Rehabilitation Research and Practice
Volume 2021 (2021), Article ID 9837505, 7 pages
https://doi.org/10.1155/2021/9837505

Research Article

Technical Innovation for Visual Assessment of Preterm Newborns in a Neonatal Intensive Care Unit: Exploratory Study

C. A. Moran,¹ V. L. Alves,² S. A. Pereira,³ and M. F. Costa⁴

¹Department of Health Sciences, Universidade Federal de Santa Catarina, Araranguá, Santa Catarina, Brazil
²Department of Physiotherapy, Faculdade de Ciências Médicas, Santa Casa de São Paulo, São Paulo, SP, Brazil
³Department of Physiotherapy, Universidade Federal do Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
⁴Department of Experimental Psychology, Center for Neurosciences and Applied Neurosciences, Institute of Psychology, Universidade de São Paulo, São Paulo, SP, Brazil

Correspondence should be addressed to C. A. Moran; cristianemoran@gmail.com

Received 14 May 2020; Revised 10 November 2020; Accepted 19 December 2020; Published 4 January 2021

Academic Editor: Nicola Smania

Copyright © 2021 C. A. Moran et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Objective. The present study is aimed at assessing heart rate variability (HRV) and its correlation with visual acuity (VA) assessment of preterm newborns (PTNB) in neonatal intensive care units. Method. Cross-sectional study analyzing HRV during assessment of VA with the aid of a Polar RS800CX heart rate monitor (Polar Electro Oy, Finland). HRV was analyzed according to time and frequency domains and the chaos domain used the autocorrelation coefficient and entropy. The sample consisted of hospitalized PTNB, and static analysis included simple regression diagnosis. Results. A total of 14 PTNB were included in the sample. VA varied between 0.23 and 1.60 cpd, and only five PTNB obtained below-expected values for age. Statistical analysis demonstrated a negative correlation between VA and time domain (SDDN and SD2) and a positive correlation between frequency domain (heart rate and hertz), but in simple linear regression analysis, these variables did not influence VA. Conclusion. The results of the study demonstrate that visual acuity was inversely correlated with SDNN and SD2 and during stimulation, showing that the higher the visual performance, the lower the autonomic modulation response.

1. Introduction

Autonomic modulation, estimated by heart rate variability (HRV) and regulated by sympathetic and parasympathetic pathways, has been increasingly used as a measure of term and preterm newborn stress and well-being [1–3].

In addition to being considered a sensitive and useful tool to determine autonomic nervous system maturation [4], it also clinically measures the ability to adapt to external events [5], maintain homeostasis [6], and conserve energy [7].

Given the conditions in neonatal intensive care units, preterm newborns are more susceptible to behavioral changes caused by exposure to pain stimuli [8], excessive light and noise [9], and invasive procedures such as mechanical ventilation [10].

When applied to preterm newborns (PTNB), these procedures favor the emergence of complications and comorbidities, including ischemic hypoxic encephalopathy [8], bronchopulmonary dysplasia [11], and visual sequelae such as retinopathy of prematurity [12].

Despite the need for a qualified professional to assess the vision of newborns in neonatal intensive care units, this does not occur in many Brazilian public hospitals and few studies compromising visual development and early PTNB rehabilitation [13, 14].

Thus, we can infer that assessing visual system maturity, using HRV, is an innovative resource, which results in a clear advantage during execution, since it uses heartbeats as a physiological parameter of vagal parasympathetic fiber actions, determining deceleration and sympathetic fiber actions caused by acceleration, because changes in the sensory system of children can be represented by a decrease in the sympathetic system and by the impairment of the parasympathetic system [15].

In addition, studies have demonstrated that organic system immaturity is directly related to gestational age, and ocular structures develop from the gestational period to postbirth [16].

Also, in the context of visual development, the ability to change the focal length of the crystalline lens is controlled nearly entirely by the parasympathetic nervous system [17]. Its parasympathetic excitation contracts the ciliary muscle, which reduces tension, allowing the crystalline lens to become more convex, thereby increasing its dioptric power and enabling the eye to focus on nearby objects [17].

Given that specific visual responses depend directly on the functions of the autonomic nervous system, our question is directly related to the possibility of using a visual marker to identify this neurophysiological function. A possible condition of imbalance could represent an important factor for visual development.

In this respect, the present study is aimed at assessing HRV and its correlation with visual acuity assessment of PTNB in neonatal intensive care units.

