Predictive value of relative fat mass algorithm for incident hypertension: a 6-year prospective study in Chinese population

ObjectivesIndividuals with obesity especially excessive visceral adiposity have high risk for incident hypertension. Recently, a new algorithm named relative fat mass (RFM) was introduced to define obesity. Our aim was to investigate whether it can predict hypertension in Chinese population and to compare its predictive power with traditional indices including body mass index (BMI), waist circumference (WC) and waist-to-height ratio (WHtR).DesignA 6-year prospective study.SettingNine provinces (Hei Long Jiang, Liao Ning, Jiang Su, Shan Dong, He Nan, Hu Bei, Hu Nan, Guang Xi and Gui Zhou) in China.ParticipantsThose without hypertension in 2009 survey and respond in 2015 survey.InterventionLogistic regression were performed to investigate the association between RFM and incident hypertension. Receiver operating characteristic (ROC) analysis was performed to compare the predictive ability of these indices and define their optimal cut-off values.Main outcome measuresIncident hypertension in 2015.ResultsThe prevalence of incident hypertension in 2015 based on RFM quartiles were 14.8%, 21.2%, 26.8% and 35.2%, respectively (p for trend <0.001). In overall population, the OR for the highest quartile compared with the lowest quartile for RFM was 2.032 (1.567–2.634) in the fully adjusted model. In ROC analysis, RFM and WHtR had the highest area under the curve (AUC) value in both sexes but did not show statistical significance when compared with AUC value of BMI and WC in men and AUC value of WC in women. The performance of the prediction model based on RFM was comparable to that of BMI, WC or WHtR.ConclusionsRFM can be a powerful indictor for predicting incident hypertension in Chinese population, but it does not show superiority over BMI, WC and WHtR in predictive power.

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A Rationale for Using Sonoelastography in Thyroid Nodular Pathology

Journal of radiology and nuclear medicine ◽

10.20862/0042-4676-2019-100-5-242-246 ◽

2019 ◽

Vol 100 (5) ◽

pp. 242-246

Author(s):

L. A. Timofeeva ◽

L. B. Shubin

Keyword(s):

Thyroid Cancer ◽

Roc Analysis ◽

Thyroid Nodules ◽

Area Under The Curve ◽

Predictive Ability ◽

Tissue Elasticity ◽

Stromal Component ◽

Collagen Level ◽

Probable Nature ◽

Benign Nodules

Objective. To provide a rationale for using sonoelastography (SEG) in the differential diagnosis of thyroid cancer (TC).Material and methods. Thirty patients with thyroid nodules of various morphological structures were examined. The authors studied the data of SEG and immunohistochemistry (IHC) with monoclonal antibodies against types III and IV collagen (they evaluated the degree of the expressed collagen fibers). Analysis of variance, ROC analysis, and logistic regression were used (by comparing with the expression of collagens) to assess the predictive ability of ultrasound.Results. The study showed that irregular and uneven contours, microcalcifications, and “the height greater than the width” were most significant among the ultrasound signs in the diagnosis of TC. Cool colors prevailed when performing SEG in the pattern of thyroid cancer. Purple-blue hues were predominantly recorded (p<0.05 with regard to benign nodules), green ones were less frequently. ROC analysis of compression elastography showed that the area under the curve was 0.785 (95% CI 0.740-0.826), sensitivity 78.1%, specificity 79.0%. Comparison of the data of IHC and SEG revealed a direct correlation of tissue elasticity with the degree of a stromal component and with the presence of collagen-containing structures.Conclusion. SEG may suppose the probable nature of thyroid nodules on the basis of their morphological features. The low degree of the stromal component and the low content of types III and IV collagen make follicular colloid goiter and adenoma soft, which is recorded at SEG. TC is characterized by a high collagen level attributable to the characteristics of the metabolism of cancer cells, which makes them solid in the mode of SEG.

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Identification of a Predictive Biomarker for Hematologic Toxicities of Gemcitabine

Journal of Clinical Oncology ◽

10.1200/jco.2008.19.9745 ◽

2009 ◽

Vol 27 (13) ◽

pp. 2261-2268 ◽

Cited By ~ 34

Author(s):

Junichi Matsubara ◽

Masaya Ono ◽

Ayako Negishi ◽

Hideki Ueno ◽

Takuji Okusaka ◽

...

