So Goto1, Naoyuki Maeda2, Shizuka Koh3, Kazuhiko Ohnuma4, Kenichi Hayashi5, Ikko Iehisa6, Toru Noda6, Kohji Nishida3. 1. Department of Ophthalmology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan; National Hospital Organization, Tokyo Medical Center, Tokyo, Japan. 2. Department of Ophthalmology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan. Electronic address: nmaeda@ophthal.med.osaka-u.ac.jp. 3. Department of Ophthalmology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan. 4. Center for Frontier Medical Engineering, Chiba University, Chiba, Japan. 5. Tomey Corporation, Nagoya, Japan. 6. National Hospital Organization, Tokyo Medical Center, Tokyo, Japan.
Abstract
PURPOSE: To evaluate the accuracy of a new formula for predicting postoperative anterior chamber depth (ACD) with preoperative angle-to-angle (ATA) depth using anterior segment (AS) optical coherence tomography (OCT) and to compare it with established methods. DESIGN: Retrospective consecutive case series. PARTICIPANTS: Three hundred four eyes (276 patients) implanted with acrylic intraocular lenses (IOLs) were divided randomly into a training set (152 eyes) and a validation set (152 eyes). METHODS: Based on the training set data, the postoperative ACD measured 1 month after surgery was analyzed via multiple linear regression analysis with 5 preoperatively measured variables: ATA depth, ATA width, preoperative ACD measured with AS OCT, axial length (AL), and corneal power. A new regression formula for predicting postoperative ACD was developed using the results of the stepwise analysis. In the validation set data, the coefficients of determination (R2) between the measured postoperative ACD and the predicted postoperative ACD obtained using the new formula were compared with those obtained using the Sanders-Retzlaff-Kraff theoretic (SRK/T) and Haigis formulas. The absolute prediction errors were compared with each formula. MAIN OUTCOME MEASURES: Postoperative ACD, median absolute prediction error of postoperative ACD, and ocular biometric parameters. RESULTS: In the training set, ATA depth yielded the highest standard partial regression coefficient value, indicating that ATA depth is the most effective parameter for predicting postoperative ACD. The new regression formula was developed with 3 variables; ATA depth, preoperative ACD, and AL. In the validation set, the postoperative ACDs of the new formula, the SRK/T formula, and Haigis formula were predicted with R2 of 0.71, 0.36, and 0.55, respectively, and the medians of the absolute prediction errors were 0.10 mm, 0.65 mm, and 0.30 mm, respectively. The absolute prediction error with the new formula was significantly smaller than those obtained with the SRK/T and Haigis formulas (P < 0.0001). CONCLUSIONS: The new formula with 3 preoperative parameters-ATA depth, preoperative ACD, and AL-predicted postoperative ACD more accurately than the SRK/T and Haigis formulas. It may be possible to improve the accuracy of IOL power calculation using an improved postoperative ACD prediction with the ATA depth measured by AS OCT.
RCT Entities:
PURPOSE: To evaluate the accuracy of a new formula for predicting postoperative anterior chamber depth (ACD) with preoperative angle-to-angle (ATA) depth using anterior segment (AS) optical coherence tomography (OCT) and to compare it with established methods. DESIGN: Retrospective consecutive case series. PARTICIPANTS: Three hundred four eyes (276 patients) implanted with acrylic intraocular lenses (IOLs) were divided randomly into a training set (152 eyes) and a validation set (152 eyes). METHODS: Based on the training set data, the postoperative ACD measured 1 month after surgery was analyzed via multiple linear regression analysis with 5 preoperatively measured variables: ATA depth, ATA width, preoperative ACD measured with AS OCT, axial length (AL), and corneal power. A new regression formula for predicting postoperative ACD was developed using the results of the stepwise analysis. In the validation set data, the coefficients of determination (R2) between the measured postoperative ACD and the predicted postoperative ACD obtained using the new formula were compared with those obtained using the Sanders-Retzlaff-Kraff theoretic (SRK/T) and Haigis formulas. The absolute prediction errors were compared with each formula. MAIN OUTCOME MEASURES: Postoperative ACD, median absolute prediction error of postoperative ACD, and ocular biometric parameters. RESULTS: In the training set, ATA depth yielded the highest standard partial regression coefficient value, indicating that ATA depth is the most effective parameter for predicting postoperative ACD. The new regression formula was developed with 3 variables; ATA depth, preoperative ACD, and AL. In the validation set, the postoperative ACDs of the new formula, the SRK/T formula, and Haigis formula were predicted with R2 of 0.71, 0.36, and 0.55, respectively, and the medians of the absolute prediction errors were 0.10 mm, 0.65 mm, and 0.30 mm, respectively. The absolute prediction error with the new formula was significantly smaller than those obtained with the SRK/T and Haigis formulas (P < 0.0001). CONCLUSIONS: The new formula with 3 preoperative parameters-ATA depth, preoperative ACD, and AL-predicted postoperative ACD more accurately than the SRK/T and Haigis formulas. It may be possible to improve the accuracy of IOL power calculation using an improved postoperative ACD prediction with the ATA depth measured by AS OCT.