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Individual Risk Prediction for Sight-Threatening Retinopathy of Prematurity Using Birth Characteristics
Univ Gothenburg, Sahlgrenska Acad, Inst Neurosci & Physiol, Dept Ophthalmol, S-41685 Gothenburg, Sweden;Stat Konsultgrp, Gothenburg, Sweden.
Univ Gothenburg, Sahlgrenska Acad, Inst Neurosci & Physiol, Dept Ophthalmol, S-41685 Gothenburg, Sweden.
Univ Gothenburg, Sahlgrenska Acad, Inst Neurosci & Physiol, Dept Ophthalmol, S-41685 Gothenburg, Sweden;Univ Gothenburg, Sahlgrenska Acad, Inst Hlth Care Sci, Gothenburg, Sweden.
Harvard Med Sch, Boston Childrens Hosp, Dept Ophthalmol, Boston, MA 02115 USA.
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2020 (English)In: JAMA ophthalmology, ISSN 2168-6165, E-ISSN 2168-6173, Vol. 138, no 1, p. 21-29Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
AMER MEDICAL ASSOC , 2020. Vol. 138, no 1, p. 21-29
National Category
Ophthalmology
Identifiers
URN: urn:nbn:se:uu:diva-403498DOI: 10.1001/jamaophthalmol.2019.4502ISI: 000506631600004PubMedID: 31697330OAI: oai:DiVA.org:uu-403498DiVA, id: diva2:1389450
Funder
Swedish Research Council, 2016-01131Knut and Alice Wallenberg FoundationAvailable from: 2020-01-30 Created: 2020-01-30 Last updated: 2020-01-30Bibliographically approved

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