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Figure 1.
Study Flowchart
Study Flowchart

GA indicates gestational age; ROP, retinopathy of prematurity; and SWEDROP, Swedish National Registry for Retinopathy of Prematurity.

Figure 2.
Crude Week-Specific and Momentary Individual Risk of Retinopathy of Prematurity (ROP) Treatment
Crude Week-Specific and Momentary Individual Risk of Retinopathy of Prematurity (ROP) Treatment

Shown is risk for gestational age (GA) less than 31 weeks.

Figure 3.
Cumulative Individual Risk for Retinopathy of Prematurity (ROP) Treatment
Cumulative Individual Risk for Retinopathy of Prematurity (ROP) Treatment

Shown is cumulative risk (95% CI) by gestational age (GA) 24 to 30 weeks for boys and girls born with birth weight SD score (BWSDS) −3 and 0.

Table 1.  
Comparison Between US Guidelines (Fierson et al) Regarding Timing of Initial Examination vs SWEDROP Data and DIGIROP-Birth Model, 2007-2018
Comparison Between US Guidelines (Fierson et al2) Regarding Timing of Initial Examination vs SWEDROP Data and DIGIROP-Birth Model, 2007-2018
Table 2.  
Final Prediction Analysis Model for Retinopathy of Prematurity Treatment for Infants Born at GA of 24 to 30 Weeks Using Poisson Regression for Time-Varying Data
Final Prediction Analysis Model for Retinopathy of Prematurity Treatment for Infants Born at GA of 24 to 30 Weeks Using Poisson Regression for Time-Varying Data
Supplement.

eAppendix 1. Prediction Model for Gestational Age <24 Weeks

eAppendix 2. Internal and External Validation

eFigure 1. AUC for DIGIROP-Birth Model Performed on Model Group for Cumulative Probabilities Estimated Over the Postnatal Age

eFigure 2. Momentary and Cumulative Individual Risk With 95% CI Over Time for ROP Treatment for Gestational Age 24 and 25 Weeks, by Sex for Different Birth WeightSDS

eFigure 3. Incidence of ROP Treatment by Gestational Age, Sex, and Birth WeightSDS (BWSDS)

eFigure 4. Estimated Risk (%) for ROP Treatment by Gestational Age and Sex

eFigure 5. ROC Curves Obtained Based on Cumulative Individual Risk for ROP Treatment for Gestational Age at Birth ≥24 Weeks From Model Group, Validation Temporal Group, Validation US Group and Validation European Group (A), by Calendar Periods in the Model Group (B), and by Race/Ethnicity in the Validation US Group (C)

eFigure 6. Calibration Plot for Observed Proportion of ROP Treatment vs Estimated Probability Obtained From the Final Prediction Model for Gestational Age at Birth ≥24 Weeks (A), and From Cross-Validation Model for Gestational Age ≥24 Weeks (B)

eFigure 7. ROC Curves Obtained Based on Cumulative Individual Risk for ROP Treatment for Gestational Age at Birth ≥24 Weeks From Validation US Group. DIGIROP-Birth vs CHOP-ROP (A), DIGIROP-Birth vs OMA-ROP (B), DIGIROP-Birth vs WINROP (C), and DIGIROP-Birth vs CO-ROP (D)

eFigure 8. Scatterplots for Individual Risk Predictions for ROP Treatment Performed on Validation US Group, Obtained From DIGIROP-Birth and CHOP-ROP Models (A), DIGIROP-Birth and CHOP-ROP Models Zoom-in Figure for Probabilities 0.0-0.1 (B), DIGIROP-Birth and OMA-ROP Models (C), and DIGIROP-Birth and WINROP Models (D).

eFigure 9. Cumulative Individual Risk With 95% CI for ROP Treatment for Gestational Age at Birth <24 Weeks

eFigure 10. ROC Curves Obtained Based on Cumulative Individual Risk for ROP Treatment for Gestational Age at Birth <24 Weeks From the Main Study, Cross-Validation, and External Population Database (A) and by Calendar Periods for the Main Study Database (B)

eFigure 11. Calibration Plot for Observed Proportion of ROP Treatment vs Estimated Probability Obtained From the Final Prediction Model for Gestational Age at Birth <24 Weeks (A), and From the Cross-Validation Model for Gestational Age <24 Weeks (B)

eTable 1. Birth Characteristics for Total SWEDROP Cohort by Study Population and by Maximum ROP Stage

eTable 2. Birth Characteristics for Validation US Group and Validation European Group

eTable 3. Number, Percentage, and Follow-up Weeks for ROP Treatment, by Sex, Gestational Age at Birth, and Birth WeightSDS

eTable 4. Prediction Models for ROP Treatment for Total SWEDROP Cohort Using Only Postnatal Age and Gestational Age – Poisson Regression for Time-Varying Data

eTable 5. Estimated Probability for ROP Treatment With 95% CI for Selected Values of Birth WeightSDS, Gestational Age at Birth, Sex, and Postnatal Age – Final Poisson Regression Model for Gestational Age at Birth ≥24 Weeks

