Quantification of Astigmatism Induced by Pterygium Using Automated Image Analysis

PURPOSE:To determine the factors influencing pterygium-induced astigmatism (PIA) and to develop a prediction model of PIA using these factors. METHODS:This cross-sectional study included 97 eyes of 97 patients who underwent a pterygium excision and a limbal conjunctival autograft. Anterior segment p...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Cornea 2016-03, Vol.35 (3), p.370-376
Hauptverfasser: Han, Sang Beom, Jeon, Hyun Sun, Kim, Moosang, Lee, Seung-Jun, Yang, Hee Kyung, Hwang, Jeong-Min, Kim, Kwang Gi, Hyon, Joon Young, Wee, Won Ryang
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:PURPOSE:To determine the factors influencing pterygium-induced astigmatism (PIA) and to develop a prediction model of PIA using these factors. METHODS:This cross-sectional study included 97 eyes of 97 patients who underwent a pterygium excision and a limbal conjunctival autograft. Anterior segment photographs were taken preoperatively, and corneal topography was done preoperatively and at 3 months postoperatively. PIA was defined as the vector difference between the topographic astigmatism preoperatively and at 3 months postoperatively. Image analysis was performed using anterior segment photographs to measure the relative length (RL) (horizontal length of pterygium invading the cornea/horizontal corneal diameter), relative width (width of pterygium invading the cornea/vertical corneal diameter), relative area (area of pterygium invading the cornea/total corneal area), and vascularity index (VI) (degree of vascularity). Association between these factors and PIA was evaluated with univariate and multivariate analyses. We also attempted to generate a model for prediction of PIA using these factors. RESULTS:Univariate analysis showed that the RL, relative width, relative area, and VI were significantly associated with PIA (P < 0.001 for all variables, Pearson coefficient (r) = 0.708, 0.555, 0.606, and 0.642, respectively). In multivariate analysis, only the RL (P < 0.001) and VI (P < 0.001) had significant correlation with PIA. A multiple regression model for PIA was generated as followsPIA = 0.080 × RL (%) + 0.039 × VI – 0.823 (r = 0.502, F = 95.71, P < 0.001). CONCLUSIONS:Larger lengths and increased vascularity were associated with larger PIA. PIA can be predicted by evaluating the length and vascularity of pterygium involving the cornea.
ISSN:0277-3740
1536-4798
DOI:10.1097/ICO.0000000000000728