Comments on ‘Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond’ by M. J. Pencina, R. B. D'Agostino Sr, R. B. D'Agostino Jr, R. S. Vasan,Statistics in Medicine (DOI: 10.1002/sim.2929)

2007 ◽  
Vol 27 (2) ◽  
pp. 188-190 ◽  
Author(s):  
Philip Greenland
Keyword(s):  
Roc Curve ◽  
Predictive Ability

scholarly journals Comparing the Predictive Ability of Prognostic Models in Ischemic Stroke; Derivation, Validation, and Discrimination Beyond the ROC Curve

2014 ◽  
Vol 5 ◽  
Author(s):  
Alireza Esteghamati ◽  
Nima Hafezi-Nejad ◽  
Sara Sheikhbahaei ◽  
Behnam Heidari ◽  
Ali Zandieh ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Roc Curve ◽  
Predictive Ability ◽  
Prognostic Models

scholarly journals Heart Score for Predicting in Hospital Major Cardiovascular Event in Patient with Non ST Segment Elevation Acute Coronary Syndrome

2020 ◽  
Vol 6 (2) ◽  
pp. 85-90
Author(s):  
Kamal Kharrazi Ilyas ◽  
Sutomo Kasiman ◽  
Harris Hasan ◽  
Zulfikri Mukhtar ◽  
Refli Hasan ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
General Hospital ◽  
Cardiovascular Event ◽  
Roc Curve ◽  
Predictive Value ◽  
Predictive Ability ◽  
St Segment Elevation ◽  
Heart Score ◽  
Coronary Syndrome ◽  
St Segment

Background: Acute Coronary Syndrome (ACS) is one of the main problems in the field of cardiovascular diseases because of high hospitalization rate, high mortality and high medical cost. Rapid and accurate risk stratification is needed to calculate the risk of complication and right now exist two most used score which is GRACE and TIMI. Heart score has 5 simple variables that can be calculated easily and this score considered to have better predictive ability compared to other score. The aim of this study is to examine HEART score as a predictor for in hospital Major Cardiovascular Event (MACE) in patient diagnosed as Non ST Segment Elevation Acute Coronary Syndrome (NSTEACS) that hospitalized at Haji Adam Malik (HAM) General Hospital Medan. Methods: This is a prospective cohort study that includes 52 NSTEACS patient that hospitalized at HAM General Hospital since November 2018 until January 2019. Patient that diagnosed as NSTEACS were calculated for GRACE, TIMI, and HEART score then observed during hospitalization. Outcome of this study is MACE during hospitalization. Statistical analysis was performed to test HEART score as MACE predictor and then comparison was done with GRACE and TIMI Results: By using ROC curve analysis, the cut-off value of HEART score was 5 (AUC 0.947, 95% CI 0.883-0.997, p<0.01). Study subject that experienced MACE with HEART score ≥5 was 21 patients (87.5%) compared to 2 patients (7.1%). HEART score ≥5 can predict MACE with sensitivity 87.5%, specificity 92.9%, negative predictive value (NPV) 89.7% and positive predictive value (PPV) 91.3%. ROC curve comparison was done between HEART with GRACE and TIMI then it was found that HEART score has better predictive ability compared to TIMI and GRACE (AUC 0.947 vs 0.829 vs 0.807, p < 0.01). Conclusion: HEART score can be used as MACE predictor which is relatively simpler but have better predictive ability compared to GRACE and TIMI.

scholarly journals Guided endodontics: accuracy of access cavity preparation and discrimination of angular and linear deviation on canal accessing ability—an ex vivo study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yinghui Su ◽  
Chenghui Chen ◽  
Chiahua Lin ◽  
Huina Lee ◽  
Kerkong Chen ◽  
...  
Keyword(s):  
Roc Curve ◽  
Ex Vivo ◽  
Predictive Ability ◽  
Angular Deviation ◽  
Cavity Preparation ◽  
Apical Position ◽  
Anterior Teeth ◽  
Significant Difference ◽  
Different Types ◽  
3D Printed

