Distinguishing myopia intervention candidates at premyopia stage in children: a nomogram based on primary refractive error screening parameters

Distinguishing myopia intervention candidates at premyopia stage in children: a nomogram based on primary refractive error screening parameters | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distinguishing myopia intervention candidates at premyopia stage in children: a nomogram based on primary refractive error screening parameters Jing Wu, Cong Zhang, Jingying Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6286151/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Aug, 2025 Read the published version in BMC Ophthalmology → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Hyperopia reserves of premyopia could be used to identify myopia intervention candidates for Chinese children. Primary refractive error screening parameters are commonly employed in clinical and community settings before cycloplegic assessment of hyperopia reserves; however, their utility in distinguishing intervention candidates at the premyopia stage remains underexplored. This study aimed to develop a nomogram based on these routinely measured parameters to support clinical decision-making for early myopia prevention. Methods: Pediatric patients (aged 4–17 years) from two medical centers in China were enrolled in this retrospective cohort study. A predictive model for the candidates of myopia intervention was developed using logistic regression with multiple imputations. The model included the following primary screening parameters: age, gender, uncorrected visual acuity (UCVA), average corneal curvature (ACC), non-cycloplegic spherical equivalent refraction (NCSER), axial length (AL), and the axial length to average corneal radius of curvature (AL/ACRC) ratio. The efficacy of the model was assessed using the area under the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA). R was employed to conduct all statistical analyses. Results: A total of 1006 participants (507 females, 499 boys) were enrolled, with 87.4% demonstrating CSER ≤ + 0.75D. In multivariate logistic regression, UCVA, NCSER, AL, and ALTOACRC were identified as independent predictors. These predictors were incorporated into a nomogram to predict the candidates of myopia intervention. The nomogram exhibited exceptional discrimination in the derivation set (AUC = 0.971, 95% CI: 0.957–0.984), whereas in the external validation set, the AUC was 0.921 (95% CI: 0.866–0.976) when a cutoff of 0.851 in derivation set was employed. Calibration was verified through the calibration curve and Hosmer-Lemeshow tests (P = 0.99 and P = 0.96, respectively), and the decision curve analysis demonstrated robust clinical utility for threshold probabilities of 0.10–1.00 in the derivation set and 0.20–1.00 in the external validation set. Conclusion: The nomogram derived from the parameters of primary refractive error screening has the potential to preliminarily predict myopia intervention candidates at the premyopia stage, thereby facilitating clinical decision-making in the context of early myopia prevention. Premyopia Nomogram primary refractive error screening myopia intervention candidates Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Myopia is among the most prevalent public health issues globally[ 1 ]. In certain Eastern nations, the prevalence of myopia ranges from 80–90%[ 2 ]. The early onset of myopia is strongly associated with high myopia[ 3 ], which markedly elevates the risk of permanent vision-threatening ocular disorders, including myopic macular degeneration, glaucoma, and cataracts [ 4 ]. Given the significant prevalence and serious consequences of myopia, its prevention is essential. Premyopia is the most prevalent refractive error in Chinese children, accounting for 52% of the population[ 5 ]. It is a distinctive transition status between hyperopia and myopia characterized by a reduction in hyperopia reserves, and a reduction in hyperopia reserves among peers is the strongest predictor of the incidence of myopia among nonmyopic school children [ 6 – 8 ]. Enrolling premyopic children in the comprehensive intervention of myopia is crucial for maintaining children in a premyopia state or delaying the onset and shift of myopia, as the fastest rate of change in refractive error occurs during the year prior to myopia onset[ 9 ] and myopia progression is challenging to manage after the onset of myopia[ 5 ]. Premyopia was defined by the International Myopia Institute[ 10 ] as a refractive state of an eye of >-0.50D and ≤ + 0.75D with other risk factors for myopia. Consequently, it is reasonable to identify Chinese children with hyperopia reserves ≤ + 0.75D as myopia intervention candidates and to identify myopia intervention candidates for eyecare providers. In clinics, hyperopia reserves of premyopia ≤ + 0.75D were confirmed by cycloplegic refraction[ 11 ]. However, many children are unsuitable for cycloplegia for many reasons, such as side effects like photophobia and tearing; ocular conditions like high ocular pressure, narrow chamber angle, and amblyopia[ 12 ]; system disease history like cardiovascular and nervous system disease history; or in some scenarios like eye health screening in schools and communities because of disturbance of studying activity during accommodation paralysis in cycloplegia[ 13 ]. Besides, in actual practice, eyecare providers would not regularly consider further cycloplegia in a child with a minor minus (positive) degree in NCSER to improve clinical efficiency. Thus, Cycloplegic refraction is not available for myopia intervention candidates at premyopia stage in practice. Instead of the golden standard of cycloplegic refraction for myopia intervention candidates, the primary refractive error screening, including visual acuity test, non cycloplegic autorefractor test, and ocular biometer test, was commonly operated in the eye clinic because of their shorter examination times, non-contact operation and no manifest side effect compared to cycloplegic refraction[ 13 ]. According to the Chinese government recommendation, the primary refractive error screening was adopted in schools or communities at least twice yearly for earlier myopia detection. Thus, Age, gender, UCVA by visual acuity test, ACC, NCSER by autorefractor test, AL, AL/ACRC by ocular biometer test were routinely gained by eyecare providers without cycloplegia. These parameters were broadly used in the myopia prediction model at the myopia stage (CSER≤-0.50 D)[ 14 – 19 ]. Nevertheless, to our knowledge, only one study has attempted to develop a model for myopia intervention candidates based on premyopia[ 20 ]. The Nanjing cohort study employed AL, AL/ACRC, and the number of parental myopia to develop a model for predicting overall survival at 1 and 2 years for premyopia, without a validation step[ 20 ]. Limited research has concentrated on the factors derived from primary refractive error screening for developing clinical prediction models for myopia intervention candidates at premyopia stage. This study mainly analyzed the parameters from primary refractive error screening to develop a prediction model. The prediction nomogram with external validation to predict the myopia intervention candidates at premyopia stage was also generated. Methods Research design and participants This retrospective cohort study included patients from 2 distinct medical centers in China. The derivation cohort comprised patients at The University of Hong Kong-Shenzhen Hospital, and the external validation cohort consisted of patients admitted at the Chongqing Shapingba District People's Hospital. Through the examination of the electronic medical records systems at the Chongqing and Shenzhen ophthalmology clinics, we picked up seven present predictors from primary refractive error screening: gender, age, UCVA, ACC, NCSER, AL, and ALTOACRC. Otherwise, most Chinese children would accept the first myopia screening after 3 years old as the Chinese government demanded, and the children after 18 years would be recognized as adults[ 21 ] with little necessity for myopia control. Hence, this study included pediatric patients aged between 4 and 17 years who underwent eye clinic examinations. Between April 2024 and October 2024, the definitive patient population for evaluation comprised 1006 individuals. Figure 1 illustrates a flow diagram of the study design. Exclusion criteria were:(1) the presence of ocular organic diseases, such as high intraocular pressure, strabismus, keratopathy, cataract, glaucoma, amblyopia, and fundus disease, which were considered to influence refractive status. (2) the presence of systemic diseases history such as hypertension, autoimmune disease, convulsions, cardiovascular system disorder, central nervous system disorder, which were considered to influence cycloplegic results. (3) the best monocular visual acuity was worse than 1.0 decimal (6/6). Diagnosis criteria of myopia intervention candidate at the premyopia stage The clinical gold standard for myopia is defined as CSER ≤-0.50D, while premyopia is characterized by CSER >-0.50D and ≤ + 0.75D, accompanied by other risk factors for myopia[ 11 ]. Given the significant prevalence of premyopia (52%) [ 5 ] among Chinese children and the unavoidable role of education as a risk factor for myopia in this population[ 22 ], premyopia represents a refractive condition with limited hyperopic reserves, placing children at an elevated risk for myopia and necessitating preventive interventions[ 2 ]. Consequently, this investigation identified Chinese individuals with CSER ≤ + 0.75D as candidates for myopia intervention. Ethics Approval This study adhered to the Declaration of Helsinki and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines. The medical ethics committee of the University of Hong Kong-Shenzhen Hospital and the Chongqing Shapingba District People's Hospital accepted this study protocol. The prerequisites for informed consent were exempted owing to the retroactive nature of the investigation and the absence of risk involved. Statistical analysis During the data preprocessing step, the spherical equivalent refraction (SER) was calculated by summing half of the cylinder power with the sphere power, while the anterior corneal curvature (ACC) was derived as the average of the horizontal and vertical meridians of corneal curvature. The ACRC was computed using the formula ACRC = 337.5 / ACC, and the AL/ACRC ratio was derived as the ratio of AL to ACRC. We chosed the right eye for the final analysis due to the significant link between the