The Rowland Universal Dementia Assessment Scale (RUDAS): a multicultural cognitive assessment scale

2004 ◽  
Vol 16 (1) ◽  
pp. 13-31 ◽  
Author(s):  
Joella E. Storey ◽  
Jeffrey T. J. Rowland ◽  
David A. Conforti ◽  
Hugh G. Dickson
Keyword(s):  
Roc Curve ◽  
Predictive Accuracy ◽  
Geriatric Medicine ◽  
Community Dwelling ◽  
Assessment Scale ◽  
Elderly Persons ◽  
Roc Curve Analysis ◽  
Simple Method ◽  
Cognitive Domains ◽  
Cut Point

Objective: To develop and validate a simple method for detecting dementia that is valid across cultures, portable and easily administered by primary health care clinicians.Design: Culture and Health Advisory Groups were used in Stage 1 to develop culturally fair cognitive items. In Stage 2, clinical testing of 42 items was conducted in a multicultural sample of consecutive new referrals to the geriatric medicine outpatient clinic at Liverpool Hospital, Sydney, Australia (n=166). In Stage 3, the predictive accuracy of items was assessed in a random sample of community-dwelling elderly persons stratified by language background and cognitive diagnosis and matched for sex and age (n=90).Measurements: A research psychologist administered all cognitive items, using interpreters when needed. Each patient was comprehensively assessed by one of three geriatricians, who ordered relevant investigations, and implemented a standardized assessment of cognitive domains. The geriatricians also collected demographic information, and administered other functional and cognitive measures. DSM-IV criteria were used to assign cognitive diagnoses. Item validity and weights were assessed using frequency and logistic regression analyses. Receiver-operating characteristic (ROC) curve analysis was used to determine overall predictive accuracy of the RUDAS and the best cut-point for detecting cognitive impairment.Results: The 6-item RUDAS assesses multiple cognitive domains including memory, praxis, language, judgement, drawing and body orientation. It appears not to be affected by gender, years of education, differential performance factors and preferred language. The area under the ROC curve for the RUDAS was 0.94 (95% CI 0.87–0.98). At a cut-point of 23 (maximum score of 30), sensitivity and specificity were 89% and 98%, respectively. Inter-rater (0.99) and test-retest (0.98) reliabilities were very high.Conclusions: The 6-item RUDAS is portable and tests multiple cognitive domains. It is easily interpreted to other languages, and appears to be culturally fair. However, further validation is needed in other settings, and in longitudinal studies to determine its sensitivity to change in cognitive function over time.

Transcultural Adaptation and Validation of the Fonseca Anamnestic Index in a Spanish Population with Temporomandibular Disorders.

2020 ◽  
Author(s):  
Carmen María Sánchez-Torrelo ◽  
Noelia Zagalaz-Anula ◽  
Roger Alonso-Royo ◽  
Alfonso Javier Ibáñez-Vera ◽  
Jesús López-Collantes ◽  
...  
Keyword(s):  
Construct Validity ◽  
Internal Consistency ◽  
Roc Curve ◽  
Temporomandibular Disorders ◽  
Concurrent Validity ◽  
Curve Analysis ◽  
Spanish Version ◽  
Spanish Population ◽  
Roc Curve Analysis ◽  
Simple Method

Abstract Background. The Fonseca Anamnestic Index (FAI) offers a simple method to screen temporomandibular disorders (TMD). This study aimed to validate the standard version of the FAI in a Spanish population and to analyze the clinimetric properties of the Spanish version of the FAI in patients with TMD.Methods. The sample consisted of 179 subjects aged over 18 years, of which 119 were diagnosed with TMD and 60 were healthy controls. Construct validity (exploratory factor analysis), internal consistency, test-retest reliability, and concurrent validity were analyzed. To discriminate between patients with and without TMD, Receiver Operating Characteristic (ROC) curve analysis was performed.Results. The Spanish version of the FAI showed construct validity formed by three factors. Cronbach’s alpha was 0.820, indicating good internal consistency. The reliability of the items measured with the weighted kappa coefficient was between 0.588 and 0.899, varying between moderate to almost perfect. The intraclass correlation coefficient (ICC) of the total score was 0.938, indicating excellent reliability. The standard error of measurement (SEM) was 6.42, with a minimum detectable change (MDC) of 12.59 points. The concurrent validity showed a significant correlation with headache, neck pain, vertigo and the Mental Component Summary (SF-12 MCS) of the SF-12. However, the relationship with the Physical Component Summary (SF-12 PCS) was not significant. The ROC curve analysis showed a good accuracy of the FAI in differentiating between healthy and TMD patients with an area under the curve (AUC) = 0.869, corresponding to a cut-off point for the FAI of >35 points, with a sensitivity = 83.19% and a specificity = 78.33%.Conclusions. The Spanish version of the FAI is a valid and reliable instrument for diagnosing people with TMD, with appropriate general clinimetric properties. Discrimination between patients with and without TMD is excellent.