Abstract Background Guided endodontics technique has been introduced for years, but the accuracy in different types of teeth has yet to be assessed. The aim of this study is to evaluate the accuracy of three dimensional (3D)-printed endodontic guides for access cavity preparation in different types of teeth, and to evaluate the predictive ability of angular and linear deviation on canal accessibility ex vivo. Method Eighty-four extracted human teeth were mounted into six jaw models and categorised into three groups: anterior teeth (AT), premolar (P), and molar (M). Preoperative cone beam computed tomography (CBCT) and surface scans were taken and matched using implant planning software. Virtual access cavity planning was performed, and templates were produced using a 3D printer. After access cavities were performed, the canal accessibility was recorded. Postoperative CBCT scans were superimposed in software. Coronal and apical linear deviations and angular deviations were measured and evaluated with nonparametric statistics. The receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of angular and linear deviation for canal accessibility in SPSS v20. Results A total of 117 guided access cavities were created and 23 of them were record as canal inaccessibility, but all canals were accessible after canal negotiation. The average linear deviation for all groups was 0.13 ± 0.21 mm at coronal position, 0.46 ± 0.4 mm at apical position, and 2.8 ± 2.6° in angular deviation. At the coronal position, the linear deviations of the AT and P groups were significantly lower than M group deviation (P < 0.05), but no statistically significant difference between AT group and P group. The same results were found in linear deviation at the apical position and in angular deviation. The area under the ROC curve was 0.975 in angular deviation, 0.562 in linear deviation at the coronal position, and 0.786 at the apical position. Statistical significance was noted in linear deviation at the apical position and in angular deviation (P < 0.001). Conclusions In conclusion, this study demonstrated that the accuracy of access cavity preparation with 3D-printed endodontic guides was acceptable. The linear and angular deviations in the M group were significantly higher than those in the other groups, which might be caused by the interference of the opposite teeth. Angular deviation best discriminated the canal access ability of guided access cavity preparation. Graphical Abstract

scholarly journals What are the optimal cut-off points of anthropometric indices for prediction of overweight and obesity? Predictive validity of waist circumference, waist-to-hip and waist-to-height ratios

2020 ◽  
Vol 10 (2) ◽  
pp. 142-147 ◽  
Author(s):  
Helda Tutunchi ◽  
Mehrangiz Ebrahimi-Mameghani ◽  
Alireza Ostadrahimi ◽  
Mohammad Asghari-Jafarabadi
Keyword(s):  
Waist Circumference ◽  
Roc Curve ◽  
Predictive Ability ◽  
Diagnostic Value ◽  
Cross Sectional Study ◽  
Overweight And Obesity ◽  
Roc Curve Analysis ◽  
Anthropometric Indices ◽  
Cross Sectional ◽  
Anthropometric Indicators

Background: Planning for obesity prevention is an important global health priority. Our aim in this study was to find the optimal cut-off points of waist circumference (WC), waist- to- hipratio (WHR) and waist- to- height ratio (WHtR), as three anthropometric indices, for prediction of overweight and obesity. We also aimed to compare the predictive ability of these indices to introduce the best choice. Methods: In this cross-sectional study, a total of 500 subjects were investigated. Anthropometric indicators were measured using a standard protocol. We considered body mass index (BMI) as the simple and most commonly used index for measuring general obesity as the comparison indicator in the present study to assess the diagnostic value for other reported obesity indices.We also performed receiver operating characteristic (ROC) curve analysis to define the optimal cut-off points of the anthropometric indicators and the best indices for overweight and obesity. Results: The proposed optimal cut-offs for WC, WHtR, and WHR were 84 cm, 0.48 and 0.78for women and 98 cm, 0.56 and 0.87 for men, respectively. The area under the ROC curve ofWHtR (women: AUC=0.97, 95% CI: 0.96-0.99 vs. men: AUC=0.97, 95%CI: 0.96-0.99) and WC(women: AUC=0.97, 95% CI, 0.95-0.99 vs. men: AUC=0.98, 95% CI: 0.97-0.99) were greater than WHR (women: AUC=0.79, 95% CI =0.74-0.85 vs. men: AUC=0.84, 95% CI=0.79-0.88). Conclusion: This study demonstrated that the WC and WHtR indicators are stronger indicators compared to the others. However, further studies using desirable and also local cutoffs against more accurate techniques for body fat measurement such as computerized tumor (CT) scans and dual-energy x-ray absorptiometry (DEXA) are required.

scholarly journals Predicting Disease in Transition Dairy Cattle Based on Behaviors Measured Before Calving

Animals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 928
Author(s):  
Mohammad W. Sahar ◽  
Annabelle Beaver ◽  
Marina A. G. von Keyserlingk ◽  
Daniel M. Weary
Keyword(s):  
Dairy Cattle ◽  
Sensitivity And Specificity ◽  
Roc Curve ◽  
Predictive Value ◽  
Predictive Ability ◽  
Training Dataset ◽  
Test Dataset ◽  
Increased Risk ◽  
High Prevalence ◽  
Random Method

