scholarly journals Value of Fractional Uric Acid Excretion in Differential Diagnosis of Hyponatremic Patients on Diuretics

2008 ◽  
Vol 93 (8) ◽  
pp. 2991-2997 ◽  
Author(s):  
Wiebke Fenske ◽  
Stefan Störk ◽  
Ann-Cathrin Koschker ◽  
Anne Blechschmidt ◽  
Daniela Lorenz ◽  
...  
Keyword(s):  
Uric Acid ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Diuretic Therapy ◽  
Isotonic Saline ◽  
Area Under The Curve ◽  
Characteristic Analysis ◽  
Diagnostic Potential ◽  
Receiver Operating

Abstract Background: The syndrome of inappropriate antidiuresis (SIAD) is the most frequent cause of hyponatremia. Its diagnosis requires decreased serum osmolality, inappropriately diluted urine (e.g. >100 mOsm/kg), clinical euvolemia, and a urinary sodium (Na) excretion (U-Na) more than 30 mmol/liter. However, in hyponatremic patients taking diuretics, this definition is unreliable due to the natriuretic effect of diuretics. Here, we examined the diagnostic potential of alternative laboratory measurements to diagnose SIAD, regardless of the use of diuretics. Methods: A total of 86 consecutive hyponatremic patients (serum Na <130 mmol/liter) was classified based on their history, clinical evaluation, osmolality, and saline response to isotonic saline into a SIAD and a non-SIAD group. U-Na, serum urate concentration, and fractional excretion (FE) of Na, urea, and uric acid (UA) were measured in all subjects. The accuracy to diagnose SIAD was assessed using receiver operating characteristic analysis. Results: A total of 31 patients (36%) had a diagnosis of SIAD, and 55 (64%) were classified as non-SIAD. There were 57 patients (68%) who were on diuretics (15 in the SIAD group, 42 in the non-SIAD group). In the absence of diuretic therapy, SIAD was accurately diagnosed using U-Na (area under the receiver operating characteristic curve 0.96; 0.92–1.02). However, in patients on diuretics, the diagnosis was unreliable (area under the curve 0.85; 0.73–0.97). There, FE-UA performed best compared with all other markers tested (area under the curve 0.96; 0.92–1.12), resulting in a positive predictive value of 100% if a cutoff value of 12% was used. Conclusion: FE-UA allows the diagnosis of SIAD with excellent specificity. Combining the information on U-Na and FE-UA leads to a very high diagnostic accuracy in hyponatremic patients with and without diuretic treatment.

scholarly journals Identification of an optimal prolactin threshold to determine prolactinoma size using receiver operating characteristic analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bianca M. Leca ◽  
Maria Mytilinaiou ◽  
Marina Tsoli ◽  
Andreea Epure ◽  
Simon J. B. Aylwin ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Tumour Size ◽  
Area Under The Curve ◽  
Serum Prolactin ◽  
Pituitary Neoplasms ◽  
Characteristic Analysis ◽  
Roc Curve Analysis ◽  
Baseline Serum ◽  
Receiver Operating

AbstractProlactinomas represent the most common type of secretory pituitary neoplasms, with a therapeutic management that varies considerably based on tumour size and degree of hyperprolactinemia. The aim of the current study was to evaluate the relationship between serum prolactin (PRL) concentrations and prolactinoma size, and to determine a cut-off PRL value that could differentiate micro- from macro-prolactinomas. A retrospective cohort study of 114 patients diagnosed with prolactinomas between 2007 and 2017 was conducted. All patients underwent gadolinium enhanced pituitary MRI and receiver operating characteristic (ROC) analyses were performed. 51.8% of patients in this study were men, with a mean age at the time of diagnosis of 42.32 ± 15.04 years. 48.2% of the total cohort were found to have microadenomas. Baseline serum PRL concentrations were strongly correlated to tumour dimension (r = 0.750, p = 0.001). When performing the ROC curve analysis, the area under the curve was 0.976, indicating an excellent accuracy of the diagnostic method. For a value of 204 μg/L (4338 mU/L), sensitivity and specificity were calculated at 0.932 and 0.891, respectively. When a cut off value of 204 μg/L (4338 mU/L) was used, specificity was 93.2%, and sensitivity 89.1%, acceptable to reliably differentiate between micro- and macro- adenomas.

scholarly journals miR-142-3p as a novel biomarker for predicting poor prognosis in renal cell carcinoma patients after surgery

2019 ◽  
Vol 34 (3) ◽  
pp. 302-308 ◽  
Author(s):  
Xiqi Peng ◽  
Xiang Pan ◽  
Kaihao Liu ◽  
Chunduo Zhang ◽  
Liwen Zhao ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Receiver Operating Characteristic ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Poor Prognosis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Receiver Operating