Keyword(s):

Pancreatic Cancer ◽

Adverse Events ◽

Statistical Significance ◽

Advanced Pancreatic Cancer ◽

Area Under The Curve ◽

Predictive Ability ◽

Predictive Biomarker ◽

Severe Neutropenia ◽

Current Standard ◽

Grade 3

PurposeGemcitabine monotherapy is the current standard for patients with advanced pancreatic cancer, but the occurrence of severe neutropenia and thrombocytopenia can sometimes be life threatening. This study aimed to discover a new diagnostic method for predicting the hematologic toxicities of gemcitabine.Patients and MethodsUsing quantitative mass spectrometry (MS), we compared the baseline plasma proteomes of 25 patients who had developed severe hematologic adverse events (grade 3 to 4 neutropenia and/or grade 2 to 4 thrombocytopenia) within the first two cycles of gemcitabine with those of 22 patients who had not (grade 0).ResultsWe identified 757 peptide peaks whose intensities were significantly different (P < .001, Welch t test) among a total of 60,888. The MS peak with the highest statistical significance (P = .0000282) was revealed to be derived from haptoglobin by tandem MS. A scoring system (nomogram) based on the values of haptoglobin, haptoglobin phenotype, neutrophil count, platelet count, and body-surface area was constructed to estimate the risk of hematologic adverse events (grade 3 to 4 neutropenia and/or grade 2 to 4 thrombocytopenia) with an area under curve value of 0.782 in a cohort of 166 patients with pancreatic cancer. Predictive ability of the system was confirmed in two independent validation cohorts consisting of 87 and 52 patients with area under the curve values of 0.655 and 0.747, respectively.ConclusionAlthough the precise mechanism responsible for the correlation of haptoglobin with the future onset of hematologic toxicities remains to be clarified, our prediction model seems to have high practical utility for tailoring the treatment of patients receiving gemcitabine.

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Receiver operating characteristic (ROC) movies, universal ROC (UROC) curves, and coefficient of predictive ability (CPA)

Machine Learning ◽

10.1007/s10994-021-06114-3 ◽

2021 ◽

Author(s):

Tilmann Gneiting ◽

Eva-Maria Walz

Keyword(s):

Receiver Operating Characteristic ◽

Roc Analysis ◽

Operating Characteristic ◽

Numerical Models ◽

Binary Classification ◽

Weather Prediction ◽

Area Under The Curve ◽

Predictive Ability ◽

Classification Problems ◽

Receiver Operating

AbstractThroughout science and technology, receiver operating characteristic (ROC) curves and associated area under the curve ($$\mathrm{AUC}$$ AUC ) measures constitute powerful tools for assessing the predictive abilities of features, markers and tests in binary classification problems. Despite its immense popularity, ROC analysis has been subject to a fundamental restriction, in that it applies to dichotomous (yes or no) outcomes only. Here we introduce ROC movies and universal ROC (UROC) curves that apply to just any linearly ordered outcome, along with an associated coefficient of predictive ability ($${\mathrm{CPA}}$$ CPA ) measure. $${\mathrm{CPA}}$$ CPA equals the area under the UROC curve, and admits appealing interpretations in terms of probabilities and rank based covariances. For binary outcomes $${\mathrm{CPA}}$$ CPA equals $$\mathrm{AUC}$$ AUC , and for pairwise distinct outcomes $${\mathrm{CPA}}$$ CPA relates linearly to Spearman’s coefficient, in the same way that the C index relates linearly to Kendall’s coefficient. ROC movies, UROC curves, and $${\mathrm{CPA}}$$ CPA nest and generalize the tools of classical ROC analysis, and are bound to supersede them in a wealth of applications. Their usage is illustrated in data examples from biomedicine and meteorology, where rank based measures yield new insights in the WeatherBench comparison of the predictive performance of convolutional neural networks and physical-numerical models for weather prediction.

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An attempt to construct the individual model of daily facial skin temperature using variational autoencoder

Artificial Life and Robotics ◽

10.1007/s10015-021-00699-7 ◽

2021 ◽

Author(s):

Ayaka Masaki ◽

Kent Nagumo ◽

Yuki Iwashita ◽

Kosuke Oiwa ◽

Akio Nozawa

Keyword(s):