eyes. Statistical analysis was conducted utilizing R software (version 4.4.2; R Foundation for Statistical Computing, Vienna, Austria). First, Missing data was treated as missing at random for the complete data in our study. Multiple imputation via chained equations was employed for the limited missing data (missing data by cases = 7.06% < 10%), creating a single imputed dataset using the R mice package for subsequent analysis. The R compareGroups , glue, tidyr , and broom packages were used to describe the character of our research cohort. Subsequently, we employed the rms package in R to conduct logistic regression. Univariate logistic regression facilitated variable selection, with variables exhibiting P < 0.05 ultimately incorporated into multivariate logistic regression analysis to develop a predictive model. We utilized the statistically significant indicators to construct a prediction model for assessing the probability of myopia intervention candidates during the premyopia stage. Otherwise, we carried out a sensitivity analysis that repeated the logistic analysis and included all patients without the multiple imputation that missing values were deleted to reconfirm the entry variables of the model. In our study, the chosen parameters exhibited statistical significance and were utilized to construct the nomogram prediction models using the replot package. Additionally, various validation techniques were employed to assess the accuracy of the risk prediction model in both the derivation and external validation cohorts. We utilized the R language pROC package for the receiver characteristic curve (ROC) analysis[ 23 ]. Furthermore, the cutoff point of the nomogram established in the derivation set was utilized as a binary variable in the external validation set for ROC analysis to evaluate the efficacy of the nomogram in identifying candidates for myopia intervention. We utilized the rms package to construct and compute the calibration curves, which were employed to assess the nomogram's calibration, supplemented by the Hosmer-Lemeshow test using the HLtest.R resource. We utilized the DCA.R resource for decision curve analysis to assess the clinical feasibility of nomograms based on net benefit across various threshold probabilities in the cohort[ 24 ]. Results Characteristics of the study cohort Among the 1006 participants in our analysis, 507(50.4%) were girls, 499 (49.6%) were boys, 127 (12.6%) had CSER > + 0.75D, while 879(87.4%) had CSER ≤ + 0.75D. 503 participants (CSER ≤ + 0.75D:82.3% ) from the Shenzhen cohort constituted the derivation set, whereas 503 participants (CSER ≤ + 0.75D: 92.4%) from the Chongqing cohort formed the external validation set. Table 1 presents the characteristics of the patients in both groups. Table 1 Demographic characteristics. ALL CSER > + 0.75D CSER ≤ + 0.75D Derivation set External validation set gender N = 1006 N = 127 (12.6%) N = 879(87.4%) N = 503 N = 503 0.925 1 girls 507 (50.4%) 65 (51.2%) 442 (50.3%) 253 (50.3%) 254 (50.5%) boys 499 (49.6%) 62 (48.8%) 437 (49.7%) 250 (49.7%) 249 (49.5%) Age(years) 10.0 [8.00;12.0] 6.00 [5.00;8.00] 10.0 [9.00;12.0] < 0.001 9.00 [7.00;11.0] 11.0 [9.00;13.0] < 0.001 UCVA 0.30 [0.20;0.60] 0.70 [0.50;0.90] 0.30 [0. 15;0.50] < 0.001 0.40 [0.20;0.60] 0.30 [0. 15;0.50] < 0.001 ACC(D) 43.2 [42.2;44.2] 43.2 [42.2;44.2] 43.2 [42.3;44.2] 0.479 43.2 [42.2;44.2] 43.4 [42.4;44.2] 0.372 NCSER(D) -1.50 [-2.75;-0.50] 0.50 [0.00;1. 13] -1.75 [-3.00;-1.00] < 0.001 -1.25 [-2.50;-0. 13] -1.75 [-2.88;-1.00] < 0.001 CSER(D) -1.25 [-2.50;-0.38] 1.75 [1.25;2.44] -1.50 [-2.75;-0.75] < 0.001 -1.25 [-2.38;0. 13] -1.50 [-2.75;-0.75] < 0.001 AL(mm) 24.1 [23.3;24.9] 22.3 [21.7;22.9] 24.3 [23.6;25.0] < 0.001 24.0 [23. 1;24.7] 24.3 [23.6;25.0] < 0.001 ALTOACRC 3.09 [3.01;3. 18] 2.87 [2.80;2.91] 3.12 [3.05;3. 19] < 0.001 3.08 [2.96;3. 17] 3.11 [3.05;3. 19] < 0.001 UCVA: uncorrected visual acuity; ACC:average corneal curvature; NCSER:non-cycloplegic spherical equivalent refraction; CSER:cycloplegic spherical equivalent refraction; AL: axial length; ACRC: average corneal radius of curvature; D, diopter. There was no statistical significance in gender and ACC between CSER > + 0.75D and CSER ≤ + 0.75D. Additionally, the derivation set (N = 503) and an external validation set (N = 503) maintain comparable gender ( P = 1.000) and ACC( P = 0.372) distributions. Nonetheless, additional baseline parameters such as Age, UCVA, NCSER, CSER, AL, and ALTOACRC underscore significant disparities between the derivation set and the external validation set: In comparison to the derivation set, the external validation set comprised an older demographic (median age 11.0 vs. median age 9) with inferior UCVA (median 0.30 vs. median 0.40), more NCSER (median − 1.75D vs. median − 1.25D), more CSER (median − 1.50D vs. median − 1.25D), increased AL (median 24.3 mm vs. median 24.0 mm), and a higher ALTOACRC (median 3.11 vs. median 3.08). Predictive model for myopia candidates at the premyopia stage Univariate regression analysis was employed to identify predictive factors from the characteristics listed in Table 1, whereas multivariate logistic regression was utilized to develop the predictive model. As Table 2 showed, UCVA (OR = 0.2; 95%CI = 0.03–1.17), NCSER (OR = 0.45;95%CI = 0.25–0.8), AL (OR = 1.95;95%CI = 1.13–3.36), and ALTOACRC (OR = 2586998.38;95%CI = 3507.89-1907861532.08) were included in the predictive model. The logistic analysis included all patients without the multiple imputation that missing values were deleted, showing that the variables in the model did not change substantially and that UCVA, NCSER, AL, and ALTOACRC were highly correlated with myopia intervention candidates (Table 3). The prediction model utilizing UCVA, NCSER, AL, and ALTOACRC was formulated as a nomogram to quantitatively assess the risk probability for candidates requiring myopia intervention (Fig. 2). For example, employing the nomogram model, a patient with UCVA of 0.9, AL of 22.56mm, NCSER of + 0.25D, and ALTO ACRC of 2.89 had an estimated probability of myopia intervention candidates of 28.9%, which was below 50%; thus, further cycloplegia or myopia intervention would not be arranged by eyecare providers in clinical practice. Table 2 Logistic Regression analysis of gender, age, UCVA, ACC,NCSER (D),AL(mm),AL/ACRC for myopia intervention candidate. Predictor variable Intercept Univariate analysis Multivariate analysis OR 95%CI P value OR 95%CI P value 0 0–0 0 myopia intervention gender 0.81 0.51–1.29 0.379 candidate(hyperopiaAge(years) 1.76 1.56-2 < 0.001 reserves ≤ + 0.75D) UCVA 0.01 0-0.03 < 0.001 0.2 0.03–1.17 0.074 ACC(D) 1.07 0.91–1.25 0.425 NCSER(D) 0.14 0.09–0.22 < 0.001 0.45 0.25–0.8 0.006 AL(mm) 8.56 5.53–13.25 < 0.001 1.95 1.13–3.36 0.016 ALTOACRC 27065959682 153182821.07- 4782299793132.69 < 0.001 2586998.38 3507.89- 1907861532.08 < 0.001 UCVA: uncorrected visual acuity; ACC:average corneal curvature; NCSER:non-cycloplegic spherical equivalent refraction; CSER:cycloplegic spherical equivalent refraction; AL: axial length; ACRC: average corneal radius of curvature; D, diopter. Table 3 Sensitive analysis:Logistic Regression analysis of gender,age,UCVA,ACC,NCSER(D),AL(mm),AL/ACRC for myopia intervention candidate(data without imputation) UCVA: uncorrected visual acuity; ACC:average corneal curvature; NCSER:non-cycloplegic spherical equivalent refraction; CSER:cycloplegic spherical equivalent refraction; AL: axial length; ACRC: average corneal radius of curvature; D, diopter. The validation of the predictive model for myopia candidates For the predictive model, the nomogram's AUC was 0.971(0.957–0.984) in the derivation set and 0.985 (0.973–0.996) in the external validation set, which indicated good performance (Fig. 3). The nomogram's cutoff point in the derivation set was 0.851; we used 0.851 as a binary variable in the external validation set and found that the AUC in the external validation set was 0.921(0.866–0.976), which was also acceptable (Table 4). Table 4 Performance of the nomogram in predicting myopia intervention candidates AUC 95%CI Best shreshhold Specificity(%) Sensitivity(%) Accuracy(%) PPV(%) NPV(%) PLR NLR Derivation set 0.971 0.957–0.984 0.851 99.6 88.2 89.7 99.2 63.7 26.16 0.12 External validation set 0.921 0.866–0.976 0.5 92.1 92 92 99.3 48.6 11.66 0.09 The calibration curves indicated that the predictive model and the validation set exhibited a satisfactory degree of fit (Fig. 4): in the derivation set, the apparent calibration curve showed near-perfect alignment with the ideal reference line throughout the entire predicted probability range (0.0–1.0), with only minor deviations at higher predicted probabilities (> 0.8); in contrast, the external validation set displayed a bias-corrected calibration curve that closely resembled the ideal line in lower-risk strata (0.0–0.5), with deviations observed at predicted probabilities between 0.5 and 0.75. The Hosmer-Lemeshow test indicated a strong consistency between predicted and actual probabilities (derivation set: Chi-square 1.92, P = 0.99; external validation set: Chi-square 3.09, P = 0.96). The DCA indicated that the model had significant practicality across a broad threshold range (0.10-1.00) in the derivation set and 0.20-1.00 in the external validation set (Fig. 5). Discussion This study effectively created and validated a predictive nomogram for assessing the likelihood of myopia intervention candidates in Chinese children aged 4–17. By incorporating routinely measured parameters from primary refractive error screening—UCVA, NCSER, AL, and AL/ACRC ratio—the model provides a robust risk assessment tool for the premyopia stage. The nomogram exhibited robust performance, indicated by a high area under the curve (AUC), well-calibrated plots, non-significant Hosmer-Lemeshow tests, and positive decision curve analysis (DCA) results. These results suggested that the model may serve as a valuable clinical tool for early identification and intervention, ultimately reducing the long-term burden of myopia. Previous studies on myopia intervention candidates have primarily focused on myopic children [ 7 , 8 , 13 – 15 , 17 , 20 , 25 , 26 ], while recent research has increasingly emphasized early identification and intervention during the premyopia stage [ 2 , 5 , 10 ]. Notably, the only existing nomogram for premyopia [ 20 ] which has not been validated was based on AL, AL/ACRC, and the number of myopic parents, without incorporating primary refractive error screening parameters such as UCVA and NCSER. Among other studies lacking nomogram construction, the findings from the Peking cohort suggested that