Relationship between tongue pressure and dysphagia diet in patients with acute stroke

A dysphagia diet is important for patients with stroke to help manage their nutritional state and prevent aspiration pneumonia. Tongue pressure measurement is a simple, non-invasive, and objective method for diagnosing dysphagia. We hypothesized that tongue pressure may be useful in making a choice of diet for patients with acute stroke. Using balloon-type equipment, tongue pressure was measured in 80 patients with acute stroke. On admission, a multidisciplinary swallowing team including doctors, nurses, speech therapists, and management dietitians evaluated and decided on the possibility of oral intake and diet form; the tongue pressure was unknown to the team. Diet form was defined and classified as dysphagia diet Codes 0 to 4 and normal form (Code 5 in this study) according to the 2013 Japanese Dysphagia Diet Criteria. In multivariate analysis, only tongue pressure was significantly associated with the dysphagia diet form (p<0.001). Receiver operating characteristic analyses revealed that the optimal cutoff tongue pressure for predicting diet Codes 1, 2, 3, 4, and 5 was 3.6 (p<0.001, area under the curve [AUC] = 0.997), 9.6 (p<0.001, AUC = 0.973), 12.8 (p<0.001, AUC = 0.963), 16.5 (p<0.001, AUC = 0.979), and 17.3 kPa (p<0.001, AUC = 0.982), respectively. Tongue pressure is one of the sensitive indicators for choosing dysphagia diet forms in patients with acute stroke. A combination of simple modalities will increase the accuracy of the swallowing assessment and choice of the diet form.

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The Optimal Cutoff Value of D-dimer Levels to Predict in Hospital Mortality in Severe Cases of Coronavirus Disease 2019

Open Access Macedonian Journal of Medical Sciences ◽

10.3889/oamjms.2021.7600 ◽

2021 ◽

Vol 9 (B) ◽

pp. 1561-1564

Author(s):

Ngakan Ketut Wira Suastika ◽

Ketut Suega

Keyword(s):

Hospital Mortality ◽

Receiver Operating Characteristic Curve ◽

Receiver Operating Characteristic ◽

Operating Characteristic ◽

Characteristic Curve ◽

Area Under The Curve ◽

Optimal Cutoff ◽

Cutoff Value ◽

Operating Characteristic Curve ◽

D Dimer

Introduction: Coronavirus disease 2019 (Covid-19) can cause coagulation parameters abnormalities such as an increase of D-dimer levels especially in severe cases. The purpose of this study is to determine the differences of D-dimer levels in severe cases of Covid-19 who survived and non-survived and determine the optimal cut-off value of D-dimer levels to predict in-hospital mortality. Method: Data were obtained from confirmed Covid-19 patients who were treated from June to September 2020. The Mann-Whitney U test was used to determine differences of D-dimer levels in surviving and non-surviving patients. The optimal cut-off value and area under the curve (AUC) of the D-dimer level in predicting mortality were obtained by the receiver operating characteristic curve (ROC) method. Results: A total of 80 patients were recruited in this study. Levels of D-dimer were significantly higher in non-surviving patients (median 3.346 mg/ml; minimum – maximum: 0.939 – 50.000 mg/ml) compared to surviving patients (median 1.201 mg/ml; minimum – maximum: 0.302 – 29.425 mg/ml), p = 0.012. D-dimer levels higher than 1.500 mg/ml are the optimal cut-off value for predicting mortality in severe cases of Covid-19 with a sensitivity of 80.0%; specificity of 64.3%; and area under the curve of 0.754 (95% CI 0.586 - 0.921; p = 0.010). Conclusions: D-dimer levels can be used as a predictor of mortality in severe cases of Covid-19.

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Baseline NIH Stroke Scale is an inferior predictor of functional outcome in the era of acute stroke intervention

International Journal of Stroke ◽

10.1177/1747493018783759 ◽

2018 ◽

Vol 13 (8) ◽

pp. 806-810 ◽

Cited By ~ 5

Author(s):

Michael E Reznik ◽

Shadi Yaghi ◽

Mahesh V Jayaraman ◽

Ryan A McTaggart ◽

Morgan Hemendinger ◽

...