Background: miR-142-3p has proved to be involved in tumorigenesis and the development of renal cell carcinoma. The present study aimed to explore the prognostic value of miR-142-3p. Methods: Total RNA was extracted from renal cell carcinoma specimens and the expression level of miR-142-3p was measured. Pearson Chi-square test, Kaplan–Meier analysis, as well as univariate and multivariate regression analysis were performed to determine the correlation between miR-142-3p and the prognosis of renal cell carcinoma patients. Receiver operating characteristic curves were constructed to evaluate the predictive efficiency of miR-142-3p for the prognosis of renal cell carcinoma patients. Data from The Cancer Genome Atlas (TCGA) were utilized to validate our findings. Results: Our results demonstrated that upregulation of miR-142-3p was correlated with shorter overall survival (P=0.002) and was, in the meantime, an independent prognostic factor for renal cell carcinoma patients (P=0.002). The receiver operating characteristic curve combining miR-142-3p expression with tumor stage showed an area under the curve of 0.633 (95% confidence interval 0.563, 0.702). The result of TCGA data was consistent with our findings. Conclusions: Our results suggest miR-142-3p expression is correlated with poor prognosis of renal cell carcinoma patients and may serve as a prognostic biomarker in the future.

A simple method to estimate the time-dependent receiver operating characteristic curve and the area under the curve with right censored data

2016 ◽  
Vol 27 (8) ◽  
pp. 2264-2278 ◽  
Author(s):  
Liang Li ◽  
Tom Greene ◽  
Bo Hu
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Sensitivity And Specificity ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Disease Onset ◽  
Area Under The Curve ◽  
Time Dependent ◽  
Operating Characteristic Curve ◽  
Receiver Operating

The time-dependent receiver operating characteristic curve is often used to study the diagnostic accuracy of a single continuous biomarker, measured at baseline, on the onset of a disease condition when the disease onset may occur at different times during the follow-up and hence may be right censored. Due to right censoring, the true disease onset status prior to the pre-specified time horizon may be unknown for some patients, which causes difficulty in calculating the time-dependent sensitivity and specificity. We propose to estimate the time-dependent sensitivity and specificity by weighting the censored data by the conditional probability of disease onset prior to the time horizon given the biomarker, the observed time to event, and the censoring indicator, with the weights calculated nonparametrically through a kernel regression on time to event. With this nonparametric weighting adjustment, we derive a novel, closed-form formula to calculate the area under the time-dependent receiver operating characteristic curve. We demonstrate through numerical study and theoretical arguments that the proposed method is insensitive to misspecification of the kernel bandwidth, produces unbiased and efficient estimators of time-dependent sensitivity and specificity, the area under the curve, and other estimands from the receiver operating characteristic curve, and outperforms several other published methods currently implemented in R packages.

scholarly journals Risk assessment and receiver operating characteristic curves

2011 ◽  
Vol 42 (5) ◽  
pp. 895-898 ◽  
Author(s):  
G. Szmukler ◽  
B. Everitt ◽  
M. Leese
Keyword(s):  
Risk Assessment ◽  
Clinical Practice ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Assessment Instruments ◽  
Receiver Operating Characteristic Curves ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Risk assessment is now regarded as a necessary competence in psychiatry. The area under the curve (AUC) statistic of the receiver operating characteristic curve is increasingly offered as the main evidence for accuracy of risk assessment instruments. But, even a highly statistically significant AUC is of limited value in clinical practice.

scholarly journals Clinical Significance of Serum HE4, CA125, CA724, and CA19-9 in Patients With Endometrial Cancer

2016 ◽  
Vol 16 (4) ◽  
pp. 435-439 ◽  
Author(s):  
Jing Bian ◽  
Xiaoxu Sun ◽  
Bo Li ◽  
Liang Ming
Keyword(s):  
Endometrial Cancer ◽  
Receiver Operating Characteristic ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
International Federation ◽  
Characteristic Analysis ◽  
Predictive Values ◽  
Gynecology And Obstetrics ◽  
Receiver Operating

