scholarly journals Walking velocity and modified rivermead mobility index as discriminatory measures for functional ambulation classification of chronic stroke patients

2019 ◽  
Vol 39 (02) ◽  
pp. 125-132
Author(s):  
Ji Young Lim ◽  
Seung Heon An ◽  
Dae-Sung Park
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Area Under The Curve ◽  
Chronic Stroke ◽  
Walking Ability ◽  
Stroke Patients ◽  
Mobility Index ◽  
Walking Velocity

Background: The cut-off values of walking velocity and classification of functional mobility both have a role in clinical settings for assessing the walking function of stroke patients and setting rehabilitation goals and treatment plans. Objective: The present study investigated whether the cut-off values of the modified Rivermead Mobility Index (mRMI) and walking velocity accurately differentiated the walking ability of stroke patients according to the modified Functional Ambulation Category (mFAC). Methods: Eighty two chronic stroke patients were included in the study. The comfortable/maximum walking velocities and mRMI were used to measure the mobility outcomes of these patients. To compare the walking velocities and mRMI scores for each mFAC point, one-way analysis of variance and the post-hoc test using Scheffe’s method were performed. The patients were categorized according to gait ability into either [Formula: see text] or mFAC[Formula: see text][Formula: see text][Formula: see text]VI group. The cut-off values for mRMI and walking velocities were calculated using a receiver-operating characteristic curve. The odds ratios of logistic regression analysis (Wald Forward) were analyzed to examine whether the cut-off values of walking velocity and mRMI can be utilized to differentiate functional walking levels. Results: Except for mFACs III and IV, maximum walking velocity differed between mFAC IV and mFAC V [Formula: see text], between mFAC V and mFAC VI [Formula: see text], and between mFAC VI and mFAC VII [Formula: see text]. The cut-off value of mRMI is [Formula: see text] and the area under the curve is 0.87, respectively; the cut-off value for comfortable walking velocity is [Formula: see text][Formula: see text]m/s and the area under the curve is 0.92, respectively; also, the cut-off value for maximum walking velocity is [Formula: see text][Formula: see text]m/s and the area under the curve is 0.97, respectively. In the logistic regression analysis, the maximum walking velocity [Formula: see text][Formula: see text]m/s, [Formula: see text] and mRMI [Formula: see text] scores, [Formula: see text] are able to distinguish [Formula: see text] from mFAC[Formula: see text][Formula: see text][Formula: see text]VI. Conclusion: The cut-off values of maximum walking velocity and mRMI are recommended as useful outcome measures for assessing ambulation levels in chronic stroke patients during rehabilitation.

14-3-3η Protein in Rheumatoid Arthritis: Promising Diagnostic Marker and Independent Risk Factor for Osteoporosis

2020 ◽  
Vol 51 (5) ◽  
pp. 529-539
Author(s):  
Tingting Zeng ◽  
Liming Tan ◽  
Yang Wu ◽  
Jianlin Yu
Keyword(s):  
Rheumatoid Arthritis ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Risk Factor ◽  
Logistic Regression Analysis ◽  
Independent Risk Factor ◽  
Multiple Logistic Regression Analysis ◽  
Area Under The Curve ◽  
Enzyme Linked Immunosorbent Assay ◽  
Disease Monitoring

Abstract Background Early identification and disease monitoring are challenges facing rheumatologists in the management of rheumatoid arthritis (RA). Methods We utilized enzyme-linked immunosorbent assay (ELISA) to determine 14-3-3η and anticyclic citrullinated peptide antibody (anti-CCP) levels, with rheumatoid factor (RF) level detected by rate nephelometry. The diagnostic value of each index was determined via receiver operating characteristic (ROC) curve, and the association between 14-3-3η and osteoporosis was assessed using multiple logistic regression analysis. Results Serum levels of 14-3-3η were 3.26 ng per mL in patients with RA. These levels were helpful in identifying patients with the disease, with the area under the curve (AUC) being 0.879 and 0.853, respectively, from all healthy control individuals and patients with RA. Combining 14-3-3η with RF or anti-CCP increased the diagnostic rate. Logistic regression analysis identified 14-3-3η as an independent risk factor for RA-related osteoporosis (odds ratio [OR], 1.503; 95% confidence interval [CI], 1.116–2.025; P <.01). Conclusions Serum 14-3-3η detection by itself or combined with other serum indices was helpful in differentiating patients with RA. Also, it was a promising biomarker for disease monitoring in RA.

