January 2019
Volume 60, Issue 1
Open Access
Anatomy and Pathology/Oncology  |   January 2019
The Association Between Retinopathy of Prematurity and Ocular Growth
Author Affiliations & Notes
  • Dimitrios Kardaras
    Department of Ophthalmology, Medical School, University of Thessaly, Larissa, Greece
  • Eleni Papageorgiou
    Department of Ophthalmology, Medical School, University of Thessaly, Larissa, Greece
  • Katerina Gaitana
    Neonatal Intensive Care Unit, University Hospital of Larissa, Larissa, Greece
  • Ioanna Grivea
    Neonatal Intensive Care Unit, University Hospital of Larissa, Larissa, Greece
  • Vasileios A. Dimitriou
    Department of Mathematics, Aristotle University, Thessaloniki, Greece
  • Sofia Androudi
    Department of Ophthalmology, Medical School, University of Thessaly, Larissa, Greece
  • Antonios Gounaris
    Neonatal Intensive Care Unit, University Hospital of Larissa, Larissa, Greece
  • Evangelia E. Tsironi
    Department of Ophthalmology, Medical School, University of Thessaly, Larissa, Greece
  • Correspondence: Evangelia E. Tsironi, Department of Ophthalmology, University of Thessaly, Mezourlo Area, Larissa 41110, Greece; e_tsironi@hotmail.com
Investigative Ophthalmology & Visual Science January 2019, Vol.60, 98-106. doi:10.1167/iovs.18-24776
  • Views
  • PDF
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Dimitrios Kardaras, Eleni Papageorgiou, Katerina Gaitana, Ioanna Grivea, Vasileios A. Dimitriou, Sofia Androudi, Antonios Gounaris, Evangelia E. Tsironi; The Association Between Retinopathy of Prematurity and Ocular Growth. Invest. Ophthalmol. Vis. Sci. 2019;60(1):98-106. doi: 10.1167/iovs.18-24776.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose: The purpose of this study was to prospectively investigate the association between retinopathy of prematurity (ROP) and ocular growth in premature infants during the earliest weeks of life.

Methods: Premature infants in the national ROP screening program were recruited and examined at 1- or 2-week intervals between 30 and 38 weeks of postmenstrual age. One hundred infants with gestational age (GA) between 24 and 35 weeks (30.04 + 2.13), and birth weight (BW) between 550 and 2060 g (1251.45 + 317.19) were included in the study. At each examination, the presence, stage, and zone of ROP were recorded along with axial length (AL), central corneal thickness (CCT), and weight gain. Biometric parameters were measured by A-scan biometry. Study variables included GA, BW, AL, CCT, weight gain, relative weight (RW), and dif_AL, dif_CCT, and dif_weight, which are the differences between two consecutive recordings of the same infant. Multiple regression analysis models were used to determine the association between the study variables and ROP.

Results: dif_AL, dif_CCT, and RW were the most appropriate variables to detect the optimal threshold points that discriminate ROP: weekly increase of AL < 0.095 mm, weekly reduction of CCT < 0.5 μm, or weekly weight gain < 7% is associated with ROP development.

Conclusions: ROP is associated with delayed ocular development, as eyes of premature infants with ROP have shorter axial lengths and thicker corneas than eyes of premature infants without ROP. The association of AL, CCT, and weight gain with ROP could be of value for future development of predictive models for ROP.

