Ranibizumab in retinopathy of prematurity – one‐year follow‐up of ophthalmic outcomes and two‐year follow‐up of neurodevelopmental outcomes from the CARE‐ROP study

Purpose The primary endpoint results from the comparing alternative ranibizumab dosages for safety and efficacy in retinopathy of prematurity (CARE-ROP) core study identified ranibizumab as an effective treatment to control acute retinopathy of prematurity (ROP). This study reports the 1- and 2-year follow-up data focusing on long-term functional outcomes and safety. Methods The CARE-ROP trial compared 0.12 mg versus 0.20 mg ranibizumab in 20 infants with ROP in a multicentric, prospective, randomized, double-blind, controlled study design. Sixteen patients entered the follow-up period. An ophthalmologic assessment at one year postbaseline was acquired from all 16 patients and a neurodevelopmental assessment at two years postbaseline was acquired from 15 patients. Results Fifteen of 16 infants were able to fixate and follow moving objects at one year postbaseline treatment. One child progressed to stage 5 ROP bilaterally between the end of the core study and the 1-year follow-up (first seen at PMA 75 weeks). Mean spherical equivalents were -1.9 diopters (D) and -0.75 D in the 0.12 mg and the 0.20 mg treatment arms. Strabismus was present in seven and nystagmus in five out of 16 infants. Mental development scores were within normal limits in six out of ten patients with available data. No statistically significant difference was observed between the two treatment arms. Conclusion Neurodevelopmental and functional ocular outcomes 1 and 2 years after treatment with ranibizumab are reassuring regarding long-term safety. Late reactivation of ROP, however, represents a challenge during the follow-up phase and it is of utmost importance that regular follow-ups are maintained

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

Question: Can a prediction model be constructed for retinopathy of prematurity needing treatment by using only birth characteristics data and applying advanced statistical methods? Findings: In this cohort study of 6947 infants born at gestational age 24 to 30 weeks, the prediction model incorporating only postnatal age, gestational age, sex, and birth weight provided a predictive ability for retinopathy of prematurity needing treatment that was comparable to current models requiring postnatal data (not always available). The risk for retinopathy of prematurity needing treatment increased up to 12 weeks' postnatal age irrespective of the infants' gestational age. Meaning: This prediction model identifying infants with a high risk for developing sight-threatening disease at an early time may improve the conditions for optimal screening. This cohort study creates and validates an easy-to-use prediction model using only birth characteristics and describes a continuous hazard function for retinopathy of prematurity treatment. 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

Einsatz von künstlicher Intelligenz im Screening auf diabetische Retinopathie an einer diabetologischen Schwerpunktklinik

Hintergrund Seit 2018 ist mit IDx-DR ein Verfahren auf dem Markt, welches den Grad der diabetischen Retinopathie (DR) mittels künstlicher Intelligenz (KI) bestimmt. Methoden Wir haben IDx-DR in die Sprechstunde an einer diabetologischen Schwerpunktklinik integriert und berichten über die Übereinstimmung zwischen IDx-DR (IDx Technologies Inc., Coralville, IA, USA) und Funduskopie sowie IDx-DR und ophthalmologischer Bildbeurteilung sowie über den Einfluss unterschiedlicher Kamerasysteme. Ergebnisse Mit der Topcon-Kamera (n = 456; NW400, Topcon Medical Systems, Oakland, NJ, USA) konnte im Vergleich zur Zeiss-Kamera (n = 47; Zeiss VISUCAM 500, Carl Zeiss Meditec AG, Jena, Deutschland) häufiger eine ausreichende Bildqualität in Miosis erreicht werden. Insgesamt war bei etwa 60 % der Patienten eine IDx-DR-Analyse in Miosis möglich. Alle Patienten, bei denen keine IDx-DR-Analyse in Miosis möglich war, konnten in Mydriasis funduskopiert werden. Innerhalb der Gruppe der auswertbaren Befunde zeigte sich eine Übereinstimmung zwischen IDx-DR und augenärztlicher Funduoskopie in ca. 55 %, ein Überschätzen des Schweregrads durch IDx-DR in ca. 40 % und ein Unterschätzen in ca. 4 %. Die Sensitivität (Spezifität) für das Erkennen einer schweren, behandlungsbedürftigen Retinopathie lag bei 95,7 % (89,1 %) für Fälle mit auswertbaren Fundusaufnahmen und bei 65,2 % (66,7 %), wenn alle Fälle betrachtet werden (inklusive derjeniger ohne verwertbare Aufnahme in Miosis). Der Kappa-Koeffizient zeigt mit 0,334 (p < 0,001) eine ausreichende Übereinstimmung zwischen IDx-DR und ärztlicher Bildauswertung anhand des Fundusfotos unter Berücksichtigung aller Patienten mit auswertbarer IDx-DR-Analyse. Der Vergleich zwischen IDx-DR mit der ärztlichen Funduskopie ergibt unter denselben Voraussetzungen eine geringe Übereinstimmung mit einem Kappa-Wert von 0,168 (p < 0,001). Schlussfolgerung Die vorliegende Studie zeigt Möglichkeiten und Grenzen des KI-gestützten DR-Screenings auf. Eine wesentliche Einschränkung liegt in der Tatsache, dass bei ca. 40 % der Patienten keine ausreichenden Aufnahmen in Miosis gewonnen werden konnten. Wenn ausreichende Aufnahmen vorlagen, stimmten IDx-DR und augenärztliche Diagnose in über 50 % der Fälle überein. Ein Unterschätzen des Schweregrades durch IDx-DR kam selten vor. Für die Integration in augenärztlich unterstützten Sprechstunden erscheint uns das System grundsätzlich geeignet. Die hohe Rate an fehlenden Aufnahmen in Miosis stellt allerdings eine Limitation dar, die einen Einsatz ohne augenärztliche Kontrollmöglichkeit schwierig erscheinen lässt.Background In 2018, IDx-DR was approved as a method to determine the degree of diabetic retinopathy (DR) using artificial intelligence (AI) by the FDA. Methods We integrated IDx-DR into the consultation at a diabetology focus clinic and report the agreement between IDx-DR and fundoscopy as well as IDx-DR and ophthalmological image assessment and the influence of different camera systems. Results Adequate image quality in miosis was achieved more frequently with the Topcon camera (n = 456; NW400, Topcon Medical Systems, Oakland, NJ, USA) compared with the Zeiss camera (n = 47; Zeiss VISUCAM 500, Carl Zeiss Meditec AG, Jena, Germany). Overall, IDx-DR analysis in miosis was possible in approximately 60% of the patients. All patients in whom IDx-DR analysis in miosis was not possible could be assessed by fundoscopy with dilated pupils. Within the group of images that could be evaluated, there was agreement between IDx-DR and ophthalmic fundoscopy in approximately 55%, overestimation of severity by IDx-DR in approximately 40% and underestimation in approximately 4%. The sensitivity (specificity) for detecting severe retinopathy requiring treatment was 95.7% (89.1%) for cases with fundus images that could be evaluated and 65.2% (66.7%) when all cases were considered (including those without images in miosis which could be evaluated). The kappa coefficient of 0.334 (p < 0.001) shows sufficient agreement between IDx-DR and physician’s image analysis based on the fundus photograph, considering all patients with IDx-DR analysis that could be evaluated. The comparison between IDx-DR and the physician’s funduscopy under the same conditions shows a low agreement with a kappa value of 0.168 (p < 0.001). Conclusion The present study shows the possibilities and limitations of AI-assisted DR screening. A major limitation is that sufficient images cannot be obtained in miosis in approximately 40% of patients. When sufficient images were available the IDx-DR and ophthalmological diagnosis matched in more than 50% of cases. Underestimation of severity by IDx-DR occurred only rarely. For integration into an ophthalmologist’s practice, this system seems suitable. Without access to an ophthalmologist the high rate of insufficient images in miosis represents an important limitation

