Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study

Background Electronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children. Methods and findings Real-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ≤ −6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records (≥3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered. Conclusions To our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia.This study was funded by the National Key R&D Program of China (2018YFC0116500), the National Natural Science Foundation of China (91546101, 81822010), the Guangdong Science and Technology Innovation Leading Talents (2017TX04R031), and Youth Pearl River Scholar in Guangdong (2016)

A multicentre study on the clinical characteristics of newborns infected with coronavirus disease 2019 during the omicron wave

ObjectiveTo investigate the clinical characteristics and outcomes of newborns infected with coronavirus disease 2019 (COVID-19) during the Omicron wave.MethodsFrom December 1, 2022, to January 4, 2023, clinical data were collected from neonates with COVID-19 who were admitted to 10 hospitals in Foshan City, China. Their epidemiological histories, clinical manifestations and outcomes were analysed. The neonates were divided into symptomatic and asymptomatic groups. The t test or χ2 test was used for comparisons between groups.ResultsA total of 286 children were diagnosed, including 166 males, 120 females, 273 full-term infants and 13 premature infants. They were 5.5 (0–30) days old on average when they were admitted to the hospital. These children had contact with patients who tested positive for COVID-19 and were infected through horizontal transmission. This study included 33 asymptomatic and 253 symptomatic patients, among whom 143 were diagnosed with upper respiratory tract infections and 110 were diagnosed with pneumonia. There were no severe or critical patients. Fever (220 patients) was the most common clinical manifestation, with a duration of 1.1 (1–6) days. The next most common clinical manifestations were cough with nasal congestion or runny nose (4 patients), cough (34 patients), poor appetite (7 patients), shortness of breath (15 patients), and poor general status (1 patient). There were no significant abnormalities in routine blood tests among the neonates infected with COVID-19 except for mononucleosis. However, compared with the asymptomatic group, in the symptomatic group, the leukocyte and neutrophil granulocyte counts were significantly decreased, and the monocyte count was significantly increased. C-reactive protein (CRP) levels were significantly increased (≥10 mg/L) in 9 patients. Myocardial enzyme, liver function, kidney function and other tests showed no obvious abnormalities.ConclusionsIn this study, neonates infected with the Omicron variant were asymptomatic or had mild disease. Symptomatic patients had lower leucocyte and neutrophil levels than asymptomatic patients

Mediator MED23 regulates basal transcription in vivo via an interaction with P-TEFb

The Mediator is a multi-subunit complex that transduces regulatory information from transcription regulators to the RNA polymerase II apparatus. Growing evidence suggests that Mediator plays roles in multiple stages of eukaryotic transcription, including elongation. However, the detailed mechanism by which Mediator regulates elongation remains elusive. In this study, we demonstrate that Mediator MED23 subunit controls a basal level of transcription by recruiting elongation factor P-TEFb, via an interaction with its CDK9 subunit. The mRNA level of Egr1, a MED23-controlled model gene, is reduced 4–5 fold in Med23−/− ES cells under an unstimulated condition, but Med23-deficiency does not alter the occupancies of RNAP II, GTFs, Mediator complex, or activator ELK1 at the Egr1 promoter. Instead, Med23 depletion results in a significant decrease in P-TEFb and RNAP II (Ser2P) binding at the coding region, but no changes for several other elongation regulators, such as DSIF and NELF. ChIP-seq revealed that Med23-deficiency partially reduced the P-TEFb occupancy at a set of MED23-regulated gene promoters. Further, we demonstrate that MED23 interacts with CDK9 in vivo and in vitro. Collectively, these results provide the mechanistic insight into how Mediator promotes RNAP II into transcription elongation

Mediator MED23 regulates basal transcription in vivo via an interaction with P-TEFb