Retinopathy of prematurity (ROP) is a leading cause of preventable blindness worldwide, and screening guidelines are currently based on two risk factors: birth weight (BW) and gestational age (GA). A variety of other risk factors have been also suggested, including maternal factors, prenatal and perinatal factors, demographics, medical interventions, comorbidities of prematurity, nutrition, and genetic factors.1,2 The recognized association between prematurity and abnormal refractive errors may be exacerbated by ROP and has been reported by many authors.35 However, the pathophysiology and timing of the mechanisms underlying these refractive errors are incompletely understood. Ultrasonic axial length (AL) measurements worldwide have been used to identify factors associated with the early growth pattern of the eye, full term or preterm.614 However, there are only a few studies distinguishing between preterm infants with and without ROP, and only some of them have provided longitudinal data.8,11,12 It has been suggested that children with ROP have smaller eyes compared with premature children without ROP and “shorter AL is correlated to severity of ROP.”8,12 Otherwise, the trend is that AL in mild ROP that resolved without treatment (up to stages 1 to 2) generally compares with what is found in preterms without ROP.15 
The purpose of this study is to prospectively investigate premature infants with and without ROP during the earliest weeks of life and to identify factors involved in ocular growth. AL and central corneal thickness (CCT) are compared between premature infants with or without ROP. Other variables that may relate to axial growth, such as GA, BW, and weight gain were also examined. 
Methods
This cohort of infants was recruited from the Neonatal Intensive Care Unit (NICU) of the University Hospital of Larissa, Greece, from October 1, 2013 to August 31, 2015 by routine screening for ROP. Infants weighing ≤ 1500 g and/or having a GA ≤ 32 weeks were included in the study. Neonates with hydrocephalus, congenital anomalies, and those who died or were lost to follow-up before development of ROP or full vascularization of the retina were excluded. Ethics Committee approval was granted from the University of Thessaly, and the research study was performed according to the Declaration of Helsinki. Following verbal and written explanation of the experimental protocol, all parents/guardians of study subjects gave their written consent. 
ROP
The screening examination for ROP followed the guidelines recommended by the Royal College of Pediatrics and Child Health and the Royal College of Ophthalmologists, United Kingdom.16 The pupils were dilated with mydriatic ophthalmic drops (cyclopentolate 0.2%, phenylephrine 1%) 1 to 2 hours before the ROP examination. One drop of each medication was instilled in each eye every 5 minutes for a total of three doses. Dilated funduscopy with scleral indentation was performed after instillation of topical anesthetic (0.5% proparacaine hydrochloride), by means of a binocular indirect ophthalmoscope, a 20-diopter (D) lens, and a lid speculum. The first screening examination was performed at 4- to 6-week chronological age from birth. Depending on the clinical findings and the presence of ROP, the infant was evaluated at 3-day to 2-week intervals, until full vascularization of the retina. ROP was categorized according to the revised international classification of ROP.17 Indications for treatment were based on the Early Treatment for ROP Trial (ET-ROP).18 In each examination, body weight, AL, and CCT were recorded. 
Biometry
AL and CCT were measured using A-scan biometry (RxP OcuScan; Alcon Laboratories, Fort Worth, TX, USA), by placing the A-scan probe gently on the center of the cornea, perpendicular to its axis, to avoid any indentation of the cornea. The average value of at least 10 measurements for AL and CCT was recorded for each eye. The average value of AL and CCT were expressed in millimeters and micrometers, respectively. 
Statistical Analysis
Statistical analysis was performed using the SPSS statistical software version 24.0 (IBM SPSS Software 2016; IBM Corp., Armonk, NY, USA).19,20 The results are demonstrated via frequencies (n), percentages (%), and means accompanied by the corresponding SD. Because our sample included repeated measurements of the same infants, and in addition there was an unavoidable occurrence of missing values (not all infants were born in the same week and consequently the first measurement did not coincide for all), generalized estimated equations (GEEs) logistic regression models were fitted in our data.20,21 At first, univariate GEE logistic regression models were used to identify significant risk factors for the appearance of ROP. The working correlation matrices for the repeated observations in the GEE models were chosen to be unstructured in our analyses, which are considered as the most efficient.21 Odds ratios (ORs) and 95% confidence intervals (CIs) are given for every risk factor. 
All the significant risk factors gathered from the analyses were included to formulate the first multivariate GEE logistic regression model. In this model, some of the initial significant factors became nonsignificant. After removing these nonsignificant factors, a second multivariate GEE logistic regression model having almost the same value of quasi-likelihood information criterion21 (QIC) (second model's QIC: 733.107 Display Formula\(\def\upalpha{\unicode[Times]{x3B1}}\)\(\def\upbeta{\unicode[Times]{x3B2}}\)\(\def\upgamma{\unicode[Times]{x3B3}}\)\(\def\updelta{\unicode[Times]{x3B4}}\)\(\def\upvarepsilon{\unicode[Times]{x3B5}}\)\(\def\upzeta{\unicode[Times]{x3B6}}\)\(\def\upeta{\unicode[Times]{x3B7}}\)\(\def\uptheta{\unicode[Times]{x3B8}}\)\(\def\upiota{\unicode[Times]{x3B9}}\)\(\def\upkappa{\unicode[Times]{x3BA}}\)\(\def\uplambda{\unicode[Times]{x3BB}}\)\(\def\upmu{\unicode[Times]{x3BC}}\)\(\def\upnu{\unicode[Times]{x3BD}}\)\(\def\upxi{\unicode[Times]{x3BE}}\)\(\def\upomicron{\unicode[Times]{x3BF}}\)\(\def\uppi{\unicode[Times]{x3C0}}\)\(\def\uprho{\unicode[Times]{x3C1}}\)\(\def\upsigma{\unicode[Times]{x3C3}}\)\(\def\uptau{\unicode[Times]{x3C4}}\)\(\def\upupsilon{\unicode[Times]{x3C5}}\)\(\def\upphi{\unicode[Times]{x3C6}}\)\(\def\upchi{\unicode[Times]{x3C7}}\)\(\def\uppsy{\unicode[Times]{x3C8}}\)\(\def\upomega{\unicode[Times]{x3C9}}\)\(\def\bialpha{\boldsymbol{\alpha}}\)\(\def\bibeta{\boldsymbol{\beta}}\)\(\def\bigamma{\boldsymbol{\gamma}}\)\(\def\bidelta{\boldsymbol{\delta}}\)\(\def\bivarepsilon{\boldsymbol{\varepsilon}}\)\(\def\bizeta{\boldsymbol{\zeta}}\)\(\def\bieta{\boldsymbol{\eta}}\)\(\def\bitheta{\boldsymbol{\theta}}\)\(\def\biiota{\boldsymbol{\iota}}\)\(\def\bikappa{\boldsymbol{\kappa}}\)\(\def\bilambda{\boldsymbol{\lambda}}\)\(\def\bimu{\boldsymbol{\mu}}\)\(\def\binu{\boldsymbol{\nu}}\)\(\def\bixi{\boldsymbol{\xi}}\)\(\def\biomicron{\boldsymbol{\micron}}\)\(\def\bipi{\boldsymbol{\pi}}\)\(\def\birho{\boldsymbol{\rho}}\)\(\def\bisigma{\boldsymbol{\sigma}}\)\(\def\bitau{\boldsymbol{\tau}}\)\(\def\biupsilon{\boldsymbol{\upsilon}}\)\(\def\biphi{\boldsymbol{\phi}}\)\(\def\bichi{\boldsymbol{\chi}}\)\(\def\bipsy{\boldsymbol{\psy}}\)\(\def\biomega{\boldsymbol{\omega}}\)\(\def\bupalpha{\unicode[Times]{x1D6C2}}\)\(\def\bupbeta{\unicode[Times]{x1D6C3}}\)\(\def\bupgamma{\unicode[Times]{x1D6C4}}\)\(\def\bupdelta{\unicode[Times]{x1D6C5}}\)\(\def\bupepsilon{\unicode[Times]{x1D6C6}}\)\(\def\bupvarepsilon{\unicode[Times]{x1D6DC}}\)\(\def\bupzeta{\unicode[Times]{x1D6C7}}\)\(\def\bupeta{\unicode[Times]{x1D6C8}}\)\(\def\buptheta{\unicode[Times]{x1D6C9}}\)\(\def\bupiota{\unicode[Times]{x1D6CA}}\)\(\def\bupkappa{\unicode[Times]{x1D6CB}}\)\(\def\buplambda{\unicode[Times]{x1D6CC}}\)\(\def\bupmu{\unicode[Times]{x1D6CD}}\)\(\def\bupnu{\unicode[Times]{x1D6CE}}\)\(\def\bupxi{\unicode[Times]{x1D6CF}}\)\(\def\bupomicron{\unicode[Times]{x1D6D0}}\)\(\def\buppi{\unicode[Times]{x1D6D1}}\)\(\def\buprho{\unicode[Times]{x1D6D2}}\)\(\def\bupsigma{\unicode[Times]{x1D6D4}}\)\(\def\buptau{\unicode[Times]{x1D6D5}}\)\(\def\bupupsilon{\unicode[Times]{x1D6D6}}\)\(\def\bupphi{\unicode[Times]{x1D6D7}}\)\(\def\bupchi{\unicode[Times]{x1D6D8}}\)\(\def\buppsy{\unicode[Times]{x1D6D9}}\)\(\def\bupomega{\unicode[Times]{x1D6DA}}\)\(\def\bupvartheta{\unicode[Times]{x1D6DD}}\)\(\def\bGamma{\bf{\Gamma}}\)\(\def\bDelta{\bf{\Delta}}\)\(\def\bTheta{\bf{\Theta}}\)\(\def\bLambda{\bf{\Lambda}}\)\(\def\bXi{\bf{\Xi}}\)\(\def\bPi{\bf{\Pi}}\)\(\def\bSigma{\bf{\Sigma}}\)\(\def\bUpsilon{\bf{\Upsilon}}\)\(\def\bPhi{\bf{\Phi}}\)\(\def\bPsi{\bf{\Psi}}\)\(\def\bOmega{\bf{\Omega}}\)\(\def\iGamma{\unicode[Times]{x1D6E4}}\)\(\def\iDelta{\unicode[Times]{x1D6E5}}\)\(\def\iTheta{\unicode[Times]{x1D6E9}}\)\(\def\iLambda{\unicode[Times]{x1D6EC}}\)\(\def\iXi{\unicode[Times]{x1D6EF}}\)\(\def\iPi{\unicode[Times]{x1D6F1}}\)\(\def\iSigma{\unicode[Times]{x1D6F4}}\)\(\def\iUpsilon{\unicode[Times]{x1D6F6}}\)\(\def\iPhi{\unicode[Times]{x1D6F7}}\)\(\def\iPsi{\unicode[Times]{x1D6F9}}\)\(\def\iOmega{\unicode[Times]{x1D6FA}}\)\(\def\biGamma{\unicode[Times]{x1D71E}}\)\(\def\biDelta{\unicode[Times]{x1D71F}}\)\(\def\biTheta{\unicode[Times]{x1D723}}\)\(\def\biLambda{\unicode[Times]{x1D726}}\)\(\def\biXi{\unicode[Times]{x1D729}}\)\(\def\biPi{\unicode[Times]{x1D72B}}\)\(\def\biSigma{\unicode[Times]{x1D72E}}\)\(\def\biUpsilon{\unicode[Times]{x1D730}}\)\(\def\biPhi{\unicode[Times]{x1D731}}\)\(\def\biPsi{\unicode[Times]{x1D733}}\)\(\def\biOmega{\unicode[Times]{x1D734}}\)\( \cong \) first model's QIC: 733.067) reveals the probability of an infant to have ROP. 
Next, receiver operating characteristic (ROC) curves were used for the determination of the optimal cutoff points of the change in AL, CCT, and weight gain to discriminate ROP.22 In this respect, new dichotomous (dummy) variables were created on the basis of the above cutoff points. χ2 tests were used to verify the dependence between these dichotomous variables and the appearance of ROP. Stacked bar charts are also provided depicting the magnitude of this dependence in our sample. 
Results
Measurements were acquired from both eyes for 100 infants (55 male and 45 female), who were examined longitudinally between 30 and 38 weeks of postmenstrual age. The GA ranged from 24 to 35 weeks (30.04 ± 2.13 weeks), and the BW ranged from 550 to 2060 g (1251.45 ± 317.19 g). Thirty-six infants developed ROP. Twelve developed stage 1, 7 developed stage 2, 12 developed stage 3, and 5 developed treatment-requiring ROP (stage 3+) and received laser therapy.17 Apart from the absolute values of body weight, AL, and CCT, the amount of change of these parameters could be a potential risk factor for the presence of ROP. In this context, we also computed and studied the following variables: dif_AL (the difference between two consecutive recordings of AL of the same infant; 652 recordings). For example, the dif_AL in the interval [30,31] (that is the interval between the 30th and the 31st week of an infant) equals the AL of week 31 − AL of week 30. Analogously, dif_CCT stands for the difference between two successive measurements of CCT (653 recordings), and dif_weight, equals to the corresponding change in the weight of an infant (648 recordings). Finally, the relative weight (RW) of an infant (648 recordings) is defined as follows:  
\begin{equation}RW\ in\ interval\left[ {i,i + 1} \right] = {{body\ weight\ in\ week\ i + 1 - body\ weight\ in\ week\ i} \over {body\ weight\ in\ week\ i}}\end{equation}
 