Skin Temperature ◽

Normal State ◽

Roc Analysis ◽

Daily Life ◽

Area Under The Curve ◽

Facial Skin ◽

Individual Model ◽

Variational Autoencoder ◽

Auc Value ◽

The Individual

AbstractFacial skin temperature (FST) has also gained prominence as an indicator for detecting anomalies such as fever due to the COVID-19. When FST is used for engineering applications, it is enough to be able to recognize normal. We are also focusing on research to detect some anomaly in FST. In a previous study, it was confirmed that abnormal and normal conditions could be separated based on FST by using a variational autoencoder (VAE), a deep generative model. However, the simulations so far have been a far cry from reality. In this study, normal FST with a diurnal variation component was defined as a normal state, and a model of normal FST in daily life was individually reconstructed using VAE. Using the constructed model, the anomaly detection performance was evaluated by applying the Hotelling theory. As a result, the area under the curve (AUC) value in ROC analysis was confirmed to be 0.89 to 1.00 in two subjects.

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High Neutrophil Percentage-To-Albumin Ratio Can Predict Occurrence of Stroke-Associated Infection

Frontiers in Neurology ◽

10.3389/fneur.2021.705790 ◽

2021 ◽

Vol 12 ◽

Author(s):

Haipeng Zhang ◽

Ti Wu ◽

Xiaolin Tian ◽

Panpan Lyu ◽

Jianfei Wang ◽

...

Keyword(s):

Statistical Significance ◽

Multivariable Analysis ◽

Area Under The Curve ◽

Adverse Outcomes ◽

Albumin Ratio ◽

Neutrophil Percentage ◽

Increased Risk ◽

Auc Value ◽

Medical University ◽

The Relationship

Purpose: Stroke-associated infection (SAI) is associated with adverse outcomes in patients with acute ischemic stroke (AIS). In this study, we aimed to evaluate the association between neutrophil percentage-to-albumin ratio (NPAR) and SAI occurrence in patients with AIS.Methods: We retrospectively analyzed all AIS patients who were admitted to the Neurology ward of The Second Hospital of Tianjin Medical University from November 2018 to October 2020. The relationship between NPAR and SAI was analyzed by multivariable analysis. The receiver operating characteristic (ROC) curve was used to compare the predicted value of albumin, neutrophil percentage, neutrophil-to-lymphocyte ratio (NLR), and NPAR.Results: We included 379 AIS patients out of which 51 (13.5%) developed SAI. The NPAR was independently associated with increased risk of SAI adjusting for confounders [adjusted odds ratio (aOR) = 10.52; 95% confidence interval (CI), 3.33–33.28; P <0.001]. The optimal cutoff value of NPAR for predicting SAI incidence was 1.64, with sensitivity and specificity of 90.2 and 55.8%, respectively. The area under the curve (AUC) value of NPAR [0.771 (0.725–0.812)] was higher than that of albumin [0.640 (0.590–0.689)], neutrophil percentage [0.747 (0.700–0.790)], and NLR [0.736 (0.689–0.780)], though the statistical significance appeared only between NPAR and albumin.Conclusions: We demonstrated that a higher NPAR could predict the occurrence of SAI. Thus, NPAR might be a more effective biomarker to predict SAI compared with albumin, neutrophil percentage, and NLR.

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Accuracy of motor tests related to musculoskeletal fitness as predictors of low back pain in adolescents: a pilot study

Manual Therapy Posturology & Rehabilitation Journal ◽

10.17784/mtprehabjournal.2017.15.542 ◽

2020 ◽

pp. 1-7

Author(s):

Raphael Gonçalves de Oliveira ◽

Rodrigo Franco de Oliveira ◽

Anne Karoline Remonte ◽

Sandy Caroline Garcia ◽

Cláudia Roberta Brunnquell Sczepanski ◽

...

Keyword(s):