AL/ACRC could serve as an alternative predictor for identifying hyperopia reserve [ 27 ]. The data from the Shanghai cohort identified AL/ACRC, UCVA, AL, and NCSER as key risk factors for premyopia [ 28 ]. These studies underscore the value of AL/ACRC, UCVA, AL, and NCSER as strong predictive markers for identifying myopia intervention candidates at the premyopia stage. In this study, we developed a validated nomogram incorporating these routinely measured screening parameters, providing a novel tool for early myopia intervention at premyopia stage. In the process of validation, the nomogram displayed good discriminative power in both the derivation set (AUC = 0.971, 95% CI: 0.957–0.984) and the external validation set (AUC = 0.921, 95% CI: 0.866–0.976). In contrast to the derivation set, which exhibited a specificity of 99.6% and a sensitivity of 88.2%, indicating potential overfitting and diminished capacity to eliminate false positives in practical scenarios, the external validation set demonstrated a more balanced performance with a specificity of 92.1% and a sensitivity of 92%, thereby preserving exceptional predictive capability and showing robust generalizability. The calibration analysis indicated that the model underestimated risk at probabilities ranging from 0.5 to 0.75 in the validation cohort, perhaps resulting in excessively cautious clinical actions. Notwithstanding the identified biases, the P value exceeding 0.05 in our calibration assessments indicated an adequate overall fit, affirming its dependability for clinical applications. The decision curve analysis revealed a superior net benefit compared to both the "None" and "All" strategies across a broad spectrum of threshold probabilities in both the derivation and external validation sets, suggesting that the nomogram would provide a significant clinical advantage in identifying candidates for myopia intervention. We found no significant correlation between gender, age, or average corneal curvature (ACC) and the identification of myopia intervention candidates at the premyopia stage. This finding aligned with previous studies. The Peking cohort [ 27 ] specifically reported no association between ACC and hyperopia reserve in premyopic children, although gender and age were considered. Moreover, In both the Nanjing study [ 20 ] and the Shanghai cohort [ 28 ], gender, ACC, and age were excluded from the multivariate models. This study's merits included the external validation of our prediction model utilizing an independent cohort from Chongqing, characterized by an older group and a more severe myopic condition relative to Shenzhen. The high accuracy in the external validation set suggested that the nomogram was likely to be applicable to diverse populations across various regions in China. Furthermore,This work developed a nomogram utilizing characteristics from primary refractive error screening often employed by eyecare providers, which may have extensive uses in clinical practice. Certain limits must also be recognized. Firstly, the current study lacked an examination of other characteristics contributing to myopia intervention candidates, including caregiver’s myopia[ 20 ], caregiver's education; other lifestyle factors such as screen time, education status, outdoor time[ 5 ], genetic factors; binocular vision factors[ 26 ]. However, these factors are not our primary aim; we aimed to construct a nomogram based on parameters from primary refractive error screening that eyecare providers have for clinical convenience use. Secondly, the nomogram was constructed on a cross-sectional study with retrospective data. Thus, the accuracy of the model we set may be limited, although we use sensitivity analysis to maximize the accuracy of our model. Thirdly, the generalizability of the model may be limited in populations with a low prevalence of myopia intervention candidates, as the model was trained by a data set having a larger percentage of myopia intervention candidates (82.3%) compared to the Chinese cohort from preschool (62.7%) [ 5 ]. Finally, large-scale longitudinal data in the school children cohort, including other predictors for myopia intervention candidates, should be further explored to obtain high-level evidence for the nomogram’s clinical application in the future. Conclusions The developed nomogram, validated externally and based on UCVA, NCSER, AL, and ALTOACRC, showed strong prediction accuracy and practicality. This can be utilized to evaluate individual candidates for myopia intervention, hence offering a reference for early intervention decisions on myopia status for eyecare providers. Abbreviations UCVA: uncorrected visual acuity;ACC:average corneal curvature, NCSER:non-cycloplegic spherical equivalent refraction;AL:axial length;AL/ACRC:axial length to average corneal radius of curvature ratio;ROC curve:receiver operating characteristic curve;DCA:decision curve analysis; Declarations Acknowledgments No. Author contributions Jingying Wang wrote the manuscript and acquired the necessary funding. Cong Zhang collected the data and guaranteed its quality. Jing Wu collected the data and guaranteed its quality. All authors have approved the final manuscript. Funding This work was supported by the Chongqing Municipal Education Commission under Grant No. KJQN202202808 and the Health Commission of Chongqing Municipality under Grant No. 2023MSXM050. Availability of data and materials The data that support the finding of this study are available from the corresponding author, Jingying Wang, upon reasonable request.This study was approved by The Medical Ethics Committee of the University of Hong Kong-Shenzhen Hospital and the Chongqing Shapingba District People's Hospital, which waived the requirement for informed consent. Ethics approval and consent to participate This study adhered to the Declaration of Helsinki and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines. The medical ethics committee of the University of Hong Kong-Shenzhen Hospital and the Chongqing Shapingba District People's Hospital accepted this study protocol. The prerequisites for informed consent were exempted owing to the retroactive nature of the investigation and the absence of risk involved. Consent for publication Not applicable. Competing interests All authors had no conflicts of interest. Author details: Cong Zhang 3 1 Department of optometry, Chongqing Medical and Pharmaceutical College, No.82, middle road of university town, Shapingba district, ChongQing 401331, China. 2 Department of ophthalmology,The University of Hong Kong-Shenzhen Hospital, No. 1, Haiyuan First Road, Futian District, Shenzhen 518053, Guangdong,China. 3 Chongqing Shapingba District People's Hospital,Xiaolongkan New Street, Shapingba District, ChongQing 400032, China. Corresponding author: Jingying Wang. Email address: [email protected] References Jonas, J.B., et al., IMI Prevention of Myopia and Its Progression. Invest Ophthalmol Vis Sci, 2021. 62 (5): p. 6. He, X., et al., Effect of Repeated Low-level Red Light on Myopia Prevention Among Children in China With Premyopia: A Randomized Clinical Trial. JAMA Netw Open, 2023. 6 (4): p. e239612. Chua, S.Y., et al., age of onset of myopia predicts risk of high myopia in later childhood in myopic Singapore children. Ophthalmic Physiol Opt, 2016. 36 (4): p. 388-94. Haarman, A.E.G., et al., The Complications of Myopia: A Review and Meta-Analysis. Invest Ophthalmol Vis Sci, 2020. 61 (4): p. 49. Wang, C.Y., et al., Premyopia at Preschool Age: Population-based Evidence of Prevalence and Risk Factors from a Serial Survey in Taiwan. Ophthalmology, 2022. 129 (8): p. 880-889. Li, S.M., et al., Annual Incidences and Progressions of Myopia and High Myopia in Chinese Schoolchildren Based on a 5-Year Cohort Study. Invest Ophthalmol Vis Sci, 2022. 63 (1): p. 8. Tsai, D.C., et al., Myopia Development Among Young Schoolchildren: The Myopia Investigation Study in Taipei. Invest Ophthalmol Vis Sci, 2016. 57 (15): p. 6852-6860. Zadnik, K., et al., Prediction of Juvenile-Onset Myopia. JAMA Ophthalmol, 2015. 133 (6): p. 683-9. Mutti, D.O., et al., Refractive error, axial length, and relative peripheral refractive error before and after the onset of myopia. Invest Ophthalmol Vis Sci, 2007. 48 (6): p. 2510-9. Sankaridurg, P., et al., IMI 2023 Digest. Invest Ophthalmol Vis Sci, 2023. 64 (6): p. 7. Flitcroft, D.I., et al., IMI - Defining and Classifying Myopia: A Proposed Set of Standards for Clinical and Epidemiologic Studies. Invest Ophthalmol Vis Sci, 2019. 60 (3): p. M20-M30. Major, E., T. Dutson, and M. Moshirfar, Cycloplegia in Children: An Optometrist's Perspective. Clin Optom (Auckl), 2020. 12 : p. 129-133. Magome, K., et al., Prediction of cycloplegic refraction for noninvasive screening of children for refractive error. PLoS One, 2021. 16 (3): p. e0248494. Lin, S., et al., Using Decision Curve Analysis to Evaluate Common Strategies for Myopia Screening in School-Aged Children. Ophthalmic Epidemiol, 2019. 26 (4): p. 286-294. Mu, J., et al., Development of a nomogram for predicting myopia risk among school-age children: a case-control study. Ann Med, 2024. 56 (1): p. 2331056. Zhang, X., et al., The distribution of refraction by age and gender in a nonmyopic Chinese children population aged 6-12 years. BMC Ophthalmol, 2020. 20 (1): p. 439. Lin, H., et al., Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study. PLoS Med, 2018. 15 (11): p. e1002674. Ma, Y., et al., Cohort study with 4-year follow-up of myopia and refractive parameters in primary schoolchildren in Baoshan District, Shanghai. Clin Exp Ophthalmol, 2018. 46 (8): p. 861-872. Chen, Y., et al., Development and Validation of a Model to Predict Who Will Develop Myopia in the Following Year as a Criterion to Define Premyopia. Asia Pac J Ophthalmol (Phila), 2023. 12 (1): p. 38-43. Liu, L., et al., Prediction of premyopia and myopia in Chinese preschool children: a longitudinal cohort. BMC Ophthalmol, 2021. 21 (1): p. 283. Wang, Y.J., et al., China Stroke Statistics: an update on the 2019 report from the National Center for Healthcare Quality Management in Neurological Diseases, China National Clinical Research Center for Neurological Diseases, the Chinese Stroke Association, National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention and Institute for Global Neuroscience and Stroke Collaborations. Stroke Vasc Neurol, 2022. 7 (5): p. 415-450. Ding, X., et al., The Causal Effect of Education on Myopia: Evidence That More Exposure to Schooling, Rather Than Increased Age, Causes the Onset of Myopia. Investigative Ophthalmology & Visual Science, 2023. 