Cluster Analysis Of Patients Research Articles

A cluster analysis of patients with axial spondyloarthritis using tumour necrosis factor alpha inhibitors based on clinical characteristics

BackgroundThis study aimed to classify the distinct group of patients with axial spondyloarthritis (SpA) on tumour necrosis factor alpha inhibitors (TNFi) according to the baseline characteristics using a clustering algorithm.MethodsThe clinical characteristics and demographic data of patients with axial SpA included in the Korean College of Rheumatology Biologics and Targeted Therapy registry were investigated. The patterns of disease manifestations were examined using divisive hierarchical cluster analysis. After clustering, we compared the clinical characteristics of patients and the drug survival of TNFi between the classified groups.ResultsA total of 1042 patients were analysed. The cluster analysis classified patients into two groups: axial group predominantly showing isolated axial manifestations (n = 828) and extra-axial group more frequently showing extra-axial symptoms (n = 214). Almost all extra-axial symptoms (peripheral arthritis, enthesitis, uveitis, and psoriasis) were more frequently observed in the extra-axial group than in the axial group. Moreover, patients in the extra-axial group had shorter disease duration, later disease onset, and higher disease activity than those in the axial group. The disease activity was comparable between the two groups after 1 year of treatment with TNFi. Interestingly, the extra-axial group had a lower drug survival with TNFi than the axial group (p = 0.001).ConclusionsCluster analysis of patients with axial SpA using TNFi classified two distinct clinical phenotypes. These clusters had different TNFi drug survival, clinical characteristics, and disease activity.

A Risk Signature Consisting of Eight m6A Methylation Regulators Predicts the Prognosis of Glioma.

Glioma progression seriously correlates to the epigenetic context. This study aims to identify glioma subtypes by clustering analysis of patients using the multi-omics data of N6-methyladenosine (m6A) methylation regulators and to construct a risk signature for investigating the role of m6A methylation regulators in the prognosis of glioma. Multi-omics data of glioma and normal control tissues were obtained through The Cancer Genome Atlas (TCGA) database. The clustering analysis of multi-omics data of patients was conducted using the R package iClusterPlus software. The risk model was constructed by univariate and multivariate Cox analysis, and the glioma expression data and related clinical data were obtained by Chinese Glioma Genome Atlas (CGGA) datasets to verify the risk model. By analyzing the glioma data in TCGA, we found that the risk signature could be constructed according to the eight genes with m6A methylation modification function, including ALKBH5, HNRNPA2B1, IGF2BP2, IGF2BP3, RBM15, WTAP, YTHDF1, and YTHDF2. Meanwhile, we found that IGF2BP2 and IGF2BP3 were highly expressed in glioma subtypes with high-risk scores and closely related to the prognosis of glioma patients. m6A methylation regulators, especially IGF2BP2 and IGF2BP3, play important roles in the malignant progression of glioma. The risk signature constructed by eight m6A methylation regulators can predict the prognosis of glioma. IGF2BP2 and IGF2BP3 may be the key regulatory factors of m6A methylation regulators involved in the occurrence and development of glioma, and can serve as molecular markers for the prognosis of glioma.

Cluster analysis of a large dataset of patients with Lennox-Gastaut syndrome

ObjectivesWe applied the Two-Step cluster analysis on a large cohort of patients with Lennox-Gastaut syndrome (LGS). We hypothesized that there are distinct clusters of patients with LGS based on their clinical characteristics. We also studied whether the seizure outcome differs between these patient clusters. MethodsThis was a longitudinal study of a prospectively developed database. All patients with a diagnosis of LGS were studied at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2020. The Two-Step cluster analysis (Schwarz's Bayesian Criterion: BIC) was applied to the whole dataset considering the demographic data, seizure types, and EEG data. In the next step, the seizure outcome was compared between the clusters of patients. ResultsTwo hundred and fifty-eight patients were studied. The Two-Step cluster analysis showed that there were three clusters of homogeneous subgroups of patients with LGS, with a fair silhouette measure of cohesion and separation. The seizure outcomes were significantly different between the clusters. In cluster 1, three out of 35 patients (8.6%) were seizure-free; in cluster 2, 13 out of 34 patients (38.2%) were seizure-free; and in cluster 3, nine out of 49 patients (18.4%) were seizure-free (p = 0.009; df: 2). ConclusionThe Two-Step cluster analysis could identify three distinct clusters of patients with LGS; these data are helpful in providing appropriate prognosis and counselling for patients and their care-givers. Future studies should also investigate the effects of etiology in cluster analysis of patients with LGS.