Low Back Pain ◽

Back Pain ◽

Statistical Significance ◽

Adult Population ◽

Area Under The Curve ◽

Predictive Ability ◽

Low Back ◽

Male Adolescents ◽

Motor Tests ◽

The One

Background: The prevalence of low back pain in adolescents is close to that found in the adult population. In view of the relationship between low back pain and the resistance of the spine stabilizing muscles, studies have sought to identify the ability of specific motor tests to predict this condition. Objectives: Our objective was to verify the predictive ability of three motor tests related to musculoskeletal fitness to identify adolescents with low back pain. Methods: The sample consisted of 150 adolescents, of both sexes, aged between 15 and 19 years. The Nordic questionnaire validated for Brazilian adolescents was applied to identify those with low back pain. Subsequently, three motor tests (one-minute sit-up test, Sorensen test and lateral plank test) were applied. Statistical analysis involved the ROC curve, to identify the Area Under the Curve (AUC), assuming a 95% confidence interval. Results: It was observed in males that all the tests had a low AUC (between 0.56 and 0.57), without statistical significance (p > 0.05). For females, AUC ranged from 0.62 to 0.66, with statistical significance (p < 0.05) for the cut-off points identified in the one-minute sit-up test (≤ 24 repetitions) and Sorensen test (≤ 28 seconds), however, without significance (p > 0.05) for the lateral plank test. Conclusion: Motor tests of abdominal and paravertebral muscle resistance were not predictors of low back pain in male adolescents. Despite the low accuracy, the cut-off points identified in the one-minute sit-up test and Sorensen test, can be used with some caution to predict low back pain in female adolescents.

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Lithology Discrimination Using Sentinel-1 Dual-Pol Data and SRTM Data

Remote Sensing ◽

10.3390/rs13071280 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1280

Author(s):

Yi Lu ◽

Changbao Yang ◽

Zhiguo Meng

Keyword(s):

Area Under The Curve ◽

Remote Sensing Data ◽

Predictive Ability ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Optical Remote Sensing ◽

Operating Characteristics ◽

Auc Value ◽

Confusion Matrices

Compared to various optical remote sensing data, studies on the performance of dual-pol Synthetic aperture radar (SAR) on lithology discrimination are scarce. This study aimed at using Sentinel-1 data to distinguish dolomite, andesite, limestone, sandstone, and granite rock types. The backscatter coefficients VV and VH, the ratio VV–VH; the decomposition parameters Entropy, Anisotropy, and Alpha were firstly derived and the Kruskal–Wallis rank sum test was then applied to these polarimetric derived matrices to assess the significance of statistical differences among different rocks. Further, the corresponding gray-level co-occurrence matrices (GLCM) features were calculated. To reduce the redundancy and data dimension, the principal component analysis (PCA) was carried out on the GLCM features. Due to the limited rock samples, before the lithology discrimination, the input variables were selected. Several classifiers were then used for lithology discrimination. The discrimination models were evaluated by overall accuracy, confusion matrices, and the area under the curve-receiver operating characteristics (AUC-ROC). Results show that (1) the statistical differences of the polarimetric derived matrices (backscatter coefficients, ratio, and decomposition parameters) among different rocks was insignificant; (2) texture information derived from Sentinel-1 had great potential for lithology discrimination; (3) partial least square discrimination analysis (PLSDA) had the highest overall accuracy (0.444) among the classification models; (4) though the overall accuracy is unsatisfactory, according to the AUC-ROC and confusion matrices, the predictive ability of PLSDA model for limestone is high with an AUC value of 0.8017, followed by dolomite with an AUC value of 0.7204. From the results, we suggest that the dual-pol Sentinel-1 data are able to correctly distinguish specific rocks and has the potential to capture the variation of different rocks.

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Coated-platelets predict stroke at 30 days following TIA

Neurology ◽

10.1212/wnl.0000000000004090 ◽

2017 ◽

Vol 89 (2) ◽

pp. 125-128 ◽

Cited By ~ 7

Author(s):

Angelia C. Kirkpatrick ◽

Andrea S. Vincent ◽

George L. Dale ◽

Calin I. Prodan

Keyword(s):

Confidence Interval ◽

Positive Predictive Value ◽

Predictive Value ◽

Roc Analysis ◽

Area Under The Curve ◽

Predictive Ability ◽

Receiver Operator Characteristic ◽

Agonist Stimulation ◽

Abcd2 Score ◽

Dual Agonist

Objective:To examine the potential for coated-platelets, a subset of highly procoagulant platelets observed on dual agonist stimulation with collagen and thrombin, for predicting stroke at 30 days in patients with TIA.Methods:Consecutive patients with TIA were enrolled and followed up prospectively. ABCD2 scores were obtained for each patient. Coated-platelet levels, reported as percent of cells converted to coated-platelets, were determined at baseline. The primary endpoint was the occurrence of stroke at 30 days. Receiver operator characteristic (ROC) analysis was used to calculate area under the curve (AUC) values for a model including coated-platelets to predict incident stroke at 30 days.Results:A total of 171 patients with TIA were enrolled, and 10 strokes were observed at 30 days. A cutoff of 51.1% for coated-platelet levels yielded a sensitivity of 0.80 (95% confidence interval [CI] 0.55–1.0), specificity of 0.73 (95% CI 0.66–0.80), positive predictive value of 0.16 (95% CI 0.06–0.26), and negative predictive value of 0.98 (95% CI 0.96–1.0). The adjusted hazard ratio of incident stroke in patients with coated-platelet levels ≥51.1% was 10.72 compared to those with levels <51.1%. ROC analysis showed significant improvement in the predictive ability of the coated-platelet model compared to ABCD2 score (AUC 0.78 ± 0.07 vs 0.54 ± 0.07, p = 0.01).Conclusions:These findings suggest a role for coated-platelets in risk stratification for stroke at 30 days after TIA.