\begin{equation}i = 30,31, \ldots ,37\end{equation}
 
At this point, we outline the reason we mention the number of recordings for each of the variables that include in their definition the difference between two consecutive measurements. When taking the difference of a variable, that is, dif_AL in interval [i,i+1] =AL in week i+1 Display Formula\( - \)AL in week i, Display Formula\(\left( {i = 30,31, \ldots ,37} \right)\)if one of these values is missing (AL in week i + 1 or AL in week i), then dif_AL cannot be defined and is also a missing value. In this sense, the maximum number of observations that we could have for dif_AL, dif_CCT, dif_weight, and RW would be 100 infants × 8-week intervals × 2 eyes = 1600 observations. However, due to the problem mentioned above (plus the fact that all the infants of our sample were not born in week 30 or 31 or some of them left the hospital before week 38), 652, 653, 648, and 648 were our recordings and 1600Display Formula\( - \)652 = 948, Display Formula\(1600 - 653 = 947,\;1600 - 648 = 952\), and 952 were the missing values for dif_AL, dif_CCT, dif_weight, and RW, respectively. In other words, variables that incorporate differences had missing values. However, the number of 653 or 653 or 648 observations still provide a sufficient number of observations for statistical analysis. Also, the adoption of the GEE logistic regression method for our analysis “allows missing values within a subject without losing all the data from the subject.”21 
The basic descriptive statistics (mean value, SD, number of observations) for the above variables with and without the presence of ROP are demonstrated for each week in Figure 1
Figure 1
 
Descriptive statistics for each potential risk factor: (A) AL measured in mm, (B) dif AL in mm, (C) CCT in μm, (D) dif_CCT in μm, (E) weight in grams, (F) dif_weight in grams and (G) RW for the presence of ROP in our sample. For each risk factor, the height of the bars demonstrate its mean value \(\bar x\) (shown in the white rectangular boxes), while error bars exhibit the interval \(\left( {\bar x - s,\bar x + s} \right),\) where s stands for the corresponding SD. These statistics are shown separately for every postmenstrual week (from 30th to 38th), and for both groups of infants (with and without ROP). In addition, the number of infants measured in each group (N) is demonstrated in a separate line per graph.
Figure 1
 