Pathological structure of visuospatial neglect: A comprehensive multivariate analysis of spatial and non-spatial aspects

SummaryVisuospatial neglect (VSN) is a neurological syndrome of higher brain functions in which an individual fails to detect stimuli on a space that is contralateral to a hemispheric lesion. We performed a comprehensive multivariate analysis based on the principal component analysis (PCA) and cluster analysis in patients with right hemisphere stroke and then performed a determination of different elements of VSN. PCA-based cluster analysis detected distinct aspects of VSN as follows: cluster 1: low arousal and attention state, cluster 2: exogenous neglect, cluster 3: spatial working memory (SWM) deficit. Lesion analysis revealed neural correlates for each cluster and highlighted “disturbance of the ventral attention network” for the stagnation of exogenous attention and “parietal damage” for SWM deficit. Our results reveal a pathological structure of VSN as multiple components of an attention network deficit, and they contribute to the understanding of the mechanisms underlying VSN.

Diagnostic and prognostic value of serum interleukins 6 and 10 integrated assessment in patients with acute period of spontaneous supratentorial intracerebral hemorrhage

Prospective cohort research that included 104 patients with spontaneous supratentorial intracerebral hemorrhage (SSICH) on the ground of conservative treatment was carried out. Diagnosis was made by using clinical and neurovisualization investigation. The diagnostic and prognostic informativeness of serum interleukin-6 (IL-6) and interleukin-10 (IL-10) integrated assessment was investigated in patients with acute period of SSICH. Laboratory fasting blood samples were taken on the 1st and 5th day of admission with further detection of serum IL-6 and IL-10 concentrations. Early neurological deterioration and unfavorable acute period spontaneous supratentorial intracerebral hemorrhage outcome as grading 4­6 scores by modified Rankin scale on the 21st day of the disease were chosen as clinical combined endpoints. Lethal outcome and unfavorable functional outcome were also recorded. Three profile types of serum IL-6 and IL-10 concentrations were identified on the ground of cluster analysis in patients with SSICH. It was revealed that profile’s types with reciprocal elevation of cytokines are associated with heavier brain structures affection and increased risk of early neurological deterioration (relative risk (95% Cl) = 3,40 (1,35-8,53), p=0,0091), lethal outcome (relative risk (95% Cl) = 7,50 (1,80-31,17), p=0,0056) and unfavorable acute period functional outcome (relative risk (95% Cl) = 2,28 (1,46-3,57), p=0,0003). Four types of IL-6 and IL-10 serum profile on the 5th day of admission were identified. In this case profiles with elevation IL-6 levels are associated with increased risk of unfavorable acute period SSICH outcome into 2,1 times (relative risk (95% Cl) = 2,13 (1,44-3,15), p=0,0002). Profile’s types of serum IL-6 and IL-10 levels in patients with SSICH are informative integrated parameter for short-term prognosis on the ground of conservative treatment. Keywords: cerebral hemorrhage, interleukin-6, interleukin-10, prognosis.