The Mediator is a multi-subunit complex that transduces regulatory information from transcription regulators to the RNA polymerase II apparatus. Growing evidence suggests that Mediator plays roles in multiple stages of eukaryotic transcription, including elongation. However, the detailed mechanism by which Mediator regulates elongation remains elusive. In this study, we demonstrate that Mediator MED23 subunit controls a basal level of transcription by recruiting elongation factor P-TEFb, via an interaction with its CDK9 subunit. The mRNA level of Egr1, a MED23-controlled model gene, is reduced 4–5 fold in Med23−/− ES cells under an unstimulated condition, but Med23-deficiency does not alter the occupancies of RNAP II, GTFs, Mediator complex, or activator ELK1 at the Egr1 promoter. Instead, Med23 depletion results in a significant decrease in P-TEFb and RNAP II (Ser2P) binding at the coding region, but no changes for several other elongation regulators, such as DSIF and NELF. ChIP-seq revealed that Med23-deficiency partially reduced the P-TEFb occupancy at a set of MED23-regulated gene promoters. Further, we demonstrate that MED23 interacts with CDK9 in vivo and in vitro. Collectively, these results provide the mechanistic insight into how Mediator promotes RNAP II into transcription elongation

Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study.

BackgroundElectronic medical records provide large-scale real-world clinical data for use in developing clinical decision systems. However, sophisticated methodology and analytical skills are required to handle the large-scale datasets necessary for the optimisation of prediction accuracy. Myopia is a common cause of vision loss. Current approaches to control myopia progression are effective but have significant side effects. Therefore, identifying those at greatest risk who should undergo targeted therapy is of great clinical importance. The objective of this study was to apply big data and machine learning technology to develop an algorithm that can predict the onset of high myopia, at specific future time points, among Chinese school-aged children.Methods and findingsReal-world clinical refraction data were derived from electronic medical record systems in 8 ophthalmic centres from January 1, 2005, to December 30, 2015. The variables of age, spherical equivalent (SE), and annual progression rate were used to develop an algorithm to predict SE and onset of high myopia (SE ≤ -6.0 dioptres) up to 10 years in the future. Random forest machine learning was used for algorithm training and validation. Electronic medical records from the Zhongshan Ophthalmic Centre (a major tertiary ophthalmic centre in China) were used as the training set. Ten-fold cross-validation and out-of-bag (OOB) methods were applied for internal validation. The remaining 7 independent datasets were used for external validation. Two population-based datasets, which had no participant overlap with the ophthalmic-centre-based datasets, were used for multi-resource validation testing. The main outcomes and measures were the area under the curve (AUC) values for predicting the onset of high myopia over 10 years and the presence of high myopia at 18 years of age. In total, 687,063 multiple visit records (≥3 records) of 129,242 individuals in the ophthalmic-centre-based electronic medical record databases and 17,113 follow-up records of 3,215 participants in population-based cohorts were included in the analysis. Our algorithm accurately predicted the presence of high myopia in internal validation (the AUC ranged from 0.903 to 0.986 for 3 years, 0.875 to 0.901 for 5 years, and 0.852 to 0.888 for 8 years), external validation (the AUC ranged from 0.874 to 0.976 for 3 years, 0.847 to 0.921 for 5 years, and 0.802 to 0.886 for 8 years), and multi-resource testing (the AUC ranged from 0.752 to 0.869 for 4 years). With respect to the prediction of high myopia development by 18 years of age, as a surrogate of high myopia in adulthood, the algorithm provided clinically acceptable accuracy over 3 years (the AUC ranged from 0.940 to 0.985), 5 years (the AUC ranged from 0.856 to 0.901), and even 8 years (the AUC ranged from 0.801 to 0.837). Meanwhile, our algorithm achieved clinically acceptable prediction of the actual refraction values at future time points, which is supported by the regressive performance and calibration curves. Although the algorithm achieved balanced and robust performance, concerns about the compromised quality of real-world clinical data and over-fitting issues should be cautiously considered.ConclusionsTo our knowledge, this study, for the first time, used large-scale data collected from electronic health records to demonstrate the contribution of big data and machine learning approaches to improved prediction of myopia prognosis in Chinese school-aged children. This work provides evidence for transforming clinical practice, health policy-making, and precise individualised interventions regarding the practical control of school-aged myopia