Purpose: Serum markers with increased sensitivity and specificity for endometrial cancer are required. To date, no good marker has met this standard. The aims of our study were to evaluate the utility of tumor markers HE4, CA125, CA724, and CA19-9 as potential markers in patients diagnosed with endometrial cancer. Methods: Blood samples from 105 patients with endometrial cancer and 87 healthy women were analyzed by Roche electrochemiluminescent immunoassay, and serum values were measured for the following biomarkers: HE4, CA125, CA724, and CA19-9. Results: Serum HE4, CA125, CA724, and CA19-9 concentrations were significantly higher in patients with endometrial cancer, compared with controls ( P < .001). In the receiver operating characteristic analysis, the area under the curve value for combination of HE4, CA125, CA724, and CA19-9 was 82.1% (95% confidence interval: 75.3%-86.2%), the maximum area of the test groups. For all stages of patients with endometrial cancer, HE4 had higher sensitivity (58%), positive predictive value (60%), and negative predictive value (67%) than any other single tumor marker, and in the combination of HE4, CA125, CA724, and CA19-9, the sensitivity and positive predictive values reached 59.1% and 88%, respectively. Meanwhile, the receiver operating characteristic area under the curve of the combination of the 4 markers was significantly increased than any other group, either in stage I or in stage II to IV cases. HE4 and CA125 both correlate with advanced age; in addition, HE4 was related to pathology subtypes and positive adnexal involvement, CA125 was related to International Federation of Gynecology and Obstetrics stage, CA19-9 was related to International Federation of Gynecology and Obstetrics stage, and CA724 was correlated with positive lymph node. Conclusion: Combination of HE4, CA125, CA724, and CA19-9 has the highest value in diagnosing endometrial cancer, and they can be a useful tissue immune marker for patients with endometrial cancer.

scholarly journals Tournament leave-pair-out cross-validation for receiver operating characteristic analysis

2018 ◽  
Vol 28 (10-11) ◽  
pp. 2975-2991 ◽  
Author(s):  
Ileana Montoya Perez ◽  
Antti Airola ◽  
Peter J Boström ◽  
Ivan Jambor ◽  
Tapio Pahikkala
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Receiver Operating Characteristic Analysis ◽  
Characteristic Curve ◽  
Diagnostic Systems ◽  
Characteristic Analysis ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Receiver operating characteristic analysis is widely used for evaluating diagnostic systems. Recent studies have shown that estimating an area under receiver operating characteristic curve with standard cross-validation methods suffers from a large bias. The leave-pair-out cross-validation has been shown to correct this bias. However, while leave-pair-out produces an almost unbiased estimate of area under receiver operating characteristic curve, it does not provide a ranking of the data needed for plotting and analyzing the receiver operating characteristic curve. In this study, we propose a new method called tournament leave-pair-out cross-validation. This method extends leave-pair-out by creating a tournament from pair comparisons to produce a ranking for the data. Tournament leave-pair-out preserves the advantage of leave-pair-out for estimating area under receiver operating characteristic curve, while it also allows performing receiver operating characteristic analyses. We have shown using both synthetic and real-world data that tournament leave-pair-out is as reliable as leave-pair-out for area under receiver operating characteristic curve estimation and confirmed the bias in leave-one-out cross-validation on low-dimensional data. As a case study on receiver operating characteristic analysis, we also evaluate how reliably sensitivity and specificity can be estimated from tournament leave-pair-out receiver operating characteristic curves.

Is the gait profile score a good marker of gait dysfunction in individuals with late effects of poliomyelitis?

2020 ◽  
pp. 1-5
Author(s):  
Anthony Supiot ◽  
François Genêt ◽  
Thomas Cattagni ◽  
Marjorie Salga ◽  
Nicolas Roche ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Late Effects ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Characteristic Analysis ◽  
Double Support ◽  
Profile Score ◽  
Gait Abnormalities ◽  
Healthy Participants ◽  
Receiver Operating

Late effects of poliomyelitis (LEoP) are characterized by new gait abnormalities that occur many years after the initial poliomyelitis illness. Currently, there is no consensus on the most appropriate evaluation to detect gait disorders following LEoP. This study aimed to assess and compare the effectivenes of the gait profile score with that of the symmetry index (SI) to charaterize gait abnormalities resulting from the LEOP. The SI for stance, swing, double-support duration and the step length, and gait profile score were computed from gait analysis of 12 poliomyelitis subjects and 12 healthy participants. Receiver operating characteristic analysis was used to measure the sensitivity and specificity of the SI and the gait profile score to discriminate patients with the LEoP and healthy participants. The area under the receiver operating characteristic curve was calculated for both gait the profile score and SI. With AUC values all above 0.83 (good discrimination), SI and GPS significantly discriminated the participants with the LEoP from the healthy participants (all p-values < 0.001). The results of this study show that both the gait profile score and SI may be used with a similar sensitivity by clinicians to identify potential gait abnormalities in patients with the LEoP.

scholarly journals COMPARISION OF BINARY DIAGNOSTIC PREDICTORS USING ENTROPY

2018 ◽  
Vol 6 (1) ◽  
pp. 440-447
Author(s):  
Kathare Alfred ◽  
Otieno Argwings ◽  
Kimeli Victor
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Gold Standard ◽  
Large Scale ◽  
Operating Characteristic ◽  
Standard Procedure ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Operating Characteristic Curve ◽  
Receiver Operating

