Purpose: To compare refractive prediction errors between phacotrabeculectomy and phacoemulsification.

Methods: Refractive prediction error was defined as the difference in spherical equivalent between the predicted value using the Barrett Universal II formula and the actual value obtained at postoperative one month. Forty-eight eyes that had undergone phacotrabeculectomy (19 eyes, open-angle glaucoma; 29 eyes, angle-closure glaucoma) were matched with 48 eyes that had undergone phacoemulsification by age, average keratometry value and axial length (AL), and their prediction errors were compared. The factors associated with prediction errors were analyzed by multivariable regression analyses.

Results: The phacotrabeculectomy group showed a larger absolute prediction error than the phacoemulsification group (0.51 ± 0.37 Diopters vs. 0.38 ± 0.22 Diopters, = 0.033). Larger absolute prediction error was associated with longer AL ( = 0.010) and higher intraocular pressure (IOP) difference ( = 0.012). Hyperopic shift (prediction error > 0) was associated with shallower preoperative anterior chamber depth (ACD) ( = 0.024) and larger IOP difference ( = 0.031). In the phacotrabeculectomy group, the prediction error was inversely correlated with AL: long eyes showed myopic shift and short eyes hyperopic shift ( = 0.002).

Conclusions: Surgeons should be aware of the possibility of worse refractive outcomes when planning phacotrabeculectomy, especially in eyes with high preoperative IOP, shallow ACD, and/or extreme AL.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10488334PMC
http://dx.doi.org/10.3390/jcm12175706DOI Listing

Publication Analysis

Top Keywords

prediction error
24
refractive prediction
12
prediction errors
12
prediction
9
factors associated
8
eyes undergone
8
phacotrabeculectomy eyes
8
phacotrabeculectomy group
8
larger absolute
8
absolute prediction
8

Similar Publications

A prediction model for electrical strength of gaseous medium based on molecular reactivity descriptors and machine learning method.

J Mol Model

January 2025

Hubei Key Laboratory·for High-Efficiency-Utilization of Solar Energy and Operation, Control of Energy-Storage System, Hubei-University of Technology, Wuhan, 430068, China.

Context: Ionization and adsorption in gas discharge are similar to electrophilic and nucleophilic reactions. The molecular descriptors characterizing reactions such as electrostatic potential descriptors are useful in predicting the electrical strength of environmentally friendly gases. In this study, descriptors of 73 molecules are employed for correlation analysis with electrical strength.

View Article and Find Full Text PDF

The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive accuracy and interpretability for brain age prediction tasks.

View Article and Find Full Text PDF

The development of optical sensors for label-free quantification of cell parameters has numerous uses in the biomedical arena. However, using current optical probes requires the laborious collection of sufficiently large datasets that can be used to calibrate optical probe signals to true metabolite concentrations. Further, most practitioners find it difficult to confidently adapt black box chemometric models that are difficult to troubleshoot in high-stakes applications such as biopharmaceutical manufacturing.

View Article and Find Full Text PDF

This paper proposes a hybridized model for air quality forecasting that combines the Support Vector Regression (SVR) method with Harris Hawks Optimization (HHO) called (HHO-SVR). The proposed HHO-SVR model utilizes five datasets from the environmental protection agency's Downscaler Model (DS) to predict Particulate Matter ([Formula: see text]) levels. In order to assess the efficacy of the suggested HHO-SVR forecasting model, we employ metrics such as Mean Absolute Percentage Error (MAPE), Average, Standard Deviation (SD), Best Fit, Worst Fit, and CPU time.

View Article and Find Full Text PDF

Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization.

Sci Rep

January 2025

Department of Oral & Maxillofacial Surgery and Diagnostic Sciences, Faculty of Dentistry, Taif University, 21944, Taif, Saudi Arabia.

This study investigates the use of machine learning models to predict solubility of rivaroxaban in binary solvents based on temperature (T), mass fraction (w), and solvent type. Using a dataset with over 250 data points and including solvents encoded with one-hot encoding, four models were compared: Gradient Boosting (GB), Light Gradient Boosting (LGB), Extra Trees (ET), and Random Forest (RF). The Jellyfish Optimizer (JO) algorithm was applied to tune hyperparameters, enhancing model performance.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!