Effect of cataract-induced refractive change on intraocular lens power formula predictions.

Cataract-induced refractive change (CIRC) is the change in refraction induced by a cataract. It can amount to several diopters (D). It alters predicted errors in refraction following cataract surgery through changes in axial length measurement. This study determined the effect of CIRC on the accuracy of intraocular lens power formula predictions of refraction in 872 eyes of 662 patients. Regression of results gave -0.030 D prediction error per 1 D of CIRC, i.e. cataract-induced myopia and hyperopia tended to yield postoperative hyperopia and myopia, respectively. Theoretical determinations with a model eye supported this result. There was significant correlation of nuclear cataract opalescence with CIRC. Although these effects are difficult to identify based on changes in refraction, if biometers were able to identify cataract density and automatically adjust axial length measurement, IOL power predictions might improve.

Standardizing sum-of-segments axial length using refractive index models.

Optical biometry uses interferometry to measure the axial length (AL) of the eye. Traditionally, one-variable regression formulas have converted the optical path length measured by a biometer to a geometric AL. An alternate calculation of axial length sums the individual segments of the eye (sum-of-segments AL). This calculation has been shown to improve predictions of some intraocular lens power formulas when used in place of traditional axial length. Sum-of-segments ALs are determined from 13 refractive index models. As measured in 1695 eyes, these yield different ocular axial lengths. A path to standardization from these models is presented.

A comparison of two methods to calculate axial length.

PURPOSE: To compare prediction accuracy with the axial length (AL) calculation method of the Lenstar biometer (traditional AL) and that of the ARGOS biometer (sum-of-segments AL). SETTING: Private practice clinic. DESIGN: Comparative case series. MAIN OUTCOME MEASURE: Mean absolute error (MAE). METHODS: Predictions were developed for nine formulas, grouping them into those derived with ultrasound (US) (SRK/T, Holladay 1 and 2, Hoffer Q, Haigis) and those derived with optical biometry (Barrett, OKULIX, Olsen from PhacoOptics, and Olsen from Lenstar). Formulas were ranked by MAE using sum-of-segments AL and traditional AL, in short eyes (traditional AL <22.0 mm), long eyes (traditional AL >26.0 mm), and all eyes. RESULTS: The study comprised 1442 eyes (54 short eyes and 67 long eyes) of 1070 patients. The best-ranking formula for long eyes was Haigis using sum-of-segments AL. For short eyes and for all eyes, OKULIX using sum-of-segments AL was best. Using sum-of-segments AL instead of traditional AL, Holladay 2 improved the most; Olsen from PhacoOptics worsened the most. CONCLUSIONS: Some biometers used traditional AL, and at least one used sum-of-segments AL. Formula accuracy varied depending on how various commercial biometers internally calculate AL. Using sum-of-segments AL instead of traditional AL improved predictions for formulas designed on US data (SRK/T, Holladay 1, Holladay 2, Hoffer Q, and Haigis), although it worsened the Barrett and Olsen formulas. OKULIX was generally improved with sum-of-segments AL. When ranking by MAE, OKULIX ranked first.

Approximating sum-of-segments axial length from a traditional optical low-coherence reflectometry measurement.

PURPOSE: To present the Cooke-modified axial length (CMALInitial) method, which closely approximates sum-of-segments AL. Notably, sum-of-segments AL has been shown to improve predictions of many intraocular lens (IOL) power formulas; however, calculating this AL requires information that is not readily available. DESIGN: Comparative case series. PATIENTS: Distinct datasets of 215 eyes and 1442 eyes, which were measured before cataract surgery with a commercially available optical biometer (Lenstar LS 900), were identified. The AL measured by this machine was labeled "traditional AL." MAIN OUTCOME MEASURE: Prediction Error. METHODS: The CMALInitial, sum-of-segments AL, and traditional AL methods with Bland-Altman plots and r2 values were compared, along with graphs of prediction errors. RESULTS: The CMALInitial was developed from 215 eyes and evaluated in the 1422-eye validation dataset. The r2 for CMALInitial versus the sum-of-segments AL was 0.99983. The predictions based on CMALInitial were compared with those based on traditional AL using the Hoffer Q, Holladay 1, SRK/T, and Holladay 2 IOL formulas. The CMALInitial produced more accurate predictions in all four formulas (P < .001). Eyes in all datasets were then combined to create the final recommendation: CMALFinal = 1.23853 + 0.95855 × traditional AL - 0.05467 × lens thickness (all measurements in millimeters). CONCLUSIONS: A modified AL method (CMAL) was easy to calculate. Using CMAL improved predictions for at least four IOL power prediction formulas, especially at extreme ALs. Caution is advised if using CMAL with other formulas.