A clinic-based cluster analysis in patients with moderate-severe obstructive sleep apnea (OSA) in Chile

RationalePatients commonly report differences in either clinical or symptomatic profiles, despite having the same severity of obstructive sleep apnea (OSA). ObjectiveTo identify clinical and symptomatic phenotypes and to evaluate cardiovascular mortality in each phenotype. MethodsData from 1370 participants (788 with moderate-severe OSA and 582 controls as a reference group) were extracted using the SantOSA database. Sixteen variables were analyzed using latent class analysis to define clinical subtypes. The association between subtypes and cardiovascular mortality was evaluated using Kaplan–Meier survival analysis and the Cox proportional hazards model. Adjusted hazard ratios (HRs) with confidence intervals (CIs) were modified by cardiovascular confounders. ResultsThe median observation period was 5.2 years. We found four clusters: cluster #1: symptomatic men with major comorbidities (n = 252); cluster #2: symptomatic women with comorbidities (n = 154); cluster #3: asymptomatic men with comorbidities (n = 143); and cluster #4: symptomatic young men without major comorbidities (n = 239). In cluster #1, mortality was 4.76% and was independently associated with age (HR 1.12; CI 1.07–1.17), type 2 diabetes mellitus (HR 3.37; CI 1.29–8.78) and coronary heart disease (HR 3.85; CI 1.27–11.56); in cluster #2, mortality was 3.89% and was independently associated with age (HR 1.12; CI 1.06–1.19) and the oxygen desaturation index (ODI, HR 1.02; CI 1.01–1.04); and in cluster #3, mortality was 3.49% (HR 3.50; CI 1.03–11.90) and was independently associated with age (HR 1.19; CI 1.10–1.29). In cluster #4, mortality was 1.25% and showed nonsignificant associations. ConclusionIn patients with moderate-severe OSA, we described four phenotypes of patients according to clinical features with different risks of cardiovascular mortality. Study registerISRCTN62293645.

Cluster Analysis of Patients’ Clinical Information for Medical Practitioners and Insurance Companies

A number of approaches have been proposed in literature to collect and classify patient related information for purpose of better clinical diagnosis and thus safer treatment and administration of related activities. This type of data collection and classification benefits doctors and the corresponding hospitals. However, no effort is made, as to our knowledge, to classify accumulated data within insurance company databases to facilitate doctors as well as insurance companies for better analysis and cost-effective treatment of patients suffering from chronic (and expensive to treat) diseases such as related to oncology. In this study, a customized self-organized data classification model is applied to an insurance company database to build clusters based on age, patient condition, tests done, etc. These clusters provide integrated analysis to doctors in providing patient-specific, disease-specific, etc., and thus cost-effective treatment. On the other side, it saves on costs to be incurred on repeated tests to be done on the patient. An experimental setup is developed to train such a network, and testing results are presented. The practical constraints are also discussed.

Prediction of S-1 adjuvant chemotherapy benefit in Stage II/III gastric cancer treatment based on comprehensive gene expression analysis.

Studies to identify predictive biomarkers of adjuvant chemotherapy with S-1 after gastrectomy in Stage II/III gastric cancer patients have been done; however, more clarity and understanding are needed. Our aim in the present study was to identify biomarkers predicting benefit due to S-1 adjuvant chemotherapy using comprehensive gene expression analysis. We retrospectively analyzed 102 patients receiving adjuvant chemotherapy with S-1 and 46 patients not receiving S-1 adjuvant chemotherapy after gastrectomy for gastric cancer treatment between January 2014 and December 2016. Hierarchical clustering analysis was performed based on the gene expression data obtained using cDNA microarray. Differentially expressed genes (DEGs) were identified using thresholds of absolute fold changes of > 4.0 and a false discovery rate P value of < 0.01. Gene Ontology (GO) analysis and GO network visualization were performed using the ClueGO app in Cytoscape. Hierarchical clustering analysis in patients treated with S-1 adjuvant chemotherapy revealed two clusters with favorable and unfavorable survival outcomes. We identified 147 upregulated DEGs and 192 downregulated DEGs in the favorable outcome group. GO analysis to identify significantly upregulated genes showed enrichment in immune-related genes and GO terms. Upregulation of these immune-related genes was not associated with survival in patients not receiving S-1 adjuvant chemotherapy. The upregulation and enrichment of immune-related genes and GO terms may be predictive biomarkers in patients who would benefit from adjuvant S-1 chemotherapy to treat Stage II/III gastric cancer.

