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

Artikel i vetenskaplig tidskrift
Författare Aldina Pivodic
Anna-Lena Hård
Chatarina Löfqvist
Lois E.H. Smith
Carolyn Wu
Marie Christine Bründer
Wolf A. Lagrèze
Andreas Stahl
Gerd Holmström
Kerstin Albertsson-Wikland
Helena Johansson
Staffan Nilsson
Ann Hellström
Publicerad i JAMA Ophthalmology
Volym 138
Nummer/häfte 1
Sidor 21-29
ISSN 2168-6165
Publiceringsår 2020
Publicerad vid Institutionen för matematiska vetenskaper
Institutionen för medicin, avdelningen för samhällsmedicin och folkhälsa
Institutionen för neurovetenskap och fysiologi
Institutionen för neurovetenskap och fysiologi, sektionen för fysiologi
Institutionen för biomedicin
Institutionen för vårdvetenskap och hälsa
Sidor 21-29
Språk en
Länkar doi.org/10.1001/jamaophthalmol.2019...
Ämneskategorier Oftalmologi

Sammanfattning

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.

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