2. Material and Methods

2.1. Study Design and Participants

This is a cross-sectional observational study with 14 PTNB, conducted between January 2016 and December 2017 in the neonatal intensive care units of three public hospitals in São Paulo, Brazil. Participation in the study was voluntary and convenience sampling was obtained from hospital admissions during the study period.

2.2. Ethics Committee

The research was approved by the Universidade de São Paulo Research Ethics Committee (protocol no. 53867016.9.0000.5551) and written informed consent was provided by all families who agreed to participate in the study.

2.3. Inclusion Criteria

Newborns with gestational age less than 37 weeks, based on the New Ballard Score [18] exhibiting clinical and hemodynamical stability were included. Newborns with variable heart rate or blood pressure, using invasive or noninvasive mechanical ventilation, taking vasoactive or sedative drugs, with grade III or IV periventricular hemorrhage Apgar score in the fifth minute less than 6 congenital malformation, were excluded.

2.4. Experimental Procedure

The experimental procedure was carried out in the intermediate care room, with temperature, humidity, and light levels monitored and without significant auditory stimulus.

Data were collected by two specialized researchers, with experience in neonatal intensive care units and qualified to perform visual assessment using Teller acuity cards [19, 20] and operate a heart rate monitor. The procedure was standardized for newborns in the supine position with 30° elevation in a common crib.

Markers were placed on the newborns’ chest with Miotec hydrogel and connected to a Polar RS800CX heart rate monitor (Polar Electro Oy, Finland) to assess heart rhythm control.

The protocol included three measuring times: (1) HRV was assessed initially with the PTNB at rest for 2 minutes to measure baseline heart rate; (2) during the visual acuity assessment protocol for grading with Teller acuity cards II for 6 minutes; and (3) a 2-minute uninterrupted recovery period monitored by the researcher (Figure 1).

Figure 1: Illustration about the newborn using the heart rate monitor.

2.5. Visual Assessment

Grating visual acuity was psychophysically measured using Teller acuity cards II (Vistech Consultants Incorporation). A complete set of Teller Acuity Cards II consists of seventeen cards, each of which has an approximately 4 mm diameter peephole at the center. Each card is gray with approximately 35% reflectance, varying slightly among cards [16]. The gratings exhibit different spatial frequencies, ranging between 0.23 and 38 cycles/degrees of visual angle, with 1/2 octave spatial frequency between cards [21].

The measure of binocular visual acuity followed the procedure modified by Salomão and Ventura (1995) [22], with the newborns positioned at a standard distance of 55 cm from the stimuli, remaining in the crib throughout the collection period.

The cards were initially presented with gratings of low spatially frequency selected according to age [21]. The task of the experimenter was to test the newborns’ reactions to the test and judge whether they looked or not at the grating.

The behavioral reactions observed were the direction of the first gaze, its duration for each stimulus, eye movements, and head movements. The examiner presented both sides of the cards to the newborns. A reaction was considered correct when the child detected the grating on one side of the card and incorrect when it was unable to detect the grating on either side presented.

After each presentation, an assistant informed the examiner whether the reaction was correct or incorrect. Visual acuity thresholds were defined as the average of the values of the last two cards with positive responses and expressed in cycles per degree. Visual acuity classification (normal or reduced) was based on Salomão et al. (2008) [21].

The timeline with the steps is described in Figure 2.

Figure 2: Timeline of the 6 data collection steps.

2.6. Assessment of Heart Rate Variability (HRV)

HRV was conducted throughout the collection period and a PTNB heart rate around 120 beats per minute was considered normal, based on the findings of Javorka et al. (2017) [6].

To assess HRV, the data were transmitted to the computer via Polar ProTrainer 5® software. Polar Pro Trainer® software generates a data table of heart rate values. To analyze the data, we used the Kubius® program, a statistical software. The analyses were performed by three exercise zones were classified as rest, assessment, and recovery.

Manual filtering was applied to discard artefacts that could influence the data to be analyzed and only the electrocardiogram data selected were then converted to digital values that reflect cyclical changes in the heart, or the R-R range, and that generate two energy spectra of clinical interest.

For the frequency domain, low frequency (LF), high frequency (HF), and the LF/HF ratio were used.

The first energy spectrum considered was the presence of low frequency wavelengths, standardized in the scientific environment as a power spectrum in the 0.04–0.15 Hz frequency range, which is mediated by the influence of the sympathetic and parasympathetic systems [23].

