Pharmacological Myopia Control Influence on Quality of Life and Psyche among Adolescents

Impact of defocus incorporated multiple segments (DIMS) spectacle lenses for myopia control on quality of life of the children: a qualitative study

OBJECTIVE

To assess the impact of DIMS (defocus incorporated multiple segments) spectacle lenses on the quality of life of children using it.

METHODS

Separate in-depth interviews were conducted with children using DIMS as a myopia control strategy for at least 1 month and their parents based on prepared guides. The recorded audio of the interviews was transcribed, and the significant data points were coded using a hybrid approach, that is, both the inductive and deductive coding methods were used to identify themes. The generated codes were further grouped, categorised and finally fitted as per relevance into the subdomains of the four domains of the WHO Quality of Life-Brief framework, namely the domains of social relationships, physical, psychological and environmental health.

RESULTS

A total of 29 interviews were conducted, 15 with children (mean age: 12.47±2.13 years) and 14 with parents. Thematic analysis was done and a total of 63 codes were generated with 2, 16, 17 and 28 codes aligning to the domains of social relationships, environmental, psychological and physical health, respectively. Most parents did not notice any change in their child's visual behaviour, yet children did experience symptoms such as peripheral blurred vision, eyestrain, headache, haloes and more during the adaptation period. High-cost, scratch-prone nature and difficulty in procurement were a few concerns raised by parents.

CONCLUSIONS

Participants were satisfied with most of the facets of social relationships, physical and psychological health domains. However, a few facets such as quality, accessibility and finance of the environmental health domain need improvement.

Health-related quality of life measurements in children and adolescents with refractive errors: A scoping review

Background

Refractive errors, particularly myopia, are the leading visual disorders worldwide, significantly affecting the quality of life (QOL) even after correction. This scoping review focuses on health-related quality of life (HRQOL) measurements for children and adolescents with refractive errors.

Main text

We explored generic and disease-specific HRQOL tools, examining their content, psychometric properties, and the impact of various interventions on QOL. Two English databases-PubMed, Embase, and one Chinese database, CNKI, were searched for relevant studies published from January 2001 to October 2023. Inclusion criteria encompassed studies using standardized instruments to assess the QOL of children aged 0-18 with refractive errors. The review compares prevalent HRQOL measurements, analyzes children's refractive error assessments, and discusses intervention effects on patient QOL.

Conclusions

The study underlines the necessity of developing disease-specific QOL instruments for very young children and serves as a practical guide for researchers in this field. The findings advocate for a targeted approach in HRQOL assessment among the pediatric population, identifying critical gaps in current methodologies.

Identifying and Exploring the Impact Factors for Intraocular Pressure Prediction in Myopic Children with Atropine Control Utilizing Multivariate Adaptive Regression Splines

PURPOSE

The treatment of childhood myopia often involves the use of topical atropine, which has been demonstrated to be effective in decelerating the progression of myopia. It is crucial to monitor intraocular pressure (IOP) to ensure the safety of topical atropine. This study aims to identify the optimal machine learning IOP-monitoring module and establish a precise baseline IOP as a clinical safety reference for atropine medication.

METHODS

Data from 1545 eyes of 1171 children receiving atropine for myopia were retrospectively analyzed. Nineteen variables including patient demographics, medical history, refractive error, and IOP measurements were considered. The data were analyzed using a multivariate adaptive regression spline (MARS) model to analyze the impact of different factors on the End IOP.

RESULTS

The MARS model identified age, baseline IOP, End Spherical, duration of previous atropine treatment, and duration of current atropine treatment as the five most significant factors influencing the End IOP. The outcomes revealed that the baseline IOP had the most significant effect on final IOP, exhibiting a notable knot at 14 mmHg. When the baseline IOP was equal to or exceeded 14 mmHg, there was a positive correlation between atropine use and End IOP, suggesting that atropine may increase the End IOP in children with a baseline IOP greater than 14 mmHg.

CONCLUSIONS

MARS model demonstrates a better ability to capture nonlinearity than classic multiple linear regression for predicting End IOP. It is crucial to acknowledge that administrating atropine may elevate intraocular pressure when the baseline IOP exceeds 14 mmHg. These findings offer valuable insights into factors affecting IOP in children undergoing atropine treatment for myopia, enabling clinicians to make informed decisions regarding treatment options.

Effect of low-dose atropine eyedrops on pupil metrics: results after half a year of treatment and cessation

Purpose

To evaluate the effect of low-dose atropine eyedrops on pupil metrics.

Methods

This study was based on a randomized, double-masked, placebo-controlled, and cross-over trial in mainland China. In phase 1, subjects received 0.01% atropine or placebo once nightly. After 1 year, the atropine group switched to placebo (atropine-placebo group), and the placebo group switched to atropine (placebo-atropine group). Ocular parameters were measured at the crossover time point (at the 12th month) and the 18th month.

Results

Of 105 subjects who completed the study, 48 and 57 children were allocated into the atropine-placebo and placebo-atropine groups, respectively. After cessation, the photopic pupil diameter (PD) and mesopic PD both decreased (− 0.46 ± 0.47 mm, P < 0.001; − 0.30 ± 0.74 mm, P = 0.008), and the constriction ratio (CR, %) increased (4.39 ± 7.54, P < 0.001) compared with values at the crossover time point of the atropine-placebo group; pupil metrics of the atropine-placebo group had no difference from the values at the crossover time point of the placebo-atropine group. After 6 months of treatment, the photopic PD and the mesopic PD increased (0.54 ± 0.67 mm, P < 0.001; 0.53 ± 0.89 mm, P < 0.001), the CR (%) decreased (− 2.53 ± 8.64, P < 0.001) compared with values at the crossover time point of the placebo-atropine group. There was no significant relationship between pupil metrics and myopia progression during 0.01% atropine treatment.

Conclusion

Pupil metrics and the CR could return to pre-atropine levels after cessation. Pupil metrics had no significant effect on myopia progression during treatment.

Evaluating the Effect of Topical Atropine Use for Myopia Control on Intraocular Pressure by Using Machine Learning

Atropine is a common treatment used in children with myopia. However, it probably affects intraocular pressure (IOP) under some conditions. Our research aims to analyze clinical data by using machine learning models to evaluate the effect of 19 important factors on intraocular pressure (IOP) in children with myopia treated with topical atropine. The data is collected on 1545 eyes with spherical equivalent (SE) less than -10.0 diopters (D) treated with atropine for myopia control. Four machine learning models, namely multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and eXtreme gradient boosting (XGBoost), were used. Linear regression (LR) was used for benchmarking. The 10-fold cross-validation method was used to estimate the performance of the five methods. The main outcome measure is that the 19 important factors associated with atropine use that may affect IOP are evaluated using machine learning models. Endpoint IOP at the last visit was set as the target variable. The results show that the top five significant variables, including baseline IOP, recruitment duration, age, total duration and previous cumulative dosage, were identified as most significant for evaluating the effect of atropine use for treating myopia on IOP. We can conclude that the use of machine learning methods to evaluate factors that affect IOP in children with myopia treated with topical atropine is promising. XGBoost is the best predictive model, and baseline IOP is the most accurate predictive factor for endpoint IOP among all machine learning approaches.