The British journal of ophthalmology · 2022
Prediction model to predict type 1 retinopathy of prematurity using gestational age and birth weight (PW-ROP).
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Iu Lawrence Pui Leung, Yip Wilson Wai Kuen, Lok Julie Ying Ching, Fan Michelle Ching Yim, Lai Connie Hong Yee, Ho Mary, Young Alvin Lerrmann
Аннотация
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.
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.