Use of percent positive biopsy cores to predict prostate cancer–specific death in patients treated with dose-escalated radiotherapy.

2011 ◽  
Vol 29 (7_suppl) ◽  
pp. 35-35
Author(s):  
Y. Qian ◽  
F. Y. Feng ◽  
S. Halverson ◽  
K. Blas ◽  
H. M. Sandler ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Roc Curve ◽  
Risk Group ◽  
Curve Analysis ◽  
External Beam Radiation ◽  
Rank Test ◽  
Roc Curve Analysis ◽  
Cut Point ◽  
Log Rank Test ◽  
Positive Biopsy

35 Background: The percent of positive biopsy cores (PPC)-considered a surrogate of local disease burden-has been shown to predict biochemical failure (BF) after external beam radiation therapy (EBRT), but most series have used conventional dose RT. Dose-escalated RT has been demonstrated to improve prostate cancer outcomes, but the value of PPC is unclear in the setting of RT doses high enough to decrease local failure. Methods: A retrospective evaluation was performed of 651 patients treated to ≥75 Gy with biopsy core information available. Patients were stratified for PPC by quartile, and differences by quartile in BF, freedom from metastasis (FFM), cause specific survival (CSS), and overall survival (OS) were assessed using the log-rank test. Receiver operated characteristic (ROC) curve analysis was utilized to determine an optimal cut-point for PPC. Cox proportional hazards multivariate regression was utilized to assess the impact of PPC on clinical outcome when adjusting for risk group. Results: With median follow-up of 62 months the median number of cores sampled was 7 (IQR: 6–12) with median PPC in 38% (IQR: 17%-67%). On log-rank test, BF, FFM, and CSS were all associated with PPC (p < 0.005 for all), with worse outcomes only for the highest PPC quartile (>67%). There was no observed difference in OS based upon PPC. ROC curve analysis confirmed a cut-point of 67% as most closely associated with CSS (p<0.001, AUC=0.71). On multivariate analysis after adjusting for NCCN risk group and ADT use, PPC>67% increased the risk for BF (p<0.0001, HR:2.1 [1.4–3.0]), FFM (p<0.05, HR:1.7 [1.1 to 2.9]), and CSS (p<0.06 (HR:2.1 [1.0–4.6]). When analyzed as a continuous variable controlling for risk group and ADT use, increasing PPC increased the risk for BF (p < 0.002), metastasis (p < 0.05), and CSS (p < 0.02), with a 1–2% increase in relative risk of recurrence for each 1% increase in the PPC. Conclusions: For patients treated with dose-escalated RT, the PPC adds prognostic value but at a higher cut-point then previously utilized. Patients with PPC >67% remain at increased risk for failure even with dose-escalated EBRT and may receive benefit from further intensification of therapy. No significant financial relationships to disclose.

scholarly journals The predictive value of serum level of cystatin C for COVID-19 severity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luanfeng Lin ◽  
Xiaoling Chen ◽  
Junnian Chen ◽  
Xiaobin Pan ◽  
Pincang Xia ◽  
...  
Keyword(s):  
Cystatin C ◽  
Roc Curve ◽  
Proportional Hazards ◽  
Predictive Accuracy ◽  
Cox Proportional Hazards ◽  
Roc Curve Analysis ◽  
Serum Cystatin C ◽  
Serum Cystatin ◽  
Increased Risk ◽  
Cox Analysis