64 . Latti, S., S. Niinivehmas, and O.T. Pentikainen, Rocker: Open source, easy-to-use tool for AUC and enrichment calculations and ROC visualization. J Cheminform, 2016. 8 (1): p. 45. Zhang, Y., et al., Establishment of a Risk Prediction Model for Non-alcoholic Fatty Liver Disease in Type 2 Diabetes. Diabetes Ther, 2020. 11 (9): p. 2057-2073. Guo, C., et al., Development and validation of a novel nomogram for predicting the occurrence of myopia in schoolchildren: A prospective cohort study. Am J Ophthalmol, 2022. 242 : p. 96-106. Wang, B., et al., Predicting the child who will become myopic - can we prevent onset? Clin Exp Optom, 2023. 106 (8): p. 815-824. Tang, T., et al., Axial length to corneal radius of curvature ratio and refractive error in Chinese preschoolers aged 4-6 years: a retrospective cross-sectional study. BMJ Open, 2023. 13 (12): p. e075115. Yin, Y., et al., Establishment of noncycloplegic methods for screening myopia and pre-myopia in preschool children. Front Med (Lausanne), 2023. 10 : p. 1291387. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Aug, 2025 Read the published version in BMC Ophthalmology → Version 1 posted Editorial decision: Revision requested 25 Apr, 2025 Reviews received at journal 24 Apr, 2025 Reviews received at journal 22 Apr, 2025 Reviewers agreed at journal 15 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Editor assigned by journal 01 Apr, 2025 Editor invited by journal 31 Mar, 2025 Submission checks completed at journal 30 Mar, 2025 First submitted to journal 30 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6286151","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444860508,"identity":"374a6547-39b3-4ca0-a0fc-3d84be6173e9","order_by":0,"name":"Jing Wu","email":"","orcid":"","institution":"The University of Hong Kong-Shenzhen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wu","suffix":""},{"id":444860510,"identity":"213657b6-97ef-4802-9fd4-75b85977dcbf","order_by":1,"name":"Cong Zhang","email":"","orcid":"","institution":"Chongqing Shapingba District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Zhang","suffix":""},{"id":444860512,"identity":"57e5e473-6578-4f65-afde-3e1c706fdc6f","order_by":2,"name":"Jingying Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACNmb+jw8kKuBcIrTwsTcYG1icIUWLHM8BM4nKNlK0sEkkpEncnGcnu3Z28wOGD2WHGfhnNxDUcthy5rZk4213jhkwzjh3mEHizgFCWhIbb0tuO5C47UYOAzNv22EGA4kEQlqSGaT/zoFq+UuUFp5jTBKSDVAtjERpYe9hNpA4BvHLwZ5z6TwSNwhokW/mYXwgUWMnu+1288MHP8qs5fhnENACA4wNEgwMB4AMHuLUw7SMglEwCkbBKMAKALFEQ7fWPNsgAAAAAElFTkSuQmCC","orcid":"","institution":"Chongqing Medical and Pharmaceutical College","correspondingAuthor":true,"prefix":"","firstName":"Jingying","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-03-23 02:53:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6286151/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6286151/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12886-025-04278-3","type":"published","date":"2025-08-28T15:56:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81699634,"identity":"8f170eb3-bf0f-425c-966b-206194a60399","added_by":"auto","created_at":"2025-04-30 13:03:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":786517,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of study design\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6286151/v1/77c3c334856d317432284d83.png"},{"id":81699719,"identity":"a36bde0c-067b-465f-842e-d282f8fe3287","added_by":"auto","created_at":"2025-04-30 13:03:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1296620,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram to predict the risk of myopia intervention candidate. *represents P value\u0026lt;0.05 , ** represents\u003c/p\u003e\n\u003cp\u003eP value\u0026lt;0.01, ***represents P value\u0026lt;0.001\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6286151/v1/f1cfa4dd9386fb92ed574501.png"},{"id":81699703,"identity":"0864893e-d456-4107-8273-02bffab71d67","added_by":"auto","created_at":"2025-04-30 13:03:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":956628,"visible":true,"origin":"","legend":"\u003cp\u003eROC validation of the prediction nomogram for myopia intervention candidates. The black line represents the perfomance of the nomogram in the derivation set (a) and external valiation set (b)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6286151/v1/336ccce1c24067c748a22e20.png"},{"id":81699865,"identity":"9c964a31-fb57-4f01-9db1-a7fd584f55d2","added_by":"auto","created_at":"2025-04-30 13:03:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1169705,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the prediction nomogram for myopia intervention candidates. The diagnoal dashed line (black) represents a perfect prediction by an ideal model, the green line represents the performance of the derivation set (a) and the external validation set(b),with results indicating that a closer fit to the diagnoal dashed line represents a better prediction.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6286151/v1/f810341e9db085aa7d2515d0.png"},{"id":81699637,"identity":"aa8d7789-a9fe-4a4f-9f24-2355b1efcc6a","added_by":"auto","created_at":"2025-04-30 13:03:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":700974,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis for the prediction nomogram for myopia intervention candidates. The thick solid line represents the assumption that all paticipants demand no myopia intervention, the thin solid line represents the assumption that all paticipancts demand myopia intervention, the dotted line represents the risk nomogram, (a) from the derivation set and (b) from the external validation set\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6286151/v1/f0d5a80843c8e3c06f25312c.png"},{"id":90344813,"identity":"ee4fcfc9-95ef-4668-bcc2-a087f479dc50","added_by":"auto","created_at":"2025-09-01 16:03:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7681402,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6286151/v1/7254c3ad-06ec-44dd-af23-1454eb05a06d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Distinguishing myopia intervention candidates at premyopia stage in children: a nomogram based on primary refractive error screening parameters","fulltext":[{"header":"Background","content":"\u003cp\u003eMyopia is among the most prevalent public health issues globally[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In certain Eastern nations, the prevalence of myopia ranges from 80\u0026ndash;90%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The early onset of myopia is strongly associated with high myopia[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], which markedly elevates the risk of permanent vision-threatening ocular disorders, including myopic macular degeneration, glaucoma, and cataracts [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Given the significant prevalence and serious consequences of myopia, its prevention is essential. Premyopia is the most prevalent refractive error in Chinese children, accounting for 52% of the population[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is a distinctive transition status between hyperopia and myopia characterized by a reduction in hyperopia reserves, and a reduction in hyperopia reserves among peers is the strongest predictor of the incidence of myopia among nonmyopic school children [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Enrolling premyopic children in the comprehensive intervention of myopia is crucial for maintaining children in a premyopia state or delaying the onset and shift of myopia, as the fastest rate of change in refractive error occurs during the year prior to myopia onset[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and myopia progression is challenging to manage after the onset of myopia[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Premyopia was defined by the International Myopia Institute[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] as a refractive state of an eye of \u0026gt;-0.50D and \u0026le;\u0026thinsp;+\u0026thinsp;0.75D with other risk factors for myopia. Consequently, it is reasonable to identify Chinese children with hyperopia reserves\u0026thinsp;\u0026le;\u0026thinsp;+\u0026thinsp;0.75D as myopia intervention candidates and to identify myopia intervention candidates for eyecare providers.\u003c/p\u003e \u003cp\u003eIn clinics, hyperopia reserves of premyopia\u0026thinsp;\u0026le;\u0026thinsp;+\u0026thinsp;0.75D were confirmed by cycloplegic refraction[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, many children are unsuitable for cycloplegia for many reasons, such as side effects like photophobia and tearing; ocular conditions like high ocular pressure, narrow chamber angle, and amblyopia[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; system disease history like cardiovascular and nervous system disease history; or in some scenarios like eye health screening in schools and communities because of disturbance of studying activity during accommodation paralysis in cycloplegia[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Besides, in actual practice, eyecare providers would not regularly consider further cycloplegia in a child with a minor minus (positive) degree in NCSER to improve clinical efficiency. Thus, Cycloplegic refraction is not available for myopia intervention candidates at premyopia stage in practice.\u003c/p\u003e \u003cp\u003eInstead of the golden standard of cycloplegic refraction for myopia intervention candidates, the primary refractive error screening, including visual acuity test, non cycloplegic autorefractor test, and ocular biometer test, was commonly operated in the eye clinic because of their shorter examination times, non-contact operation and no manifest side effect compared to cycloplegic refraction[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. According to the Chinese government recommendation, the primary refractive error screening was adopted in schools or communities at least twice yearly for earlier myopia detection. Thus, Age, gender, UCVA by visual acuity test, ACC, NCSER by autorefractor test, AL, AL/ACRC by ocular biometer test were routinely gained by eyecare providers without cycloplegia. These parameters were broadly used in the myopia prediction model at the myopia stage (CSER\u0026le;-0.50 D)[\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Nevertheless, to our knowledge, only one study has attempted to develop a model for myopia intervention candidates based on premyopia[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The Nanjing cohort study employed AL, AL/ACRC, and the number of parental myopia to develop a model for predicting overall survival at 1 and 2 years for premyopia, without a validation step[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Limited research has concentrated on the factors derived from primary refractive error screening for developing clinical prediction models for myopia intervention candidates at premyopia stage. This study mainly analyzed the parameters from primary refractive error screening to develop a prediction model. The prediction nomogram with external validation to predict the myopia intervention candidates at premyopia stage was also generated.