Descriptive statistics for each potential risk factor: (A) AL measured in mm, (B) dif AL in mm, (C) CCT in μm, (D) dif_CCT in μm, (E) weight in grams, (F) dif_weight in grams and (G) RW for the presence of ROP in our sample. For each risk factor, the height of the bars demonstrate its mean value \(\bar x\) (shown in the white rectangular boxes), while error bars exhibit the interval \(\left( {\bar x - s,\bar x + s} \right),\) where s stands for the corresponding SD. These statistics are shown separately for every postmenstrual week (from 30th to 38th), and for both groups of infants (with and without ROP). In addition, the number of infants measured in each group (N) is demonstrated in a separate line per graph.
First, univariate GEE logistic analyses were performed for each potential risk factor for the development of ROP. The OR for each factor, along with the corresponding CI, and P value are shown in Table 1. As one can easily see from Table 1, neither the sex (male, female) nor the eye (left, right) were significant risk factors for ROP. On the contrary, the postmenstrual age of an infant from its 30th week until its 35th week is a significant factor, always in comparison to a 38-week-old infant (reference category). For instance, the odds of an infant developing ROP at the age of 30 weeks are 16.227 times higher than a 38-week-old infant. Furthermore, there is a significant association between the magnitude of AL, as well as its change (i.e., dif_AL), with the appearance of ROP. CCT was not a significant risk factor for ROP. However, its change, that is dif_CCT, is significantly associated with the development of ROP. Finally, the weight of an infant, the increase/decrease of an infant's weight (dif_weight), and its RW are all significant risk factors for ROP. 
Table 1
 
Univariate GEE Logistic Regression Analysis of Risk Factors for the Manifestation of ROP
Table 1
 
Univariate GEE Logistic Regression Analysis of Risk Factors for the Manifestation of ROP
All significant risk factors were included into a multivariate GEE logistic regression model. In this model, the age of an infant (variable week) was considered as a continuous variable (covariate) instead of a factor, because it reduced the values of QIC and corrected quasi-likelihood information criterion21 (QICC) of the model. The results are shown in Table 2. The risk factors AL, dif_weight, and RW are nonsignificant in the multivariate model. This fact was rather anticipated, as the same amount of information was included in AL and dif_AL, and therefore it was expected that only one of them would be significant in the multivariate model. The same explanation applies to the risk factors weight, dif_weight, and RW. By removing the nonsignificant factors from the first model, a second model was developed (Table 3). According to the latter model, the probability of an infant to develop ROP, given its age (i.e., week 30, 31,…, 38), weight, and change in its AL and CCT equals20:  
\begin{equation}P(an\ infant\ develops\ ROP)={1\over{1+e^{-(18.692-0.682\cdot Week+1.35\cdot dif\_AL-0.011\cdot dif\_CCT+0.002\cdot weight)}}}\end{equation}
 
Table 2
 
Multivariate GEE Logistic Regression Analysis, Model 1 With QIC = 733.067, of Risk Factors for the Development of ROP
Table 2
 
Multivariate GEE Logistic Regression Analysis, Model 1 With QIC = 733.067, of Risk Factors for the Development of ROP
Table 3
 
Multivariate GEE Logistic Regression Analysis, Model 2 With QIC = 733.107, of All Significant Risk Factors Associated With the Presence of ROP
Table 3
 
Multivariate GEE Logistic Regression Analysis, Model 2 With QIC = 733.107, of All Significant Risk Factors Associated With the Presence of ROP
In the sequel, ROC curve analyses revealed the best discriminative cutoff points for dif_AL, dif_CCT, and RW concerning the development of ROP. These three variables were considered as the most appropriate ones to detect the optimal threshold points that discriminate ROP. The criterion used for this reason was the Youden index.22 The Youden index is defined as22  
\begin{equation}Youden\ Index = sensitivity + specificity - 1\end{equation}
 
The criterion of the Youden index maximizes the above quantity and “is the more commonly used criterion because this index reflects the intension to maximize the correct classification rate and is easy to calculate.”22 For dif_AL, the area under the ROC curve was 0.704 (95% CI: 0.661 to 0.776; P < 0.0001), and the optimal threshold point was 0.095 with sensitivity and specificity being 0.498 and 0.955, respectively. For dif_CCT, the area under the ROC curve was 0.577 (95% CI: 0.531 to 0.622; P = 0.001), the best cutoff point was Display Formula\( - 0.5\), and the sensitivity and specificity values were 0.274 and 0.944, respectively. Finally, the area under the ROC curve for RW was 0.609 (95% CI: 0.565 to 0.654; P < 0.0001), with the best threshold being 0.0713 and sensitivity and specificity being 0.385 and 0.871, respectively. 
Finally, our patient sample was divided into two subgroups, each time on the grounds of the above cutoff points. First, the sample was divided into two subgroups according to dif_AL: a group with dif_AL Display Formula\( \le \) 0.095 (potential group of developing ROP) and a group with dif_AL Display Formula\( \gt \) 0.095. Second, the sample was divided into two subgroups according to dif_CCT: a group with dif_CCT Display Formula\( \ge - 0.5\) (potential group of developing ROP) and a group with dif_CCT Display Formula\( \lt - 0.5.\) The patient sample was also divided into a group with RW Display Formula\( \le 0.0713\) (potential group of developing ROP) and a group with RW > 0.0713. A χ2 test was performed to determine whether there is a significant relationship between the above groups and the appearance of ROP. Infants with dif_AL Display Formula\( \le \) 0.095 had significantly (Display Formula\({{\rm{\upchi }}^2}\left( 1 \right) = 175.744;\) P < 0.0001; Fig. 2) higher risk for developing ROP (90.3%) than those with dif_AL Display Formula\( \gt \) 0.095 (30.8%). Similarly, infants with dif_CCT Display Formula\( \ge - 0.5\) also had significantly higher (Display Formula\({{\rm{\upchi }}^2}\left( 1 \right) = 58.311;\) P < 0.0001; Fig. 3) risk for developing ROP (80.4%) in comparison to infants with dif_CCT Display Formula\( \lt - 0.5\) (39.4%). Finally, infants with RW Display Formula\( \le 0.0713\) had a significant (Display Formula\({{\rm{\upchi }}^2}\left( 1 \right) = 56.612;\) P < 0.001) higher risk for developing ROP (71.9%) in comparison to the group with RW Display Formula\( \gt 0.0713\) (37.7%; Fig. 4). 
Figure 2
 
100% stacked bar chart (i.e., each bar in the chart represents the whole [100%] of a group of infants in our sample) and segments in the bar represent the parts (percentages) with and without ROP. More specifically, the left bar shows the group of infants whose change of axial length between two weekly interval measurements was more than the cutoff point of 0.095 mm. On the contrary, the right bar shows the group of infants whose axial length between two weekly interval measurements was ≤0.095 mm.
Figure 2
 