Abstract TP176: Risk Factors and Clinical Significances of the Quantitative CTA Vascular Calcification in Ischemic Stroke Patients

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Takashi Shimoyama ◽  
Sibaji Gaj ◽  
Kunio Nakamura ◽  
Shivakrishna Kovi ◽  
Ken Uchino
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Vascular Calcification ◽  
Logistic Regression Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Stroke Patients ◽  
Age Category

Background and Purpose: Intracranial arterial calcification is a marker of atherosclerosis burden in the general population. The aim of the study is to investigate risk factor profiles of vascular calcification in ischemic stroke patients. Methods: We identified ischemic stroke patients who underwent complete CTA from a prospective single-hospital stroke registry in 2018. Automatic artery and calcification segmentation method measured calcification volumes in the intracranial, extracranial, and aortic arteries using deep-learning U-net model and region-grow algorithms. Severe vascular calcification was defined as patients in the upper quartile calcification volume. The prevalence of severe vascular calcification and mean calcification volume were investigated by age category (<60 years, 60-70 years, 70-80 years, 80 years ≥). The relation between each potential risk factors and severe vascular calcification was assessed using the multivariate logistic regression analysis adjusted for age, sex, NIHSS score, and TOAST stroke subtypes. Results: Of the 558 consecutive acute ischemic stroke patients, 388 patients (212 males; mean age 66.6±14.2 years) met inclusion and with quantitative CTA calcification. The prevalence of severe vascular calcification (CTA calcification volume> 812 mm 3 ) increased with increasing age category (<60 years: 6.8% (7/103), 60-70 years: 15.7% (18/115), 70-80 years: 39.6% (38/105), 80 years ≥: 45.9% (34/74), P<0.001 for χ 2 test). Over age 80 years subsets had significantly higher mean calcification volume with 1213 mm 3 than other age category (<60 years: 225 mm 3 , P<0.001; 60-70 years: 462 mm 3 , P<0.001; 70-79 years: 817 mm 3 , P=0.020 for t-test). In the multivariate logistic regression analysis, age (OR 1.096, 95% CI 1.066-1.128, P<0.001), smoking (OR 3.430, 95% CI 1.833-6.419, P<0.001), and large artery atherosclerosis (LAA) (OR 4.260, 95% CI 1.963-9.247, P<0.001) were independently associated with severe vascular calcification. Conclusion: In the quantitative CTA analysis of calcification volume, older age and smoking were high risk for severe atherosclerotic calcium burden in ischemic stroke patients. Moreover, severe vascular calcification may differentiate LAA from other stroke etiology.

Abstract 189: ADAMTS13 Activity Predicts Response to Thrombolysis in the Acute Stroke Setting

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Alejandro Bustamante ◽  
Victor Llombart ◽  
Cristina Boada ◽  
Anna Penalba ◽  
Alba Simats ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Acute Stroke ◽  
Logistic Regression Analysis ◽  
Arterial Occlusion ◽  
Adamts13 Activity ◽  
Stroke Patients ◽  
Vessel Patency ◽  
A Disintegrin And Metalloproteinase ◽  
Von Willebrand