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch design and participants\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study included patients from 2 distinct medical centers in China. The derivation cohort comprised patients at The University of Hong Kong-Shenzhen Hospital, and the external validation cohort consisted of patients admitted at the Chongqing Shapingba District People's Hospital. Through the examination of the electronic medical records systems at the Chongqing and Shenzhen ophthalmology clinics, we picked up seven present predictors from primary refractive error screening: gender, age, UCVA, ACC, NCSER, AL, and ALTOACRC. Otherwise, most Chinese children would accept the first myopia screening after 3 years old as the Chinese government demanded, and the children after 18 years would be recognized as adults[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] with little necessity for myopia control. Hence, this study included pediatric patients aged between 4 and 17 years who underwent eye clinic examinations. Between April 2024 and October 2024, the definitive patient population for evaluation comprised 1006 individuals. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates a flow diagram of the study design.\u003c/p\u003e \u003cp\u003eExclusion criteria were:(1) the presence of ocular organic diseases, such as high intraocular pressure, strabismus, keratopathy, cataract, glaucoma, amblyopia, and fundus disease, which were considered to influence refractive status. (2) the presence of systemic diseases history such as hypertension, autoimmune disease, convulsions, cardiovascular system disorder, central nervous system disorder, which were considered to influence cycloplegic results. (3) the best monocular visual acuity was worse than 1.0 decimal (6/6).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDiagnosis criteria of myopia intervention candidate at the premyopia stage\u003c/h3\u003e\n\u003cp\u003eThe clinical gold standard for myopia is defined as CSER \u0026le;-0.50D, while premyopia is characterized by CSER \u0026gt;-0.50D and \u0026le;\u0026thinsp;+\u0026thinsp;0.75D, accompanied by other risk factors for myopia[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Given the significant prevalence of premyopia (52%) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] among Chinese children and the unavoidable role of education as a risk factor for myopia in this population[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], premyopia represents a refractive condition with limited hyperopic reserves, placing children at an elevated risk for myopia and necessitating preventive interventions[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Consequently, this investigation identified Chinese individuals with CSER\u0026thinsp;\u0026le;\u0026thinsp;+\u0026thinsp;0.75D as candidates for myopia intervention.\u003c/p\u003e\n\u003ch3\u003eEthics Approval\u003c/h3\u003e\n\u003cp\u003e This study adhered to the Declaration of Helsinki and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines. The medical ethics committee of the University of Hong Kong-Shenzhen Hospital and the Chongqing Shapingba District People's Hospital accepted this study protocol. The prerequisites for informed consent were exempted owing to the retroactive nature of the investigation and the absence of risk involved.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDuring the data preprocessing step, the spherical equivalent refraction (SER) was calculated by summing half of the cylinder power with the sphere power, while the anterior corneal curvature (ACC) was derived as the average of the horizontal and vertical meridians of corneal curvature. The ACRC was computed using the formula ACRC\u0026thinsp;=\u0026thinsp;337.5 / ACC, and the AL/ACRC ratio was derived as the ratio of AL to ACRC. We chosed the right eye for the final analysis due to the significant link between the eyes.\u003c/p\u003e \u003cp\u003eStatistical analysis was conducted utilizing R software (version 4.4.2; R Foundation for Statistical Computing, Vienna, Austria). First, Missing data was treated as missing at random for the complete data in our study. Multiple imputation via chained equations was employed for the limited missing data (missing data by cases\u0026thinsp;=\u0026thinsp;7.06% \u0026lt; 10%), creating a single imputed dataset using the R \u003cem\u003emice\u003c/em\u003e package for subsequent analysis. The R \u003cem\u003ecompareGroups\u003c/em\u003e, \u003cem\u003eglue, tidyr\u003c/em\u003e, and \u003cem\u003ebroom\u003c/em\u003e packages were used to describe the character of our research cohort. Subsequently, we employed the \u003cem\u003erms\u003c/em\u003e package in R to conduct logistic regression. Univariate logistic regression facilitated variable selection, with variables exhibiting P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 ultimately incorporated into multivariate logistic regression analysis to develop a predictive model. We utilized the statistically significant indicators to construct a prediction model for assessing the probability of myopia intervention candidates during the premyopia stage. Otherwise, we carried out a sensitivity analysis that repeated the logistic analysis and included all patients without the multiple imputation that missing values were deleted to reconfirm the entry variables of the model. In our study, the chosen parameters exhibited statistical significance and were utilized to construct the nomogram prediction models using the \u003cem\u003ereplot\u003c/em\u003e package.\u003c/p\u003e \u003cp\u003eAdditionally, various validation techniques were employed to assess the accuracy of the risk prediction model in both the derivation and external validation cohorts. We utilized the R language \u003cem\u003epROC\u003c/em\u003e package for the receiver characteristic curve (ROC) analysis[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, the cutoff point of the nomogram established in the derivation set was utilized as a binary variable in the external validation set for ROC analysis to evaluate the efficacy of the nomogram in identifying candidates for myopia intervention. We utilized the \u003cem\u003erms\u003c/em\u003e package to construct and compute the calibration curves, which were employed to assess the nomogram's calibration, supplemented by the Hosmer-Lemeshow test using the HLtest.R resource. We utilized the DCA.R resource for decision curve analysis to assess the clinical feasibility of nomograms based on net benefit across various threshold probabilities in the cohort[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eCharacteristics of the study cohort\u003c/h2\u003e\n \u003cp\u003eAmong the 1006 participants in our analysis, 507(50.4%) were girls, 499 (49.6%) were boys, 127 (12.6%) had CSER\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;0.75D, while 879(87.4%) had CSER\u0026thinsp;\u0026le;\u0026thinsp;+\u0026thinsp;0.75D. 503 participants (CSER\u0026thinsp;\u0026le;\u0026thinsp;+\u0026thinsp;0.75D:82.3% ) from the Shenzhen cohort constituted the derivation set, whereas 503 participants (CSER\u0026thinsp;\u0026le;\u0026thinsp;+\u0026thinsp;0.75D: 92.4%) from the Chongqing cohort formed the external validation set. Table 1 presents the characteristics of the patients in both groups.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDemographic characteristics.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eALL CSER\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;0.75D CSER\u0026thinsp;\u0026le;\u0026thinsp;+\u0026thinsp;0.75D\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u003cbr\u003e\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDerivation set External validation set\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;127 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;879(87.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u0026thinsp;=\u0026thinsp;503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egirls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e507 (50.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (51.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e442 (50.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e253 (50.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254 (50.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eboys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e499 (49.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (48.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e437 (49.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250 (49.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249 (49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0 [8.00;12.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.00 [5.00;8.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0 [9.00;12.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.00 [7.00;11.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0 [9.00;13.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUCVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30 [0.20;0.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70 [0.50;0.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30 [0. 15;0.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40 [0.20;0.60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30 [0. 15;0.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACC(D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.2 [42.2;44.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.2 [42.2;44.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.2 [42.3;44.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.2 [42.2;44.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.4 [42.4;44.2]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNCSER(D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.50 [-2.75;-0.50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50 [0.00;1. 