The second energy spectrum (high frequencies) was also previously established as energy spectra in the 0.15-1.00 Hz frequency range, which are predominantly influenced by parasympathetic activation [23].

The variables analyzed in the time domain were the standard deviation between normal beats (SDNN), the square root of the mean squared difference between adjacent RR intervals (RMSDD), the relationship between standard deviations 1 and 2 obtained by the Poincará-Bendixson graph (SD1/SD2), and the percentage of adjacent intervals with a difference greater than 50 milliseconds (pNN50).

Assessment in the chaos domain used the autocorrelation coefficient (tau) and entropy (ApEn).

2.7. Statistical Analysis

The data collected were analyzed using the Statistical Package for the Social Sciences (SPSS)®, version 21.0 (SPSS for Windows, Chicago, USA). The Shapiro-Wilk test was used to determine the normality of the variables. Descriptive statistics was conducted for all the variables and presented as mean and standard deviation. Comparison of heart rates at the different times was analyzed by the ANOVA repeated measures, and additionally, simple regression diagnoses were carried out to determine the predictive ability between the acuity value measured by Teller acuity cards and the significant covariables in bivariate analysis. We considered and 95% CI for all the stages.

3. Results

A total of 25 PTNB were screened, 14 of whom were included in the sample, 11 were excluded for being in a deep sleep at the moment of collection (6) or scheduled for discharge before the researchers’ visit (5). This sample size has a statistical power of .195 (Figure 3).

Figure 3: Sample flowchart.

Of the 14 PTNB assessed, 7 (50%) were boys with an average chronological age of 7 weeks (±6 weeks). The descriptive data for the sample are presented in Table 1.

Table 1: Descriptive characteristics of the sample.

Variable
Mean (SD)

Gestational age (weeks)
()

Birth weight (kg)
1,619.00 (±770.21)

Current weight (kg)
2,584.08 (±952.63)

VA (cpd)
0.877 (±0.528)

cpd: cycles per degree; kg: kilograms; SD: standard deviation; VA: visual assessment.

VA varied between 0.23 and 1.60 cpd, and only five PTNB obtained below-expected values for age (Tables 2 and 3).

Table 2: Individual sample values.

Identification
Sex
Apgar in the first minute
Apgar in the fifth minute
Gestational age (weeks)
Days of life
Birth weight (g)
Resting HR (BPM)
HR during Teller (BPM)
Recovery HR (BPM)

1
F
4
7
29
785
3460
179
180
178

2
M
7
9
30
980
2015
122
124
128

3
F
7
9
30
940
1875
144
146
145

4
M
6
7
37 6/7
3410
3255
141
157
168

5
F
7
8
32
1595
1930
171
165
166

6
F
7
9
33 4/7
2185
2150
138
175
153

7
F
7
9
32 4/7
1400
2580
146
141
128

8
F
9
10
35
1970
1975
160
165
160

9
M
8
9
32 5/7
1760
1970
142
137
146

10
F
7
8
34
1905
2354
160
161
166

11
M
7
9
30
1350
2320
166
167
167

12
M
4
7
36 6/7
3620
3620
124
132
130

13
M
1
5
31 5/7
1015
1955
184
192
188

14
M
7
9
33 6/7
2115
4955
146
149
156

Table 3: Continuation of individual sample values.

Identification
Resting (Hz)
Hz during Teller
Recovery Hz
Assessment with Teller (cpd)
Initial reference value (cpd)
Reference value (average-cpd)
Reference value (lower-cpd)
Final result

1
2.98
2.3
2.97
1.3
0.44
3.89
1.49
0

2
2.04
2.06
2.13
1.6
0.44
3.89
1.49
0

3
2.4
2.44
2.41
0.43
0.44
3.89
1.49
0

4
2.35
2.62
2.79
0.23
0.3
0.66
0.27
0

5
2.86
2.75
2.83
0.43
0.3
0.66
0.27
0

6
2.3
2.92
2.55
0.23
0.3
0.66
0.27
0

7
2.44
2.36
2.14
0.86
0.3
0.66
0.27
1

8
2.67
2.76
2.67
0.86
0.3
0.66
0.27
1

9
2.36
2.29
2.43
1.6
0.3
0.66
0.27
1

10
2.66
2.68
2.76
0.23
0.3
0.66
0.27
0

11
2.77
2.85
2.79
0.32
0.3
2.02
0.26
0

12
2.07
2.19
2.17
1.6
0.3
0.66
0.27
1

13
3.07
3.2
3.13
0.86
0.3
0.66
0.27
1

14
2.44
2.49
2.6
0.43
0.3
0.66
0.27
0

BPM: beats per minute; cpg: cycles per degree; cpd: cycles per degree; F: female; M: male; HR: heart rate; Hz: hertz; min: minute; VA: visual acuity value; initial, average, and lower reference value: normative VA values reported by Salomão, 1995 [22]; 0: visual acuity expected for age; 1: altered visual acuity.