Keyword(s):

Acute Stroke ◽

Receiver Operating Characteristic Curve ◽

Receiver Operating Characteristic ◽

Functional Outcome ◽

Operating Characteristic ◽

Mechanical Thrombectomy ◽

Characteristic Curve ◽

Area Under The Curve ◽

Curve Analysis ◽

Operating Characteristic Curve

Background and aims Baseline National Institutes of Health Stroke Scale (NIHSS) scores have frequently been used for prognostication after ischemic stroke. With the increasing utilization of acute stroke interventions, we aimed to determine whether baseline NIHSS scores are still able to reliably predict post-stroke functional outcome. Methods We retrospectively analyzed prospectively collected data from a high-volume tertiary-care center. We tested strength of association between NIHSS scores at baseline and 24 h with discharge NIHSS using Spearman correlation, and diagnostic accuracy of NIHSS scores in predicting favorable outcome at three months (defined as modified Rankin Scale 0–2) using receiver operating characteristic curve analysis with area under the curve. Results There were 1183 patients in our cohort, with median baseline NIHSS 8 (IQR 3–17), 24-h NIHSS 4 (IQR 1–11), and discharge NIHSS 2 (IQR 1–8). Correlation with discharge NIHSS was r = 0.60 for baseline NIHSS and r = 0.88 for 24-h NIHSS. Of all patients with follow-up data, 425/1037 (41%) had favorable functional outcome at three months. Receiver operating characteristic curve analysis for predicting favorable outcome showed area under the curve 0.698 (95% CI 0.664–0.732) for baseline NIHSS, 0.800 (95% CI 0.772–0.827) for 24-h NIHSS, and 0.819 (95% CI 0.793–0.845) for discharge NIHSS; 24 h and discharge NIHSS maintained robust predictive accuracy for patients receiving mechanical thrombectomy (AUC 0.846, 95% CI 0.798–0.895; AUC 0.873, 95% CI 0.832–0.914, respectively), while accuracy for baseline NIHSS decreased (AUC 0.635, 95% CI 0.566–0.704). Conclusion Baseline NIHSS scores are inferior to 24 h and discharge scores in predicting post-stroke functional outcomes, especially in patients receiving mechanical thrombectomy.

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Choroidal Neovascularization Screening on OCT-Angiography Choriocapillaris Images by Convolutional Neural Networks

Applied Sciences ◽

10.3390/app11199313 ◽

2021 ◽

Vol 11 (19) ◽

pp. 9313

Author(s):

Kawther Taibouni ◽

Alexandra Miere ◽

Abdourahmane Samake ◽

Eric Souied ◽

Eric Petit ◽

...

Keyword(s):

Choroidal Neovascularization ◽

Operating Characteristic ◽

Area Under The Curve ◽

Developed Countries ◽

Age Related Macular Degeneration ◽

Test Set ◽

Non Invasive ◽

Age Related ◽

Wet Amd

Choroidal Neovascularization (CNV) is the advanced stage of Age-related Macular Degeneration (AMD), which is the leading cause of irreversible visual loss for elder people in developed countries. Optical Coherence Tomography Angiography (OCTA) is a recent non-invasive imaging technique widely used nowadays in diagnosis and follow-up of CNV. In this study, an automatic screening of CNV based on deep learning is performed using OCTA choriocapillaris images. CNV eyes (advanced wet AMD) are diagnosed among healthy eyes (no AMD) and eyes with drusen (intermediate AMD). An OCTA dataset of 1396 images is used to train and evaluate the model. A pre-trained convolutional neural network (CNN) is fine-tuned and validated on 80% of the dataset while the remaining 20% is used independently for predictions. The model can accurately detect CNV on the test set with an accuracy of 89.74%, precision of 0.96 and 0.99 area under the curve of the receiver operating characteristic. A good overall classification accuracy of 88.46% is obtained on a balanced test set. Detailed analysis of misclassified images shows that they are also considered ambiguous images for expert clinicians. This novel CNN-based application is truly a breakthrough to assist clinicians in the challenging task of screening for neovascular complications.

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Abstract TMP91: Prediction of Recurrent Embolism and Major Hemorrhage in Patients With Acute Stroke and Atrial Fibrillation by Risk Stratification Schema: The Alessa Score Study

Stroke ◽

10.1161/str.48.suppl_1.tmp91 ◽

2017 ◽

Vol 48 (suppl_1) ◽

Author(s):

Maurizio Paciaroni ◽

Keyword(s):