13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.75 [-3.00;-1.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.25 [-2.50;-0. 13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.75 [-2.88;-1.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCSER(D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.25 [-2.50;-0.38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75 [1.25;2.44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.50 [-2.75;-0.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.25 [-2.38;0. 13]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.50 [-2.75;-0.75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAL(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.1 [23.3;24.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.3 [21.7;22.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.3 [23.6;25.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.0 [23. 1;24.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.3 [23.6;25.0]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALTOACRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.09 [3.01;3. 18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.87 [2.80;2.91]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.12 [3.05;3. 19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.08 [2.96;3. 17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.11 [3.05;3. 19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eUCVA: uncorrected visual acuity; ACC:average corneal curvature; NCSER:non-cycloplegic spherical equivalent refraction;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eCSER:cycloplegic spherical equivalent refraction; AL: axial length; ACRC: average corneal radius of curvature; D, diopter.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003eThere was no statistical significance in gender and ACC between CSER\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;0.75D and CSER\u0026thinsp;\u0026le;\u0026thinsp;+\u0026thinsp;0.75D. Additionally, the derivation set (N\u0026thinsp;=\u0026thinsp;503) and an external validation set (N\u0026thinsp;=\u0026thinsp;503) maintain comparable gender (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.000) and ACC(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.372) distributions. Nonetheless, additional baseline parameters such as Age, UCVA, NCSER, CSER, AL, and ALTOACRC underscore significant disparities between the derivation set and the external validation set: In comparison to the derivation set, the external validation set comprised an older demographic (median age 11.0 vs. median age 9) with inferior UCVA (median 0.30 vs. median 0.40), more NCSER (median \u0026minus;\u0026thinsp;1.75D vs. median \u0026minus;\u0026thinsp;1.25D), more CSER (median \u0026minus;\u0026thinsp;1.50D vs. median \u0026minus;\u0026thinsp;1.25D), increased AL (median 24.3 mm vs. median 24.0 mm), and a higher ALTOACRC (median 3.11 vs. median 3.08).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePredictive model for myopia candidates at the premyopia stage\u003c/h3\u003e\n\u003cp\u003eUnivariate regression analysis was employed to identify predictive factors from the characteristics listed in Table 1, whereas multivariate logistic regression was utilized to develop the predictive model. As Table 2 showed, UCVA (OR\u0026thinsp;=\u0026thinsp;0.2; 95%CI\u0026thinsp;=\u0026thinsp;0.03\u0026ndash;1.17), NCSER (OR\u0026thinsp;=\u0026thinsp;0.45;95%CI\u0026thinsp;=\u0026thinsp;0.25\u0026ndash;0.8), AL (OR\u0026thinsp;=\u0026thinsp;1.95;95%CI\u0026thinsp;=\u0026thinsp;1.13\u0026ndash;3.36), and ALTOACRC (OR\u0026thinsp;=\u0026thinsp;2586998.38;95%CI\u0026thinsp;=\u0026thinsp;3507.89-1907861532.08) were included in the predictive model. The logistic analysis included all patients without the multiple imputation that missing values were deleted, showing that the variables in the model did not change substantially and that UCVA, NCSER, AL, and ALTOACRC were highly correlated with myopia intervention candidates (Table 3). The prediction model utilizing UCVA, NCSER, AL, and ALTOACRC was formulated as a nomogram to quantitatively assess the risk probability for candidates requiring myopia intervention (Fig. 2). For example, employing the nomogram model, a patient with UCVA of 0.9, AL of 22.56mm, NCSER of +\u0026thinsp;0.25D, and ALTO ACRC of 2.89 had an estimated probability of myopia intervention candidates of 28.9%, which was below 50%; thus, further cycloplegia or myopia intervention would not be arranged by eyecare providers in clinical practice.\u003c/p\u003e\n\u003cdiv\u003e\u0026nbsp;\u003cbr\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eLogistic Regression analysis of gender, age, UCVA, ACC,NCSER (D),AL(mm),AL/ACRC for myopia intervention candidate.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003cp\u003evariable\u003c/p\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e OR 95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0 0\u0026ndash;0\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emyopia intervention gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u0026ndash;1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecandidate(hyperopiaAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.56-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ereserves\u0026thinsp;\u0026le;\u0026thinsp;+\u0026thinsp;0.75D) UCVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u0026ndash;1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.074\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACC(D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u0026ndash;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNCSER(D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u0026ndash;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u0026ndash;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAL(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.53\u0026ndash;13.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u0026ndash;3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALTOACRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27065959682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153182821.07-\u003c/p\u003e\n \u003cp\u003e4782299793132.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2586998.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3507.89-\u003c/p\u003e\n \u003cp\u003e1907861532.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eUCVA: uncorrected visual acuity; ACC:average corneal curvature; NCSER:non-cycloplegic spherical equivalent refraction;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eCSER:cycloplegic spherical equivalent refraction; AL: axial length; ACRC: average corneal radius of curvature; D, diopter.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/caption\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 3\u0026nbsp;Sensitive analysis:Logistic Regression analysis \u0026nbsp;of\u0026nbsp;gender,age,UCVA,ACC,NCSER(D),AL(mm),AL/ACRC\u0026nbsp;for myopia intervention candidate(data without imputation)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eUCVA: uncorrected visual acuity; \u0026nbsp;ACC:average corneal curvature; NCSER:non-cycloplegic spherical equivalent refraction;\u003c/p\u003e\n\u003cp\u003eCSER:cycloplegic spherical equivalent refraction; AL: axial length; ACRC: average corneal radius of curvature; D, diopter.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe validation of the predictive model for myopia candidates\u003c/p\u003e\n\u003cp\u003eFor the predictive model, the nomogram\u0026apos;s AUC was 0.971(0.957\u0026ndash;0.984) in the derivation set and 0.985 (0.973\u0026ndash;0.996) in the external validation set, which indicated good performance (Fig. 3). The nomogram\u0026apos;s cutoff point in the derivation set was 0.851; we used 0.851 as a binary variable in the external validation set and found that the AUC in the external validation set was 0.921(0.866\u0026ndash;0.976), which was also acceptable (Table 4).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePerformance of the nomogram in predicting myopia intervention candidates\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBest shreshhold\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eSpecificity(%) Sensitivity(%) Accuracy(%) PPV(%) NPV(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDerivation set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.957\u0026ndash;0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExternal\u003c/p\u003e\n \u003cp\u003evalidation set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.866\u0026ndash;0.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe calibration curves indicated that the predictive model and the validation set exhibited a satisfactory degree of fit (Fig. 4): in the derivation set, the apparent calibration curve showed near-perfect alignment with the ideal reference line throughout the entire predicted probability range (0.0\u0026ndash;1.0), with only minor deviations at higher predicted probabilities (\u0026gt;\u0026thinsp;0.8); in contrast, the external validation set displayed a bias-corrected calibration curve that closely resembled the ideal line in lower-risk strata (0.0\u0026ndash;0.5), with deviations observed at predicted probabilities between 0.5 and 0.75. The Hosmer-Lemeshow test indicated a strong consistency between predicted and actual probabilities (derivation set: Chi-square 1.92, P\u0026thinsp;=\u0026thinsp;0.99; external validation set: Chi-square 3.09, P\u0026thinsp;=\u0026thinsp;0.96). The DCA indicated that the model had significant practicality across a broad threshold range (0.10-1.00) in the derivation set and 0.20-1.00 in the external validation set (Fig. 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study effectively created and validated a predictive nomogram for assessing the likelihood of myopia intervention candidates in Chinese children aged 4\u0026ndash;17. By incorporating routinely measured parameters from primary refractive error screening\u0026mdash;UCVA, NCSER, AL, and AL/ACRC ratio\u0026mdash;the model provides a robust risk assessment tool for the premyopia stage. The nomogram exhibited robust performance, indicated by a high area under the curve (AUC), well-calibrated plots, non-significant Hosmer-Lemeshow tests, and positive decision curve analysis (DCA) results. These results suggested that the model may serve as a valuable clinical tool for early identification and intervention, ultimately reducing the long-term burden of myopia.