Dairy cattle are particularly susceptible to metritis, hyperketonemia (HYK), and mastitis in the weeks after calving. These high-prevalence transition diseases adversely affect animal welfare, milk production, and profitability. Our aim was to use prepartum behavior to predict which cows have an increased risk of developing these conditions after calving. The behavior of 213 multiparous and 105 primiparous Holsteins was recorded for approximately three weeks before calving by an electronic feeding system. Cows were also monitored for signs of metritis, HYK, and mastitis in the weeks after calving. The data were split using a stratified random method: we used 70% of our data (hereafter referred to as the “training” dataset) to develop the model and the remaining 30% of data (i.e., the “test” dataset) to assess the model’s predictive ability. Separate models were developed for primiparous and multiparous animals. The area under the receiver operating characteristic (ROC) curve using the test dataset for multiparous cows was 0.83, sensitivity and specificity were 73% and 80%, positive predictive value (PPV) was 73%, and negative predictive value (NPV) was 80%. The area under the ROC curve using the test dataset for primiparous cows was 0.86, sensitivity and specificity were 71% and 84%, PPV was 77%, and NPV was 80%. We conclude that prepartum behavior can be used to predict cows at risk of metritis, HYK, and mastitis after calving.

scholarly journals Time-Effect of Donor and Recipient Characteristics on Graft Survival after Kidney Transplantation

2019 ◽  
Vol 28 (04) ◽  
pp. 249-254
Author(s):  
Jingyan Yang ◽  
Christine L. Sardo Molmenti ◽  
Joaquin Cagliani ◽  
Harish Datta ◽  
Elliot Grodstein ◽  
...  
Keyword(s):  
Graft Survival ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Predictive Ability ◽  
Roc Curves ◽  
Time Dependent ◽  
First Year ◽  
Kidney Allocation System ◽  
Donor Characteristics ◽  
United Network

AbstractThe kidney allocation system (KAS) is based on quality-based “longevity matching” strategies that provide only a momentary snapshot of expected outcomes at the time of transplantation. The purpose of our study was to define on a continuous timeline the relative and mutual interactions of donor and recipient characteristics on graft survival.Total 39,108 subjects who underwent kidney transplant between October 25, 1999 and January 1, 2007 were identified in the United Network for Organ Sharing dataset. Our primary outcome was graft survival. Time-dependent receiver operating characteristic (ROC) curves and area under time-dependent ROC curve (AUC) were used to compare the predictive ability of the two allocation systems.During the first year after transplantation, both donor and recipient models showed identical relevance. From the first to the sixth years, although the two ROC curves were nearly identical, the donor model outweighed the recipient model. Both models intersected again at the sixth year. From that time onward, the ROC curve for recipient characteristics model predominated over the ROC curve for donor characteristics model. The predictive value of the recipient model (AUC = 0.752) was greater than that of the donor model (AUC = 0.673)We hope that this model will provide additional guidance and risk stratification to further optimize organ allocation based on the dynamic interaction of both donor and recipient characteristics over time.

scholarly journals Anthropometric Measures and Frailty Prediction in the Elderly: An Easy-to-Use Tool

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Vera Elizabeth Closs ◽  
Patricia Klarmann Ziegelmann ◽  
João Henrique Ferreira Flores ◽  
Irenio Gomes ◽  
Carla Helena Augustin Schwanke
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Roc Curve ◽  
Predictive Ability ◽  
Network Models ◽  
The Elderly ◽  
Neural Network Models ◽  
Anthropometric Measures ◽  
Cross Sectional ◽  
Sagittal Abdominal Diameter

Purpose. Anthropometry is a useful tool for assessing some risk factors for frailty. Thus, the aim of this study was to verify the discriminatory performance of anthropometric measures in identifying frailty in the elderly and to create an easy-to-use tool. Methods. Cross-sectional study: a subset from the Multidimensional Study of the Elderly in the Family Health Strategy (EMI-SUS) evaluating 538 older adults. Individuals were classified using the Fried Phenotype criteria, and 26 anthropometric measures were obtained. The predictive ability of anthropometric measures in identifying frailty was identified through logistic regression and an artificial neural network. The accuracy of the final models was assessed with an ROC curve. Results. The final model comprised the following predictors: weight, waist circumference, bicipital skinfold, sagittal abdominal diameter, and age. The final neural network models presented a higher ROC curve of 0.78 (CI 95% 0.74–0.82) (P<0.001) than the logistic regression model, with an ROC curve of 0.71 (CI 95% 0.66–0.77) (P<0.001). Conclusion. The neural network model provides a reliable tool for identifying prefrailty/frailty in the elderly, with the advantage of being easy to apply in the primary health care. It may help to provide timely interventions to ameliorate the risk of adverse events.

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