eTable 6. DIGIROP-Birth vs CHOP-ROP, OMA-ROP, WINROP and CO-ROP. AUC, Sensitivity, Specificity, PPV and NPV

eTable 7. Final Prediction Model for ROP Treatment for Infants With Gestational Age at Birth <24 Weeks – Poisson Regression for Time-Varying Data

eTable 8. Estimated Probability for ROP Treatment With 95% CI for Selected Values of Birth Weight, Gestational Age at Birth, Sex, and Postnatal Age – Final Poisson Regression Model for Gestational Age at Birth <24 Weeks

eReferences

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Mora  JS, Waite  C, Gilbert  CE, Breidenstein  B, Sloper  JJ.  A worldwide survey of retinopathy of prematurity screening.  Br J Ophthalmol. 2018;102(1):9-13. doi:10.1136/bjophthalmol-2017-310709PubMedGoogle ScholarCrossref
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Fierson  WM; American Academy of Pediatrics Section on Ophthalmology; American Academy of Ophthalmology; American Association for Pediatric Ophthalmology and Strabismus; American Association of Certified Orthoptists.  Screening examination of premature infants for retinopathy of prematurity.  Pediatrics. 2018;142(6):e20183061. doi:10.1542/peds.2018-3061PubMedGoogle ScholarCrossref
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Holmström  G, Tornqvist  K, Al-Hawasi  A, Nilsson  Å, Wallin  A, Hellström  A.  Increased frequency of retinopathy of prematurity over the last decade and significant regional differences.  Acta Ophthalmol. 2018;96(2):142-148. doi:10.1111/aos.13549PubMedGoogle ScholarCrossref
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Mehta  M, Adams  GG, Bunce  C, Xing  W, Hill  M.  Pilot study of the systemic effects of three different screening methods used for retinopathy of prematurity.  Early Hum Dev. 2005;81(4):355-360. doi:10.1016/j.earlhumdev.2004.09.005PubMedGoogle ScholarCrossref
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Hellström  A, Smith  LE, Dammann  O.  Retinopathy of prematurity.  Lancet. 2013;382(9902):1445-1457. doi:10.1016/S0140-6736(13)60178-6PubMedGoogle ScholarCrossref
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Hellström  A, Hård  AL, Engström  E,  et al.  Early weight gain predicts retinopathy in preterm infants: new, simple, efficient approach to screening.  Pediatrics. 2009;123(4):e638-e645. doi:10.1542/peds.2008-2697PubMedGoogle ScholarCrossref
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Löfqvist  C, Hansen-Pupp  I, Andersson  E,  et al.  Validation of a new retinopathy of prematurity screening method monitoring longitudinal postnatal weight and insulinlike growth factor I.  Arch Ophthalmol. 2009;127(5):622-627. doi:10.1001/archophthalmol.2009.69PubMedGoogle ScholarCrossref
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Wu  C, Vanderveen  DK, Hellström  A, Löfqvist  C, Smith  LE.  Longitudinal postnatal weight measurements for the prediction of retinopathy of prematurity.  Arch Ophthalmol. 2010;128(4):443-447. doi:10.1001/archophthalmol.2010.31PubMedGoogle ScholarCrossref
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Zepeda-Romero  LC, Lundgren  P, Gutierrez-Padilla  JA,  et al.  Oxygen monitoring reduces the risk for retinopathy of prematurity in a Mexican population.  Neonatology. 2016;110(2):135-140. doi:10.1159/000445040PubMedGoogle ScholarCrossref
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Biniwale  M, Weiner  A, Sardesai  S, Cayabyab  R, Barton  L, Ramanathan  R.  Early postnatal weight gain as a predictor for the development of retinopathy of prematurity.  J Matern Fetal Neonatal Med. 2019;32(3):429-433. doi:10.1080/14767058.2017.1381902PubMedGoogle ScholarCrossref
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Binenbaum  G, Ying  GS, Tomlinson  LA; Postnatal Growth and Retinopathy of Prematurity (G-ROP) Study Group.  Validation of the Children’s Hospital of Philadelphia Retinopathy of Prematurity (CHOP ROP) model.  JAMA Ophthalmol. 2017;135(8):871-877. doi:10.1001/jamaophthalmol.2017.2295PubMedGoogle ScholarCrossref
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McCauley  K, Chundu  A, Song  H, High  R, Suh  D.  Implementation of a clinical prediction model using daily postnatal weight gain, birth weight, and gestational age to risk stratify ROP.  J Pediatr Ophthalmol Strabismus. 2018;55(5):326-334. doi:10.3928/01913913-20180405-02PubMedGoogle ScholarCrossref
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Cao  JH, Wagner  BD, Cerda  A,  et al.  Colorado Retinopathy of Prematurity model: a multi-institutional validation study.  J AAPOS. 2016;20(3):220-225. doi:10.1016/j.jaapos.2016.01.017PubMedGoogle ScholarCrossref
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Holmström  G, Hellström  A, Jakobsson  P, Lundgren  P, Tornqvist  K, Wallin  A.  Evaluation of new guidelines for ROP screening in Sweden using SWEDROP: a national quality register.  Acta Ophthalmol. 2015;93(3):265-268. doi:10.1111/aos.12506PubMedGoogle ScholarCrossref
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Wu  C, Löfqvist  C, Smith  LEH, VanderVeen  DK, Hellström  A; WINROP Consortium.  Importance of early postnatal weight gain for normal retinal angiogenesis in very preterm infants: a multicenter study analyzing weight velocity deviations for the prediction of retinopathy of prematurity.  Arch Ophthalmol. 2012;130(8):992-999. doi:10.1001/archophthalmol.2012.243PubMedGoogle ScholarCrossref