INTRODUCTION: Biological markers predicting tPA response in acute stroke could be used for dose adjustments or early selection of patients for endovascular procedures. ADAMTS13 (A Disintegrin And Metalloproteinase with a ThromboSpondin type 1 motif, member 13) inactivates Von Willebrand Factor by cleaving it, and its deficiency may generate prothrombotic diseases such as thrombotic thrombocytopenic purpura. We aimed to analyze ADAMTS13 activity in acute stroke patients and its relation to vessel patency among those treated with intravenous tPA. METHODS: Acute ischemic stroke patients (n=104) with documented arterial occlusion by transcranial Doppler (TCD), who received tPA within the first 4.5 hours after symptoms onset were recruited and compared with 38 age-matched healthy subjects. Samples were collected at baseline, before thrombolytic treatment, and ADAMTS13 activity was measured by ELISA and expressed as %. A temporal profile of ADAMTS13 activity was determined at 24 hours and 3 months in a subset of 10 patients. Recanalization was assessed 2 hours after tPA bolus by TCD, using thrombolysis in brain ischemia (TIBI) flow grading system. Logistic regression analysis was conducted to determine independent predictors of 2-hour recanalization in patients with proximal arterial occlusions. RESULTS: ADAMTS13 activity was consistently lower in stroke patients than in healthy controls (p<0.001), and remained lower at 24 hours and 3 months. For those patients who presented arterial recanalization at 2 hours, higher baseline ADAMTS13 activity (p=0.032) was noted. In logistic regression analysis from 72 patients with proximal MCA occlusion, ADAMTS13 activity >74.72% was an independent predictor of recanalization [OR=5.148 (1.463-18.111), p=0.011], together with early ischemic signs at baseline neuroimaging [OR=0.065 (0.006-0.712), p=0.025] and OCSP classification (TACI vs. PACI) [OR=0.072 (0.011-0.45), p=0.005]. CONCLUSIONS: In stroke patients treated with tPA, ADAMTS13 activity may be used to monitor the treatment as well as to make decisions regarding more aggressive reperfusion therapies when the absence of a response to intravenous tPA is anticipated.

Abstract TP200: Urinary Albumin/creatinine Ratio Should Be Associated With White Matter Lesion Severity in First-ever Stroke Patients

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Satoshi Suda ◽  
Takashi Shimoyama ◽  
Yohei Takayama ◽  
Takahiro Ouchi ◽  
Masafumi Arakawa ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
White Matter ◽  
Logistic Regression Analysis ◽  
White Matter Lesion ◽  
Urinary Albumin ◽  
Small Vessel ◽  
Creatinine Ratio ◽  
Stroke Patients ◽  
Albumin Creatinine Ratio

Background and purpose: White matter lesion (WML) is an indicator of small vessel disease, however, the underlying pathological mechanisms has not been fully understood. In recent years, experimental and epidemiological studies have suggested that chronic kidney disease (CKD) is associated with endothelial dysfunction; thereby, a CKD state may initiate small vessel damage. Our aim was to investigate the association of estimated glomerular filtration rate (eGFR), urinary albumin/creatinine ratio (UACR), and WML in first-ever stroke patients. Methods: We retrospectively enrolled 284 consecutive patients (177 male; median age 72 years) admitted to our stroke center between May 2010 and January 2012. eGFR and UACR measurements were performed on admission. WML were assessed using the Fazekas classification. Severe WML was defined as Fazekas gradings of 2 or higher. The impact of the eGFR and UACR on WML was evaluated using multiple logistic regression analysis. Separate analyses were conducted according to severe WML and trichotomized eGFR level [60 mL/min ≤ eGFR (reference), 45 mL/min ≤ eGFR < 60 mL/min, and eGFR < 45 mL/min)] and UACR level [UACR < 30.0 mg/g creatinine (reference), 30.0 mg/g creatinine ≤ UACR < 300 mg/g creatinine, and 300 mg/g creatinine ≤ UACR]. Results: According to the Fazekas gradings, 91 patients (32.0%) had scale 0; 90 patients (31.7%), scale 1; 59 patients (20.8%), stage 2; and 44 patients (15.5%), scale 3. Age ( P < 0.0001), sex ( P = 0.0094), eGFR ( P = 0.0173), UACR ( P = 0.0001), hypertension ( P = 0.0436), and brain natriuretic peptide ( P = 0.0354) were significantly associated with severe WML. On multivariable logistic regression analysis, high UACR (≥ 39.6 mg/g creatinine), but not low eGFR (≤ 74 mL/min/1.73 m 2 ), was independently associated with severe WML. In comparisons between trichotomized UACR level, severe WML were more frequent in UACR ≥ 300 mg/g creatinine group than in UACR < 30.0 mg/g creatinine group after multivariate adjustment (OR, 2.25; 95% CI, 1.04-5.00; P = 0.039). On the other hand, there was no significant association with trichotomized eGFR level and severe WML (OR, 1.51; 95% CI, 0.62-3.77; P = 0.3672). Conclusions: Our data suggest that a high UACR, but not eGFR, is independently associated with severe WML.

