Research Article Logistic Regression Model Using Scheimpflug-Placido Cornea Topographer Parameters to Diagnose Keratoconus Emre Altinkurt , 1 Ozkan Avci , 1 Orkun Muftuoglu , 2 Adem Ugurlu , 3 Zafer Cebeci , 1 and Kemal Turgay Ozbilen 1 1 Istanbul University, Istanbul Faculty of Medicine, Department of Ophthalmology, Fatih/Capa, Istanbul 34093, Turkey 2 FEBO, Professor of Ophthalmology, Koc University, Faculty of Medicine Department of Ophthalmology, Zeytinburnu/ ˙ Istanbul 34010, Turkey 3 Erzincan University, Faculty of Medicine, Department of Ophthalmology, Fatih, Erzincan 24100, Turkey Correspondence should be addressed to Emre Altinkurt; altinkurtemre@gmail.com Received 13 February 2021; Revised 11 April 2021; Accepted 10 May 2021; Published 19 May 2021 Academic Editor: Vincent M. Borderie Copyright©2021EmreAltinkurtetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Purpose. Diagnose keratoconus by establishing an effective logistic regression model from the data obtained with a Scheimpflug- Placido cornea topographer. Methods. Topographical parameters of 125 eyes of 70 patients diagnosed with keratoconus by clinical or topographical findings were compared with 120 eyes of 63 patients who were defined as keratorefractive surgery candidates. e receiver operating character (ROC) curve analysis was performed to determine the diagnostic ability of the topographic pa- rameters. e data set of parameters with an AUROC (area under the ROC curve) value greater than 0.9 was analyzed with logistic regression analysis (LRA) to determine the most predictive model that could diagnose keratoconus. A logit formula of the model wasbuilt,andthelogitvaluesofeveryeyeinthestudywerecalculatedaccordingtothisformula.en,anROCanalysisofthelogit values was done. Results. Baiocchi Calossi Versaci front index (BCV f ) had the highest AUROC value (0.976) in the study. e LRA model, which had the highest prediction ability, had 97.5% accuracy, 96.8% sensitivity, and 99.2% specificity. e most significant parameters were found to be BCV f (p � 0.001), BCV b (Baiocchi Calossi Versaci back) (p � 0.002), posterior rf (apical radius of the flattest meridian of the aspherotoric surface in 4.5 mm diameter of the cornea) (p � 0.005), central corneal thickness (p � 0.072), and minimum corneal thickness (p � 0.494). Conclusions. e LRA model can distinguish keratoconus corneas from normal ones with high accuracy without the need for complex computer algorithms. 1. Introduction Keratoconus is an ectatic disorder that is characterized by progressive stromal thinning and protrusion of the cornea with irregular astigmatism [1]. Ectasia progression after refractive surgery in patients with keratoconus has been reported in previous studies [2]. e prevalence of kerato- conus is higher among refractive surgery candidates com- pared to the general population [3], and operating on a cornea with keratoconus can cause corneal ectasia after refractive surgery [4]. Topography systems are very useful in the diagnosis of keratoconus. Still, an exact diagnosis is difficult because threshold criteria remain to be defined. Moreover, examining each parameter in the topography device one by one takes time. is study aimed to gauge the most useful parameters of the Sirius Scheimpflug-Placido topographer in determining keratoconus eyes from normal eyes and to find an accurate logistic regression model for diagnosing keratoconus with these parameters. 2. Patients and Methods e study was conducted in agreement with the ethical standards established in the Declaration of Helsinki and endorsed by the local clinical research ethics committee. Informed consent was obtained from all patients. e study consisted of 120 eyes from 63 patients who had undergone keratorefractive surgery (normal group) and 125 eyes from Hindawi Journal of Ophthalmology Volume 2021, Article ID 5528927, 7 pages https://doi.org/10.1155/2021/5528927