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Larsen  PP, Bründer  MC, Petrak  M,  et al.  Screening for retinopathy of prematurity: trends over the past 5 years in two German university hospitals  [in German].  Ophthalmologe. 2018;115(6):469-475. doi:10.1007/s00347-018-0675-3PubMedGoogle ScholarCrossref
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Engle  WA; American Academy of Pediatrics Committee on Fetus and Newborn.  Age terminology during the perinatal period.  Pediatrics. 2004;114(5):1362-1364. doi:10.1542/peds.2004-1915PubMedGoogle ScholarCrossref
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Niklasson  A, Albertsson-Wikland  K.  Continuous growth reference from 24th week of gestation to 24 months by gender.  BMC Pediatr. 2008;8:8. doi:10.1186/1471-2431-8-8PubMedGoogle ScholarCrossref
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Shukla  A, Sonnie  C, Worley  S,  et al.  Comparison of biphasic vs static oxygen saturation targets among infants with retinopathy of prematurity.  JAMA Ophthalmol. 2019;137(4):417-423. doi:10.1001/jamaophthalmol.2018.7021PubMedGoogle ScholarCrossref
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International Committee for the Classification of Retinopathy of Prematurity.  The International Classification of Retinopathy of Prematurity revisited.  Arch Ophthalmol. 2005;123(7):991-999. doi:10.1001/archopht.123.7.991PubMedGoogle ScholarCrossref
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Early Treatment for Retinopathy of Prematurity Cooperative Group.  Revised indications for the treatment of retinopathy of prematurity: results of the Early Treatment for Retinopathy of Prematurity randomized trial.  Arch Ophthalmol. 2003;121(12):1684-1694. doi:10.1001/archopht.121.12.1684PubMedGoogle ScholarCrossref
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Steyerberg  EW, Moons  KGM, van der Windt  DA,  et al; PROGRESS Group.  Prognosis Research Strategy (PROGRESS) 3: prognostic model research.  PLoS Med. 2013;10(2):e1001381. doi:10.1371/journal.pmed.1001381PubMedGoogle Scholar
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FRAX: fracture risk assessment tool. https://www.sheffield.ac.uk/FRAX/index.aspx
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Albertsson-Wikland  K, Mårtensson  A, Sävendahl  L,  et al.  Mortality is not increased in rhGH-treated patients when adjusting for birth characteristics.  J Clin Endocrinol Metab. 2016;101(5):2149-2159. doi:10.1210/jc.2015-3951PubMedGoogle ScholarCrossref
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Welcome to DIGIROP. http://www.digirop.com. Accessed October 2019.
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Good  WV, Hardy  RJ, Dobson  V,  et al; Early Treatment for Retinopathy of Prematurity Cooperative Group.  The incidence and course of retinopathy of prematurity: findings from the Early Treatment for Retinopathy of Prematurity Study.  Pediatrics. 2005;116(1):15-23. doi:10.1542/peds.2004-1413PubMedGoogle ScholarCrossref
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Palmer  EA, Flynn  JT, Hardy  RJ,  et al; Cryotherapy for Retinopathy of Prematurity Cooperative Group.  Incidence and early course of retinopathy of prematurity.  Ophthalmology. 1991;98(11):1628-1640. doi:10.1016/S0161-6420(91)32074-8PubMedGoogle ScholarCrossref
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Austeng  D, Källen  K, Ewald  U, Wallin  A, Holmström  G.  Treatment of ROP in a population of preterm infants born before the 27th week of gestation in Sweden.  Br J Ophthalmol. 2010;94(9):1136-1139. doi:10.1136/bjo.2009.170704PubMedGoogle ScholarCrossref
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Holmström  G, Hellström  A, Jakobsson  P, Lundgren  P, Tornqvist  K, Wallin  A.  Five years of treatment for retinopathy of prematurity in Sweden: results from SWEDROP, a national quality register.  Br J Ophthalmol. 2016;100(12):1656-1661. doi:10.1136/bjophthalmol-2015-307263PubMedGoogle ScholarCrossref
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Quinn  GE, Ying  GS, Bell  EF,  et al; G-ROP Study Group.  Incidence and early course of retinopathy of prematurity: secondary analysis of the Postnatal Growth and Retinopathy of Prematurity (G-ROP) study.  JAMA Ophthalmol. 2018;136(12):1383-1389. doi:10.1001/jamaophthalmol.2018.4290PubMedGoogle ScholarCrossref
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Norman  M, Hellström  A, Hallberg  B,  et al.  Prevalence of severe visual disability among preterm children with retinopathy of prematurity and association with adherence to best practice guidelines.  JAMA Netw Open. 2019;2(1):e186801. doi:10.1001/jamanetworkopen.2018.6801PubMedGoogle Scholar
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Reynolds  JD, Hardy  RJ, Kennedy  KA, Spencer  R, van Heuven  WA, Fielder  AR; Light Reduction in Retinopathy of Prematurity (LIGHT-ROP) Cooperative Group.  Lack of efficacy of light reduction in preventing retinopathy of prematurity.  N Engl J Med. 1998;338(22):1572-1576. doi:10.1056/NEJM199805283382202PubMedGoogle ScholarCrossref
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    Original Investigation
    November 7, 2019