Comparison of 9 intraocular lens power calculation formulas.

PURPOSE: To evaluate the accuracy of 9 intraocular lens (IOL) calculation formulas using 2 optical biometers. SETTING: Private practice, Saint Joseph, Michigan, USA. DESIGN: Retrospective consecutive case series. METHODS: Nine IOL power formula predictions with observed refractions after cataract surgery were compared using 1 IOL platform. The performance of each formula was ranked for accuracy by machine and by axial length (AL). The Olsen was further divided by a preinstalled version (OlsenOLCR) and a purchased version (OlsenStandalone). The Holladay 2 was divided by whether a refraction was entered (Holladay 2PreSurgRef) or not (Holladay 2NoRef). The OLCR device used in the study was the Lenstar L5 900 and the PCI device, the IOLMaster. RESULTS: The formulas were ranked by the standard deviation of the prediction error (optical low-coherence reflectometry [OLCR], partial coherence interferometry [PCI]) as follows: OlsenStandalone (0.361, 0.446), Barrett Universal II (0.365, 0.387), OlsenOLCR (0.378, not applicable), Haigis (0.393, 0.401), T2 (0.397, 0.404), Super Formula (0.403, 0.410), Holladay 2NoRef (0.404, 0.417), Holladay 1 (0.408, 0.414), Holladay 2PreSurgRef (0.423, 0.432), Hoffer Q (0.428, 0.432), and SRK/T (0.433, 0.44). CONCLUSIONS: The formulas gave different results depending on which machine measurements were used. The Olsen formula was the most accurate with OLCR measurements, significantly better than the best formula with PCI measurements. The Olsen was better, regardless of AL. If only PCI measurements (without lens thickness) were available, the Barrett Universal II performed the best and the Olsen formula performed the worst. The preinstalled version of Olsen was not as good as the standalone version. The Holladay 2 formula performed better when the preoperative refraction was excluded. FINANCIAL DISCLOSURE: Neither author has a financial or proprietary interest in any material or method mentioned.

Prediction accuracy of preinstalled formulas on 2 optical biometers.

PURPOSE: To evaluate how well partial coherence interferometry (PCI) (IOLMaster) and optical low-coherence reflectometry (OLCR) (Lenstar LS 900) predict postoperative refractions using only the formulas that come preinstalled on the machines. SETTING: Private practice, Saint Joseph, Michigan, USA. DESIGN: Retrospective consecutive case series. METHODS: Eyes were measured with 2 biometers before cataract surgery. Six formulas were ranked by machine. Formulas were also ranked for extremely long and short eyes by averaging the ranks of 6 statistics (mean error, mean absolute error, standard deviation [SD], maximum error, and percentage of eyes within ±0.5 diopter [D] and ±1.0 D of prediction). RESULTS: Formulas were ranked by the SD of the prediction errors. The OLCR device outperformed the PCI device using the preinstalled formulas. The Olsen formula performed the best (0.378) but was preinstalled on the OLCR device only. Other formulas had the following SDs (OLCR device first, PCI device second): Haigis (0.393, 0.401), Holladay 1 (0.408, 0.414), Hoffer Q (0.428, 0.432), SRK/T (0.433, 0.440), and SRK II (0.623, 0.633). Rankings for long eye were (first to last) were Olsen, Haigis, SRK/T, Hoffer Q, and Holladay 1. Rankings for short eyes were Olsen, Haigis, Holladay 1, SRK/T, and Hoffer Q. CONCLUSIONS: The OLCR device outperformed the PCI device using the Olsen formula. The Olsen formula also ranked first for short eyes and long eyes. Other formulas performed about the same on both machines. The SRK II formula should be avoided.