The use of gold standard procedures in screening may be costly, risky or even unethical. It is, therefore, not admissible for large scale application. In this case, a more acceptable diagnostic predictor is applied to a sample of subjects alongside a gold standard procedure. The performance of the predictor is then evaluated using Receiver Operating Characteristic curve. The area under the curve, then, provides a summative measure of the performance of the predictor. The Receiver Operating Characteristic curve is a trade-off between sensitivity and specificity which in most cases are of different clinical significance. Also, the area under the curve is criticized for lack of coherent interpretation. In this study, we proposed the use of entropy as a summary index measure of uncertainty to compare diagnostic predictors. Noting that a diseased subject who is truly identified with the disease at a lower cut-off will also be identified at a higher cut-off, we substituted time variable in survival analysis for cut-offs in a binary predictor. We then derived the entropy of the functions of diagnostic predictors. Application of the procedure to real data showed that entropy was a strong measure for quantifying the amount of uncertainty engulfed in a set of cut-offs of binary diagnostic predictor.

ATL

2014 ◽  
Vol 24 (4) ◽  
pp. 643-648 ◽  
Author(s):  
Da Zhu ◽  
Xiao-Hui Jiang ◽  
Yun-Hui Jiang ◽  
Wen-Cheng Ding ◽  
Chang-lin Zhang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Receiver Operating Characteristic ◽  
Test Performance ◽  
Copy Number ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Intraepithelial Neoplasia ◽  
Characteristic Analysis ◽  
Diagnostic Test Performance ◽  
Receiver Operating

ObjectiveBiopsy confirmed that cervical intraepithelial neoplasia (CIN) may naturally regress or progress. Currently, the risk assessment for CIN progression to cervical cancer is still not satisfactory in clinical practice. We investigated copy number and protein expression of TP63 and MYC and explored the possibility to use them as progression biomarkers.MethodsCopy numbers of TP63 and MYC, as well as human papilloma virus (HPV) integration status, were determined by fluorescence in situ hybridization in 39 patients with CIN and 66 patients with cervical cancer. Corresponding protein expressions were analyzed by immunohistochemistry. Receiver operating characteristic curves were used to measure the diagnostic test performance for the detection of cervical cancer from CIN. Sensitivity and specificity values of biomarkers were calculated.ResultsThe average copy number and expression of TP63 and MYC, as well as the HPV integration rate, increased in the progression of CIN to cervical cancer. Receiver operating characteristic analysis for detection of cervical cancer resulted in area under the curve (AUC) values of TP63 copy number (AUC, 0.96; 95% confidence interval [CI], 0.91–1.00), MYC copy number (AUC, 0.92; 95% CI, 0.85–0.96), TP63 expression (AUC, 0.73; 95% CI, 0.61–0.85), and HPV-16 integration (AUC, 0.73; 95% CI, 0.60–0.85). MYC expression was not able to statistically distinguish cancer from CIN (P = 0.393). The combinations increased the specificity slightly but not sensitivity. Among them, TP63 amplification showed the best diagnostic performance.ConclusionsAmplification and overexpression of TP63 and MYC, and HPV integration rate, are associated with the transition of CIN to cervical cancer. Future studies on these biomarkers will help to assess the risk of CIN progression.

The area under the generalized receiver-operating characteristic curve

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Pablo Martínez-Camblor ◽  
Sonia Pérez-Fernández ◽  
Susana Díaz-Coto
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Asymptotic Variance ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Finite Samples ◽  
Real World Problem ◽  
Graphical Tool ◽  
Receiver Operating

Abstract The receiver operating-characteristic (ROC) curve is a well-known graphical tool routinely used for evaluating the discriminatory ability of continuous markers, referring to a binary characteristic. The area under the curve (AUC) has been proposed as a summarized accuracy index. Higher values of the marker are usually associated with higher probabilities of having the characteristic under study. However, there are other situations where both, higher and lower marker scores, are associated with a positive result. The generalized ROC (gROC) curve has been proposed as a proper extension of the ROC curve to fit these situations. Of course, the corresponding area under the gROC curve, gAUC, has also been introduced as a global measure of the classification capacity. In this paper, we study in deep the gAUC properties. The weak convergence of its empirical estimator is provided while deriving an explicit and useful expression for the asymptotic variance. We also obtain the expression for the asymptotic covariance of related gAUCs and propose a non-parametric procedure to compare them. The finite-samples behavior is studied through Monte Carlo simulations under different scenarios, presenting a real-world problem in order to illustrate its practical application. The R code functions implementing the procedures are provided as Supplementary Material.

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