Cluster analysis of patients with cough, mainly cough variant asthma, showing a good response to asthma therapy

Cluster analysis of patients with cough, mainly cough variant asthma, showing a good response to asthma therapy

Application of Cluster Analysis to Distant Metastases from Lung Cancer

In patients with lung cancer, there has been no study that treated 'distant metastases' as 'metastatic patterns'. This study aimed to evaluate if specific 'metastatic patterns' exist in lung cancer patients. Data were collected from lung cancer patients between 2009 and 2018. Metastatic patterns were analyzed using cluster analysis in patients with epidermal growth factor receptor (EGFR) mutation-positive lung adenocarcinoma, those with small cell lung cancer (SCLC), and those with squamous cell lung cancer (SqCLC). In 313 patients (127 patients with EGFR mutation, 87 patients with SCLC, and 99 patients with SqCLC), metastatic patterns existed in each of the three subset groups, and metastatic patterns of these groups were statistically different. The knowledge of the metastatic patterns might be useful for clinical practice in the foreseeable future, as it enables a more efficient detection of metastatic disease through imaging, and a more effective treatment at predicted metastatic sites.

821P - Prediction of S-1 adjuvant chemotherapy efficacy in stage II/III gastric cancer treatment based on comprehensive gene expression analysis

BackgroundStudies to identify predictive biomarkers of adjuvant chemotherapy with S-1 after gastrectomy in Stage II/III gastric cancer patients have been done; however, more clarity and understanding are needed. Our aim in the present study was to identify biomarkers predicting benefit due to S-1 adjuvant chemotherapy using comprehensive gene expression analysis. MethodsWe retrospectively analyzed 102 patients receiving adjuvant chemotherapy with S-1 and 46 patients not receiving S-1 adjuvant chemotherapy after gastrectomy for gastric cancer treatment between January 2014 and December 2016. Hierarchical clustering analysis was performed based on the gene expression data obtained using DNA microarrays. Differentially expressed genes (DEGs) were identified using thresholds of absolute fold changes (FCs) of>4.0 and a false discovery rate (FDR) P value of<0.01. Gene Ontology (GO) analysis and GO network visualization were performed using the ClueGO app in Cytoscape. ResultsHierarchical clustering analysis in patients treated with S-1 adjuvant chemotherapy revealed two clusters with favorable and unfavorable survival outcomes. We identified 147 upregulated DEGs and 192 downregulated DEGs in the favorable outcome group. GO analysis to identify significantly upregulated genes showed enrichment in immune-related genes and GO terms. Upregulation of these immune-related genes was not associated with survival in patients not receiving S-1 adjuvant chemotherapy. ConclusionsThe upregulation and enrichment of immune-related genes and GO terms may be a predictive biomarker in patients who would benefit from adjuvant S-1 chemotherapy to treat Stage II/III gastric cancer. Legal entity responsible for the studyThe authors. FundingHas not received any funding. DisclosureM. Terashima: Honoraria (self): Taiho; Honoraria (self): Chugai; Honoraria (self): Yakult; Honoraria (self): ONo; Honoraria (self): BMS; Honoraria (self): Eli-Lilly; Honoraria (self): Takeda; Honoraria (self): Daiichi-Sankyo; Honoraria (self): Kyowa Hakko Kirin; Honoraria (self): Nihon Kayaku; Honoraria (self): Pfizer. All other authors have declared no conflicts of interest.

Exploring syndrome differentiation using non-negative matrix factorization and cluster analysis in patients with atopic dermatitis

