized by myeloperoxidase-ANCA negativity (n = 42), without s-Cr elevation (< 1.3 mg/dL) (n = 157), s-Cr elevation (≥ 1.3 mg/dL) with high CRP (> 10 mg/dL) (n = 71), or s-Cr elevation (≥ 1.3 mg/dL) without high CRP (≤ 10 mg/dL) (n = 157). Overall, renal, and relapse-free survival rates were significantly different across the four clusters in model 2. ENT, cutaneous, and renal symptoms may be useful in characterization of Japanese AAV patients with myeloperoxidase-ANCA. The combination of s-Cr and CRP levels may be predictive of prognosis.

scholarly journals Classification of Polish voivodeships with regards to the accident rate in the construction industry

2018 ◽  
Vol 174 ◽  
pp. 04007 ◽  
Author(s):  
Tomasz Nowobilski ◽  
Irena Bagińska ◽  
Krzysztof Gawron
Keyword(s):  
Cluster Analysis ◽  
Construction Industry ◽  
Occupational Safety ◽  
Statistical Data ◽  
Occupational Accidents ◽  
Accident Rate ◽  
Central Statistical ◽  
Central Statistical Office ◽  
And Cluster Analysis

The article classifies Polish voivodeships into appropriate groups with a similar level of occupational safety in the construction industry. The basis for the adopted classification was statistical data published by the Central Statistical Office regarding population, employment in the construction industry, the value of construction production and the number of occupational accidents. The conducted research allowed a logical and correct, in terms of content, division of the Polish territory to be made, taking into account the aspect of occupational safety in the construction industry. Statistica software and cluster analysis were used to solve the problem.

scholarly journals Achieving the best method to classify Eosinophilic Chronic Rhinosinusitis: a systematic review

2021 ◽  
Vol 59 (4) ◽  
pp. 330-339
Author(s):  
M.D.C. Toro ◽  
M.A. Antonio ◽  
M.G. Alves Dos Reis ◽  
M.S. de Assumpcao ◽  
E. Sakano
Keyword(s):  
Cluster Analysis ◽  
Chronic Rhinosinusitis ◽  
High Power Field ◽  
Eosinophilic Chronic Rhinosinusitis ◽  
Multiple Biopsy ◽  
Quality Of Life Index ◽  
Field Methodology ◽  
Polyp Recurrence ◽  
And Cluster Analysis

Background: Chronic Rhinosinusitis is currently classified into eosinophilic and non-eosinophilic, according to the histologic quantification of the number of eosinophils in nasal mucosa biopsy. There is a lack of unanimous histopathologic criteria and methodology for this classification and no consensus regarding a cut-off point for Eosinophils per High power field. Methodology: A systematic electronic search was performed on BVS, PUBMED, PUBMED PMC, SCOPUS, WEB OF SCIENCE, EMBASE, COCHRANE and PROQUEST databases looking for studies that reported a cut point for classification of Eosinophilic Chronic Rhinosinusitis (eCRS), and data concerning methodology of classification was extracted. Results: We identified 142 studies that reported 29 different cut-off values for classification of eCRS, and different methods of histologic analysis. Out of these studies 13 reported their own methodology to establish the cut-off point, and used different reference standards as polyp recurrence, asthma and allergy, immunocytochemistry, quality of life index, standard deviation of the control population and cluster analysis. Conclusions: Further studies are needed to determine a precise cut-off point, especially international multicentered cluster analysis. Moreover, methodologic standardization of biopsy and analysis is needed to certify comparable results. Multiple biopsy sites, densest cellular infiltration area examination and oral steroids restriction at least four weeks before sampling are advisable.

scholarly journals Mental State of Inpatients with COVID-19: a Computational Psychiatry Approach

2021 ◽  
Author(s):  
M.Yu. Sorokin ◽  
E.I. Palchikova ◽  
A.A. Kibitov ◽  
E.D. Kasyanov ◽  
M.A. Khobeysh ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Mental State ◽  
Dispersion Analysis ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Computational Psychiatry ◽  
Mental Disturbances ◽  
The Relationship ◽  
Respiration Function

ABSTRACTThe overload of healthcare systems around the world and the danger of infection have limited the ability of researchers to obtain sufficient and reliable data on psychopathology in hospitalized patients with COVID-19. The relationship between severe SARS-CoV-2 infection and specific mental disturbances remains poorly understood.Aimto reveal the possibility of identifying the typology and frequency of psychiatric syndromes associated with acute COVID-19 using cluster analysis of discrete psychopathological phenomena.Materials and methodsDescriptive data on the mental state of 55 inpatients with COVID-19 were obtained by young-career physicians with psychiatric backgrounds. Classification of observed clinical phenomena was performed with k-means cluster analysis of variables codded from the main psychopathological symptoms. Dispersion analysis with p-level 0.05 was used to reveal the cluster’s differences in demography, parameters of inflammation and respiration function collected on the basis of the original medical records.ResultsThree resulting clusters of patients were identified: persons with anxiety, disorders of fluency and tempo of thinking, mood, attention, motor-volitional sphere, reduced insight, and pessimistic plans for the future (n=11); persons without psychopathology (n=37); persons with disorientation, disorders of memory, attention, fluency, and tempo of thinking, reduced insight (n=7). The development of a certain type of impaired mental state was specifically associated with: age, lung lesions according to computed tomography, saturation, respiratory rate, C-reactive protein level, platelet count.ConclusionThe prevalence and typology of psychiatric disorders in patients with acute COVID-19 were described using the computational psychiatry approach.

Does Mutual Fund Management in India Correspond to its Investment Objective Classification?

2005 ◽  
Vol 08 (04) ◽  
pp. 659-685 ◽  
Author(s):  
Luis Ferruz Agudo ◽  
Cristina Ortiz Lázaro
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Mutual Fund ◽  
The Other ◽  
Fund Management ◽  
Institutional Control ◽  
Other Hand ◽  
Objective Classification ◽  
And Cluster Analysis

The aim of this article is to investigate the mutual fund market in India and verify whether or not the fund classification obtained from the name given to identify them corresponds to that which would be obtained were prior management to be taken into account. This industry has undergone spectacular growth in recent years, making this study extremely interesting, not least because institutional control could be less in times of expansion. The methodologies employed in the study are factor analysis and cluster analysis. The former determines that risk would clearly identify two groups of funds in the same manner as public classification of the funds. Cluster analysis, on the other hand, identifies funds that are, in fact, very close to one another, when for the bulk of investors they are not.

Sign in / Sign up

Export Citation Format

Share Document