Comparison of Uveitis Incidence by Medication in Juvenile Idiopathic Arthritis and Implications for Screening.

PURPOSE: To determine uveitis incidence in juvenile idiopathic arthritis (JIA) patients treated with disease-modifying antirheumatic drugs (DMARD) medications, and to evaluate uveitis risk-stratification protocols. DESIGN: Retrospective clinical cohort study. METHODS: Medical records of patients with JIA seen by ophthalmology at a single institution from April 2014 to April 2022 and ≥18 months' follow-up were reviewed. Exclusion criteria included uveitis history prior to study period, Still disease, or <18 months' follow-up. Patient characteristics, medications, and uveitis status were recorded. Factors associated with uveitis development were analyzed and statistically significant metrics used to determine empiric risk-stratification criteria. These criteria and American College of Rheumatology (ACR) risk-stratification guidelines were applied retroactively to determine predictive power. RESULTS: One hundred eighty-four patients met inclusion criteria and were included. Twenty-one new cases of uveitis developed during the study period. There were no statistically significant differences between no DMARD treatment, methotrexate (MTX), and etanercept (ETA) groups in uveitis incidence, whereas the adalimumab (ADA) and other biologics groups had no uveitis cases. Under the empirically determined criteria, the ratio of uveitis incidence between high- and low-risk groups was 8.21 (2.68-33.55; P < .0001), whereas it was 1.90 (0.72-4.93; P = .15) under the ACR criteria. CONCLUSION: Patients on MTX, ETA, and no DMARD treatment were comparable in JIA-associated uveitis incidence, whereas there were no new cases with ADA or other biologics. Further, we found increased predictive power in the empiric criteria in comparison to current ACR risk stratification.

Prediction for Cycloplegic Refractive Error in Chinese School Students: Model Development and Validation.

Purpose: To predict cycloplegic refractive error using measurements obtained under noncycloplegic conditions. Method: Refractive error was measured in 5- to 18-year-old Chinese students using a NIDEK autorefractor before and after administration of 0.5% tropicamide. Spherical equivalent (SER) in diopters (D) was calculated as sphere plus half cylinder. A multivariable prediction model for cycloplegic SER was developed using data from students in Jinyun (n = 1938) and was validated using data from students in Hangzhou (n = 1498). The performance of the prediction model was evaluated using R2, mean difference between predicted and measured cycloplegic SER, and sensitivity and specificity for predicting myopia (cycloplegic SER ≤ -0.5 D). Results: Among 3436 students (mean age, 9.7 years; 51% female), the mean (SD) noncycloplegic and cycloplegic SER values were -1.12 (1.97) D and -0.20 (2.19) D, respectively. The prediction model that included demographics, noncycloplegic SER, axial length/corneal curvature radius ratio, uncorrected visual acuity (UCVA), and intraocular pressure predicted cycloplegic SER with R2 of 0.93 in the development dataset and 0.92 in the validation dataset. The mean (SD) differences between predicted and measured cycloplegic SER were 0.0 (0.55) D in the development dataset and 0.06 (0.64) D in the validation dataset. In both the development and validation datasets, the combination of predicted SER and UCVA yielded high sensitivity (91.4% and 91.9%, respectively) and specificity (95.0% and 90.1%, respectively) for detecting myopia. Conclusions: Cycloplegic refractive error can be predicted using measurements obtained under noncycloplegic conditions. The prediction model could potentially be used to correct the myopia prevalence in epidemiological studies in which administering cycloplegic agent on all participants is not feasible. Translational Relevance: The prediction model may provide a tool for correcting the overestimation of myopia from noncycloplegic refractive error in future epidemiological studies in which administering cycloplegic agent on all participants is not feasible.

Effect of Cycloplegia on Refractive Error Measure in Chinese School Students.

PURPOSE: To determine differences in cycloplegic vs. non-cycloplegic refractive error and factors associated with these differences in Chinese school students. METHOD: In this cross-sectional school-based study, refractive error was measured in school students using a NIDEK autorefractor before and after administration of 0.5% tropicamide. Spherical equivalent (SER) in diopters (D) was calculated as sphere plus half cylinder. SER differences before vs. after cycloplegia were evaluated using mean, standard deviation (SD), 95% limits of agreement. Univariable and multivariable regression models were used to determine factors associated with SER differences. RESULTS: Among 3604 students, 3450 (95.7%) provided data for analysis. Mean age (SD) was 9.7 (3.6) years. The mean SER (SD) was -1.12 (1.97) D before cycloplegia, and -0.20 (2.19) D after cycloplegia, with a mean difference of 0.92 D (95% limits of agreement: -0.93 to 2.78 D). Among 196 eyes with non-cycloplegic SER -6.0 D or worse (e.g., met high myopia definition), 71.4% had cycloplegic SER -6.0 D or worse, and among 3607 eyes with non-cycloplegic SER -0.5 D or worse (e.g., met myopia definition), 62.1% eyes had cycloplegic SER -0.5 D or worse. Cycloplegic SER was more correlated with axial length than non-cycloplegic SER (Pearson r = 0.82 vs. 0.72, p < .0001). In multivariable analysis, larger SER differences were associated with more hyperopic refractive error and smaller axial length (all p < .0001). CONCLUSION: Non-cycloplegic refractive error overestimates myopia by approximately one diopter. This overestimation increases with more hyperopic refractive error and smaller axial length. Non-cycloplegic refractive error should not be used for evaluating pediatric myopia. ABBREVIATIONS: BCVA = best corrected visual acuity; D = diopter; SD = standard deviation; SE = standard error; SER = spherical equivalent; CI = confidence interval.

Predictive Value of Clinician "Gestalt" in Pediatric Community-Acquired Pneumonia.

OBJECTIVES: Validated prognostic tools for pediatric community-acquired pneumonia (CAP) do not exist. Thus, clinicians rely on "gestalt" in management decisions for children with CAP. We sought to determine the ability of clinician gestalt to predict severe outcomes. METHODS: We performed a prospective cohort study of children 3 months to 18 years old presenting to a pediatric emergency department (ED) with lower respiratory infection and receiving a chest radiograph for suspected CAP from 2013 to 2017. Clinicians reported the probability that the patient would develop severe complications of CAP (defined as respiratory failure, empyema or effusion, lung abscess or necrosis, metastatic infection, sepsis or septic shock, or death). The primary outcome was development of severe complications. RESULTS: Of 634 children, 37 (5.8%) developed severe complications. Of children developing severe complications after the ED visit, 62.1% were predicted as having <10% risk by the ED clinician. Sensitivity was >90% at the <1% predicted risk threshold, whereas specificity was >90% at the 10% risk threshold. Gestalt performance was poor in the low-intermediate predicted risk category (1%-10%). Clinicians had only fair ability to discriminate children developing complications from those who did not (area under the receiver operator characteristic curve 0.747), with worse performance from less experienced clinicians (area under the receiver operator characteristic curve 0.693). CONCLUSIONS: Clinicians have only fair ability to discriminate children with CAP who develop severe complications from those who do not. Clinician gestalt performs best at very low or higher predicted risk thresholds, yet many children fall in the low-moderate predicted risk range in which clinician gestalt is limited. Evidence-based prognostic tools likely can improve on clinician gestalt, particularly when risk is low-moderate.