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

    Author Affiliations
    • 1Department of Ophthalmology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
    • 2Statistiska Konsultgruppen, Gothenburg, Sweden
    • 3Institute of Health Care Science, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
    • 4Department of Ophthalmology, Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts
    • 5Department of Ophthalmology, University Medical Center Greifswald, Greifswald, Germany
    • 6Eye Center, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
    • 7Unit of Ophthalmology, Department of Neuroscience, University Hospital, Uppsala, Sweden
    • 8Unit of Endocrinology, Department of Physiology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
    • 9McKillop Health Institute, Australian Catholic University, Melbourne, Australia
    • 10Department of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
    • 11Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
    • 12Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
    JAMA Ophthalmol. Published online November 7, 2019. doi:https://doi.org/10.1001/jamaophthalmol.2019.4502
    Key Points

    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.

    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.

    Introduction

    Retinopathy of prematurity (ROP) is a potentially blinding disease, and screening programs for detecting sight-threatening ROP needing treatment have been established worldwide.1,2 Infants with lower gestational age (GA) have a higher risk of sight-threatening ROP; in Sweden, the recommendation is to screen infants with GA less than 31 weeks and severely ill infants if older. Data are registered in the Swedish National Registry for Retinopathy of Prematurity (SWEDROP). Between 2008 and 2015, only 5.7% of screened infants in Sweden were treated for ROP.3 Screening includes retinal examinations by specially trained ophthalmologists and is often stressful for the infant4; without risk prediction, some infants may not be screened and treated at the appropriate time. Individualized risk estimates would allow for optimization of timing and frequency of the screening processes from the health care and economics perspectives. Improving the timing of screening visits could avoid unnecessary examinations of low-risk infants and optimize identification of those at high risk.

    Risk and severity of ROP vary by prenatal and postnatal factors,5 including poor prenatal and postnatal weight gain. For this reason, the prediction algorithm WINROP (weight, insulinlike growth factor 1, neonatal, ROP), which is based on accumulated postnatal weight gain, has been validated and broadly used.6-9 Similar tools based on longitudinal postnatal weight gain also have been developed.10-13 The objectives of this study were to create, then to internally and externally validate, and to describe the clinical implications of a prediction model for individual momentary and cumulative risks of ROP treatment based on birth characteristics alone, including infants born at GA less than 31 weeks.

    Methods
    Study Population

    Infants born between January 1, 2007, and August 7, 2018, at GA less than 31 weeks and with completed ROP screening registered in SWEDROP14 were included as part of the Swedish Neonatal Quality Register,15 started in 2007, which has approximately 97% coverage and contains perinatal data, screening outcomes, and treatment information.3 All data are registered through standardized protocols, in most settings by a trained pediatric ophthalmologist who has performed the screening examination. A validation of 85 randomly selected infants screened in 2018 showed 100% correctly reported values for variables used in this study. This retrospective cohort study was approved by the ethics committee at Uppsala University, Uppsala, Sweden, who also waived written informed consent because all the data were deidentified.

    Model Development Group

    In total, data for 8784 infants born between January 1, 2007, and October 31, 2017, were retrieved from SWEDROP for the prediction model development. Of those, data for 1372 of 8784 infants (15.6%) were excluded for having GA at least 31 weeks at birth, and 126 of 8784 infants (1.4%) were excluded for missing data. This left 7286 of 8784 infants (82.9%) eligible for the model development group. Of those, 6947 of 7286 infants (95.3%) had GA 24 to 30 weeks (Figure 1).

    Validation Groups

    The group used for temporal validation consisted of infants born between November 1, 2017, and August 7, 2018, and registered in SWEDROP. Among infants born at GA 24 to 30 weeks, 308 of 323 (95.4%) were eligible and served as the validation temporal group (Figure 1).

