Implementation of a Clinical Prediction Model Using Daily Postnatal Weight Gain, Birth Weight, and Gestational Age to Risk Stratify ROP

Retinopathy of Prematurity Outcomes of Neonates Meeting Only a Single Screening Criterion: Proposal of the TWO-ROP Algorithm

PURPOSE

To assess the rates of retinopathy of prematurity (ROP) and treatment-warranted ROP in a modern set of patients meeting 0 or 1 of the current ROP screening criteria.

DESIGN

Retrospective cohort study.

METHODS

Single-center study of 9350 infants screened for ROP from 2009 to 2019. Rates of ROP and treatment-warranted ROP were evaluated in group 1 (birth weight [BW] <1500 g and gestational age [GA] ≥30 weeks), group 2 (BW ≥1500 g and GA <30 weeks), and group 3 (BW ≥1500 g and GA ≥30 weeks).

RESULTS

Of 7520 patients with reported BW and GA, 1612 (21.4%) patients met the inclusion criteria. The number of patients in groups 1, 2, and 3 was 466 (6.19%), 23 (0.31%), and 1123 (14.93%), respectively. The number of patients diagnosed with ROP was 20 (4.29%) in group 1, 1 (4.35%) in group 2, and 12 (1.07%) in group 3 (P < .001). The mean interval between birth and ROP diagnosis was 36.25 days (range 12-75 days) in group 1, 47 days in group 2, and 23.33 days (range 10-39 days) in group 3 (P = .05). No cases of stage 3, zone 1, or plus disease were recorded. No patients met the treatment criteria.

CONCLUSIONS

Patients meeting 1 screening criterion had a low rate of ROP (<5%), with no stage 3, zone 1, or plus disease. No patients required treatment. We propose a possible algorithm (TWO-ROP) in appropriate neonatal intensive care units, with an amendment in screening protocol for this low-risk population to include only an outpatient screening examination within 1 week of discharge, or at 40 weeks if inpatient, to decrease the inpatient ROP screening burden while maintaining safety. Further external validation of this protocol would be required.

Validation of DIGIROP models and decision support tool for prediction of treatment for retinopathy of prematurity on a contemporary Swedish cohort

BACKGROUND/AIMS

Retinopathy of prematurity (ROP) is currently diagnosed through repeated eye examinations to find the low percentage of infants that fulfil treatment criteria to reduce vision loss. A prediction model for severe ROP requiring treatment that might sensitively and specifically identify infants that develop severe ROP, DIGIROP-Birth, was developed using birth characteristics. DIGIROP-Screen additionally incorporates first signs of ROP in different models over time. The aim was to validate DIGIROP-Birth, DIGIROP-Screen and their decision support tool on a contemporary Swedish cohort.

METHODS

Data were retrieved from the Swedish national registry for ROP (2018-2019) and two Swedish regions (2020), including 1082 infants born at gestational age (GA) 24 to <31 weeks. The predictors were GA at birth, sex, standardised birth weight and age at the first sign of ROP. The outcome was ROP treatment. Sensitivity, specificity and area under the receiver operating characteristic curve (AUC) with 95% CI were described.

RESULTS

For DIGIROP-Birth, the AUC was 0.93 (95% CI 0.90 to 0.95); for DIGIROP-Screen, it ranged between 0.93 and 0.97. The specificity was 49.9% (95% CI 46.7 to 53.0) and the sensitivity was 96.5% (95% CI 87.9 to 99.6) for the tool applied at birth. For DIGIROP-Screen, the cumulative specificity ranged between 50.0% and 78.7%. One infant with Beckwith-Wiedemann syndrome who fulfilled criteria for ROP treatment and had no missed/incomplete examinations was incorrectly flagged as not needing screening.

CONCLUSIONS

DIGIROP-Birth and DIGIROP-Screen showed high predictive ability in a contemporary Swedish cohort. At birth, 50% of the infants born at 24 to <31 weeks of gestation were predicted to have low risk of severe ROP and could potentially be released from ROP screening examinations. All routinely screened treated infants, excluding those screened for clinical indications of severe illness, were correctly flagged as needing ROP screening.

Prediction model to predict type 1 retinopathy of prematurity using gestational age and birth weight (PW-ROP)

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.

Retinopathy of Prematurity: Advances in the Screening and Treatment of Retinopathy of Prematurity Using a Single Center Approach

PURPOSE

To focus on the longitudinal evaluation of high-risk infants for the development of retinopathy of prematurity (ROP) at a single tertiary neonatal intensive care unit (NICU), and to evaluate evolving demographics of ROP and the transition of treatment-warranted disease.

DESIGN

Retrospective cohort study.

METHODS

A consecutive retrospective review was performed of all infants screened for ROP between 1990 and 2019 at the Jackson Memorial Hospital neonatal intensive care unit. All inborn infants meeting a birth criteria of <32 weeks' gestational age (GA) or a birthweight (BW) of 1500 g were included. Longitudinal demographic, diagnostic, and treatment data were reported.