Atrial Fibrillation ◽

Logistic Regression ◽

Multivariate Analysis ◽

Acute Stroke ◽

Area Under The Curve ◽

Major Hemorrhage ◽

Ischemic Event ◽

Atrial Enlargement ◽

Recurrent Ischemic Event ◽

Ischemic Events

Background: We aim to derive and validate a score that predicts early ischemic events or major hemorrhage in acute stroke patients with atrial fibrillation (AF). Methods and results: The derivation cohort consisted of 854 patients with acute ischemic stroke and AF (mean age 76.3 y, 46.6% M) included between January 2012 and March 2014. Older age (HR 1.06 for each additional year, p=0.0025) and severe atrial enlargement (HR 2.05, p= 0.027) resulted being predictors for recurrent ischemic event (stroke, TIA, systemic embolism) within 90 days from acute stroke. Small infarct size (≤1.5 cm) was inversely correlated with both severe bleeding (HR 0.39, p=0.03) and recurrent ischemic events (HR 0.44, p=0.01). Considering the magnitude of the effect, we assigned 2 points to age ≥80 y; 1 point to 70-79 y; 1 point to presence of ischemic index lesion >1.5 cm; 1 point to severe atrial enlargement (ALESSA score). An increase in this score was associated with recurrent ischemic event but not major hemorrhage. A logistic regression with the ROC graph procedure (C-statistic) showed an area under the curve of 0.697 (0.632-0.763), p=0.0001 for ischemic outcome event and 0.585 (0.493-0.678), p=0.47 for major hemorrhage. On multivariate analysis, ALESSA >2 was associated with recurrent ischemic event (OR: 2.5, 95% CI 1.4-4.4, p=0.001) but not major hemorrhage (OR: 1.1, 95% CI 0.5-2.4, p=0.9). The validation cohort included 994 patients with acute stroke and AF (mean age 75.8 y, 46.0% M) included between April 2014 and June 2016. Also in this cohort, a higher ALESSA score was associated with recurrent ischemic event but not major hemorrhage. Logistic regression with the ROC graph procedure showed an area under the curve of 0.646 (0.529-0.763), p=0.009 for recurrent ischemic event and 0.407 (0.275-0.540), p=0.14 for major hemorrhage. On multivariate analysis, ALESSA >2 barely lacked being significantly correlated with recurrent ischemic event (OR: 2.07, 95% CI 0.93-4.67, p=0.07). Conclusions: A higher ALESSA score is associated with a higher risk of recurrent ischemic event but not with major hemorrhage. Therefore, patients with acute stroke and AF and an ALESSA score >2 may be candidates for early anticoagulation treatment. Further validations of this schema need to be performed.

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Urinary MicroRNAs as Potential Markers for Non-Invasive Diagnosis of Bladder Cancer

International Journal of Molecular Sciences ◽

10.3390/ijms21113814 ◽

2020 ◽

Vol 21 (11) ◽

pp. 3814 ◽

Cited By ~ 2

Author(s):

Kati Erdmann ◽

Karsten Salomo ◽

Anna Klimova ◽

Ulrike Heberling ◽

Andrea Lohse-Fischer ◽

...

Keyword(s):

Bladder Cancer ◽

Operating Characteristic ◽

Area Under The Curve ◽

High Specificity ◽

Specific Expression ◽

Predictive Values ◽

Non Invasive ◽

Diagnostic Potential ◽

Urinary Levels ◽

Sensitivity Specificity

Currently, voided urine cytology (VUC) serves as the gold standard for the detection of bladder cancer (BCa) in urine. Despite its high specificity, VUC has shortcomings in terms of sensitivity. Therefore, alternative biomarkers are being searched, which might overcome these disadvantages as a useful adjunct to VUC. The aim of this study was to evaluate the diagnostic potential of the urinary levels of selected microRNAs (miRs), which might represent such alternative biomarkers due to their BCa-specific expression. Expression levels of nine BCa-associated microRNAs (miR-21, -96, -125b, -126, -145, -183, -205, -210, -221) were assessed by quantitative PCR in urine sediments from 104 patients with primary BCa and 46 control subjects. Receiver operating characteristic (ROC) curve analyses revealed a diagnostic potential for miR-96, -125b, -126, -145, -183, and -221 with area under the curve (AUC) values between 0.605 and 0.772. The combination of the four best candidates resulted in sensitivity, specificity, positive and negative predictive values (NPV), and accuracy of 73.1%, 95.7%, 97.4%, 61.1%, and 80.0%, respectively. Combined with VUC, sensitivity and NPV could be increased by nearly 8%, each surpassing the performance of VUC alone. The present findings suggested a diagnostic potential of miR-125b, -145, -183, and -221 in combination with VUC for non-invasive detection of BCa in urine.

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Non-invasive assessment of exfoliated kidney cells extracted from urine using multispectral autofluorescence features

Scientific Reports ◽

10.1038/s41598-021-89758-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Saabah B. Mahbub ◽

Long T. Nguyen ◽

Abbas Habibalahi ◽

Jared M. Campbell ◽

Ayad G. Anwer ◽

...

