PURPOSE: Empirical soft toric contact lens fitting based on manifest refraction and keratometry often presents unanticipated fitting and power errors upon initial lens dispensing. However, corneal topography may provide features that influence soft toric lens performance, flexure, and back vertex power in situ, which may assist in improved fitting guidelines. In this study, quantitative topographic descriptors were generated and analyzed as potential variables in predicting soft toric fitting success. METHODS: One hundred five eyes of 54 patients were empirically fit with back surface toric, prism ballasted, soft contact lenses after videokeratography was performed with the EyeSys 2000 (v. 4.0) or Humphrey Atlas (v. A6) instrument. Custom software was written to generate 54 separate quantitative descriptors of shape and astigmatism from the raw data files. A logistic regression was used to determine which variables significantly contributed to a successful or failed fit. RESULTS: Two types of empirical fitting failures were identified: loose fit (n = 15) and power errors (n = 17). The following variables were associated with a fitting failure: flat simulated keratometry (SimKf2b) within the central 3 mm zone, steep simulated keratometry (SimKs2b) within the central 3 mm zone, a difference between central and peripheral flat meridian axis (DIFFAXIS), and a difference between central and peripheral astigmatism (DIFFASTIG). For fitting failures caused by power errors, a larger steep SimKs2b (p< 0.01) and smaller DIFFAXIS (p< 0.05) were associated with a failed fit. For failures caused by physical fit of a selected base curve, a smaller DIFFAXIS (p< 0.05), larger steep SimKs2b (p< 0.05), and larger DIFFASTIG (p< 0.01) were associated with a failed fit. CONCLUSIONS: Novel quantitative descriptors of corneal shape and toricity derived from topography are associated with empirical soft toric contact lens fitting failures. Future algorithms or recommendations for improved soft toric lens selection may be derived from such indices to develop a predictive model for successful soft toric lens fitting using corneal topography data.
PURPOSE: Empirical soft toric contact lens fitting based on manifest refraction and keratometry often presents unanticipated fitting and power errors upon initial lens dispensing. However, corneal topography may provide features that influence soft toric lens performance, flexure, and back vertex power in situ, which may assist in improved fitting guidelines. In this study, quantitative topographic descriptors were generated and analyzed as potential variables in predicting soft toric fitting success. METHODS: One hundred five eyes of 54 patients were empirically fit with back surface toric, prism ballasted, soft contact lenses after videokeratography was performed with the EyeSys 2000 (v. 4.0) or Humphrey Atlas (v. A6) instrument. Custom software was written to generate 54 separate quantitative descriptors of shape and astigmatism from the raw data files. A logistic regression was used to determine which variables significantly contributed to a successful or failed fit. RESULTS: Two types of empirical fitting failures were identified: loose fit (n = 15) and power errors (n = 17). The following variables were associated with a fitting failure: flat simulated keratometry (SimKf2b) within the central 3 mm zone, steep simulated keratometry (SimKs2b) within the central 3 mm zone, a difference between central and peripheral flat meridian axis (DIFFAXIS), and a difference between central and peripheral astigmatism (DIFFASTIG). For fitting failures caused by power errors, a larger steep SimKs2b (p< 0.01) and smaller DIFFAXIS (p< 0.05) were associated with a failed fit. For failures caused by physical fit of a selected base curve, a smaller DIFFAXIS (p< 0.05), larger steep SimKs2b (p< 0.05), and larger DIFFASTIG (p< 0.01) were associated with a failed fit. CONCLUSIONS: Novel quantitative descriptors of corneal shape and toricity derived from topography are associated with empirical soft toric contact lens fitting failures. Future algorithms or recommendations for improved soft toric lens selection may be derived from such indices to develop a predictive model for successful soft toric lens fitting using corneal topography data.