\u003c/p\u003e \u003cp\u003ePrevious studies on myopia intervention candidates have primarily focused on myopic children [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], while recent research has increasingly emphasized early identification and intervention during the premyopia stage [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Notably, the only existing nomogram for premyopia [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] which has not been validated was based on AL, AL/ACRC, and the number of myopic parents, without incorporating primary refractive error screening parameters such as UCVA and NCSER. Among other studies lacking nomogram construction, the findings from the Peking cohort suggested that AL/ACRC could serve as an alternative predictor for identifying hyperopia reserve [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The data from the Shanghai cohort identified AL/ACRC, UCVA, AL, and NCSER as key risk factors for premyopia [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These studies underscore the value of AL/ACRC, UCVA, AL, and NCSER as strong predictive markers for identifying myopia intervention candidates at the premyopia stage. In this study, we developed a validated nomogram incorporating these routinely measured screening parameters, providing a novel tool for early myopia intervention at premyopia stage.\u003c/p\u003e \u003cp\u003eIn the process of validation, the nomogram displayed good discriminative power in both the derivation set (AUC\u0026thinsp;=\u0026thinsp;0.971, 95% CI: 0.957\u0026ndash;0.984) and the external validation set (AUC\u0026thinsp;=\u0026thinsp;0.921, 95% CI: 0.866\u0026ndash;0.976). In contrast to the derivation set, which exhibited a specificity of 99.6% and a sensitivity of 88.2%, indicating potential overfitting and diminished capacity to eliminate false positives in practical scenarios, the external validation set demonstrated a more balanced performance with a specificity of 92.1% and a sensitivity of 92%, thereby preserving exceptional predictive capability and showing robust generalizability. The calibration analysis indicated that the model underestimated risk at probabilities ranging from 0.5 to 0.75 in the validation cohort, perhaps resulting in excessively cautious clinical actions. Notwithstanding the identified biases, the \u003cem\u003eP\u003c/em\u003e value exceeding 0.05 in our calibration assessments indicated an adequate overall fit, affirming its dependability for clinical applications. The decision curve analysis revealed a superior net benefit compared to both the \"None\" and \"All\" strategies across a broad spectrum of threshold probabilities in both the derivation and external validation sets, suggesting that the nomogram would provide a significant clinical advantage in identifying candidates for myopia intervention.\u003c/p\u003e \u003cp\u003eWe found no significant correlation between gender, age, or average corneal curvature (ACC) and the identification of myopia intervention candidates at the premyopia stage. This finding aligned with previous studies. The Peking cohort [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] specifically reported no association between ACC and hyperopia reserve in premyopic children, although gender and age were considered. Moreover, In both the Nanjing study [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and the Shanghai cohort [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], gender, ACC, and age were excluded from the multivariate models.\u003c/p\u003e \u003cp\u003eThis study's merits included the external validation of our prediction model utilizing an independent cohort from Chongqing, characterized by an older group and a more severe myopic condition relative to Shenzhen. The high accuracy in the external validation set suggested that the nomogram was likely to be applicable to diverse populations across various regions in China. Furthermore,This work developed a nomogram utilizing characteristics from primary refractive error screening often employed by eyecare providers, which may have extensive uses in clinical practice.\u003c/p\u003e \u003cp\u003eCertain limits must also be recognized. Firstly, the current study lacked an examination of other characteristics contributing to myopia intervention candidates, including caregiver\u0026rsquo;s myopia[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], caregiver's education; other lifestyle factors such as screen time, education status, outdoor time[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], genetic factors; binocular vision factors[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, these factors are not our primary aim; we aimed to construct a nomogram based on parameters from primary refractive error screening that eyecare providers have for clinical convenience use. Secondly, the nomogram was constructed on a cross-sectional study with retrospective data. Thus, the accuracy of the model we set may be limited, although we use sensitivity analysis to maximize the accuracy of our model. Thirdly, the generalizability of the model may be limited in populations with a low prevalence of myopia intervention candidates, as the model was trained by a data set having a larger percentage of myopia intervention candidates (82.3%) compared to the Chinese cohort from preschool (62.7%) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Finally, large-scale longitudinal data in the school children cohort, including other predictors for myopia intervention candidates, should be further explored to obtain high-level evidence for the nomogram\u0026rsquo;s clinical application in the future.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe developed nomogram, validated externally and based on UCVA, NCSER, AL, and ALTOACRC, showed strong prediction accuracy and practicality. This can be utilized to evaluate individual candidates for myopia intervention, hence offering a reference for early intervention decisions on myopia status for eyecare providers.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eUCVA: uncorrected visual acuity;ACC:average corneal curvature, NCSER:non-cycloplegic spherical equivalent refraction;AL:axial length;AL/ACRC:axial length to average corneal radius of curvature ratio;ROC curve:receiver operating characteristic curve;DCA:decision curve analysis;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJingying Wang wrote the manuscript and acquired the necessary funding. Cong Zhang collected the data and guaranteed its quality.\u003csup\u003e\u0026nbsp;\u003c/sup\u003eJing Wu collected the data and guaranteed its quality. All authors have approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Chongqing Municipal Education Commission under Grant No. KJQN202202808 and the Health Commission of Chongqing Municipality under Grant No. 2023MSXM050.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the finding of this study are available from the corresponding author, Jingying Wang, upon reasonable request.This study was approved by The Medical Ethics Committee of the University of Hong Kong-Shenzhen Hospital and the Chongqing Shapingba District People\u0026apos;s Hospital, which waived the requirement for informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adhered to the Declaration of Helsinki and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines. The medical ethics committee of the University of Hong Kong-Shenzhen Hospital and the Chongqing Shapingba District People\u0026apos;s Hospital accepted this study protocol. The prerequisites for informed consent were exempted owing to the retroactive nature of the investigation and the absence of risk involved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors had no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCong Zhang\u003csup\u003e3\u003c/sup\u003e \u003csup\u003e1\u0026nbsp;\u003c/sup\u003eDepartment of optometry, Chongqing Medical and Pharmaceutical College, No.82, middle road of university town, Shapingba district, ChongQing 401331, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of ophthalmology,The University of Hong Kong-Shenzhen Hospital, No. 1, Haiyuan First Road, Futian District, Shenzhen 518053, Guangdong,China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eChongqing Shapingba District People\u0026apos;s Hospital,Xiaolongkan New Street, Shapingba District, ChongQing 400032, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJingying Wang. Email address:[email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJonas, J.B., et al., \u003cem\u003eIMI Prevention of Myopia and Its Progression.\u003c/em\u003e Invest Ophthalmol Vis Sci, 2021. \u003cstrong\u003e62\u003c/strong\u003e(5): p. 6.\u003c/li\u003e\n\u003cli\u003eHe, X., et al., \u003cem\u003eEffect of Repeated Low-level Red Light on Myopia Prevention Among Children in China With Premyopia: A Randomized Clinical Trial.\u003c/em\u003e JAMA Netw Open, 2023. \u003cstrong\u003e6\u003c/strong\u003e(4): p. e239612.\u003c/li\u003e\n\u003cli\u003eChua, S.Y., et al., \u003cem\u003eage of onset of myopia predicts risk of high myopia in later childhood in myopic Singapore children.\u003c/em\u003e Ophthalmic Physiol Opt, 2016. \u003cstrong\u003e36\u003c/strong\u003e(4): p. 388-94.\u003c/li\u003e\n\u003cli\u003eHaarman, A.E.G., et al., \u003cem\u003eThe Complications of Myopia: A Review and Meta-Analysis.\u003c/em\u003e Invest Ophthalmol Vis Sci, 2020. \u003cstrong\u003e61\u003c/strong\u003e(4): p. 49.\u003c/li\u003e\n\u003cli\u003eWang, C.Y., et al., \u003cem\u003ePremyopia at Preschool Age: Population-based Evidence of Prevalence and Risk Factors from a Serial Survey in Taiwan.\u003c/em\u003e Ophthalmology, 2022. \u003cstrong\u003e129\u003c/strong\u003e(8): p. 880-889.\u003c/li\u003e\n\u003cli\u003eLi, S.M., et al., \u003cem\u003eAnnual Incidences and Progressions of Myopia and High Myopia in Chinese Schoolchildren Based on a 5-Year Cohort Study.\u003c/em\u003e Invest Ophthalmol Vis Sci, 2022. \u003cstrong\u003e63\u003c/strong\u003e(1): p. 8.\u003c/li\u003e\n\u003cli\u003eTsai, D.C., et al., \u003cem\u003eMyopia Development Among Young Schoolchildren: The Myopia Investigation Study in Taipei.\u003c/em\u003e Invest Ophthalmol Vis Sci, 2016. \u003cstrong\u003e57\u003c/strong\u003e(15): p. 6852-6860.\u003c/li\u003e\n\u003cli\u003eZadnik, K., et al., \u003cem\u003ePrediction of Juvenile-Onset Myopia.\u003c/em\u003e JAMA Ophthalmol, 2015. \u003cstrong\u003e133\u003c/strong\u003e(6): p. 683-9.\u003c/li\u003e\n\u003cli\u003eMutti, D.O., et al., \u003cem\u003eRefractive error, axial length, and relative peripheral refractive error before and after the onset of myopia.