Assessment of Retinopathy of Prematurity Regression and Reactivation Using an Artificial Intelligence–Based Vascular Severity Score

Importance: One of the biggest challenges when using anti–vascular endothelial growth factor (VEGF) agents to treat retinopathy of prematurity (ROP) is the need to perform long-term follow-up examinations to identify eyes at risk of ROP reactivation requiring retreatment. Objective: To evaluate whether an artificial intelligence (AI)–based vascular severity score (VSS) can be used to analyze ROP regression and reactivation after anti-VEGF treatment and potentially identify eyes at risk of ROP reactivation requiring retreatment. Design, Setting, and Participants: This prognostic study was a secondary analysis of posterior pole fundus images collected during the multicenter, double-blind, investigator-initiated Comparing Alternative Ranibizumab Dosages for Safety and Efficacy in Retinopathy of Prematurity (CARE-ROP) randomized clinical trial, which compared 2 different doses of ranibizumab (0.12 mg vs 0.20 mg) for the treatment of ROP. The CARE-ROP trial screened and enrolled infants between September 5, 2014, and July 14, 2016. A total of 1046 wide-angle fundus images obtained from 19 infants at predefined study time points were analyzed. The analyses of VSS were performed between January 20, 2021, and November 18, 2022. Interventions: An AI-based algorithm assigned a VSS between 1 (normal) and 9 (most severe) to fundus images. Main Outcomes and Measures: Analysis of VSS in infants with ROP over time and VSS comparisons between the 2 treatment groups (0.12 mg vs 0.20 mg of ranibizumab) and between infants who did and did not receive retreatment for ROP reactivation. Results: Among 19 infants with ROP in the CARE-ROP randomized clinical trial, the median (range) postmenstrual age at first treatment was 36.4 (34.7-39.7) weeks; 10 infants (52.6%) were male, and 18 (94.7%) were White. The mean (SD) VSS was 6.7 (1.9) at baseline and significantly decreased to 2.7 (1.9) at week 1 (P < .001) and 2.9 (1.3) at week 4 (P < .001). The mean (SD) VSS of infants with ROP reactivation requiring retreatment was 6.5 (1.9) at the time of retreatment, which was significantly higher than the VSS at week 4 (P < .001). No significant difference was found in VSS between the 2 treatment groups, but the change in VSS between baseline and week 1 was higher for infants who later required retreatment (mean [SD], 7.8 [1.3] at baseline vs 1.7 [0.7] at week 1) vs infants who did not (mean [SD], 6.4 [1.9] at baseline vs 3.0 [2.0] at week 1). In eyes requiring retreatment, higher baseline VSS was correlated with earlier time of retreatment (Pearson r = −0.9997; P < .001). Conclusions and Relevance: In this study, VSS decreased after ranibizumab treatment, consistent with clinical disease regression. In cases of ROP reactivation requiring retreatment, VSS increased again to values comparable with baseline values. In addition, a greater change in VSS during the first week after initial treatment was found to be associated with a higher risk of later ROP reactivation, and high baseline VSS was correlated with earlier retreatment. These findings may have implications for monitoring ROP regression and reactivation after anti-VEGF treatment