    The validation US group included 1485 of 1535 eligible infants (96.7%) born at GA 24 to 30 weeks from 12 US centers between 2005 and 2010 (Figure 1).16 The validation European group included 329 of 354 eligible infants (92.9%) born at GA 24 to 30 weeks from Freiburg, Germany, with retrospective screening data collected between 2011 and 2017 (Figure 1).17

    Study Procedures

    The estimation of GA was based on fetal ultrasonographic results. The chronological (postnatal) age, postmenstrual age, and GA are defined according to the American Academy of Pediatrics’ issued policy.18 An SD score (SDS) of expected reference weight (birth weight SDS [BWSDS]) was calculated based on GA, sex, and birth weight for all healthy singletons born at GA at least 24 weeks between 1990 and 1999 in Sweden and registered in the Medical Birth Register (800 000 healthy infants of approximately 1 million born).19 Hence, BWSDS was not calculated for infants born at GA less than 24 weeks because of a lack of reference for this extremely preterm population. Infants born at GA less than 24 weeks are at high risk of severe ROP requiring treatment, partly owing to a larger proportion of avascular retinal area at birth,20 and prediction models are not as useful in this cohort. Therefore, a simpler prediction model was developed for this group and is presented along with the results in eAppendix 1 (which references eFigures 9-11 and eTables 7 and 8) in the Supplement. Small for GA19 was defined as BWSDS less than −2.

    Study Outcome

    The prediction model was developed to estimate risk for treatment of sight-threatening ROP. The International Classification of Retinopathy of Prematurity21 and Early Treatment for Retinopathy of Prematurity (ETROP)22 criteria for treatment were used.

    Statistical Analysis
    General Methodology

    Number and percentage are given for categorical variables; for continuous variables, the mean, SD, median, range, and interquartile range are provided, where applicable. For comparison between 2 groups, we used the Fisher exact test for dichotomous variables, Mantel-Haenszel χ2 trend test for ordered categorical variables, and Mann-Whitney test for continuous variables. The Jonckheere-Terpstra test was applied for identifying trends between ordered categorical and continuous variables. The crude week-specific risk of ROP treatment (number of infants with the event divided by number of infants at risk) was analyzed based on postnatal age and postmenstrual age (GA plus postnatal age) by GA at birth. The modeling process consisted of (1) prediction model development, (2) internal and external validation, and (3) clinical implication.23 The prediction model for ROP treatment, called DIGIROP-Birth (Digital ROP), was developed using Poisson regression for time-varying data, from which we obtained a continuous hazard function, h(t), describing momentary risk for ROP treatment.24,25 From the hazard function, the survival function

    Image description not available.

    and its complement, the cumulative risk function F(t) = 1 − S(t), were estimated. The 95% CI for F(t) was obtained via repeated sampling (1000 samples) of the model parameters from a multivariate normal distribution using a covariance matrix estimated by the Poisson regression models. Parameter estimates, SEs, and hazard ratios (HRs) with 95% CIs are presented. The predictive ability of the continuous cumulative risks was checked and was found to be similarly high after postnatal age 15 weeks (eFigure 1 in the Supplement). Given this information and the knowledge about the studied hazard function, the cumulative risks of ever needing ROP treatment during 20 postnatal weeks were used for interpretation.

    All tests above were 2-tailed and conducted at the .05 significance level, with no adjustments for multiple comparisons. All analyses were performed using SAS statistical software, version 9.4 (SAS Institute Inc).

    DIGIROP-Birth Prediction Model for GA 24 to 30 Weeks Development and Validation

    Based on the crude risks for ROP treatment over time stratified by GA, we found that postnatal age was the most appropriate time axis. The final model for GA 24 to 30 weeks included the following: piecewise linear current postnatal age (break points, 8 and 12 weeks), piecewise linear continuous GA given in weeks and days (break point, 27 weeks), sex, piecewise linear BWSDS (break point, −1 SDS), postnatal age × piecewise linear GA interaction, sex × GA interaction, and postnatal age × piecewise linear BWSDS interaction. The break points for the variables were selected based on graphical review of univariable hazard functions. The final model was built by gradually expanding the models, starting only with postnatal age and further keeping interactions with P < .10.

    Internal, temporal, and geographical external validations were performed. The model fit and adaptation were described by the area under the receiver operating characteristic curve (hereinafter referred to as AUC) overall, by calendar periods, and by race/ethnicity. We performed cross-validation and evaluated calibration plots; calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV); and compared DIGIROP-Birth with 4 other published prediction models (CHOP-ROP [Children’s Hospital of Philadelphia–ROP],11 OMA-ROP [Omaha-ROP],12 WINROP [weight, insulinlike growth factor 1, neonatal, ROP],7 and CO-ROP [Colorado-ROP]13) using GA, birth weight, and different weight gain variables in the algorithms, as described in more detail in eAppendix 2 in the Supplement.

    Results
    Study Population

    Birth characteristics for the whole SWEDROP cohort, model development group, and validation temporal group, as well as by maximum ROP stage, are listed in eTable 1 in the Supplement. Among 7609 patients, 4155 (54.6%) were boys, the mean (SD) GA was 28.1 (2.1) weeks, and the mean (SD) birth weight was 1119 [353] g. Of those born at GA at least 24 weeks, 1510 of 7255 (20.8%) were small for GA. In total, 354 of 7609 (4.7%) were born at GA less than 24 weeks, and 2806 of 7609 (36.9%) were born at GA 24 to less than 28 weeks. Birth characteristics were numerically balanced between the model development group and the validation temporal group. Birth characteristics for the validation US group and the validation European group are listed in eTable 2 in the Supplement.