AbstractTo investigate the potential prognostic value of Serum cystatin C (sCys C) in patients with COVID-19 and determine the association of sCys C with severe COVID-19 illness. We performed a retrospective review of medical records of 162 (61.7 ± 13.5 years) patients with COVID-19. We assessed the predictive accuracy of sCys C for COVID-19 severity by the receiver operating characteristic (ROC) curve analysis. The participants were divided into two groups based on the sCys C cut-off value. We evaluated the association between high sCys C level and the development of severe COVID-19 disease, using a COX proportional hazards regression model. The area under the ROC curve was 0.708 (95% CI 0.594–0.822), the cut-off value was 1.245 (mg/L), and the sensitivity and specificity was 79.1% and 60.7%, respectively. A multivariable Cox analysis showed that a higher level of sCys C (adjusted HR 2.78 95% CI 1.25–6.18, p = 0.012) was significantly associated with an increased risk of developing a severe COVID-19 illness. Patients with a higher sCys C level have an increased risk of severe COVID-19 disease. Our findings suggest that early assessing sCys C could help to identify potential severe COVID-19 patients.

The Rowland Universal Dementia Assessment Scale (RUDAS) and the Folstein MMSE in a multicultural cohort of elderly persons

2006 ◽  
Vol 18 (1) ◽  
pp. 111-120 ◽  
Author(s):  
Jeffrey T. Rowland ◽  
David Basic ◽  
Joella E. Storey ◽  
David A. Conforti
Keyword(s):  
Rating Scale ◽  
Characteristic Curve ◽  
Positive Likelihood Ratio ◽  
Language Education ◽  
Community Dwelling ◽  
Assessment Scale ◽  
Elderly Persons ◽  
Likelihood Ratios ◽  
Clinical Dementia Rating ◽  
And Gender

Objective: To compare the accuracy of the Rowland Universal Dementia Assessment Scale (RUDAS) and the Folstein Mini-mental State Examination (MMSE) for diagnosis of dementia in a multicultural cohort of elderly persons.Methods: A total of 129 community-dwelling persons were selected at random from a database of referrals to an aged-care team. Subjects were stratified according to language background and cognitive diagnosis, and matched for age and gender. The RUDAS and the MMSE were administered to each subject in random order. Within several days, a geriatrician assessed each subject for dementia (DSM-IV criteria) and disease severity (Clinical Dementia Rating Scale). All assessments were carried out independent and blind. The geriatrician also administered the Modified Barthel Index and the Lawton Instrumental Activities of Daily Living Scale, and screened all participants for non-cognitive disorders that might affect instrument scores.Results: The area under the receiver operating characteristic curve (AUC) for the RUDAS [0.92, 95% confidence interval (95%CI) 0.85–0.96] was similar to the AUC for the MMSE (0.91, 95%CI 0.84–0.95). At the published cut-points (RUDAS < 23/30, MMSE < 25/30), the positive and negative likelihood ratios for the RUDAS were 19.4 and 0.2, and for the MMSE 2.1 and 0.14, respectively. The MMSE, but not the RUDAS, scores were influenced by preferred language (p = 0.015), total years of education (p = 0.016) and gender (p = 0.044).Conclusions: The RUDAS is at least as accurate as the MMSE, and does not appear to be influenced by language, education or gender. The high positive likelihood ratio for the RUDAS makes it particularly useful for ruling-in disease.

scholarly journals Modified N Stage of Esophageal Cancer Based on the Evaluation of the Hezode Rate of the Negative and Positive Lymph Node

2020 ◽  
Author(s):  
Jinling Zhang ◽  
Hongyan Li ◽  
Liangjian Zhou ◽  
lianling Yu ◽  
Fengyuan Che ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Lymph Node ◽  
Roc Curve ◽  
Cross Validation ◽  
Predictive Accuracy ◽  
Positive Lymph Node ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Validation Method ◽  
N Stage

