scholarly journals Platelet and Red Blood Cell Indices in Patients with Panic Disorder: A Receiver Operating Characteristic Analysis

2020 ◽  
Vol 11 (02) ◽  
pp. 261-266 ◽  
Author(s):  
Ramdas S. Ransing ◽  
Neha Gupta ◽  
Girish Agrawal ◽  
Nilima Mahapatro
Keyword(s):  
Panic Disorder ◽  
Blood Cell ◽  
Receiver Operating Characteristic ◽  
Red Blood Cell ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Healthy Controls ◽  
Distribution Width ◽  
Rbc Indices ◽  
Receiver Operating

Abstract Objective Panic disorder (PD) is associated with changes in platelet and red blood cell (RBC) indices. However, the diagnostic or predictive value of these indices is unknown. This study assessed the diagnostic and discriminating value of platelet and RBC indices in patients with PD. Materials and Methods In this cross-sectional study including patients with PD (n = 98) and healthy controls (n = 102), we compared the following blood indices: mean platelet volume (MPV), platelet distribution width (PDW), and RBC distribution width (RDW). The receiver operating characteristic (ROC) curve was used to calculate the area under the ROC curve (AUC), sensitivity, specificity, and likelihood ratio for the platelet and RBC indices. Results Statistically significant increase in PDW (17.01 ± 0.91 vs. 14.8 ± 2.06; p < 0.0001) and RDW (16.56 ± 2.32 vs. 15.12 ± 2.43; p < 0.0001) levels were observed in patients with PD. PDW and mean corpuscular hemoglobin concentration had larger AUC (0.89 and 0.74, respectively) and Youden’s index (0.65 and 0.39, respectively), indicating their higher predictive capacity as well as higher sensitivity in discriminating patients with PD from healthy controls. Conclusion PDW can be considered a “good” diagnostic or predictive marker in patients with PD.

scholarly journals Analysis of Eyelid Blink Characteristics in Patients with Ptosis Using a Smartphone Camera

2021 ◽  
Vol 62 (7) ◽  
pp. 873-880
Author(s):  
Taesung Joo ◽  
Jin-Ho Joo ◽  
In-Ki Park ◽  
Jae-Ho Shin
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Curve ◽  
Healthy Subjects ◽  
Operating Characteristic ◽  
Control Group ◽  
Palpebral Fissure ◽  
Blink Rate ◽  
Healthy Controls ◽  
Healthy Control ◽  
Receiver Operating

Purpose: To compare eyelid blink characteristics between patients with ptosis and healthy controls using a smartphone camera. Methods: The ptosis group consisted of 20 senile aponeurotic ptosis patients with margin reflex distance1 ≤2.5 mm and the control group consisted of 10 healthy subjects without ptosis. The ptosis group was further divided into two groups based on an age cutoff of 70 years. Palpebral fissure height, levator function, margin reflex distance1, inter-blink interval, blink duration, blink rate, and blink velocity were measured and compared between the three groups based on photographs of the eyelids and videos of blinking taken with a smartphone camera. Results: The palpebral fissure height, levator function, margin reflex distance1, and blink velocity were lower in the ptosis groups than in the control group but these values did not differ between the two ptosis groups. The palpebral fissure height, levator function, and margin reflex distance1 were correlated with blink velocity. In the receiver operating characteristic (ROC) curve of blink velocity, the area under the receiver operating characteristic (AUROC) curve value was as high as 0.969 and the cut-off value was 32.36 mm/s. Conclusions: It is possible to analyze eyelid blink characteristics using a smartphone camera and the results confirmed that palpebral fissure height, levator function, margin reflex distance1, and blink velocity were lower in the senile aponeurotic ptosis group than in the healthy control group and were unaffected by age. Additionally, blink velocity is valuable for diagnosis of ptosis due to the correlation between the degree of ptosis, blink velocity, and the ROC curve of blink velocity.

scholarly journals Comparative Assessment of Three Common Algorithms for Estimating the Variance of the Area under the Nonparametric Receiver Operating Characteristic Curve

2002 ◽  
Vol 2 (3) ◽  
pp. 280-289 ◽  
Author(s):  
Mario A. Cleves
Keyword(s):  
Receiver Operating Characteristic ◽  
Diagnostic Test ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Simulated Data ◽  
Small Samples ◽  
Binary Outcomes ◽  
Discriminatory Accuracy ◽  
Receiver Operating

The area under the receiver operating characteristic (ROC) curve is often used to summarize and compare the discriminatory accuracy of a diagnostic test or modality, and to evaluate the predictive power of statistical models for binary outcomes. Parametric maximum likelihood methods for fitting of the ROC curve provide direct estimates of the area under the ROC curve and its variance. Nonparametric methods, on the other hand, provide estimates of the area under the ROC curve, but do not directly estimate its variance. Three algorithms for computing the variance for the area under the nonparametric ROC curve are commonly used, although ambiguity exists about their behavior under diverse study conditions. Using simulated data, we found similar asymptotic performance between these algorithms when the diagnostic test produces results on a continuous scale, but found notable differences in small samples, and when the diagnostic test yields results on a discrete diagnostic scale.

Intraoperative scrape cytology: Comparison with frozen sections, using receiver operating characteristic (ROC) curve

2000 ◽  
Vol 23 (2) ◽  
pp. 134-139 ◽  
Author(s):  
Vinod Shidham ◽  
Dilip Gupta ◽  
Lorenzo M. Galindo ◽  
Marian Haber ◽  
Carolyn Grotkowski ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Frozen Sections ◽  
Scrape Cytology ◽  
Receiver Operating
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