\u003c/em\u003e Invest Ophthalmol Vis Sci, 2007. \u003cstrong\u003e48\u003c/strong\u003e(6): p. 2510-9.\u003c/li\u003e\n\u003cli\u003eSankaridurg, P., et al., \u003cem\u003eIMI 2023 Digest.\u003c/em\u003e Invest Ophthalmol Vis Sci, 2023. \u003cstrong\u003e64\u003c/strong\u003e(6): p. 7.\u003c/li\u003e\n\u003cli\u003eFlitcroft, D.I., et al., \u003cem\u003eIMI - Defining and Classifying Myopia: A Proposed Set of Standards for Clinical and Epidemiologic Studies.\u003c/em\u003e Invest Ophthalmol Vis Sci, 2019. \u003cstrong\u003e60\u003c/strong\u003e(3): p. M20-M30.\u003c/li\u003e\n\u003cli\u003eMajor, E., T. Dutson, and M. Moshirfar, \u003cem\u003eCycloplegia in Children: An Optometrist\u0026apos;s Perspective.\u003c/em\u003e Clin Optom (Auckl), 2020. \u003cstrong\u003e12\u003c/strong\u003e: p. 129-133.\u003c/li\u003e\n\u003cli\u003eMagome, K., et al., \u003cem\u003ePrediction of cycloplegic refraction for noninvasive screening of children for refractive error.\u003c/em\u003e PLoS One, 2021. \u003cstrong\u003e16\u003c/strong\u003e(3): p. e0248494.\u003c/li\u003e\n\u003cli\u003eLin, S., et al., \u003cem\u003eUsing Decision Curve Analysis to Evaluate Common Strategies for Myopia Screening in School-Aged Children.\u003c/em\u003e Ophthalmic Epidemiol, 2019. \u003cstrong\u003e26\u003c/strong\u003e(4): p. 286-294.\u003c/li\u003e\n\u003cli\u003eMu, J., et al., \u003cem\u003eDevelopment of a nomogram for predicting myopia risk among school-age children: a case-control study.\u003c/em\u003e Ann Med, 2024. \u003cstrong\u003e56\u003c/strong\u003e(1): p. 2331056.\u003c/li\u003e\n\u003cli\u003eZhang, X., et al., \u003cem\u003eThe distribution of refraction by age and gender in a nonmyopic Chinese children population aged 6-12 years.\u003c/em\u003e BMC Ophthalmol, 2020. \u003cstrong\u003e20\u003c/strong\u003e(1): p. 439.\u003c/li\u003e\n\u003cli\u003eLin, H., et al., \u003cem\u003ePrediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study.\u003c/em\u003e PLoS Med, 2018. \u003cstrong\u003e15\u003c/strong\u003e(11): p. e1002674.\u003c/li\u003e\n\u003cli\u003eMa, Y., et al., \u003cem\u003eCohort study with 4-year follow-up of myopia and refractive parameters in primary schoolchildren in Baoshan District, Shanghai.\u003c/em\u003e Clin Exp Ophthalmol, 2018. \u003cstrong\u003e46\u003c/strong\u003e(8): p. 861-872.\u003c/li\u003e\n\u003cli\u003eChen, Y., et al., \u003cem\u003eDevelopment and Validation of a Model to Predict Who Will Develop Myopia in the Following Year as a Criterion to Define Premyopia.\u003c/em\u003e Asia Pac J Ophthalmol (Phila), 2023. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 38-43.\u003c/li\u003e\n\u003cli\u003eLiu, L., et al., \u003cem\u003ePrediction of premyopia and myopia in Chinese preschool children: a longitudinal cohort.\u003c/em\u003e BMC Ophthalmol, 2021. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 283.\u003c/li\u003e\n\u003cli\u003eWang, Y.J., et al., \u003cem\u003eChina Stroke Statistics: an update on the 2019 report from the National Center for Healthcare Quality Management in Neurological Diseases, China National Clinical Research Center for Neurological Diseases, the Chinese Stroke Association, National Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention and Institute for Global Neuroscience and Stroke Collaborations.\u003c/em\u003e Stroke Vasc Neurol, 2022. \u003cstrong\u003e7\u003c/strong\u003e(5): p. 415-450.\u003c/li\u003e\n\u003cli\u003eDing, X., et al., \u003cem\u003eThe Causal Effect of Education on Myopia: Evidence That More Exposure to Schooling, Rather Than Increased Age, Causes the Onset of Myopia.\u003c/em\u003e Investigative Ophthalmology \u0026amp; Visual Science, 2023. \u003cstrong\u003e64\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eLatti, S., S. Niinivehmas, and O.T. Pentikainen, \u003cem\u003eRocker: Open source, easy-to-use tool for AUC and enrichment calculations and ROC visualization.\u003c/em\u003e J Cheminform, 2016. \u003cstrong\u003e8\u003c/strong\u003e(1): p. 45.\u003c/li\u003e\n\u003cli\u003eZhang, Y., et al., \u003cem\u003eEstablishment of a Risk Prediction Model for Non-alcoholic Fatty Liver Disease in Type 2 Diabetes.\u003c/em\u003e Diabetes Ther, 2020. \u003cstrong\u003e11\u003c/strong\u003e(9): p. 2057-2073.\u003c/li\u003e\n\u003cli\u003eGuo, C., et al., \u003cem\u003eDevelopment and validation of a novel nomogram for predicting the occurrence of myopia in schoolchildren: A prospective cohort study.\u003c/em\u003e Am J Ophthalmol, 2022. \u003cstrong\u003e242\u003c/strong\u003e: p. 96-106.\u003c/li\u003e\n\u003cli\u003eWang, B., et al., \u003cem\u003ePredicting the child who will become myopic - can we prevent onset?\u003c/em\u003e Clin Exp Optom, 2023. \u003cstrong\u003e106\u003c/strong\u003e(8): p. 815-824.\u003c/li\u003e\n\u003cli\u003eTang, T., et al., \u003cem\u003eAxial length to corneal radius of curvature ratio and refractive error in Chinese preschoolers aged 4-6 years: a retrospective cross-sectional study.\u003c/em\u003e BMJ Open, 2023. \u003cstrong\u003e13\u003c/strong\u003e(12): p. e075115.\u003c/li\u003e\n\u003cli\u003eYin, Y., et al., \u003cem\u003eEstablishment of noncycloplegic methods for screening myopia and pre-myopia in preschool children.\u003c/em\u003e Front Med (Lausanne), 2023. \u003cstrong\u003e10\u003c/strong\u003e: p. 1291387.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-ophthalmology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"boph","sideBox":"Learn more about [BMC Ophthalmology](http://bmcophthalmol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/boph","title":"BMC Ophthalmology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Premyopia, Nomogram, primary refractive error screening, myopia intervention candidates","lastPublishedDoi":"10.21203/rs.3.rs-6286151/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6286151/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eHyperopia reserves of premyopia could be used to identify myopia intervention candidates for Chinese children. Primary refractive error screening parameters are commonly employed in clinical and community settings before cycloplegic assessment of hyperopia reserves; however, their utility in distinguishing intervention candidates at the premyopia stage remains underexplored. This study aimed to develop a nomogram based on these routinely measured parameters to support clinical decision-making for early myopia prevention.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003ePediatric patients (aged 4\u0026ndash;17 years) from two medical centers in China were enrolled in this retrospective cohort study. A predictive model for the candidates of myopia intervention was developed using logistic regression with multiple imputations. The model included the following primary screening parameters: age, gender, uncorrected visual acuity (UCVA), average corneal curvature (ACC), non-cycloplegic spherical equivalent refraction (NCSER), axial length (AL), and the axial length to average corneal radius of curvature (AL/ACRC) ratio. The efficacy of the model was assessed using the area under the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA). R was employed to conduct all statistical analyses.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eA total of 1006 participants (507 females, 499 boys) were enrolled, with 87.4% demonstrating CSER\u0026thinsp;\u0026le;\u0026thinsp;+\u0026thinsp;0.75D. In multivariate logistic regression, UCVA, NCSER, AL, and ALTOACRC were identified as independent predictors. These predictors were incorporated into a nomogram to predict the candidates of myopia intervention. The nomogram exhibited exceptional discrimination in the derivation set (AUC\u0026thinsp;=\u0026thinsp;0.971, 95% CI: 0.957\u0026ndash;0.984), whereas in the external validation set, the AUC was 0.921 (95% CI: 0.866\u0026ndash;0.976) when a cutoff of 0.851 in derivation set was employed. Calibration was verified through the calibration curve and Hosmer-Lemeshow tests (P\u0026thinsp;=\u0026thinsp;0.99 and P\u0026thinsp;=\u0026thinsp;0.96, respectively), and the decision curve analysis demonstrated robust clinical utility for threshold probabilities of 0.10\u0026ndash;1.00 in the derivation set and 0.20\u0026ndash;1.00 in the external validation set.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThe nomogram derived from the parameters of primary refractive error screening has the potential to preliminarily predict myopia intervention candidates at the premyopia stage, thereby facilitating clinical decision-making in the context of early myopia prevention.\u003c/p\u003e","manuscriptTitle":"Distinguishing myopia intervention candidates at premyopia stage in children: a nomogram based on primary refractive error screening parameters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 12:09:55","doi":"10.21203/rs.3.rs-6286151/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-25T04:10:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-24T11:42:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-22T07:18:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88484742374999360223265504804869669145","date":"2025-04-16T01:03:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283795124276818229133126913436097986368","date":"2025-04-01T21:43:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T06:49:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-01T06:35:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-31T05:54:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-30T05:54:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Ophthalmology","date":"2025-03-30T05:53:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-ophthalmology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"boph","sideBox":"Learn more about [BMC Ophthalmology](http://bmcophthalmol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/boph","title":"BMC Ophthalmology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"95c5a5d3-e8f3-466c-b8c5-c5c8fbfb5ab6","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T15:58:47+00:00","versionOfRecord":{"articleIdentity":"rs-6286151","link":"https://doi.org/10.1186/s12886-025-04278-3","journal":{"identity":"bmc-ophthalmology","isVorOnly":false,"title":"BMC Ophthalmology"},"publishedOn":"2025-08-28 15:56:55","publishedOnDateReadable":"August 28th, 2025"},"versionCreatedAt":"2025-04-30 12:09:55","video":"","vorDoi":"10.1186/s12886-025-04278-3","vorDoiUrl":"https://doi.org/10.1186/s12886-025-04278-3","workflowStages":[]},"version":"v1","identity":"rs-6286151","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6286151","identity":"rs-6286151","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}