Keyword(s):

Proximal Tubule ◽

Operating Characteristic ◽

Multispectral Imaging ◽

Estimated Glomerular Filtration Rate ◽

Area Under The Curve ◽

Kidney Cells ◽

Proximal Tubule Cells ◽

Non Invasive ◽

Renal Proximal Tubule Cells ◽

Characteristic Area

AbstractOptimally preserved urinary exfoliated renal proximal tubule cells were assessed by multispectral imaging of cell autofluorescence. We demonstrated different multispectral autofluorescence signals in such cells extracted from the urine of patients with healthy or diseased kidneys. Using up to 10 features, we were able to differentiate cells from individuals with heathy kidneys and impaired renal function (indicated by estimated glomerular filtration rate (eGFR) values) with the receiver operating characteristic area under the curve (AUC) of 0.99. Using the same method, we were also able to discriminate such urine cells from patients with and without renal fibrosis on biopsy, where significant differences in multispectral autofluorescence signals (AUC = 0.90) were demonstrated between healthy and diseased patients (p < 0.05). These findings show that multispectral assessment of the cell autofluorescence in urine exfoliated proximal tubule kidney cells has the potential to be developed as a sensitive, non-invasive diagnostic method for CKD.

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Three plasma-based microRNAs as potent diagnostic biomarkers for endometrial cancer

Cancer Biomarkers ◽

10.3233/cbm-200972 ◽

2021 ◽

pp. 1-12

Author(s):

Xingchen Fan ◽

Minmin Cao ◽

Cheng Liu ◽

Cheng Zhang ◽

Chunyu Li ◽

...

Keyword(s):

Endometrial Cancer ◽

Operating Characteristic ◽

Characteristic Curve ◽

External Validation ◽

Diagnostic Value ◽

Diagnostic Biomarkers ◽

Non Invasive ◽

Polymerase Chain ◽

Public Datasets ◽

Plasma Mirnas

BACKGROUND: MicroRNAs (miRNAs), with noticeable stability and unique expression pattern in plasma of patients with various diseases, are powerful non-invasive biomarkers for cancer detection including endometrial cancer (EC). OBJECTIVE: The objective of this study was to identify promising miRNA biomarkers in plasma to assist the clinical screening of EC. METHODS: A total of 93 EC and 79 normal control (NC) plasma samples were analyzed using Quantitative Real-time Polymerase Chain Reaction (qRT-PCR) in this four-stage experiment. The receiver operating characteristic curve (ROC) analysis was conducted to evaluate the diagnostic value. Additionally, the expression features of the identified miRNAs were further explored in tissues and plasma exosomes samples. RESULTS: The expression of miR-142-3p, miR-146a-5p, and miR-151a-5p was significantly overexpressed in the plasma of EC patients compared with NCs. Areas under the ROC curve of the 3-miRNA signature were 0.729, 0.751, and 0.789 for the training, testing, and external validation phases, respectively. The diagnostic performance of the identified signature proved to be stable in the three public datasets and superior to the other miRNA biomarkers in EC diagnosis. Moreover, the expression of miR-151a-5p was significantly elevated in EC plasma exosomes. CONCLUSIONS: A signature consisting of 3 plasma miRNAs was identified and showed potential for the non-invasive diagnosis of EC.

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A [68Ga]Ga-DOTANOC PET/CT Radiomic Model for Non-Invasive Prediction of Tumour Grade in Pancreatic Neuroendocrine Tumours

Diagnostics ◽

10.3390/diagnostics11050870 ◽

2021 ◽

Vol 11 (5) ◽

pp. 870

Author(s):

Alessandro Bevilacqua ◽

Diletta Calabrò ◽

Silvia Malavasi ◽

Claudio Ricci ◽

Riccardo Casadei ◽

...