Correlation analysis showed a negative correlation between VA and SDNN (, ), and SD2 (, ), and a positive correlation between HR (, ) and HZ (, ), during Teller assessment, but in simples linear regression analysis, these variables did not influence VA (Table 4).

Table 4: Simple linear regression analysis between visual acuity and different HRV domains.

Model

95% confidence interval

Lower CI
Upper CI

HR
10.577
.395
-.289
.648

HZ
-9.871
.425
-37.965
17.941

SDNN
-.175
.508
-.048
.026

SD2
. 491
.884
-367
.415

Dependent variable: visual acuity; independent: frequency domain variables (HR: heart rate and HZ: hertz) and time domain variables (SDNN: standard deviation between normal beats; SD2: standard deviation) during test; : beta; : value. Linear regression analysis.

The RR intervals were performed between 2 : 05 and 14 : 28 minutes (average 8 : 03 minutes), with an overall average of 156 beats (124 to 192 beats).

Repeated measures analysis revealed no difference between the 3 assessment times (before, during, and after) in the time and frequency domains (Table 5).

Table 5: Repeated measures analysis.

Variables
Rest
Assessment
Recovery

HR

158 ± 5.3
0.29

HZ

0.24

LF (ms²)

0.42

HF (ms²)

0.51

LF/HR

0.77

SDNN (ms)

0.72

RMSSD (ms)

0.21

PNN50

0.75

SD1

0.24

SD2

0.88

SD1/SD2

0.1

APEN

0.95

ANOVA repeated measures; ApEn: entropy; HR: heart rate; Hz: hertz; HF: high frequency; LF: low frequency; ms: milliseconds; PNN50: percentage of differences between adjacent normal RR intervals greater than 50 milliseconds; RMSSD: root mean square of the successive differences between adjacent RR intervals; SDNN: standard deviation between normal beats; SD: standard deviation.

4. Discussion

The results of the study demonstrate that visual acuity was inversely correlated with SDNN and SD2 and during stimulation, showing that the higher the visual performance, the lower the autonomic modulation response. Given this dependence, an inference could be made in both directions, that is, newborns with autonomic dysfunction are at risk of visual impairment, and vice versa. One of the explanations is found in Smith et al. (2013) [5], who suggest that preterm newborns in intensive care units are submitted to several stress stimuli, which, in turn, hinder autonomic nervous system maturation; it can be explained by the possible allostatic load resulting in the impairment of the physiological functioning of several systems of the organism, mainly in the cerebral development [15].

The data show a relationship between visual function and autonomic nervous system modulation and the stress level to which these NBs were submitted. An important contribution of our findings is the fact that we can infer, with a moderate likelihood of success, that visually impaired NBs are more affected by environmental stress. As such, visual measures can be used as a screening tool for risk of physiological stress in this population.

There is enough scientific evidence showing that compromised visual development in preterm [24, 25] and small-for-gestational-age newborns [26] is directly related to the precocity of gestational age. Thus, all preterm newborns are more susceptible to developing visual disorders [27], evident in their poorer visual measures when compared to term newborns [28].

Vision development is a learned skill, which starts at birth in term newborns. The visual pathway from the retina to the cortex is vulnerable during the neonatal period, whereby very low weight newborns are more likely to develop visual disorders such as retinopathy of prematurity [25].

However, although the study included three different intensive care units, only two newborns were visually compromised with retinopathy of prematurity, hindering broader analysis of HRV in NBs with visual disorders.

HRV makes it possible to determine autonomic nervous system maturation and, indirectly, the ability of a NB to adapt to external events. Sympathetic activity is dominant in premature babies, while parasympathetic activity, specifically the vagus nerve, increases with gestational age [3].

