Related Experiment Video
Updated: May 20, 2025

Binocular Dynamic Visual Acuity in Eyeglass-Corrected Myopic Patients
Published on: March 29, 2022
Multiformula Prediction Range: a univariate predictor of IOL Power Calculation Accuracy
Daniel Romero1, Alicia Cárceles Montoya1, Jorge L Alió2
1Department of Opthalmology, General University Hospital of Elche (Spain).
Purpose:
The purpose of this study was to evaluate the influence of range between the predictions of 5 different calculation formulas in IOL power calculation accuracy.
Settings:
General University Hospital of Elche, Spain.
Design:
Retrospective Sequential Cohort.
Methods:
In this retrospective sequential cohort, the LenStar LS900 (Haag-Streit, Koeniz, Switzerland) was used for the preoperative biometry. The predicted spherical equivalent refraction of the implanted IOL were calculated for 5 different formulas: Barrett Universal II, Emmetropia Verifying Optical (EVO) 2.0, Hill RBF-3.0, Kane, PEARL-DGS. Multiformula Prediction Range was defined as the range of the refractive error predicted by the 5 formulas. According to the median of the Multiformula Prediction Range the sample was divided into a low and high spread group (LS and HS respectively).
Results:
278 eyes were included. The standard deviation of the prediction error was significantly lower in the LS group for all included formulas. For the Barrett Universal II, EVO 2.0, RBF-3.0, Kane and PEARL-DGS formulae, the median absolute error (MdAE) was significantly lower in the LS group compared to the HS group (p-values of 0.001, 0.027, 0.004, 0.028 and 0.035, respectively). The percentage of eyes within the ±0.50D PE range was significantly higher in the LS group for all five analyzed formulas.
Conclusions:
Multiformula Prediction Range can be a novel univariate predictor of IOL power calculation accuracy and a potential determinant for identifying patient suitable for immediate sequential cataract surgery. Accuracy was consistently higher for all five included formulas in the Low Spread group.
More Related Videos
Related Concept Videos
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
Range Rule of Thumb to Interpret Standard Deviation
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Expected Frequencies in Goodness-of-Fit Tests
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
Standard Deviation of Calculated Results
A broad Gaussian distribution curve has a wider standard deviation, representing a data set with...

