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THESS-ROP study: Development of a prediction model for treatment-warranted retinopathy of prematurity and external
Stella Moutzouri1, Anna-Bettina Haidich2, Maria Lithoxopoulou3
12nd Department of Ophthalmology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Insights
A new prediction model, THESS-ROP, accurately identifies infants at risk for treatment-warranted retinopathy of prematurity (TW-ROP). This model aids in optimizing screening for preterm infants.
Area of Science:
- Neonatology
- Ophthalmology
- Medical Informatics
Background:
- Retinopathy of prematurity (ROP) is a significant cause of vision impairment in preterm infants.
- Early detection and treatment are crucial to prevent severe visual outcomes.
- Existing prediction models for ROP require validation in diverse populations.
Purpose of the Study:
- To develop and validate THESS-ROP, a novel prediction model for treatment-warranted retinopathy of prematurity (TW-ROP).
- To compare the performance of THESS-ROP against established ROP prediction models.
- To assess the model's utility in a Greek cohort of preterm infants.
Main Methods:
- An ambispective cohort study of 526 preterm infants screened for ROP.
- Development of a penalized ridge logistic regression model using gestational age, birth weight, sepsis, and hydrocephalus.
- Internal validation via bootstrap resampling and external validation of existing models (CO-ROP, G-ROP, ROPScore).
Main Results:
- THESS-ROP demonstrated high predictive accuracy with an optimism-corrected AUC of 0.935.
- The model achieved 98.0% pooled sensitivity and 58.0% pooled specificity for TW-ROP.
- Sensitivity reached 100% for type 1 ROP and treated ROP, outperforming some established models in specific metrics.
Conclusions:
- The THESS-ROP model effectively stratifies risk for TW-ROP using readily available clinical data.
- It shows potential for optimizing ROP screening protocols in neonatal intensive care units.
- Further external validation in independent populations is recommended prior to clinical implementation.
Purpose:
To develop THESS-ROP, a novel prediction model for treatment-warranted retinopathy of prematurity (TW-ROP) and compare its performance with four established models in a Greek cohort of consecutively screened preterm infants.
Methods:
This ambispective cohort study included all 526 infants screened for ROP (2016-2022) at the 2nd Neonatology Department of Aristotle University of Thessaloniki, a tertiary neonatal intensive care unit in Greece. The primary outcome was TW-ROP (type 1 or 2 or aggressive ROP). Secondary outcomes included type 1 ROP and ROP treatment. A penalized ridge logistic regression model was developed using gestational age, birth weight, sepsis, and hydrocephalus up to 4 weeks postnatal age. Missing data were addressed using 10-fold multiple imputation. Internal validation used bootstrap resampling with optimism correction. External validation of CO-ROP, G-ROP, G-ROP 180 g, and ROPScore was performed in the same cohort. Performance was assessed using area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value.
Results:
TW-ROP developed in 34 infants (6.5%; 24 type 1, 10 type 2), with 29 receiving treatment. THESS-ROP demonstrated excellent discrimination [optimism-corrected AUC 0.935, 95% CI (0.905-0.965)], pooled sensitivity 98.0% and pooled specificity 58.0%. Sensitivity reached 100% for type 1 ROP and treated ROP. In external validation, CO-ROP, G-ROP, G-ROP 180 g, and ROPScore achieved pooled sensitivities of 90.9%, 98.8%, 98.8%, and 100.0% with specificities of 63.4%, 34.4%, 26.1%, and 62.1%, respectively.
Conclusion:
THESS-ROP model, incorporating easily available clinical data, enables effective early risk stratification at 4 weeks PNA and may optimize ROP screening. External validation in independent populations is required before clinical implementation.