Abstract Objective:The study aimed to propose a modified N stage of esophageal cancer (EC) on basis of based on the number of positive lymph node (PLN) and the number of negative lymph node (NLN) simultaneously. Method:Data from 13,491 patients with EC registered in the SEER database were reviewed. The parameters related to prognosis were investigated using a Cox proportional hazards regression model. A modified N stage was proposed based on the cut-off number of the re-adjusted ratio of the number of PLN (numberPLN) to the number of NLN (numberNLN), which derived from the comparison of the hezode rate (HR) of numberPLN and numberNLN. The modified N stage was confirmed using the cross-validation method with the training and validation cohort, and it was also compared to the N stage from the American Joint Committee on Cancer (AJCC) staging system (7th edition) using Receiver Operating Characteristic (ROC) curve analysis.Results:The numberPLN on prognosis was 1.042, while numberNLN was 0.968. The modified N stage was defined as follows: N1 stage: the ratio range was from 0 to 0.21; N2 stage: more than 0.21, but no more than 0.48; N3 stage: more than 0.48. Cross-validation method within the cohort identified the predictive accuracy of this modified N stage, and ROC curve analysis demonstrated the superiority of this modified N stage over that of the AJCC.Conclusion:The modified N stage based on the re-adjusted ratio of numberPLN to numberNLN can evaluate tumor stage more accurately than the traditional N stage.

Screening for cognitive deficits with the Evaluation of Cognitive Processes involved in Disability in Schizophrenia scale

2018 ◽  
Vol 33 (1) ◽  
pp. 113-119 ◽  
Author(s):  
Paul Roux ◽  
Mathieu Urbach ◽  
Sandrine Fonteneau ◽  
Fabrice Berna ◽  
Lore Brunel ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Cognitive Processes ◽  
Roc Curve ◽  
Standardized Test ◽  
Curve Analysis ◽  
Index Test ◽  
Neuropsychological Battery ◽  
Roc Curve Analysis ◽  
Cognitive Domains ◽  
The Mean

Objective: This study aimed to evaluate the validity of the Evaluation of Cognitive Processes involved in Disability in Schizophrenia scale (ECPDS) to discriminate for cognitive impairment in schizophrenia. Design: This multicentre cross-sectional study used a validation design with receiver operating characteristic (ROC) curve analysis. Settings: The study was undertaken in a French network of seven outward referral centres. Subjects: We recruited individuals with clinically stable schizophrenia diagnosed based on the Structured Clinical Interview for assessing Diagnostic and Statistical Manual of Mental Disorders (4th ed., rev.; DSM-IV-R) criteria. Main measures: The index test for cognitive impairment was ECPDS (independent variable), a 13-item scale completed by a relative of the participant. The reference standard was a standardized test battery that evaluated seven cognitive domains. Cognitive impairment was the dependent variable and was defined as an average z-score more than 1 SD below the normative mean in two or more cognitive domains. Results: Overall, 97 patients were included (67 with schizophrenia, 28 with schizoaffective disorder, and 2 with schizophreniform disorder). The mean age was 30.2 (SD 7.7) years, and there were 75 men (77.3%). There were 59 (60.8%) patients with cognitive impairment on the neuropsychological battery, and the mean ECPDS score was 27.3 (SD 7.3). The ROC curve analysis showed that the optimal ECPDS cut-off was 29.5. The area under the curve was 0.77, with 76.3% specificity and 71.1% sensitivity to discriminate against cognitive impairment. Conclusion: The ECPDS is a valid triage tool for detecting cognitive impairment in schizophrenia, before using an extensive neuropsychological battery, and holds promise for use in everyday clinical practice.

scholarly journals The role of peroxisome proliferator-activated receptor gamma and adiponectin in children with Kawasaki disease

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199492
Author(s):  
Ji-Yong Zhang ◽  
Hong Peng ◽  
Si-Tang Gong ◽  
Yong-Mei Zeng ◽  
Miao Huang ◽  
...  
Keyword(s):  
Kawasaki Disease ◽  
Roc Curve ◽  
Predictive Accuracy ◽  
Mrna Level ◽  
Curve Analysis ◽  
Peroxisome Proliferator ◽  
Mrna Levels ◽  
Roc Curve Analysis ◽  
Peroxisome Proliferator Activated Receptor ◽  
Paediatric Patients

