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Slow postnatal growth is a surrogate measure for low serum insulin-like growth factor 1, which is an important risk factor for severe retinopathy of prematurity (ROP).
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Risk models that consider postnatal weight gain, along with birth weight and gestational age, include WINROP, ROPScore, and CHOP ROP.
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These models predict severe ROP with much greater specificity than current ROP screening guidelines.
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Two important limitations are study sample size and poor generalizability in countries with
Algorithms for the Prediction of Retinopathy of Prematurity Based on Postnatal Weight Gain
Section snippets
Key points
Growth, insulin-like growth factor-1, and ROP
In an effort to improve this specificity, postnatal growth-based ROP risk models have been developed. The models are based on groundbreaking work by Smith, Hellstrom, Lofqvist, and colleagues, who have provided the current understanding of the pathophysiology underlying ROP.
ROP develops in 2 phases, a hypoxic preclinical phase, during which slow postnatal growth can be used to predict risk, and a subsequent proliferative clinical phase. These phases result from alterations in serum insulin-like
Postnatal growth ROP models
Specific postnatal growth-based ROP predictive models are discussed later in this article. In evaluating these models, it is most useful to consider their performance with regard to sensitivity for detecting severe ROP and the reduction in children requiring eye examinations that would have resulted from their use, which is a more clinically intuitive measure than specificity. Severe ROP has been variably defined as Early Treatment of ROP Study type 1 ROP, treated ROP, or stage 3 ROP in the
WINROP
On the basis of their work on IGF-1 and ROP, Lofqvist and colleagues22 developed a computer-based ROP risk algorithm named WINROP to detect slowdowns in postnatal weight gain, predict severe ROP, and greatly reduce the number of infants requiring examinations.45, 46, 47 WINROP uses a cumulative-deviations statistical approach in multiple steps: each week the infant’s actual weight is compared with an expected growth curve of infants who developed no or mild ROP; the differences or deviations
ROPScore
Eckert and colleagues51 recently developed a less complex model named ROPScore. The model consists of a logistic regression equation, which is used to calculate risk only once per child, using an Excel spreadsheet. The model includes continuous rather than dichotomized terms for BW and GA, weight gain at a single time point (6 weeks postnatal age) as a proportion of BW, and dichotomous terms for blood transfusion and use of oxygen in mechanical ventilation during the first 6 weeks of life.
PINT ROP and CHOP ROP
Binenbaum and colleagues30 developed a simpler logistic regression-based model named PINT ROP. Prospectively collected data from 367 infants with BW <1000 g in the Premature Infants in Need of Transfusion (PINT) randomized controlled trial were used to develop a model containing terms only for BW, GA, and daily rate of weight gain, which was calculated from the current and prior week’s weight measurements. The equation is calculated on a weekly basis, and if the predicted risk of ROP is greater
Model development and complexity
The creation of a usable clinical prediction model is a stepwise process.1 First, a development study is undertaken, containing as large a sample size as possible to avoid overfitting of the model.1, 53 Overfitting may occur when the complexity of the model is high relative to the number of outcome events (cases of severe ROP) in the cohort, and the model effectively describes random error rather than a true association. Such a model will not perform well in a new group of patients. Second, the
Sample size: an important limitation
Despite studies in cohorts ranging from 300 to 1700 infants, sample size remains a limiting factor. Although a point estimate of sensitivity of 100% has been reported, the confidence interval (CI) around that point estimate is likely too wide for clinicians to have confidence that infants with severe ROP would not be missed. The reason is that the width of the CI is driven by the number of cases of severe ROP, not the overall number of infants. For example, the CHOP ROP study involved 524
Generalizability: a second important limitation
There are several reasons a predictive model may behave poorly in new patients, including the methods used to design the model and differences in health care systems and patient characteristics.54 There is good evidence that the generalizability of models such as WINROP and CHOP ROP to countries where higher BW and GA infants develop severe ROP will be limited, and that separate model development studies need to be performed in such populations. When WINROP was applied to a Brazilian cohort, it
Benefits and future studies
Improved ROP risk assessment could have broad-reaching benefits in neonatology, ophthalmology, and public health. Revised ROP screening guidelines might reduce both the number of children requiring stressful diagnostic eye examinations and the frequency of examinations for lower risk infants. Professional and infrastructure resources may be better allocated to high-risk infants, particularly in areas and countries with limited resources, where better targeted resources could lower the burden of
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