Edsel B Ing1,2, Royce Ing2. 1. Department of Ophthalmology & Vision Sciences, University of Toronto, Toronto, Ontario, Canada. 2. Toronto Eyelid, Strabismus & Orbit Surgery Clinic, Toronto, Ontario, Canada.
Abstract
OBJECTIVE: To illustrate the utility of a nomogram for the prediction of giant cell arteritis (GCA). METHOD: A nomogram was constructed from a multivariable logistic regression prediction model with 10 covariates: age, sex, clinical temporal artery abnormality, new-onset headache, jaw claudication, vision loss, diplopia, erythrocyte sedimentation rate, C-reactive protein, and platelet level. RESULTS: The magnitude and location of the nomogram scale for each predictor variable graphically illustrates the net effect of each covariate and is especially useful for continuous variables such as age and bloodwork values. CONCLUSIONS: Nomograms allow integration and synthesis of the relative importance of clinical variables and provide a graphic representation of the odds ratios, p values, and confidence intervals of logistic regression prediction models. Although nomograms and prediction rules cannot substitute for clinical judgment, they help objectify and optimize the individualized risk assessments for patients with suspected GCA.
OBJECTIVE: To illustrate the utility of a nomogram for the prediction of giant cell arteritis (GCA). METHOD: A nomogram was constructed from a multivariable logistic regression prediction model with 10 covariates: age, sex, clinical temporal artery abnormality, new-onset headache, jaw claudication, vision loss, diplopia, erythrocyte sedimentation rate, C-reactive protein, and platelet level. RESULTS: The magnitude and location of the nomogram scale for each predictor variable graphically illustrates the net effect of each covariate and is especially useful for continuous variables such as age and bloodwork values. CONCLUSIONS: Nomograms allow integration and synthesis of the relative importance of clinical variables and provide a graphic representation of the odds ratios, p values, and confidence intervals of logistic regression prediction models. Although nomograms and prediction rules cannot substitute for clinical judgment, they help objectify and optimize the individualized risk assessments for patients with suspected GCA.
Authors: Kevin L Rieck; Tanaz A Kermani; Kristine M Thomsen; William S Harmsen; Matthew J Karban; Kenneth J Warrington Journal: J Oral Maxillofac Surg Date: 2010-07-31 Impact factor: 1.895
Authors: Tim Spelman; Claire Meyniel; Juan Ignacio Rojas; Alessandra Lugaresi; Guillermo Izquierdo; Francois Grand'Maison; Cavit Boz; Raed Alroughani; Eva Havrdova; Dana Horakova; Gerardo Iuliano; Pierre Duquette; Murat Terzi; Pierre Grammond; Raymond Hupperts; Jeannette Lechner-Scott; Celia Oreja-Guevara; Eugenio Pucci; Freek Verheul; Marcela Fiol; Vincent Van Pesch; Edgardo Cristiano; Thor Petersen; Fraser Moore; Tomas Kalincik; Vilija Jokubaitis; Maria Trojano; Helmut Butzkueven Journal: Mult Scler Date: 2016-11-25 Impact factor: 6.312
Authors: Edsel B Ing; Dan Ni Wang; Abirami Kirubarajan; Etienne Benard-Seguin; Jingyi Ma; James P Farmer; Michel J Belliveau; Galina Sholohov; Nurhan Torun Journal: Neuroophthalmology Date: 2018-06-19
Authors: Edsel B Ing; Neil R Miller; Angeline Nguyen; Wanhua Su; Lulu L C D Bursztyn; Meredith Poole; Vinay Kansal; Andrew Toren; Dana Albreki; Jack G Mouhanna; Alla Muladzanov; Mikaël Bernier; Mark Gans; Dongho Lee; Colten Wendel; Claire Sheldon; Marc Shields; Lorne Bellan; Matthew Lee-Wing; Yasaman Mohadjer; Navdeep Nijhawan; Felix Tyndel; Arun N E Sundaram; Martin W Ten Hove; John J Chen; Amadeo R Rodriguez; Angela Hu; Nader Khalidi; Royce Ing; Samuel W K Wong; Nurhan Torun Journal: Clin Ophthalmol Date: 2019-02-21
Authors: Fadila Zerka; Samir Barakat; Sean Walsh; Marta Bogowicz; Ralph T H Leijenaar; Arthur Jochems; Benjamin Miraglio; David Townend; Philippe Lambin Journal: JCO Clin Cancer Inform Date: 2020-03