    ROP Treatment Incidence in Screened Infants

    Altogether, 2427 of 7609 infants (31.9%) developed any ROP, which regressed spontaneously in 1985 of 7609 (26.1%) and was treated in 442 of 7609 (5.8%) (eTable 3 in the Supplement). Among infants with GA less than 24 weeks, 142 of 354 (40.1%) were treated, 287 of 2806 (10.2%) among those with GA 24 to less than 28 weeks and 13 of 4449 (0.3%) among those with GA at least 28 weeks. The incidence of ROP treatment for infants born at GA 24 to 30 weeks was 125 of 1485 (8.4%) in the validation US group and 17 of 329 (5.2%) in the validation European group.

    Momentary Individual Risk of ROP Treatment for GA Less Than 31 Weeks

    Figure 2A and B show crude week-specific risk of ROP treatment for the SWEDROP population. Table 1 lists the observed timing for ROP treatment applying postnatal age and postmenstrual age as time axes. The ROP treatment risk peaked at postnatal week 12 regardless of GA at birth, but no specific pattern by GA was seen for postmenstrual age.

    From the Poisson regression model based on the total SWEDROP population, including postnatal age and adjusting for GA, the risk for ROP treatment increased by 54% (HR, 1.54; 95% CI, 1.39-1.70) per week from postnatal weeks 8 through 12. Afterward, it decreased by 30% (HR, 0.70; 95% CI, 0.67-0.74) per week (Figure 2C and eTable 4 in the Supplement).

    Cumulative Individual Risk of ROP Treatment for GA 24 to 30 Weeks

    Table 2 summarizes the final DIGIROP-Birth model for ROP treatment in infants born at GA 24 to 30 weeks. The estimated cumulative risks were 60.0% and 35.1%, respectively, for a girl with BWSDS −3 and 0 born at GA 24 weeks and were 27.8% and 14.2%, respectively, if she was born at GA 25 weeks (Figure 3 and eFigure 2 and eTable 5 in the Supplement). Corresponding figures for a boy with the same background data were 57.7% and 33.4%, respectively, and 32.5% and 16.9%, respectively. Greater decreasing risk was observed for girls than for boys with increasing GA (P for interaction = .02), with HRs of 0.83 (95% CI, 0.64-1.07) at 25 weeks and 0.50 (95% CI, 0.33-0.76) at 27 weeks (crude incidences are shown in eFigure 3 in the Supplement, and predicted cumulative risks are shown in eFigure 4 in the Supplement). The cumulative risk estimates with 95% CIs are available online for public use,26 requiring input of GA in weeks and days, sex, and birth weight for the infant.

    Internal and External Validation of DIGIROP-Birth for GA 24 to 30 Weeks

    eFigure 5 in the Supplement shows AUCs from the internal and external validations, indicating whether the model discriminates well between receiving or not receiving treatment. The AUC for the model development group was 0.90 (95% CI, 0.89-0.92), and the AUC for the cross-validation model was 0.90 (95% CI, 0.89-0.91). The AUCs for different calendar periods ranged from 0.87 to 0.92. The calibration plots, examining overestimation or underestimation of risks in different regions, showed the model as being overall well adapted (eFigure 6 in the Supplement). Temporal validation of DIGIROP-Birth showed an AUC of 0.94 (95% CI, 0.90-0.98). Geographical external validation resulted in an AUC of 0.87 (95% CI, 0.84-0.89) for the validation US group and an AUC of 0.90 (95% CI, 0.85-0.95) for the validation European group. The AUCs for stratified analysis on race/ethnicity categories in the validation US group were 0.79 for Hispanic infants, 0.85 for Asian infants, 0.86 for non-Hispanic infants, 0.88 for white infants, and 0.90 for black infants.

    DIGIROP-Birth for GA 24 to 30 Weeks vs Existing ROP Models (Requiring Postnatal Longitudinal Data)

    The comparisons of DIGIROP-Birth vs CHOP-ROP, OMA-ROP, WINROP, and CO-ROP were performed on the validation US group, enabling the use of longitudinal weight data. These results are summarized in eFigures 7 and 8 and eTable 6 in the Supplement.

    Applying the CHOP-ROP algorithm (AUC, 0.89; 95% CI, 0.87-0.92) and categorizing the probabilities based on the recommended cutoff of 0.0140, similar prediction ability was observed compared with DIGIROP-Birth (AUC, 0.88; 95% CI, 0.86-0.91), and a cutoff of 0.0083 obtained the same sensitivity (95 of 96 [99.0%] for both CHOP-ROP and DIGIROP-Birth. Specificity was 598 of 1346 (44.4%) vs 658 of 1346 (48.9%), respectively. Applying the same cutoff on the complete SWEDROP database, the model showed 97.7% (95% CI, 95.3%-99.1%) sensitivity and 59.5% (95% CI, 58.4%-60.7%) specificity. Applying a cutoff of 0.00083 for 100% (95% CI, 98.8%-100%) sensitivity in the cohort, a specificity of 19.0% (95% CI, 18.1%-20.0%) was obtained.