Syndrome differentiation (SD) results in a diagnostic conclusion based on a cluster of concurrent symptoms and signs, including pulse form and tongue color. In Korea, there is a strong interest in the standardization of Traditional Medicine (TM). In order to standardize TM treatment, standardization of SD should be given priority. The aim of this study was to explore the SD, or symptom clusters, of patients with atopic dermatitis (AD) using non-negative factorization methods and k-means clustering analysis.We screened 80 patients and enrolled 73 eligible patients. One TM dermatologist evaluated the symptoms/signs using an existing clinical dataset from patients with AD. This dataset was designed to collect 15 dermatologic and 18 systemic symptoms/signs associated with AD. Non-negative matrix factorization was used to decompose the original data into a matrix with three features and a weight matrix. The point of intersection of the three coordinates from each patient was placed in three-dimensional space. With five clusters, the silhouette score reached 0.484, and this was the best silhouette score obtained from two to nine clusters. Patients were clustered according to the varying severity of concurrent symptoms/signs. Through the distribution of the null hypothesis generated by 10,000 permutation tests, we found significant cluster-specific symptoms/signs from the confidence intervals in the upper and lower 2.5% of the distribution. Patients in each cluster showed differences in symptoms/signs and severity. In a clinical situation, SD and treatment are based on the practitioners' observations and clinical experience. SD, identified through informatics, can contribute to development of standardized, objective, and consistent SD for each disease.

Extended phenotype and clinical subgroups in unilateral Meniere disease: A cross-sectional study with cluster analysis.

To define clinical subgroups by cluster analysis in patients with unilateral Meniere disease (MD) and to compare them with the clinical subgroups found in bilateral MD. A cross-sectional study with a two-step cluster analysis. A tertiary referral multicenter study. Nine hundred and eighty-eight adult patients with unilateral MD. best predictors to define clinical subgroups with potential different aetiologies. We established five clusters in unilateral MD. Group 1 is the most frequently found, includes 53% of patients, and it is defined as the sporadic, classic MD without migraine and without autoimmune disorder (AD). Group 2 is found in 8% of patients, and it is defined by hearing loss, which antedates the vertigo episodes by months or years (delayed MD), without migraine or AD in most of cases. Group 3 involves 13% of patients, and it is considered familial MD, while group 4, which includes 15% of patients, is linked to the presence of migraine in all cases. Group 5 is found in 11% of patients and is defined by a comorbid AD. We found significant differences in the distribution of AD in clusters 3, 4 and 5 between patients with uni- and bilateral MD. Cluster analysis defines clinical subgroups in MD, and it extends the phenotype beyond audiovestibular symptoms. This classification will help to improve the phenotyping in MD and facilitate the selection of patients for randomised clinical trials.

Retraction: Distinct Phenotypes of Cigarette Smokers Identified by Cluster Analysis of Patients with Severe Asthma.

Retraction: Distinct Phenotypes of Cigarette Smokers Identified by Cluster Analysis of Patients with Severe Asthma.

Distinct Phenotypes of Cigarette Smokers Identified by Cluster Analysis of Patients with Severe Asthma.

Smoking may have multifactorial effects on asthma phenotypes, particularly in severe asthma. Cluster analysis has been applied to explore novel phenotypes, which are not based on any a priori hypotheses. To explore novel severe asthma phenotypes by cluster analysis when including cigarette smokers. We recruited a total of 127 subjects with severe asthma, including 59 current or ex-smokers, from our university hospital and its 29 affiliated hospitals/pulmonary clinics. Twelve clinical variables obtained during a 2-day hospital stay were used for cluster analysis. After clustering using clinical variables, the sputum levels of 14 molecules were measured to biologically characterize the clinical clusters. Five clinical clusters were identified, including two characterized by high pack-year exposure to cigarette smoking and low FEV1/FVC. There were marked differences between the two clusters of cigarette smokers. One had high levels of circulating eosinophils, high IgE levels, and a high sinus disease score. The other was characterized by low levels of the same parameters. Sputum analysis revealed increased levels of IL-5 in the former cluster and increased levels of IL-6 and osteopontin in the latter. The other three clusters were similar to those previously reported: young onset/atopic, nonsmoker/less eosinophilic, and female/obese. Key clinical variables were confirmed to be stable and consistent 1 year later. This study reveals two distinct phenotypes of severe asthma in current and former cigarette smokers with potentially different biological pathways contributing to fixed airflow limitation. Clinical trial registered with www.umin.ac.jp (000003254).

Muscle Quantitative MR Imaging and Clustering Analysis in Patients with Facioscapulohumeral Muscular Dystrophy Type 1.

