We developed a predictive model (PINT-ROP model) and preliminary nomograms that use BW, GA, and postnatal weight gain measurements to predict the risk of severe ROP and determine the need for eye examinations. In a high-risk cohort, this preliminary model reduced the number of infants with BW < 1000 g who required eye examinations by 30%, missed 1 infant with severe ROP, and identified all infants who required laser surgery.
Although current guidelines treat BW and GA as dichotomous variables, the PINT-ROP model accounts for the known inverse relationship between risk of severe ROP and BW or GA. Infants with very low BW or GA continue to receive examinations on the basis of their degree of prematurity at birth. In contrast, larger BW and older GA infants at increased risk for severe ROP are identified by another factor, slow postnatal weight gain. Numerous additional factors were associated with severe ROP in univariate analysis but were no longer significant in multivariate analysis. We hypothesize that many previously described risk factors for ROP act via a common pathway, lowering IGF-1, and therefore are “captured” through weight measurement as a surrogate for IGF-1 levels. For example, sepsis has been associated with both low IGF-1 and the subsequent development of severe ROP. If low serum IGF-1 lies within the causal pathway tying sepsis with ROP, then measuring its surrogate, poor postnatal weight gain, might be expected to supplant sepsis as a predictor in a risk model for ROP. This hypothesis requires additional study. As recommended by principles of prognostic model development, BW was retained in our model as an established strong risk factor for ROP.38
Its marginal statistical significance (P
= .1) may result from the relatively small range of birth weights (all < 1000 g) represented in the study.
The PINT cohort was at high risk for severe ROP (median BW: 800 g; maximum BW: 995 g). Yet application of the model would have resulted in a 30% reduction in the number of infants requiring eye examinations. Because the greatest reduction in examinations occurs in larger BW infants, we anticipate even greater reductions when the model is applied to a broader cohort, inclusive of infants who meet current screening guidelines (BW < 1501 g). For example, in a much lower-risk Swedish cohort (median BW: 1290 g), Lofqvist et al13
reported a reduction of 76% for the WINROP model.
Among the ways we explored to treat weight gain, we found that rate of daily weight gain calculated from the current and previous weeks' measurements produced the most robust model. This logistic-regression approach is methodologically distinct from the statistical methods used in WINROP,13
which are a cumulative-deviations based model. A reference model of expected weights is created with linear regression and data from infants who develop no or mild ROP. Weights from new infants are compared with the reference model on a weekly basis, and the differences summed over time. When the cumulative differences surpass a threshold level, an alarm is sounded, and, in a final stratification, BW and GA cutoffs are applied to determine a need for examinations. These methods involve calculations that require the use of a computer-based algorithm. One advantage of a logistic-regression based model is that it permits direct calculation of risk and may be represented as a nomogram ( and ).
Nomograms provide a graphical representation of mathematical relationships or formulas, such as a multivariate logistic regression-based clinical predictive model, and can be used to calculate risk of disease without use of a calculator or computer.43
Nomograms have been developed to predict treatment response in breast cancer,44
prostate cancer lymph-node metastasis,45
bladder cancer recurrence,46
and self-assessed melanoma risk.43
For the PINT-ROP model, a need for eye examinations would be determined by setting a risk cutoff level, above which an infant requires examinations, and the user could directly see how the risk of severe ROP is altered by changes in BW, GA, or weight gain rate. However, we stress that the nomograms in and should be considered preliminary and are not intended for clinical use at this time. In particular, the restriction to infants with BW < 1000 g limits the ability to reliably model risk for higher BW and GA infants. It is possible that with additional development in a broader BW and GA cohort, and with subsequent validation, such nomograms may eventually be used as a simple, paper-based clinical tool in lieu of a computer-based algorithm. Future studies should also assess whether an even simpler clinical tool is equally predictive, such as a single cutoff for weight gain per day in target BW or GA populations.
Strengths of this study include use of prospectively collected clinical data and a diverse, multicenter cohort of infants. In addition, the bootstrap internal validation results suggest that there is minimal optimistic bias in the estimates of sensitivity and specificity for detecting severe ROP. However, important limitations need to be addressed before clinical implementation of this or any weight-based ROP risk model, including sample size, outcome criteria, false-negative signals, and generalizability. The development of a predictive model preferably employs a data set with hundreds of outcome events.47
Much larger development studies must be pursued before subsequent validation studies can be undertaken. With regards to outcome, stage 3 ROP inadequately defines severe ROP; zone I disease and plus disease should be included. Adding “treated ROP” to the outcome criteria helped somewhat to address this issue, but treatment decisions were made using “threshold ROP” criteria, not type 1 ROP according to current Early Treatment of ROP criteria,6
during the study period. A third limitation is that clinical factors that cause weight gain but are not associated with increased IGF-1 may generate false-negative signals and have yet to be clearly identified. With regards to generalizability, no predictive model should be used clinically until validated in new patients. Although our multicenter population was diverse, the cohort was at high risk for severe ROP. The low number of infants with a GA > 28 weeks, and low number of severe ROP outcomes among them, limited our ability to model-risk for those infants. The model will likely require recalibration when applied to a broader case-mix sample. Finally, it is important to recognize that in countries with developing neonatal care systems, severe ROP occurs in infants with much higher BW and GA.2
Indeed, accurate assessment of GA may not even be possible in some regions. Application of ROP risk models will require the completion of separate, additional development and validation studies in those populations.
Growth-based ROP prediction modeling is early in its development, but preliminary results are promising. Models such as PINT-ROP and WINROP have the potential to reduce the ROP examination burden, enable better health care resource allocation, and identify early infants who may benefit from preventive interventions, such as IGF-1 supplementation and intensive nutritional management. However, before these goals can be realized, much larger studies must be undertaken to ensure that any proposed changes to screening practices continue to identify with very high sensitivity those infants who may require treatment to prevent lifelong blindness.