scholarly journals Evaluation of Ocular Biomechanical Indices to Distinguish Normal from Keratoconus Eyes

2012 ◽  
Vol 1 (3) ◽  
pp. 145-150
Author(s):  
Bernardo Lopes ◽  
Allan Luz ◽  
Bruno Fontes ◽  
Isaac C Ramos ◽  
Fernando Correia ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Receiver Operating Characteristic ◽  
Linear Model ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Corneal Resistance Factor ◽  
Corneal Deformation ◽  
Eye Examination ◽  
The Individual

ABSTRACT Purpose To compare and assess the ability of pressure-derived parameters and corneal deformation waveform signal-derived parameters of the ocular response analyzer (ORA) measurement to distinguish between keratoconus and normal eyes, and to develop a combined parameter to optimize the diagnosis of keratoconus. Materials and methods One hundred and seventy-seven eyes (177 patients) with keratoconus (group KC) and 205 normal eyes (205 patients; group N) were included. One eye from each subject was randomly selected for analysis. Patients underwent a complete clinical eye examination, corneal topography (Humphrey ATLAS), tomography (Pentacam Oculus) and biomechanical evaluations (ORA Reichert). Differences in the distributions between the groups were assessed using the Mann- Whitney test. The receiver operating characteristic (ROC) curve was used to identify cutoff points that maximized sensitivity and specificity in discriminating keratoconus from normal corneas. Logistic regression was used to identify a combined linear model (Fisher 1.0). Results Significant differences in all studied parameters were detected (p < 0.05), except for W2. For the corneal resistance factor (CRF): Area under the ROC curve (AUROC) 89.1%, sensitivity 81.36%, specificity 84.88%. For the p1area: AUROC 91.5%, sensitivity 87.1%, specificity 81.95%. Of the individual parameters, the highest predictive accuracy was for the Fisher 1.0, which represents the combination of all parameters (AUROC 95.5%, sensitivity 88.14%, specificity 93.17%). Conclusion Waveform-derived ORA parameters displayed greater accuracy than pressure-derived parameters for identifying keratoconus. Corneal hysteresis (CH) and CRF, a diagnostic linear model that combines different parameters, provided the greatest accuracy for differentiating keratoconus from normal corneas. How to cite this article Luz A, Fontes B, Ramos IC, Lopes B, Correia F, Schor P, Ambrósio R. Evaluation of Ocular Biomechanical Indices to Distinguish Normal from Keratoconus Eyes. Int J Kerat Ect Cor Dis 2012;1(3):145-150.

scholarly journals Comparison of three statistical methods for earthquake-induced landslides susceptibility in Lushan region China

2020 ◽  
Vol 198 ◽  
pp. 02024
Author(s):  
Rui Liu ◽  
Luyao Li ◽  
Zili Lai ◽  
Xin Yang
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Receiver Operating Characteristic ◽  
Statistical Methods ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Support Vector ◽  
Susceptibility Assessment ◽  
Distribution Rule

This paper adopts three models including the logistic regression (LR), support vector machine (SVM), and random forest (RF) to study the susceptibility distribution rule of susceptibility distribution of earthquakes induced landslides. The Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and Ratio were used for evaluating the model’s accuracy and mapping availability susceptibility assessment. The result shows that RF has the best performance in the susceptibility assessment of earthquake-induced landslides in the Lushan region of China.

scholarly journals Predicting Burn Mortality Using a Simple Novel Prediction Model

2021 ◽  
Author(s):  
Sneha Sharma ◽  
Raman Tandon
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Logistic Regression Analysis ◽  
Operating Characteristic ◽  
Binary Logistic Regression ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Binary Logistic Regression Analysis

Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

A Predictive Model of Intrinsic Factors Associated with Long-Stay Nursing Home Care After Hospitalization

2020 ◽  
pp. 105477382098527
Author(s):  
Jane Flanagan ◽  
Marie Boltz ◽  
Ming Ji
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Receiver Operating Characteristic ◽  
Predictive Model ◽  
Operating Characteristic ◽  
Resource Planning ◽  
Term Care ◽  
Intrinsic Factors ◽  
Patients At Risk ◽  
Receiver Operating

We aimed to build a predictive model with intrinsic factors measured upon admission to skilled nursing facilities (SNFs) post-acute care (PAC) to identify older adults transferred from SNFs to long-term care (LTC) instead of home. We analyzed data from Massachusetts in 23,662 persons admitted to SNFs from PAC in 2013. Explanatory logistic regression analysis identified single “intrinsic predictors” related to LTC placement. To assess overfitting, the logistic regression predictive model was cross-validated and evaluated by its receiver operating characteristic (ROC) curve. A 12-variable predictive model with “intrinsic predictors” demonstrated both high in-sample and out-of-sample predictive accuracy in the receiver operating characteristic ROC and area under the ROC among patients at risk of LTC placement. This predictive model may be used for early identification of patients at risk for LTC after hospitalization in order to support targeted rehabilitative approaches and resource planning.

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.

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.

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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