Keyword(s):

Neuroendocrine Tumour ◽

Area Under The Curve ◽

Tumour Volume ◽

Assessment Method ◽

Second Order ◽

Tumour Grade ◽

Non Invasive ◽

Pet Ct ◽

Median Test ◽

Fdg Pet Ct

Predicting grade 1 (G1) and 2 (G2) primary pancreatic neuroendocrine tumour (panNET) is crucial to foresee panNET clinical behaviour. Fifty-one patients with G1-G2 primary panNET demonstrated by pre-surgical [68Ga]Ga-DOTANOC PET/CT and diagnostic conventional imaging were grouped according to the tumour grade assessment method: histology on the whole excised primary lesion (HS) or biopsy (BS). First-order and second-order radiomic features (RFs) were computed from SUV maps for the whole tumour volume on HS. The RFs showing the lowest p-values and the highest area under the curve (AUC) were selected. Three radiomic models were assessed: A (trained on HS, validated on BS), B (trained on BS, validated on HS), and C (using the cross-validation on the whole dataset). The second-order normalized homogeneity and entropy was the most effective RFs couple predicting G2 and G1. The best performance was achieved by model A (test AUC = 0.90, sensitivity = 0.88, specificity = 0.89), followed by model C (median test AUC = 0.87, sensitivity = 0.83, specificity = 0.82). Model B performed worse. Using HS to train a radiomic model leads to the best prediction, although a “hybrid” (HS+BS) population performs better than biopsy-only. The non-invasive prediction of panNET grading may be especially useful in lesions not amenable to biopsy while [68Ga]Ga-DOTANOC heterogeneity might recommend FDG PET/CT.

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Evaluation of alpha-l-fucosidase for the diagnosis of hepatocellular carcinoma based on meta-analysis

LaboratoriumsMedizin ◽

10.1515/labmed-2019-0152 ◽

2020 ◽

Vol 0 (0) ◽

Cited By ~ 1

Author(s):

Lei Xi ◽

Chunqing Yang

Keyword(s):

Hepatocellular Carcinoma ◽

Operating Characteristic ◽

Meta Analysis ◽

Area Under The Curve ◽

Diagnostic Odds Ratio ◽

Diagnostic Value ◽

Receiver Operating Characteristic Curves ◽

Sensitivity Specificity ◽

Review Manager

AbstractObjectivesThe main aim of the present study was to assess the diagnostic value of alpha-l-fucosidase (AFU) for hepatocellular carcinoma (HCC).MethodsStudies that explored the diagnostic value of AFU in HCC were searched in EMBASE, SCI, and PUBMED. The sensitivity, specificity, and DOR about the accuracy of serum AFU in the diagnosis of HCC were pooled. The methodological quality of each article was evaluated with QUADAS-2 (quality assessment for studies of diagnostic accuracy 2). Receiver operating characteristic curves (ROC) analysis was performed. Statistical analysis was conducted by using Review Manager 5 and Open Meta-analyst.ResultsEighteen studies were selected in this study. The pooled estimates for AFU vs. α-fetoprotein (AFP) in the diagnosis of HCC in 18 studies were as follows: sensitivity of 0.7352 (0.6827, 0.7818) vs. 0.7501 (0.6725, 0.8144), and specificity of 0.7681 (0.6946, 0.8283) vs. 0.8208 (0.7586, 0.8697), diagnostic odds ratio (DOR) of 7.974(5.302, 11.993) vs. 13.401 (8.359, 21.483), area under the curve (AUC) of 0.7968 vs. 0.8451, respectively.ConclusionsAFU is comparable to AFP for the diagnosis of HCC.

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Statistical Assessment of Discrimination Capabilities of a Fractional Calculus Based Image Watermarking System for Gaussian Watermarks

Entropy ◽

10.3390/e23020255 ◽

2021 ◽

Vol 23 (2) ◽

pp. 255

Author(s):

Mario Gonzalez-Lee ◽

Hector Vazquez-Leal ◽

Luis J. Morales-Mendoza ◽

Mariko Nakano-Miyatake ◽

Hector Perez-Meana ◽

...

Keyword(s):

Fractional Calculus ◽

Roc Curve ◽

Gaussian Noise ◽

False Positive ◽

Operating Characteristic ◽

Image Watermarking ◽

Area Under The Curve ◽

Statistical Assessment ◽

Watermarking Scheme ◽

Fractional Equation

In this paper, we explore the advantages of a fractional calculus based watermarking system for detecting Gaussian watermarks. To reach this goal, we selected a typical watermarking scheme and replaced the detection equation set by another set of equations derived from fractional calculus principles; then, we carried out a statistical assessment of the performance of both schemes by analyzing the Receiver Operating Characteristic (ROC) curve and the False Positive Percentage (FPP) when they are used to detect Gaussian watermarks. The results show that the ROC of a fractional equation based scheme has 48.3% more Area Under the Curve (AUC) and a False Positives Percentage median of 0.2% whilst the selected typical watermarking scheme has 3%. In addition, the experimental results suggest that the target applications of fractional schemes for detecting Gaussian watermarks are as a semi-fragile image watermarking systems robust to Gaussian noise.

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