Objective To investigate the relationship between peroxisome proliferator-activated receptor gamma (PPARγ) mRNA, serum adiponectin (ADP) and lipids in paediatric patients with Kawasaki disease (KD). Methods This prospective study enrolled paediatric patients with KD and grouped them according to the presence or absence of coronary artery lesions (CAL). A group of healthy age-matched children were recruited as the control group. The levels of PPARγ mRNA, serum ADP and lipids were compared between the groups. Receiver operating characteristic (ROC) curve analysis was undertaken to determine if the PPARγ mRNA level could be used as a predictive biomarker of CAL prognosis. Results The study enrolled 42 patients with KD (18 with CAL [CAL group] and 24 without CAL [NCAL group]) and 20 age-matched controls. PPARγ mRNA levels in patients with KD were significantly higher than those in the controls; but significantly lower in the CAL group than the NCAL group. ROC curve analysis demonstrated that the PPARγ mRNA level provided good predictive accuracy for the prognosis of CAL. There was no association between PPARγ, ADP and lipid levels. Conclusion There was dyslipidaemia in children with KD, but there was no correlation with PPARγ and ADP. PPARγ may be a predictor of CAL in patients with KD with good predictive accuracy.

scholarly journals Identification of Cross-Talk and Pyroptosis-Related Genes Linking Periodontitis and Rheumatoid Arthritis Revealed by Transcriptomic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-29
Author(s):  
Yongbin Jing ◽  
Dong Han ◽  
Chunyang Xi ◽  
Jinglong Yan ◽  
Jinpeng Zhuang
Keyword(s):  
Rheumatoid Arthritis ◽  
Cross Talk ◽  
Roc Curve ◽  
Predictive Accuracy ◽  
Curve Analysis ◽  
Differentially Expressed ◽  
Roc Curve Analysis ◽  
The Core ◽  
The Cross ◽  
Blue Module

Background. The current study is aimed at identifying the cross-talk genes between periodontitis (PD) and rheumatoid arthritis (RA), as well as the potential relationship between cross-talk genes and pyroptosis-related genes. Methods. Datasets for the PD (GSE106090, GSE10334, GSE16134) and RA (GSE55235, GSE55457, GSE77298, and GSE1919) were downloaded from the GEO database. After batch correction and normalization of datasets, differential expression analysis was performed to identify the differentially expressed genes (DEGs). The cross-talk genes linking PD and RA were obtained by overlapping the DEGs dysregulated in PD and DEGs dysregulated in RA. Genes involved in pyroptosis were summarized by reviewing literatures, and the correlation between pyroptosis genes and cross-talk genes was investigated by Pearson correlation coefficient. Furthermore, the weighted gene coexpression network analysis (WGCNA) was carried out to identify the significant modules which contained both cross-talk genes and pyroptosis genes in both PD data and RA data. Thus, the core cross-talk genes were identified from the significant modules. Receiver-operating characteristic (ROC) curve analysis was performed to identify the predictive accuracy of these core cross-talk genes in diagnosing PD and RA. Based on the core cross-talk genes, the experimentally validated protein-protein interaction (PPI) and gene-pathway network were constructed. Results. A total of 40 cross-talk genes were obtained. Most of the pyroptosis genes were not differentially expressed in disease and normal samples. By selecting the modules containing both cross-talk genes or pyroptosis genes, the blue module was identified to be significant module. Three genes, i.e., cross-talk genes (TIMP1, LGALS1) and pyroptosis gene-GPX4, existed in the blue module of PD network, while two genes (i.e., cross-talk gene-VOPP1 and pyroptosis gene-AIM2) existed in the blue module of RA network. ROC curve analysis showed that three genes (TIMP1, VOPP1, and AIM2) had better predictive accuracy in diagnosing disease compared with the other two genes (LGALS1 and GPX4). Conclusions. This study revealed shared mechanisms between RA and PD based on cross-talk and pyroptosis genes, supporting the relationship between the two diseases. Thereby, five modular genes (TIMP1, LGALS1, GPX4, VOPP1, and AIM2) could be of relevance and might serve as potential biomarkers. These findings are a basis for future research in the field.

