scholarly journals Comparison of Photopic Negative Response of Full-Field and Focal Electroretinograms in Detecting Glaucomatous Eyes

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Shigeki Machida ◽  
Kunifusa Tamada ◽  
Taku Oikawa ◽  
Yasutaka Gotoh ◽  
Tomoharu Nishimura ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Negative Response ◽  
Photopic Negative Response ◽  
Diagnostic Ability ◽  
Full Field ◽  
Receiver Operating Characteristic Curves ◽  
Advanced Glaucoma ◽  
Blue Background ◽  
Higher Sensitivity

Purpose. To compare the photopic negative response (PhNR) of the full-field electroretinogram (ERG) to the PhNR of the focal ERGs in detecting glaucoma.Methods. One hundred and three eyes with glaucoma and 42 normal eyes were studied. Full-field ERGs were elicited by red stimuli on a blue background. The focal ERGs were elicited by a15∘white stimulus spot centered on the macula, the superotemporal or the inferotemporal areas of the macula.Results. In early glaucoma, the areas under the receiver operating characteristic curves (AUCs) were significantly larger for the focal PhNR (0.863–0.924) than those for the full-field PhNR (0.666–0.748) (P<.05). The sensitivity was significantly higher for the focal PhNR than for the full-field PhNR in early (P<.01) and intermediate glaucoma (P<.05). In advanced glaucoma, there was no difference in the AUCs and sensitivities between the focal and full-field PhNRs.Conclusions. The focal ERG has the diagnostic ability with higher sensitivity in detecting early and intermediate glaucoma than the full-field ERG.

A conditional approach for the receiver operating characteristic curve construction to evaluate diagnostic test performance in a family-matched case–control design

2021 ◽  
pp. 096228022199595
Author(s):  
Yalda Zarnegarnia ◽  
Shari Messinger
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Case Control ◽  
Paired Data ◽  
Characteristic Curves ◽  
Receiver Operating Characteristic Curves ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Receiver operating characteristic curves are widely used in medical research to illustrate biomarker performance in binary classification, particularly with respect to disease or health status. Study designs that include related subjects, such as siblings, usually have common environmental or genetic factors giving rise to correlated biomarker data. The design could be used to improve detection of biomarkers informative of increased risk, allowing initiation of treatment to stop or slow disease progression. Available methods for receiver operating characteristic construction do not take advantage of correlation inherent in this design to improve biomarker performance. This paper will briefly review some developed methods for receiver operating characteristic curve estimation in settings with correlated data from case–control designs and will discuss the limitations of current methods for analyzing correlated familial paired data. An alternative approach using conditional receiver operating characteristic curves will be demonstrated. The proposed approach will use information about correlation among biomarker values, producing conditional receiver operating characteristic curves that evaluate the ability of a biomarker to discriminate between affected and unaffected subjects in a familial paired design.

Development and Validation of a Nomogram for Renal Involvement in Primary Sjögren Syndrome Patients: A Retrospective Analysis

2021 ◽  
Author(s):  
Xinshi Huang ◽  
Xiaobing Wang ◽  
Dinglai Yu
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Renal Involvement ◽  
Calibration Plot ◽  
Receiver Operating Characteristic Curves ◽  
Logistic Analysis ◽  
Validation Set ◽  
Multivariate Logistic Analysis

Abstract Objective To establish and validate a nomogram for individualized prediction of renal involvement in pSS patients. Methods A total of 1293 patients with pSS from the First Affiliated Hospital of Wenzhou Medical University between January 2008 to January 2020 were recruited and further analyzed retrospectively. The patients were randomly divided into a development set (70%, n = 910) and a validation set (30%, n = 383). The univariable and multivariate logistic regression were performed to analyze the risk factors of renal involvement in pSS. Based on the regression β coefficients derived from multivariate logistic analysis, an individualized nomogram prediction model was developed. The prediction model of discrimination and calibration was evaluated with the area under the receiver operating characteristic curves and Calibration plot. Results Multivariate logistic analysis showed that hypertension, anemia, albumin, uric acid, anti-Ro52, hematuria and Chisholm-Mason grade were independent risk factors of renal involvement in pSS. The area under the receiver operating characteristic curves were 0.797 and 0.750 respectively in development set and validation set, indicating the nomogram had a good discrimination capacity. The Calibration plot showed nomogram had a strong concordance performance between the prediction probability and the actual probability. Conclusion The individualized nomogram for pSS patients those who had renal involvement could be used by clinicians to predict the risk of pSS patients developing into renal involvement and improve early screening and intervention.

scholarly journals Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device

2021 ◽  
Vol 8 ◽  
Author(s):  
Tina Diao ◽  
Fareshta Kushzad ◽  
Megh D. Patel ◽  
Megha P. Bindiganavale ◽  
Munam Wasi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Optic Neuropathy ◽  
Negative Response ◽  
Photopic Negative Response ◽  
Learning Approaches ◽  
Data Set ◽  
Full Field ◽  
Handheld Device ◽  
Technical Requirements

The photopic negative response of the full-field electroretinogram (ERG) is reduced in optic neuropathies. However, technical requirements for measurement and poor classification performance have limited widespread clinical application. Recent advances in hardware facilitate efficient clinic-based recording of the full-field ERG. Time series classification, a machine learning approach, may improve classification by using the entire ERG waveform as the input. In this study, full-field ERGs were recorded in 217 eyes (109 optic neuropathy and 108 controls) of 155 subjects. User-defined ERG features including photopic negative response were reduced in optic neuropathy eyes (p &lt; 0.0005, generalized estimating equation models accounting for age). However, classification of optic neuropathy based on user-defined features was only fair with receiver operating characteristic area under the curve ranging between 0.62 and 0.68 and F1 score at the optimal cutoff ranging between 0.30 and 0.33. In comparison, machine learning classifiers using a variety of time series analysis approaches had F1 scores of 0.58–0.76 on a test data set. Time series classifications are promising for improving optic neuropathy diagnosis using ERG waveforms. Larger sample sizes will be important to refine the models.

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