scholarly journals Red Blood Cell Distribution Width as a Predictive Marker for Coronary Artery Lesions in Patients with Kawasaki Disease

2021 ◽  
Author(s):  
Li Ming ◽  
Hui-ling Cao ◽  
Qiushu Li ◽  
Gengsheng Yu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Area Under The Curve ◽  
Predictive Marker ◽  
Cell Distribution ◽  
Multivariate Logistic Regression ◽  
Distribution Width ◽  
Independent Marker

AbstractThis study aimed to investigate the association between red blood cell distribution width (RDW) and the risk of coronary artery lesions (CALs) in patients with Kawasaki disease (KD). A total of 1355 patients who met the diagnostic criteria for KD were reviewed between January 2018 and December 2019, including 636 patients with CALs and 719 patients without CALs. Blood samples for RDW were obtained at admission (before intravenous immunoglobulin treatment). A logistic regression analysis was performed, and a receiver operating characteristic curve was constructed to determine the prognostic value of RDW standard deviation (RDW-SD) and RDW coefficient of variation (RDW-CV). The study was registered at www.chictr.org.cn, No.: ChiCTR 2000040980. The results showed that RDW-SD increased in patients with complete KD and CALs compared with patients with complete KD without CALs (39 fL vs. 38 fL, respectively; p = 0.000). RDW-CV in patients with complete KD and CALs was significantly higher compared with patients with completed KD without CALs (p = 0.000). Further multivariate logistic regression analysis revealed that RDW-SD was an independent marker of CALs in patients with complete KD (p = 0.001), but no association was found between RDW-CV and CALs. The area under the curve of RDW-SD for predicting CALs in patients with complete KD was 0.606 (95% confidence interval 0.572–0.640; p = 0.000) with a sensitivity and specificity of 61% and 55%, respectively, when the optimal cut-off value of RDW-SD was 38.5 fL. RDW-CV increased in patients with incomplete KD and CALs compared with patients without CALs (13.55% vs 13.3%, respectively; p = 0.004), and multivariate logistic regression analysis revealed that RDW-CV was an independent marker of CALs in patients with incomplete KD (p = 0.021). The area under the curve of RDW-CV for predicting CALs in patients with incomplete KD was 0.597 (95% confidence interval 0.532–0.661; p = 0.004) with a sensitivity and specificity of 40% and 77%, respectively, when the optimal cut-off value of RDW-SD was 13.85%. Conclusion: RDW can be used as an independent predictive marker of CALs in patients with KD, but the type of KD should be considered. RDW-SD was an independent marker of CALs in patients with complete KD, while RDW-CV was a predictor of incomplete KD.

scholarly journals Perbandingan Analisis Diskriminan dan Analisis Regresi Logistik Ordinal dalam Prediksi Klasifikasi Kondisi Kesehatan Bank

2017 ◽  
Vol 7 (2) ◽  
pp. 92
Author(s):  
Fajri Zufa ◽  
Sigit Nugroho ◽  
Mudin Simanihuruk
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Discriminant Analysis ◽  
Logistic Regression Analysis ◽  
Prediction Accuracy ◽  
Secondary Data ◽  
Ordinal Logistic Regression ◽  
Capital Adequacy ◽  
Ordinal Logistic Regression Analysis

The purpose of this research is to compare the accuracy of bank classification prediction based on Capital Adequacy Ratio (CAR), Earning Asset Quality (EAQ), Non Performing Loan (NPL), Return on Assets (ROA), Net Interest Margin (NIM), Short Term Mismatch (STM) and Loan to Deposit Ratio (LDR). Discriminant analysis and ordinal logistic regression analysis are compared in classifying the prediction. The data used are secondary data, namely data classification of bank conditions in Indonesia in 2014 obtained from research institute PT Infovesta Utama. Based on Apparent Error Rate (APER) score obtained, it can be said that discriminant analysis is better in predicting the classification of bank conditions in Indonesia than that of ordinal logistic regression analysis. Discriminant analysis has the average prediction accuracy of 80%, while ordinal logistic regression analysis has the average prediction accuracy of 74,38%.