BackgroundFacioscapulohumeral muscular dystrophy type 1 (FSHD1) is the third most common inherited muscular dystrophy. Considering the highly variable clinical expression and the slow disease progression, sensitive outcome measures would be of interest.Methods and FindingsUsing muscle MRI, we assessed muscular fatty infiltration in the lower limbs of 35 FSHD1 patients and 22 healthy volunteers by two methods: a quantitative imaging (qMRI) combined with a dedicated automated segmentation method performed on both thighs and a standard T1-weighted four-point visual scale (visual score) on thighs and legs. Each patient had a clinical evaluation including manual muscular testing, Clinical Severity Score (CSS) scale and MFM scale. The intramuscular fat fraction measured using qMRI in the thighs was significantly higher in patients (21.9 ± 20.4%) than in volunteers (3.6 ± 2.8%) (p<0.001). In patients, the intramuscular fat fraction was significantly correlated with the muscular fatty infiltration in the thighs evaluated by the mean visual score (p<0.001). However, we observed a ceiling effect of the visual score for patients with a severe fatty infiltration clearly indicating the larger accuracy of the qMRI approach. Mean intramuscular fat fraction was significantly correlated with CSS scale (p≤0.01) and was inversely correlated with MMT score, MFM subscore D1 (p≤0.01) further illustrating the sensitivity of the qMRI approach. Overall, a clustering analysis disclosed three different imaging patterns of muscle involvement for the thighs and the legs which could be related to different stages of the disease and put forth muscles which could be of interest for a subtle investigation of the disease progression and/or the efficiency of any therapeutic strategy.ConclusionThe qMRI provides a sensitive measurement of fat fraction which should also be of high interest to assess disease progression and any therapeutic strategy in FSHD1 patients.

Cluster Analysis in Patients with GOLD 1 Chronic Obstructive Pulmonary Disease.

BackgroundWe hypothesized that heterogeneity exists within the Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1 spirometric category and that different subgroups could be identified within this GOLD category.MethodsPre-randomization study participants from two clinical trials were symptomatic/asymptomatic GOLD 1 chronic obstructive pulmonary disease (COPD) patients and healthy controls. A hierarchical cluster analysis used pre-randomization demographics, symptom scores, lung function, peak exercise response and daily physical activity levels to derive population subgroups.ResultsConsiderable heterogeneity existed for clinical variables among patients with GOLD 1 COPD. All parameters, except forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC), had considerable overlap between GOLD 1 COPD and controls. Three-clusters were identified: cluster I (18 [15%] COPD patients; 105 [85%] controls); cluster II (45 [80%] COPD patients; 11 [20%] controls); and cluster III (22 [92%] COPD patients; 2 [8%] controls). Apart from reduced diffusion capacity and lower baseline dyspnea index versus controls, cluster I COPD patients had otherwise preserved lung volumes, exercise capacity and physical activity levels. Cluster II COPD patients had a higher smoking history and greater hyperinflation versus cluster I COPD patients. Cluster III COPD patients had reduced physical activity versus controls and clusters I and II COPD patients, and lower FEV1/FVC versus clusters I and II COPD patients.ConclusionsThe results emphasize heterogeneity within GOLD 1 COPD, supporting an individualized therapeutic approach to patients.Trial registrationwww.clinicaltrials.gov. NCT01360788 and NCT01072396.

Kampo Traditional Pattern Diagnosis and the Clustering Analysis of Patients with Insomnia

Kampo Traditional Pattern Diagnosis and the Clustering Analysis of Patients with Insomnia

Kampo Traditional Pattern Diagnosis and the Clustering Analysis of Patients with Cold Sensation

Kampo Traditional Pattern Diagnosis and the Clustering Analysis of Patients with Cold Sensation

FRI0395 Five phenotype profiles are revealed by cluster analysis in patients with gout. Results from a cohort of 2762 patients

BackgroundDiet and genetic predisposition seem to be the main causal factors of primary gout. Gout is associated with hypertension, diabetes mellitus, metabolic syndrome, and renal and cardiovascular diseases that may...