    Compared with OMA-ROP (AUC, 0.77; 95% CI, 0.72-0.82), a cutoff of 23 g per day in weight gain, with a corresponding cutoff of 0.0200 for DIGIROP-Birth (AUC, 0.90; 95% CI, 0.87-0.92), a sensitivity of 90 of 92 (97.8%) was obtained. Specificity was 173 of 771 (22.4%) for OMA-ROP vs 448 of 771 (58.1%) for DIGIROP-Birth.

    Compared with WINROP (AUC, 0.81; 95% CI, 0.78-0.84), the alarm category of WINROP score 2 or 3 provided a sensitivity of 121 of 125 (96.8%), with a corresponding cutoff of 0.0089 for DIGIROP-Birth (AUC, 0.87; 95% CI, 0.84-0.89). Specificity was 487 of 1360 (35.8%) for WINROP vs 671 of 1360 (49.3%) for DIGIROP-Birth.

    The specificity was 141 of 1341 (10.5%) for the CO-ROP algorithm and 642 of 1341 (47.9%) for DIGIROP-Birth. Both had a sensitivity of 122 of 124 (98.4%).

    Clinical Practice Implications of DIGIROP-Birth for GA 24 to 30 Weeks

    Based on ROP treatment timing in the SWEDROP cohort (2007-2018) and the DIGIROP-Birth model, we compared the results with the current US recommendations, based on studies that are more than 20 years old,2 for postnatal age and postmenstrual age at initial examination (Table 1). The maximum age for estimated risk less than 0.001 corresponds well to the observed minimum age for ROP treatment, except for GA 24 weeks, for which a somewhat higher risk at a younger age was estimated. Recommending that the initial examination should start 1 week before the earliest observed ROP treatment per GA week in our cohort would potentially have avoided 14 867 of 135 061 visits (11.0%), assuming 1 visit per week. For GA of at least 27 weeks, with a ROP treatment incidence of 33 of 5398 (0.6%), the difference between the US recommendations and this study resulted in 14 066 of 93 052 examinations (15.1%) potentially being avoided.

    Discussion

    We have created and validated the DIGIROP-Birth prediction model, available free of charge online26 based on 6947 infants born at GA 24 to 30 weeks, estimating the individual momentary and cumulative risks for ROP treatment. The model using only available data at birth but more advanced statistical methods was at least as accurate as 4 of the ROP prediction models now in use based on longitudinal weight measurements, which are not always readily available to ophthalmologists.

    Surprisingly, the momentary risk of ROP treatment peaked at 12 weeks’ postnatal age regardless of GA at birth, while no specific pattern was observed for postmenstrual age. This observation is particularly interesting because the ETROP study27 found that the progression of prethreshold ROP was highly associated with postmenstrual age, similar to the finding in the CRYO-ROP (Cryotherapy for ROP) study28 15 years earlier. However, it should be emphasized that infants included in the CRYO-ROP study were born at higher GA, and no GA-specific hazard functions were studied for ROP outcome. Other Swedish studies29,30 have reported that lower GA at birth is associated with lower GA at treatment, but the momentary risk in relation to postnatal age and postmenstrual age was not analyzed. Recently, in a large North American cohort, the timing of ROP treatment was presented only in relation to postmenstrual age and not postnatal age.31

    The identification of a peak risk at 12 postnatal weeks in infants with GA less than 31 weeks might be clinically useful because it was recently shown that inadequate screening or treatment was identified in 11 of 17 cases with blindness from ROP (64.7%).32 Hence, clinicians and parents could be alerted during this period to ensure that timely screening occurs to reduce the risk of blindness.

    National patient registries are valuable sources for estimation of treatment risks. Herein, the DIGIROP-Birth model was compared with a validation US group and a validation European group and showed high predictive ability and generalizability both for individuals with the same and with different reported race/ethnicity.

    The ROP prediction models may also be used to reduce screening frequency in infants at low risk. The latest US policy statement for ROP screening2 was issued in 2018. The recommendations for the timing of the first examination were based on the CRYO-ROP study28 published in 1991 and the LIGHT-ROP (Light Reduction in ROP) study33 published in 1998. In those periods, fewer extremely preterm infants survived, more mature infants were treated, and treatment criteria were different from those used today. Based on the results of our study, if the initial examination was performed 1 week before the earliest observed postnatal age at ROP treatment, 14 867 of 135 061 stressful early examinations (11.0%) could be avoided (assuming 1 examination per week) compared with US recommendations.2 For GA of at least 27 weeks, with a ROP treatment incidence herein less than 1%, 14 066 of 93 052 examinations (15.1%) could have been avoided while capturing all cases of ROP treatment (100% sensitivity). Notably, reaching 100% sensitivity in such models of real-life, large data sets is accompanied by low specificity. Based on approximately as large a cohort as in our study, the updated CHOP-ROP11 model, which uses longitudinal weight data and birth data, achieved 11.2% specificity for 100% sensitivity and 36.4% specificity for 98.5% sensitivity; DIGIROP-Birth (using only readily obtained birth data) showed 19.0% specificity for 100% sensitivity and 53.8% specificity for 99.0% sensitivity.