scholarly journals Novel Method of Classification in Knee Osteoarthritis: Machine Learning Application Versus Logistic Regression Model

2020 ◽  
Vol 44 (6) ◽  
pp. 415-427
Author(s):  
Jung Ho Yang ◽  
Jae Hyeon Park ◽  
Seong-Ho Jang ◽  
Jaesung Cho
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Knee Osteoarthritis ◽  
Logistic Regression Analysis ◽  
Support Vector ◽  
Svm Classifier ◽  
Machine Learning Method ◽  
Learning Method

Objective To present new classification methods of knee osteoarthritis (KOA) using machine learning and compare its performance with conventional statistical methods as classification techniques using machine learning have recently been developed.Methods A total of 84 KOA patients and 97 normal participants were recruited. KOA patients were clustered into three groups according to the Kellgren-Lawrence (K-L) grading system. All subjects completed gait trials under the same experimental conditions. Machine learning-based classification using the support vector machine (SVM) classifier was performed to classify KOA patients and the severity of KOA. Logistic regression analysis was also performed to compare the results in classifying KOA patients with machine learning method.Results In the classification between KOA patients and normal subjects, the accuracy of classification was higher in machine learning method than in logistic regression analysis. In the classification of KOA severity, accuracy was enhanced through the feature selection process in the machine learning method. The most significant gait feature for classification was flexion and extension of the knee in the swing phase in the machine learning method.Conclusion The machine learning method is thought to be a new approach to complement conventional logistic regression analysis in the classification of KOA patients. It can be clinically used for diagnosis and gait correction of KOA patients.

Abstract P715: Vertebral Artery Occlusive Dissection Score in Acute Ischemic Stroke

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Hajime Ikenouchi ◽  
Sohei Yoshimura ◽  
Kaori Miwa ◽  
Tetsuya Chiba ◽  
Satoshi Hosoki ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Magnetic Resonance ◽  
Vertebral Artery ◽  
Logistic Regression Analysis ◽  
Area Under The Curve ◽  
Receiver Operating Curve ◽  
Discriminative Performance ◽  
Stroke Registry

Background and purpose: The discrimination of vertebral artery (VA) occlusive dissection from other etiologies is critical for acute stroke management but sometimes difficult. We aimed to assess the factors associated with VA occlusive dissection and develop the discrimination score. Methods: We examined consecutive patients with acute posterior ischemic stroke due to unilateral VA occlusion from our prospective stroke registry between 2012 and 2019. Unilateral VA occlusion was confirmed by magnetic resonance angiography and cervical ultrasonography. The diagnosis of VA dissection was based on the magnetic resonance imaging or digital subtraction angiography. Dissection score was developed from associated factors to discriminate VA occlusive dissection by logistic regression analysis. Discriminative performance was analyzed by receiver operating curve (ROC) analysis. Results: Consecutive 84 patients (70±13 years; male, 77%) involved 16 (19%) with VA occlusive dissection. On logistic regression analysis, each of younger age ( ≤ 70 years), absence of hypertension, absence of dyslipidemia, head or neck pain, medullary infarction and non-dominance side VA occlusion were significantly associated with VA occlusive dissection (Table). Dissection score was created with these factors by assigning respective points based on the corresponding regression coefficients, and the score were ranged from 0 to 9 (Table). High discriminative performance for VA occlusive dissection was observed (area under the curve: 0.91) and optimal cut-off value was 5 or more (accuracy, 79%; sensitivity, 94%; specificity, 75%). Conclusions: In patients with acute posterior ischemic stroke due to unilateral VA occlusion, dissection score had high discriminative performance for diagnosing VA dissection.

Sign in / Sign up

Export Citation Format

Share Document