RESULTS

Between January 1, 1990, and June 20, 2019, a total of 25,567 examinations were performed and 7436 patients were included. Longitudinal trends over 3 decades demonstrated a decreasing incidence of ROP (P < .05). Although the mean BW and GA increased over 3 decades, patients with ROP demonstrated lower BW and GA over time (P < .05). The prevalence of micro-premature infants (as defined by BW <750 g) continues to rise over time. Micro-preemies demonstrated increasing severity of zone and stage grading, plus disease, and propensity to require treatment (P < .05). The rate of progression of ROP to stage 4 and 5 disease has decreased over time, and there has been an associated increased adoption of intravitreal bevacizumab as primary and salvage therapy.

CONCLUSIONS

Understanding the evolution of ROP infants and treatment over time is critical in identifying high-risk infants and in reducing the incidence of severe-stage ROP. Micro-prematurity is one of the significant risk factors for treatment-warranted ROP that continues to increase as neonatal care improves. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.

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

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.

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.

Development and validation of a new clinical decision support tool to optimize screening for retinopathy of prematurity

BACKGROUND/AIMS

Prematurely born infants undergo costly, stressful eye examinations to uncover the small fraction with retinopathy of prematurity (ROP) that needs treatment to prevent blindness. The aim was to develop a prediction tool (DIGIROP-Screen) with 100% sensitivity and high specificity to safely reduce screening of those infants not needing treatment. DIGIROP-Screen was compared with four other ROP models based on longitudinal weights.

METHODS

Data, including infants born at 24-30 weeks of gestational age (GA), for DIGIROP-Screen development (DevGroup, N=6991) originate from the Swedish National Registry for ROP. Three international cohorts comprised the external validation groups (ValGroups, N=1241). Multivariable logistic regressions, over postnatal ages (PNAs) 6-14 weeks, were validated. Predictors were birth characteristics, status and age at first diagnosed ROP and essential interactions.

RESULTS

ROP treatment was required in 287 (4.1%)/6991 infants in DevGroup and 49 (3.9%)/1241 in ValGroups. To allow 100% sensitivity in DevGroup, specificity at birth was 53.1% and cumulatively 60.5% at PNA 8 weeks. Applying the same cut-offs in ValGroups, specificities were similar (46.3% and 53.5%). One infant with severe malformations in ValGroups was incorrectly classified as not needing screening. For all other infants, at PNA 6-14 weeks, sensitivity was 100%. In other published models, sensitivity ranged from 88.5% to 100% and specificity ranged from 9.6% to 45.2%.

CONCLUSIONS

DIGIROP-Screen, a clinical decision support tool using readily available birth and ROP screening data for infants born GA 24-30 weeks, in the European and North American populations tested can safely identify infants not needing ROP screening. DIGIROP-Screen had equal or higher sensitivity and specificity compared with other models. DIGIROP-Screen should be tested in any new cohort for validation and if not validated it can be modified using the same statistical approaches applied to a specific clinical setting.

ROP Screening Tool Assessment and Validation in a Third-Level Hospital in Argentina: A Pilot Study

PURPOSE

To evaluate whether a mathematical tool that predicts severe retinopathy of prematurity (ROP) using clinical parameters at 6 weeks of life (ROPScore calculator smartphone application; PABEX Corporation) can be useful to predict severe ROP in a population of premature infants in Argentina.

METHODS

In this retrospective study, data from the clinical records of all premature infants examined between 2012 and 2018 in the ophthalmology department of a public third-level hospital in Córdoba, Argentina, were obtained. ROPScore screening was applied using a Microsoft Excel spreadsheet (Microsoft Corporation). The sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values of the algorithm were analyzed.

RESULTS

Between 2012 and 2018, a total of 2,894 pre-term infants were examined and 411 met the inclusion criteria, of whom 34% (n = 139) presented some form of ROP and 6% (n = 25) developed severe forms that required treatment. The sensitivity of the algorithm for any ROP and severe ROP was 100%. The PPV and NPV were 35.64% and 100%, respectively, for any ROP and 9.88% and 100% for severe ROP.

CONCLUSIONS

One-time only calculation of the ROPScore algorithm could identify severe cases after validation, reducing the number of screened infants by 38% in infants with a birth weight of 1,500 g or less or a gestational age of 32 weeks or younger. [J Pediatr Ophthalmol Strabismus. 2021;58(1):55-61.].

Screening and novel therapies for retinopathy of prematurity - A review

With current screening for sight threatening retinopathy of prematurity (ROP) <10% of screened infants need treatment. Prediction models based on birth characteristics, postnatal weight gain and other factors have been developed to reduce examinations in low-risk infants. A model based on advanced statistics using data from >7000 infants registered in the Swedish ROP registry is being developed. Based on birth characteristics only, it appears to predict total risk of ROP-treatment as well as models including weight measurements. Treatment risk peaked at 12 weeks of age. Laser therapy is the method of choice for severe ROP. Anti-VEGF therapies are implemented worldwide despite insufficient knowledge of choice of drug, dosage and long term systemic effects. Prevention of ROP may be achieved through oxygen control and provision of the mother's breastmilk. Other interventions such as supplementation with long chain polyunsaturated fatty acids and preservation of fetal haemoglobin are investigated.