    Strengths and Limitations

    The strengths of our study include the unique and complete cohort of preterm infants born in Sweden between January 2007 and August 2018. Also, our statistical model includes 3 basic measurements (GA, sex, and birth weight). The postnatal age for ROP treatment or censoring (discontinued follow-up) is included in the hazard function estimation but is not required as an input variable. Hence, the input data are simple, facilitating their general use, even though the method is more advanced, taking into account the underlying hazard function and the important interactions that contribute to adjustment of heterogeneity, which is novel in ROP research. The DIGIROP-Birth has shown strong predictive ability in internal, temporal, and geographical external validations. If found not acceptable in future validations among a population, a subgroup-specific model designed for optimal predictions in that population might be developed using our methods. Finally, DIGIROP-Birth has been shown to be equal to or better than 4 other ROP prediction models and is accessible online.26

    Our study has some limitations. One limitation is the use of registry retrospective data. However, the registry showed high coverage and successful validation of data for 85 randomly selected infants screened in 2018. In addition, infants born at GA less than 24 weeks could not be included in the prediction model because of the lack of a reference algorithm for birth weight, preventing BWSDS calculations. Given the small sample size, only a simple model could be developed for these infants, resulting in low predictive ability. Close monitoring of such infants is mandatory irrespective of calculated risk, making prediction models less important for this group.

    Conclusions

    We created and validated the DIGIROP-Birth model, an individualized early prediction model for infants with GA 24 to 30 weeks, which estimates momentary and cumulative risks for receiving ROP treatment based on simple birth characteristics. A surprising finding was that postnatal age was the best predictive variable for the temporal risk of ROP treatment. The DIGIROP-Birth model is an accessible online application that appears to be generalizable and to have at least as good test statistics as other models that require longitudinal neonatal data, which are not always readily available to ophthalmologists.

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    Article Information

    Accepted for Publication: September 15, 2019.

    Published Online: November 7, 2019. doi:10.1001/jamaophthalmol.2019.4502

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Pivodic A et al. JAMA Ophthalmology.

    Corresponding Author: Ann Hellström, MD, PhD, Department of Ophthalmology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, S-416 85 Gothenburg, Sweden (ann.hellstrom@medfak.gu.se).

    Author Contributions: Ms Pivodic and Dr Hellström had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Pivodic, Hård, Löfqvist, Smith, Albertsson-Wikland, Johansson, Hellström.

    Acquisition, analysis, or interpretation of data: Pivodic, Hård, Löfqvist, Wu, Bründer, Lagrèze, Stahl, Holmström, Albertsson-Wikland, Johansson, Nilsson, Hellström.

    Drafting of the manuscript: Pivodic, Hård, Albertsson-Wikland.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Pivodic, Johansson, Nilsson, Hellström.

    Obtained funding: Albertsson-Wikland, Hellström.

    Administrative, technical, or material support: Wu, Lagrèze, Stahl, Hellström.

    Supervision: Stahl, Albertsson-Wikland, Johansson, Nilsson, Hellström.

    Conflict of Interest Disclosures: Dr Stahl reported receiving grants or personal fees (honoraria for consultancy work and travel reimbursement) from Novartis, Bayer, and Recordati Rare Diseases. No other disclosures were reported.

    Funding/Support: This study was supported by the Swedish Research Council (grant 2016-01131), Gothenburg Medical Society, government grants under the ALF agreements (grants ALFGBG-717971 and ALFGBG-812951). This work was also supported by the Knut and Alice Wallenbergs Foundation, and De Blindas Vänner unrestricted grant (Dr Hellström), and by EY024864, EY017017, EY017017-13S1 and 1U54HD090255 from the National Institutes of Health (Dr Smith).

    Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Contributions: Professor emeritus Anders Odén (no affiliation at the time of the study) originally developed the Poisson regression method for time-varying data, and Anton Mårtensson, BSc (Statistiska konsultgruppen, Gothenburg, Sweden), translated the method into an SAS macro. Aimon Niklasson, MD, PhD (Gothenburg Pediatric Growth Research Center [GP-GRC], Department of Pediatrics, Institute of Clinical Sciences, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden), provided valuable discussions regarding infant growth patterns, and Swedish National Registry for Retinopathy of Prematurity (SWEDROP) group members Lotta Gränse, MD, PhD (Department of Clinical Sciences, Ophthalmology, Skåne University Hospital, Lund University, Lund, Sweden), Kristina Tornqvist, MD, PhD (Department of Clinical Sciences, Ophthalmology, Skåne University Hospital, Lund University, Lund, Sweden), Abbas Al-Hawasi, MD (Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden), Pia Lundgren, MD, PhD (Section for Ophthalmology, Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden), and Agneta Wallin, MD (St Erik Eye Hospital, Stockholm, Sweden), assisted with clinical data collection. They were not compensated for their contributions. We thank all of the retinopathy of prematurity screening ophthalmologists in Sweden who work daily with the infants included in our study.

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