100% stacked bar chart (i.e., each bar in the chart represents the whole [100%] of a group of infants in our sample) and segments in the bar represent the parts (percentages) with and without ROP. More specifically, the left bar shows the group of infants whose change of axial length between two weekly interval measurements was more than the cutoff point of 0.095 mm. On the contrary, the right bar shows the group of infants whose axial length between two weekly interval measurements was ≤0.095 mm.
Figure 3
 
100% stacked bar displaying the percentages of infants with and without ROP within each of the following two groups in our sample: the group of infants whose difference of CCT between two weekly interval measurements was less than the threshold of \( - 0.5\) μm (left bar) and the group of infants whose difference of CCT between two weekly interval measurements was equal or more than the threshold of \( - 0.5\) μm (right bar).
Figure 3
 
100% stacked bar displaying the percentages of infants with and without ROP within each of the following two groups in our sample: the group of infants whose difference of CCT between two weekly interval measurements was less than the threshold of \( - 0.5\) μm (left bar) and the group of infants whose difference of CCT between two weekly interval measurements was equal or more than the threshold of \( - 0.5\) μm (right bar).
Figure 4
 
100% stacked bar exhibiting the percentages of infants with and without ROP within each of the next two groups in our sample: the group of infants with RW (i.e., percentage weight change) between two weekly interval measurements above the cutoff point of \(0.0713\) (left bar) and the group of infants with RW between two weekly interval measurements below or equal the cutoff point of \(0.0713\) (right bar).
Figure 4
 
