Individual Risk Prediction for Sight-Threatening Retinopathy of Prematurity Using Birth Characteristics

Abstract

IMPORTANCE To prevent blindness, repeated infant eye examinations are performed to detect severe retinopathy of prematurity (ROP), yet only a small fraction of those screened need treatment. Early individual risk stratification would improve screening timing and efficiency and potentially reduce the risk of blindness. OBJECTIVES To create and validate an easy-to-use prediction model using only birth characteristics and to describe a continuous hazard function for ROP treatment. DESIGN, SETTING, AND PARTICIPANTS In this retrospective cohort study, Swedish National Patient Registry data from infants screened for ROP (born between January 1, 2007, and August 7, 2018) were analyzed with Poisson regression for time-varying data (postnatal age, gestational age [GA], sex, birth weight, and important interactions) to develop an individualized predictive model for ROP treatment (called DIGIROP-Birth [Digital ROP]). The model was validated internally and externally (in US and European cohorts) and compared with 4 published prediction models. MAIN OUTCOMES AND MEASURES The study outcome was ROP treatment. The measures were estimated momentary and cumulative risks, hazard ratios with 95% CIs, area under the receiver operating characteristic curve (hereinafter referred to as AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS Among 7609 infants (54.6% boys; mean [SD] GA, 28.1 [2.1] weeks; mean [SD] birth weight, 1119 [353] g), 442 (5.8%) were treated for ROP, including 142 (40.1%) treated of 354 born at less than 24 gestational weeks. Irrespective of GA, the risk for receiving ROP treatment increased during postnatal weeks 8 through 12 and decreased thereafter. Validations of DIGIROP-Birth for 24 to 30 weeks' GA showed high predictive ability for the model overall (AUC, 0.90 [95% CI, 0.89-0.92] for internal validation, 0.94 [95% CI, 0.90-0.98] for temporal validation, 0.87 [95% CI, 0.84-0.89] for US external validation, and 0.90 [95% CI, 0.85-0.95] for European external validation) by calendar periods and by race/ethnicity. The sensitivity, specificity, PPV, and NPV were numerically at least as high as those obtained from CHOP-ROP (Children's Hospital of Philadelphia-ROP), OMA-ROP (Omaha-ROP), WINROP (weight, insulinlike growth factor 1, neonatal, ROP), and CO-ROP (Colorado-ROP), models requiring more complex postnatal data. CONCLUSIONS AND RELEVANCE This study validated an individualized prediction model for infants born at 24 to 30 weeks' GA, enabling early risk prediction of ROP treatment based on birth characteristics data. Postnatal age rather than postmenstrual age was a better predictive variable for the temporal risk of ROP treatment. The model is an accessible online application that appears to be generalizable and to have at least as good test statistics as other models requiring longitudinal neonatal data not always readily available to ophthalmologists.

Validation of The DIGIROP-Birth Model in A Chinese Cohort

“…The present study validated the DIGIROP-Birth model in Chinese preterm infants, and found that the model had less satisfactory performance than previously reported (AUC = 0.634 in this study vs. AUC = 0.85 in the study by Pivodic et al) [23].…”

“…The cutoff value that resulted in the maximal sum of sensitivity and speci city was chosen and assessed. In addition, the cutoff values previously tested by Pivodic et al were also selected for validation [23]. The performance of the DIGIROP-Birth model for identifying TR-ROP infants was also evaluated by gestational age and birth weight.…”

Translational Research in Retinopathy of Prematurity: From Bedside to Bench and Back Again

“…Although various studies have been conducted from a clinical perspective to prevent blindness caused by ROP, including the identification of risk factors, development of predictive models, and diagnosis of the disease stage with artificial intelligence [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ], this review will focus on the translational research that has progressed from basic research to clinical trials.…”

Risk Calculator for Retinopathy of Prematurity Requiring Treatment

“…Majority show high sensitivity and medium to low specificity ( 7 – 10 ). Authors themselves have made clarifications regarding their own models, claiming that no predictive model will work properly at a universal level ( 11 ); in many cases newborns of ≥31 weeks or <24 weeks of pregnancy, who quite often show a wide area of non-vascularized retina after birth, are excluded ( 12 ). However, there is not a single model of practical applicability and adaptability currently available in the usual clinical practice ( 13 ) that helps physicians identify newborns with higher risk of severe ROP requiring treatment and guides the planning of checkup frequency.…”

“…The DIGIROP model concluded that one of the best variables for predicting risk of requiring ROP treatment is postnatal chronologicalage ( 12 ). Conversely, the Early Treatment for Retinopathy Of Prematurity Study showed that the wider the avascular area (zone I), the higher the risk of requiring treatment ( 14 ).…”