Multivariate Regression Analysis to Predict Postoperative Refractive Astigmatism in Cataract Surgery

The Preoperative Factors for the Undercorrection of Myopia in an Extend Depth-of-Focus Intraocular Lens: A Case-Control Study

We aim to investigate the potential risk factors for undercorrection in those who have received extend depth-of-focus (EDOF) intraocular lens (IOL) implantation. A retrospective case-control study was conducted in which patients who had received one type of EDOF IOL implantation were included. The patients were divided into the residual group and non-residual group according to the final postoperative sphere power. The preoperative data include the refractive, topographic, endothelial, and biometric parameters obtained. A generalized linear model was generated to yield the adjusted odds ratio (aOR) and 95% confidence interval (CI) of each parameter of the residual myopia. One month postoperatively, the UDVA was better in the non-residual group than in the residual group (p = 0.010), and the final SE was significantly higher in the residual group than in the non-residual group (p < 0.001). In the multivariable analysis, the high preoperative cycloplegia sphere power, higher TCRP, higher corneal cylinder power, and longer AXL significantly correlated to the presence of postoperative residual myopia (all p < 0.05). Furthermore, the higher preoperative cycloplegia sphere power, higher TCRP, higher corneal cylinder power, longer AXL, larger ACD, and larger WTW were significantly associated with postoperative residual myopia in the high-myopia population (all p < 0.001), while the higher preoperative cycloplegia sphere power, higher TCRP, and longer AXL were related to postoperative residual myopia in the low-myopia population (all p < 0.05). In conclusion, high preoperative myopia and corneal refractive power correlate to high risk of residual myopia after EDOF IOL implantation, especially in the high-myopia population.

Prediction of postoperative refractive astigmatism before toric intraocular lens implantation

Background

To determine the preoperative factors influencing refractive astigmatism after cataract surgery for astigmatism correction by toric intraocular lens (IOL) implantation and to evaluate the prediction model using these factors.

Methods

Prospective, observational case series. The right eyes of forty consecutive patients with preoperative corneal astigmatism of the total cornea of 1.5 diopters (D) or more in magnitude and scheduled for implantation of a non-toric IOL during cataract surgery with a 2.4-mm temporal clear corneal incision were examined prospectively. The vertical/horizontal astigmatism component (J0) and oblique astigmatism component (J45) of refractive and corneal astigmatism were converted using power vector analysis. Multivariate regression analysis was performed with refractive astigmatism at three months postoperatively as the dependent variable, and preoperative parameters including age, sex, refractive astigmatism, corneal astigmatism, sphere, spherical equivalent, intraocular pressure, corneal thickness, anterior chamber depth, lens thickness, lens positions (tilt and decentration), axial length, and corneal higher order aberrations as independent variables. The root mean square (RMS) errors were calculated to express the regression model fit.

Results

The regression model for the J0 component was \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ Postoperative\kern0.34em refractive\kern0.2em J0=1.05\times Coneal\kern0.2em J0-0.14 $$\end{document}PostoperativerefractiveJ0=1.05×ConealJ0−0.14 (R² = 0.96, P < 0.001). The model for the J45 component was \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ Postoperative\kern0.34em refractive\kern0.2em J45=0.68\times Coneal\kern0.2em J45+0.19\times Preoperative\kern0.34em refractive\kern0.2em J45-0.06 $$\end{document}PostoperativerefractiveJ45=0.68×ConealJ45+0.19×PreoperativerefractiveJ45−0.06 (R² = 0.72, P < 0.001). The mean RMS errors for preoperative corneal astigmatism alone and the multivariate model were 0.58 D and 0.46 D, respectively. There was a statistically significant difference between them (P = 0.02).

Conclusions

Refractive astigmatism after implantation of a toric IOL can be predicted by the regression model more accurately than by corneal astigmatism alone. However, the prediction of oblique astigmatism remains a challenge.

A Data-Driven Predictive Machine Learning Model for Efficiently Storing Temperature-Sensitive Medical Products, Such as Vaccines: Case Study: Pharmacies in Rwanda

Temperature control is the key element during medicine storage. Pharmacies sell some medical products which are kept in fridges. The opening and closing of the fridge while taking some medicine makes the outside hot air enter the fridge, which will increase the inner fridge temperature. When the frequency of opening and closing of the fridge is increased, the temperature may go beyond the allowed storage temperature range. In this paper, we are proposing a model with the help of machine learning that will be used in multiple chambers fridges to keep indicating the time remaining for the inner temperature to go beyond the allowed range, and if the time is short, the system will propose to the pharmacist not to open that particular room and proposes a room that has enough time slots (time to reach the upper limit temperature). By using training data got from a thermoelectric cooler-based fridge, we constructed a multiple linear regression model that can predict the required time for a given room to reach the cut-off temperature in case that room is opened. The built model was evaluated using the coefficient of determination R 2 and is found to be 77%, and then it can be used to develop a multiple room smart fridge for efficiently storing highly sensitive medical products.