100% stacked bar exhibiting the percentages of infants with and without ROP within each of the next two groups in our sample: the group of infants with RW (i.e., percentage weight change) between two weekly interval measurements above the cutoff point of \(0.0713\) (left bar) and the group of infants with RW between two weekly interval measurements below or equal the cutoff point of \(0.0713\) (right bar).
Discussion
Although the incidence of ROP is increasing with increased survival of infants at a very early GA, its pathogenesis is still poorly understood. Current biometric studies aim at identifying changes in ocular growth patterns in ROP-affected eyes, which could potentially explain refractive errors later in life. Our study showed that ROP is associated with an altered growth pattern of AL and CCT. To the best of our knowledge, this is the first prospective longitudinal study to provide information on weekly development, quantitative data (cutoff points) on weight gain, AL, and CCT development in preterm babies, and investigate the correlation of these parameters with the presence of ROP. Specifically for AL, weekly increase <0.095 mm is correlated with ROP development (diff_AL < 0.095 mm). Additionally, weekly reduction of CCT < 0.5 μm (diff_CCT < 0.5 μm) and weekly weight gain < 7% are associated with ROP development (RW < 7%). Previous predictive models and preliminary nomograms have used BW, GA, and postnatal weight gain measurements to predict the risk for ROP (i.e., WINROP, PINT-ROP, CHOP-ROP).2325 We created a predictive model that comprises biometrical characteristics into this equation. Our logistic model includes weight, postmenstrual age, and the variation of AL and CCT. Taking into account all the abovementioned factors, this model allowed us to estimate the probability of a premature infant to develop ROP. 
In agreement with previous reports, AL displays a linear growth pattern in both groups of infants in the present study.7,9 The rate of growth in AL was lower in preterm infants with ROP (0.11 mm/wk) than in preterm infants without ROP (0.24 mm/wk). A review of the literature on AL in prematurity reveals that, although several authors have addressed this topic, there are few data on week by week measurement of AL in the first weeks after birth, and studies distinguishing between infants with and without ROP are lacking. Previous studies on premature babies without ROP have reported a weekly growth of AL ranging from 0.18 to 0.19 mm/wk, with an outlier value of 0.30 mm/wk given in a single study.7,9,26 In another study on ocular growth development in premature infants without ROP, AL was found to increase at a rate of 0.16 mm/wk.27 However, in this study, infants were examined up to week 52, and it has been suggested that the weekly elongation of the eyeball in premature babies is slower after week 40.8,9,15 This may explain the higher weekly growth rate in the present study (0.24 mm/wk), as we recorded AL values only up to week 38. O'Brien et al.,9 who have not included infants with ROP, have reported a weekly elongation of 0.20 mm up to week 40, and 0.14 mm thereafter. Fledelius et al.,15 who have included infants with stages 1 and 2 ROP, have found an AL growth rate of 0.19 and 0.13 mm/wk, before and after week 40, respectively. Laws et al.,8 who have included premature babies with ROP stages 1, 2, and 3, with some of them requiring cryotherapy or diode laser treatment, reported a weekly growth rate of 0.18 mm/wk with shorter AL in infants undergoing treatment. Cook et al.,11 who have also included babies up to stage 3+ ROP, reported a weekly elongation of 0.152 mm/wk. In our group of premature babies with ROP, we found a lower weekly AL growth rate of 0.11 mm/wk. However, a direct comparison with the previous studies is not possible because their values are not reported separately for the ROP and non-ROP groups. 
The difference in AL growth rate that was noted between the two groups in our study resulted in a difference in the final AL. At the 38th postgestational week, AL in preterm infants with and without ROP was 16.40 ± 0.52 and 17.0 5 ± 0.42 mm, respectively. Regarding the final AL at term, there is fair agreement between most studies, suggesting that the mean axial length in a full-term infant at term (week 40) is approximately 16.6 to 17 mm. AL at term in our group without ROP (17.05 mm) is slightly higher than previous values ranging between 16.60 and 16.84 mm.7,9,2729 Our results are in best agreement with those of Fledelius et al.,15 who reported that at 40 weeks, the AL of 101 preterm infants (including 25 infants with regressed stage 1 and 2 ROP) and 25 term infants measured 17.02 and 17.03 mm, respectively. Our value for the AL at 38 weeks in the ROP group (16.40 mm) is slightly lower than that of Laws et al.8 (16.65 mm). However, the latter study also included infants without ROP, and values were recorded up to week 41. At week 40, Cook et al.11 reported AL values between 16.60 and 16.80 mm for stages 1 to 3 and at 16.40 mm for stage 3+. 
Based on results of our study, the presence of ROP is associated with shorter AL at term. This finding suggests that the ROP eye is characterized by delayed, abnormal growth and the abnormally avascular peripheral ROP retina may underpin the abnormalities in the ROP eye's growth.30 Evidence from simian eyes strongly suggests that the peripheral retina is most important to the process of emmetropization.30 Notably, the peripheral retinal vasculature is abnormal (or even absent) in eyes with ROP.30 The above features may be interpreted as delayed development of the globe due to the biologic stress of the retinopathy.31 It has been also postulated that the ROP lesion, being located in the part of the eye undergoing maximum growth during late fetal and early neonatal life, may exert a mechanical effect on the anterior sclera and anterior segment.31,32 We have not performed anterior chamber measurements in the present study, but our results on reduced AL growth rate in babies with ROP support the theory of restricted globe elongation due to ROP. 
CCT is expected to decrease weekly until full-term age.3335 Linear reduction of CCT was confirmed over our study period. However, the rate of decrease was reduced in infants with ROP, as minimum or no CCT reduction was recorded. CCT weekly decrease <0.5 μm was indicative of ROP development. Interestingly, in some infants with ROP, a small increase in CCT was recorded. The rate of CCT decrease is lower in preterm infants with ROP (10.93 μm/wk) than in preterm infants without ROP (16.11 μm/wk), resulting in different final CCT at the 38th postmenstrual week (571.52 ± 37.05 μm in preterm infants with ROP and 541.85 ± 77.56 μm in preterm infants without ROP). Acar et al.36 reported a mean CCT value of 590.67 μm in 470 infants who underwent screening for ROP. Those infants were measured just one time at a mean postconceptional age of 35.94 ± 4 weeks, and no correlation with ROP was made.36 However, in conjunction with our findings, a dramatic decline in CCT from 653.99 μm in the group <32 weeks to 554.27 μm in the group 37 to 40 weeks was also found.36 In 33 infants, Uva et al.37 found mean CCT values of 599 ± 36 μm at 34 ± 3 weeks and 576 ± 26 μm at 40 ± 1 weeks, which are partially consistent with our values of 593 μm at 35 weeks and 541 μm at 38 weeks in the non-ROP group and 606 μm at 35 weeks and 571 μm at 38 weeks. Similar results have been obtained by Kirwan et al.,38 who found a mean CCT of 691 μm in 35 infants at 31 weeks compared with 636 and 667 μm in our non-ROP and ROP groups at 31 weeks, respectively. Our finding of a final mean CCT of 541.85 μm at 38 weeks for the non-ROP group is slightly lower than published values, because previous reports have pooled babies with and without ROP. Previous studies have also shown that GA and chronologic age of infants were negatively correlated with CCT.36,38 We here expand this knowledge by demonstrating that ROP delays the expected decrease of CCT in preterm infants the first weeks after birth. Increased CCT in neonates is thought to be related to prolonged eye closure in utero, and decreasing thickness after birth is due to better control of corneal hydration, evaporation, and corneal remodeling, with stretching of collagen fibers, which are possibly disturbed by the retarded development of the ROP eye.38,39 
Analysis of weight fluctuation in our study confirmed previous knowledge, establishing poor postnatal weight gain and low caloric intake as risk factors for ROP.4043 The explanation is presumably related to insulin growth factor-1 (IGF-1), which is necessary for retinal vascular growth.2 In our study cohort, mean weekly weight gain was 276.46 and 186.29 g in the non-ROP and ROP groups, respectively. In accordance with previous reports, the RW was 11 ± 8% and 9 ± 8% for the non-ROP and ROP groups, respectively, resulting in a final weight of 3057.76 ± 454.17 and 2447.70 ± 546.69 g, respectively, at the 38th postmenstrual week.44 Our results reveal that the odds of an infant to develop ROP are higher in the first weeks of screening, from the 30th to the 34th week. More specifically, by using the postmenstrual age of an infant as an independent factor, it was found that the younger postmenstrual age, the greater the risk of ROP development, as the odds of an infant to develop ROP were very high in the first weeks of screening, especially the 30th and 31st week (16.227 and 15.513 times higher than a 38-week-old infant). Hence, our analysis confirmed previous reports on this result.17,45 This underlines the importance of time for the screening examinations, as ROP develops over a relatively narrow postmenstrual age range.46 