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Clinical performance and utility of a NNMT-based urine test for bladder cancer

The International Journal of Biological Markers ◽

10.5301/ijbm.5000311 ◽

2017 ◽

Vol 33 (1) ◽

pp. 94-101 ◽

Cited By ~ 9

Author(s):

Valentina Pozzi ◽

Giulia Di Ruscio ◽

Davide Sartini ◽

Roberto Campagna ◽

Riccardo Seta ◽

...

Keyword(s):

Bladder Cancer ◽

Diagnostic Accuracy ◽

Roc Analysis ◽

Clinical Performance ◽

Histological Grade ◽

Area Under The Curve ◽

Inverse Correlation ◽

Cutoff Value ◽

Healthy Donors ◽

Auc Value

Background: Bladder cancer (BC) represents the most common neoplasm of the urinary tract. Although cystoscopy and urine cytology represent the gold standard methods to monitor BC, both procedures have limitations. Therefore, the identification of reliable biomarkers for early and noninvasive detection of BC is urgently required. Methods: In this study, we analyzed nicotinamide N-methyltransferase (NNMT) expression in urine samples from 55 BC patients and 107 controls, using real-time polymerase chain reaction (PCR). Receiver operating characteristic (ROC) analysis was used to identify the best cutoff value to discriminate BC patients from healthy donors, and to evaluate the diagnostic accuracy of a urine-based NNMT test. Results: The results demonstrated that urinary NNMT expression was significantly (p<0.05) higher in BC patients. Moreover, a significant (p<0.05) inverse correlation was found between NNMT expression and histological grade. The ROC analysis revealed that a ΔCq of 13.3 was the best cutoff value, since it was associated with the highest combination of sensitivity and specificity. Moreover, the area under the curve (AUC) value was 0.913 (p<0.05), indicating the excellent diagnostic accuracy of a urine-based NNMT test. Conclusions: Our data indicate that NNMT is a promising biomarker that could be used to support the early and noninvasive diagnosis of BC.

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The effect of troponin values on prognosis in acute pulmonary embolism

International Journal of Clinical Trials ◽

10.18203/2349-3259.ijct20204479 ◽

2020 ◽

Vol 7 (4) ◽

pp. 229

Author(s):

Gulay Dasdemir Ilkhan ◽

Hakan Celikhisar

Keyword(s):

Pulmonary Embolism ◽

Sensitivity And Specificity ◽

Significant Relationship ◽

Roc Analysis ◽

Operating Characteristic ◽

Area Under The Curve ◽

Survivor Group ◽

Acute Pulmonary Thromboembolism ◽

Auc Value ◽

Low Sensitivity

Background: The objective of this study is to evaluate the clinical usefulness of cardiac troponin levels in acute pulmonary thromboembolism (PTE) prognosis.Methods: Thorax computed tomography (CT) angiography was performed and reported by the radiologist as pulmonary embolism and 193 patients older than 18 years of age who were considered PTE by the physician of chest diseases were included in the study. Patients diagnosed with PTE were divided into two groups as those who died within 30 days and did not die within 30 days. As a result of the statistically significant relationship between troponin and mortality, receiver operating characteristic (ROC) analysis was performed to determine the prognosis level of troponin and appropriate sensitivity and specificity cut-off values were determined.Results: We determined that troponin levels of patients diagnosed with PTE in the emergency department were statistically significantly higher in the group with mortality (p=0.031). Since the area under the curve (AUC) value was calculated as 0.636, troponin value was found to have a weak-medium significance in terms of predicting 30-day mortality.Conclusions: Troponin values are statistically significantly higher in patients with a one-month period than the survivor group in this period. However, we concluded that troponin values are not clinically usable as mortality markers due to their low sensitivity and specificity rates. However, due to its significant relationship with increased mortality, patients with PTE with high troponin values should be hospitalized and monitored closely.

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