scholarly journals Identification of a 5-Gene-Based Scoring System by WGCNA and LASSO to Predict Prognosis for Rectal Cancer Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
He Huang ◽  
Shilei Xu ◽  
Aidong Chen ◽  
Fen Li ◽  
Jiezhong Wu ◽  
...  
Keyword(s):  
Risk Score ◽  
Roc Curve ◽  
Predictive Accuracy ◽  
Risk Group ◽  
Survival Curve ◽  
Curve Analysis ◽  
The Cancer Genome Atlas ◽  
Molecular Signature ◽  
Prognostic Signature ◽  
Roc Curve Analysis

Background. Although accumulating evidence suggested that a molecular signature panel may be more effective for the prognosis prediction than routine clinical characteristics, current studies mainly focused on colorectal or colon cancers. No reports specifically focused on the signature panel for rectal cancers (RC). Our present study was aimed at developing a novel prognostic signature panel for RC. Methods. Sequencing (or microarray) data and clinicopathological details of patients with RC were retrieved from The Cancer Genome Atlas (TCGA-READ) or the Gene Expression Omnibus (GSE123390, GSE56699) database. A weighted gene coexpression network was used to identify RC-related modules. The least absolute shrinkage and selection operator analysis was performed to screen the prognostic signature panel. The prognostic performance of the risk score was evaluated by survival curve analyses. Functions of prognostic genes were predicted based on the interaction proteins and the correlation with tumor-infiltrating immune cells. The Human Protein Atlas (HPA) tool was utilized to validate the protein expression levels. Results. A total of 247 differentially expressed genes (DEGs) were commonly identified using TCGA and GSE123390 datasets. Brown and yellow modules (including 77 DEGs) were identified to be preserved for RC. Five DEGs (ASB2, GPR15, PRPH, RNASE7, and TCL1A) in these two modules constituted the optimal prognosis signature panel. Kaplan-Meier curve analysis showed that patients in the high-risk group had a poorer prognosis than those in the low-risk group. Receiver operating characteristic (ROC) curve analysis demonstrated that this risk score had high predictive accuracy for unfavorable prognosis, with the area under the ROC curve of 0.915 and 0.827 for TCGA and GSE56699 datasets, respectively. This five-mRNA classifier was an independent prognostic factor. Its predictive accuracy was also higher than all clinical factor models. A prognostic nomogram was developed by integrating the risk score and clinical factors, which showed the highest prognostic power. ASB2, PRPH, and GPR15/TCL1A were predicted to function by interacting with CASQ2/PDK4/EPHA67, PTN, and CXCL12, respectively. TCL1A and GPR15 influenced the infiltration levels of B cells and dendritic cells, while the expression of PRPH was positively associated with the abundance of macrophages. HPA analysis supported the downregulation of PRPH, RNASE7, CASQ2, EPHA6, and PDK4 in RC compared with normal controls. Conclusion. Our immune-related signature panel may be a promising prognostic indicator for RC.

scholarly journals Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Ilker Unal
Keyword(s):  
Diagnostic Accuracy ◽  
Sensitivity And Specificity ◽  
Roc Curve ◽  
Simulated Data ◽  
Real Data ◽  
Roc Curve Analysis ◽  
New Approach ◽  
Cut Point ◽  
Alternative Approach ◽  
The Difference

ROC curve analysis is often applied to measure the diagnostic accuracy of a biomarker. The analysis results in two gains: diagnostic accuracy of the biomarker and the optimal cut-point value. There are many methods proposed in the literature to obtain the optimal cut-point value. In this study, a new approach, alternative to these methods, is proposed. The proposed approach is based on the value of the area under the ROC curve. This method defines the optimal cut-point value as the value whose sensitivity and specificity are the closest to the value of the area under the ROC curve and the absolute value of the difference between the sensitivity and specificity values is minimum. This approach is very practical. In this study, the results of the proposed method are compared with those of the standard approaches, by using simulated data with different distribution and homogeneity conditions as well as a real data. According to the simulation results, the use of the proposed method is advised for finding the true cut-point.

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