As our work constitutes an ongoing prospective study, the refractive errors of children will be followed longitudinally in regular intervals until late childhood to relate the present data with the refractive status of preterm children. Numerous studies have revealed that premature infants show a tendency toward myopia from an early age and are likely to be myopic into the preschool and early school years.6,4749 This is known as myopia of prematurity, and it has been attributed to shallower anterior chambers,11,50 increased lens power,6 an anteriorly displaced lens,27 increased corneal curvature,50 and a shorter AL than would be expected for the dioptric value of the eye.27 To better understand the development of the preterm eye and the effect of ROP on ocular growth, future studies should attempt to prospectively document changes in all the biometric components along with refractive state from the earliest weeks of life throughout adolescence. 
This paper reports that ROP is associated with delayed axial elongation and increased CCT and additionally provides cutoff points on the above variables. We presented more precise longitudinal weekly data than previous studies on AL development between 30 and 38 weeks of postmenstrual age. Our results pertain to a specific subset of premature infants, who satisfied the criteria for ROP screening and therefore differ from other published reports in the selection of subjects for study. We suggest that ROP plays a role in the development of ocular dimensions. Additionally, the noninvasive ultrasound examination of AL and CCT in premature infants during the early weeks of life could provide important information for understanding the growth of biometric variables and the development of ROP. In accordance with previous reports,8 the tendency toward shorter AL, increased CCT, and poor weight gain was noted before and during the development of ROP. Hence, this study does not identify definite causative factors but reveals an association of AL, CCT, and weight gain with ROP, which could be of value for future development of predictive models for ROP. 
Acknowledgments
The authors thank the two anonymous reviewers who helped with suggestions and comments to improve the technical quality and quality of presentation of this paper. 
Disclosure: D. Kardaras, None; E. Papageorgiou, None; K. Gaitana, None; I. Grivea, None; V.A. Dimitriou, None; S. Androudi, None; A. Gounaris, None; E.E. Tsironi, None 
References
Kim SJ, Port AD, Swan R, Campbell JP, Chan RVP, Chiang MF. Retinopathy of prematurity: a review of risk factors and their clinical significance. Surv Ophthalmol. 2018; 19: 639–657.
Hellstrom A, Smith LE, Dammann O. Retinopathy of prematurity. Lancet. 2013; 382: 1445–1457.
Page JM, Schneeweiss S, Whyte HEA, Harvey P. Ocular sequelae in premature infants. Pediatrics. 1993; 92: 787–790.
Laws D, Shaw DE, Robinson J, Jones HS, Ng YK, Fielder AR. Retinopathy of prematurity: a prospective study. Review at six months. Eye. 1992; 6: 477–483.
Nissenkorn I, Yassur Y, Mashkowski I, Sherf I, Ben-Sira I. Myopia in premature babies with and without retinopathy of prematurity. Br J Ophthalmol. 1983; 67: 170–173.
Gordon RA, Donzis PB. Myopia associated with retinopathy of prematurity. Ophthalmology. 1986; 93: 1593–1598.
Tucker SM, Enzenauer RW, Levin AV, Morin JD, Hellmann J. Corneal diameter, axial length and intraocular pressure in premature infants. Ophthalmology. 1992; 99: 1296–1300.
Laws DE, Haslett R, Ashby D, O'Brien C, Clark D. Axial length biometry in infants with retinopathy of prematurity. Eye. 1994; 8: 427–430.
O'Brien C, Clark D. Ocular biometry in pre-term infants without retinopathy of prematurity. Eye. 1994; 8: 662–665.
Isenberg SJ, Neumann D, Cheong PYY, Ling YLF, McCall LC, Ziffer AJ. Growth of the internal and external eye in term and preterm infants. Ophthalmology. 1994; 102: 827–830.
Cook A, White S, Batterbury M, Clark D. Ocular growth and refractive error development in premature infants without and without retinopathy of prematurity. Invest Ophthalmol Vis Sci. 2008; 49: 5199–5120.
Fledelius HC, Fledelius C. Eye size in threshold retinopathy of prematurity, based on a Danish preterm infant series: early axial eye growth, pre and postnatal aspects. Invest Ophthalml Vis Sci. 2012; 53: 4177–4184.
Azad RV, Lakshminarayana P, Kumar H, Talwar D, Pal N, Chandra P. Ocular growth pattern in cryotherapy- and laser treated infants with prethreshold retinopathy of prematurity. J Pediatr Ophthalmol Strabismus. 2005; 42: 149–154.
Axer-Siegel R, Bourla D, Sirota L, Weinberger D, Snir M. Ocular growth in premature infants conceived by in vitro fertilization versus natural conception. Invest Ophthalmol Vis Sci. 2005; 46: 1163–1169.
Fledelius HC. Pre-term delivery and the growth of the eye. An oculometric study of eye size around term-time. Acta Ophthalmol Suppl. 1992; 204: 10–15.
Wilkinson AR, Haines L, Head K, Fielder AR. UK retinopathy of prematurity guideline. Eye. 2009; 23: 2137–2139.
International Committee for the Classification of Retinopathy of Prematurity. The International Classification of Retinopathy of Prematurity revisited. Arch Ophthalmol. 2005; 123: 991–999.
Good WV; Early Treatment for Retinopathy of Prematurity Cooperative Group . Final results of the Early Treatment for Retinopathy of Prematurity (ETROP) randomized trial. Trans Am Ophthalmol Soc. 2004; 102: 233–250.
IBM SPSS Statistics for Windows, Version 24.0. Available at: https://www.ibm.com/analytics/data-science/predictive-analytics/spss-statistical-software.
Field A. Discovering Statistics Using SPSS. 3rd ed. London: Sage; 2009.
Wilson JR, Lorenz KA. Modeling Binary Correlated Responses using SAS, SPSS and R. New York: Springer; 2015.
Kumar R, Indrayan A. Receiver operating characteristic (ROC) curve for medical researchers. Ind Pediatr. 2011; 48: 277–287.
Löfqvist C, Hansen-Pupp I, Andersson E, et al. Validation of a new retinopathy of prematurity screening method monitoring longitudinal postnatal weight and insulinlike growth factor I. Arch Ophthalmol. 2009; 127: 622–627.
Binenbaum G, Ying GS, Quinn GE, et al. A clinical prediction model to stratify retinopathy of prematurity risk using postnatal weight gain. Pediatrics. 2011; 127: 607–614.
Binenbaum G, Ying GS, Quinn GE, et al. The CHOP postnatal weight gain, birth weight, and gestational age retinopathy of prematurity risk model. Arch Ophthalmol. 2012; 130: 1560–1565.
Harayama K, Amemiya T, Nishimura H. Development of the eyeball during fetal life. J Paediatr Ophthalmol Strabismus. 1981; 18: 37–40.
Cook A, White S, Batterbury M, Clark D. Ocular growth and refractive error development in premature infants without retinopathy of prematurity. Invest Ophthalmol Vis Sci. 2003; 44: 953–960.
Gordon RA, Donzis PB. Myopia associated with retinopathy of prematurity. Ophthalmology. 1986; 93: 1593–1598.
Yamamoto Y, Hirano S, Kaburagi F. Ultra-sonic observation of eyes in premature infants IV. Folia Ophthalmol Jpn. 1978; 29: 141–148.
Munro RJ, Fulton AB, Chui TY, et al. Eye growth in term- and preterm-born eyes modeled from magnetic resonance images. Invest Ophthalmol Vis Sci. 2015; 56: 3121–3131.
Fielder A, Quinn G. Myopia of prematurity: nature, nurture or disease? Br J Ophthalmol. 1997; 81: 2–3.
Kent D, Pennie F, Laws D, White S, Clark D. The influence of retinopathy of prematurity on ocular growth. Eye. 2000; 14: 23–29.
Jethani J, Shah K, Jethani M. Evaluating the change in central corneal thickness in neonates (term and preterm) in Indian population and the factors affecting it. Indian J Ophthalmol. 2015; 63: 501–503.
Remon L, Cristobal JA, Castillo J, et al. Central and peripheral cornea thickness in full-term newborns by ultrasound pachymetry. Invest Ophthalmol Vis Sci. 1992; 33: 3080–3083.
Ehlers N, Sorensen T, Bramsen T, Poulsen EH. Central cornea thickness in newborns and children. Acta Ophthalmol. 1976; 54: 285–290.
Acar DE, Acar U, Tunay ZO, Ozdemir O, Dolgun A, Erdurmus M. The intraocular pressure and central corneal thickness in healthy premature infants. J AAPOS. 2015; 19: 108–111.
Uva MG, Reibaldi M, Longo A, et al. Intraocular pressure and central corneal thickness in premature and full-term newborns. J AAPOS. 2011; 15: 367–369.
Kirwan C, O'Keefe M, Fitzsimon S. Central corneal thickness and corneal diameter in premature infants. Acta Ophthalmol Scand. 2005; 83: 751–753.
Al-Umran KU, Pandolfi MF. Corneal diameter in premature infants. Br J Ophthalmol. 1992; 76: 292–293.
Fortes Filho JB, Bonomo PP, Maia M, Procianoy RS. Weight gain measured at 6 weeks after birth as a predictor for severe retinopathy of prematurity: study with 317 very low birth weight preterm babies. Graefes Arch Exp Ophthalmol. 2009; 247: 831–836.
Wallace DK, Kylstra JA, Phillips SJ, Hall JG. Poor postnatal weight gain: a risk factor for severe retinopathy of prematurity. J AAPOS. 2000; 4: 343–347.
Allegaert K, Vanhole C, Casteels I, et al. Perinatal growth characteristics and associated risk of developing threshold retinopathy of prematurity. J AAPOS. 2003; 7: 34–37.
Stolt Sjostrom E, Lundgren P, Ohlund P, Holmstrom G, Hellstrom A, Domellof M. Low energy intake during the first 4 weeks of life increases the risk for severe retinopathy of prematurity in extremely preterm infant. Arch Dis Child Fetal Neonatal Ed. 2016; 101: 108–113.
Wang Z-H, Gao P-F, Bai H, Li Y-Y. Postnatal weight gain in very low weight infants in Beijing and the risk of retinopathy of prematurity. Int J Ophthalmol. 2015; 18: 1207–1210.
Hutchinson AK, Saunders RA, O'Neil JW, Lovering A, Wilson ME. Timing of initial screening examinations for retinopathy of prematurity. Arch Ophthalmol. 1998; 116: 608–612.
Fierson WM. Screening examination of premature infants for retinopathy of prematurity. Pediatrics. 2013; 13: 185–195.
Wang J, Ren X, Shen L, Yanni SE, Leffler JN, Birch EE. Development of refractive error in individual children with regressed retinopathy of prematurity. Invest Ophthalmol Vis Sci. 2013; 54: 6018–6024.
Fledelius HC. Myopia of prematurity, clinical patterns. A follow-up of Danish children now aged 3-9 years. Acta Ophthalmol. 1995; 73: 402–406.
Quinn GE, Dobson V, Davitt BV, et al.; Early Treatment for Retinopathy of Prematurity Cooperative Group. Progression of myopia and high myopia in the Early Treatment for Retinopathy of Prematurity study: findings at 4 to 6 years of age. J AAPOS. 2013; 17: 124–128.
Fledelius HC. Pre-term delivery and subsequent ocular development: 7–10 year follow-up of children screened 1982–84 for ROP. Acta Ophthalmol Scand. 1996; 74: 301–305.
Figure 1
 
