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Prediction model to predict type 1 retinopathy of prematurity using gestational age and birth weight (PW-ROP)
  1. Lawrence Pui Leung Iu1,2,
  2. Wilson Wai Kuen Yip1,2,
  3. Julie Ying Ching Lok1,2,
  4. Michelle Ching Yim Fan3,
  5. Connie Hong Yee Lai4,5,
  6. Mary Ho1,2,
  7. Alvin Lerrmann Young1,2
  1. 1Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong, China
  2. 2Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
  3. 3Department of Ophthalmology, Tung Wah Eastern Hospital, Hong Kong, China
  4. 4Department of Ophthalmology, Queen Mary Hospital, Hong Kong, China
  5. 5Department of Ophthalmology, The University of Hong Kong, Hong Kong, China
  1. Correspondence to Dr Lawrence Pui Leung Iu, Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong, China; dr.lawrenceiu{at}gmail.com

Abstract

Purpose To develop a prediction model for type 1 retinopathy of prematurity (ROP) from an Asian population.

Methods This retrospective cohort study included 1043 premature infants who had ROP screening in a tertiary hospital in Hong Kong from year 2006 to 2018. The ROP prediction model was developed by multivariate logistic regression analyses on type 1 ROP. The cut-off value and the corresponding sensitivity and specificity were determined by receiver operating characteristic curve analysis. A validation group of 353 infants collected from another tertiary hospital in another region of Hong Kong from year 2014 to 2017 was used for external validation.

Results There were 1043 infants in the study group. The median gestational age (GA) was 30 weeks and 1 day and median birth weight (BW) was 1286 g. The prediction model required only GA and BW as parameters (prematurity-birth weight ROP (PW-ROP)). The area under curve value was 0.902. The sensitivity and specificity were 87.4% and 79.3%, respectively. Type 1 ROP developed in 0.9%, 17.4% and 50% of infants with PW-ROP scores<0, between 0 and <300, and ≥300 respectively (p<0.001). On external validation, our prediction model correctly predicted 95.8% of type 1 ROP (sensitivity=95.8%, specificity=74.8%) in the validation group.

Conclusion The PW-ROP model is a simple model which could predict type 1 ROP with high sensitivity and specificity. Incorporating this model to ROP examination would help identify infants at risk for ROP treatment.

  • retina
  • child health (paediatrics)

Data availability statement

Data are available on reasonable request. Data are available from the corresponding author.

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Data availability statement

Data are available on reasonable request. Data are available from the corresponding author.

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Footnotes

  • Contributors LPLI had substantial contribution to the conception and design of the work; acquisition and analysis of data; drafting of the manuscript. WWKY, JYCL, MH and ALY had substantial contribution to the conception and design of the work; acquisition of data; critical review and revision of the manuscript. MCYF and CHYL had substantial contribution to the acquisition and analysis of data; critical review and revision of the manuscript. LPLI is responsible for the overall content as the guarantor, accepts full responsibility for the work and the conduct of the study, had access to the data, and controlled the decision to publish.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; internally peer reviewed.