Descriptive statistics for each potential risk factor: (A) AL measured in mm, (B) dif AL in mm, (C) CCT in μm, (D) dif_CCT in μm, (E) weight in grams, (F) dif_weight in grams and (G) RW for the presence of ROP in our sample. For each risk factor, the height of the bars demonstrate its mean value \(\bar x\) (shown in the white rectangular boxes), while error bars exhibit the interval \(\left( {\bar x - s,\bar x + s} \right),\) where s stands for the corresponding SD. These statistics are shown separately for every postmenstrual week (from 30th to 38th), and for both groups of infants (with and without ROP). In addition, the number of infants measured in each group (N) is demonstrated in a separate line per graph.
Figure 1
 
Descriptive statistics for each potential risk factor: (A) AL measured in mm, (B) dif AL in mm, (C) CCT in μm, (D) dif_CCT in μm, (E) weight in grams, (F) dif_weight in grams and (G) RW for the presence of ROP in our sample. For each risk factor, the height of the bars demonstrate its mean value \(\bar x\) (shown in the white rectangular boxes), while error bars exhibit the interval \(\left( {\bar x - s,\bar x + s} \right),\) where s stands for the corresponding SD. These statistics are shown separately for every postmenstrual week (from 30th to 38th), and for both groups of infants (with and without ROP). In addition, the number of infants measured in each group (N) is demonstrated in a separate line per graph.
Figure 2
 
100% stacked bar chart (i.e., each bar in the chart represents the whole [100%] of a group of infants in our sample) and segments in the bar represent the parts (percentages) with and without ROP. More specifically, the left bar shows the group of infants whose change of axial length between two weekly interval measurements was more than the cutoff point of 0.095 mm. On the contrary, the right bar shows the group of infants whose axial length between two weekly interval measurements was ≤0.095 mm.
Figure 2
 
100% stacked bar chart (i.e., each bar in the chart represents the whole [100%] of a group of infants in our sample) and segments in the bar represent the parts (percentages) with and without ROP. More specifically, the left bar shows the group of infants whose change of axial length between two weekly interval measurements was more than the cutoff point of 0.095 mm. On the contrary, the right bar shows the group of infants whose axial length between two weekly interval measurements was ≤0.095 mm.
Figure 3
 
100% stacked bar displaying the percentages of infants with and without ROP within each of the following two groups in our sample: the group of infants whose difference of CCT between two weekly interval measurements was less than the threshold of \( - 0.5\) μm (left bar) and the group of infants whose difference of CCT between two weekly interval measurements was equal or more than the threshold of \( - 0.5\) μm (right bar).
Figure 3
 
100% stacked bar displaying the percentages of infants with and without ROP within each of the following two groups in our sample: the group of infants whose difference of CCT between two weekly interval measurements was less than the threshold of \( - 0.5\) μm (left bar) and the group of infants whose difference of CCT between two weekly interval measurements was equal or more than the threshold of \( - 0.5\) μm (right bar).
Figure 4
 
100% stacked bar exhibiting the percentages of infants with and without ROP within each of the next two groups in our sample: the group of infants with RW (i.e., percentage weight change) between two weekly interval measurements above the cutoff point of \(0.0713\) (left bar) and the group of infants with RW between two weekly interval measurements below or equal the cutoff point of \(0.0713\) (right bar).
Figure 4
 
100% stacked bar exhibiting the percentages of infants with and without ROP within each of the next two groups in our sample: the group of infants with RW (i.e., percentage weight change) between two weekly interval measurements above the cutoff point of \(0.0713\) (left bar) and the group of infants with RW between two weekly interval measurements below or equal the cutoff point of \(0.0713\) (right bar).
Table 1
 
Univariate GEE Logistic Regression Analysis of Risk Factors for the Manifestation of ROP
Table 1
 
Univariate GEE Logistic Regression Analysis of Risk Factors for the Manifestation of ROP
Table 2
 
Multivariate GEE Logistic Regression Analysis, Model 1 With QIC = 733.067, of Risk Factors for the Development of ROP
Table 2
 
Multivariate GEE Logistic Regression Analysis, Model 1 With QIC = 733.067, of Risk Factors for the Development of ROP
Table 3
 
Multivariate GEE Logistic Regression Analysis, Model 2 With QIC = 733.107, of All Significant Risk Factors Associated With the Presence of ROP
Table 3
 
Multivariate GEE Logistic Regression Analysis, Model 2 With QIC = 733.107, of All Significant Risk Factors Associated With the Presence of ROP
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×