Kuerger et al. (2010) [29] compared preterm newborns between 28 and 34 weeks of age with their term counterparts and found that the autonomic nervous system in this period exhibited maturation, particularly of the parasympathetic pathway [29], in line with the profile of the present sample, which was evaluated at a corrected age of 40 weeks. Newborns with gestational age of more than 36 weeks demonstrated significantly greater HRV than their younger counterparts [6].

Although studies showed sympathetic pathway activity in the preterm newborns of the present study, it was predominantly parasympathetic, which may be explained by visual acuity stimulation from the Teller acuity cards, revealing autonomic maturation at a frequency above 0.15 hertz, confirmed by the positive correlation, represented by the increase in HR and HZ, and good visual performance of premature infants.

However, we found a negative correlation between SDNN and SD2, showing that the better the visual acuity, the lower the autonomic system activation. SDNN represents a balance between the sympathetic and parasympathetic nervous systems, whereas SD2 is equivalent to activation of the sympathetic nervous system.

Average SDNN in the present study was 13 ms, similar to that reported by Selig et al. 2011 [7], in preterm infants. This finding may be due to the fact that only a single assessment was carried out in the first days of life, with no monitoring of neurological maturation, and the significant difference in the visual system.

Moreover, the lower vagal response, represented by the sympathetic system, is directly related to morbidities, which influence neurotransmitter release into the central nervous system, since the sympathetic nervous system is responsible for releasing energy as well as participating in glucose and fat metabolization [30]. The fact that our sample consisted of stable newborns, with definitive diagnosis and average weight of 2.584 kg, suggests a state of physiological stability and well-being at assessment.

Although gender-related HRV factors are already present at birth, the literature findings remain controversial [6]. However, in the present study, no significant gender differences were found.

Visual assessment of newborns in intensive care units is routinely conducted by neonatologists via red reflex, the procedure recommended by the American Academy of Pediatrics, aimed at early detecting of visual disorders and carried out as a protocol to identify eye diseases [31]. However, measuring visual acuity by Teller cards is a rapid clinical method, validated for the Brazilian infants population [22], and enables estimating visual acuity by the spatial frequency of the best grating, which can be observed in the present study.

Early detection of visual disorders and effective treatment in rehabilitation programs results in better motor and cognitive performance and social inclusion of the child [32], making it essential to implement low-cost, easy-to-apply tools.

4.1. Study Limitation

The main study limitation was recruiting the sample in neonatal intensive care units, due to the severity of the clinical cases and the transfer of several newborns to other wards in the hospital. Nevertheless, the tool is easy to apply and noninvasive, thereby enabling better understanding of preterm newborn development.

In addition, another study limitation is the lack of evidence to confirm biomarkers of HRV as inducers of autonomic nervous system responses in newborns, given the complex nonlinear interactions between the sympathetic and parasympathetic pathways [33, 34].

Thus, the visual assessment can complement analysis of autonomic nervous system maturation. In addition, physiological biomarkers can complement visual tests and be used to noninvasively measure behavioral visual acuity and track NBs at risk of impaired nervous system development.

During VA assessment, PTNB had a predominantly parasympathetic activity, which may be explained by stimulation from the Teller acuity cards. The results suggest an ability of the crystalline lens to increase its dioptric power and enabling the eye to focus on Teller acuity cards, but further studies need to confirm this hypothesis.

5. Conclusion

The results of the study demonstrate that visual acuity was inversely correlated with SDNN and SD2 and during stimulation, showing that the higher the visual performance, the lower the autonomic modulation response.

Data Availability

The data base is available with author no. 1 and could be asked in anytime by e-mail.

Additional Points

Official Institution to Which the Work is Linked. Federal University of the Santa Catarina.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This study was financed in part by the “Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.

References

L. F. M. de Souza Filho, J. C. M. de Oliveira, M. K. A. Ribeiro et al., “Evaluation of the autonomic nervous system by analysis of heart rate variability in the preterm infants,” BMC Cardiovascular Disorders, vol. 19, no. 1, p. 198, 2019.

V. Oliveira, W. von Rosenberg, P. Montaldo et al., “Early postnatal heart rate variability in healthy newborn infants,” Frontiers in Physiology, vol. 10, 2019.

P. Fister, M. Nolimal, H. Lenasi, and M. Klemenc, “The effect of sleeping position on heart rate variability in newborns,” BMC Pediatrics, vol. 20, no. 1, 2020.

S. B. Mulkey, R. B. Govindan, L. Hitchings et al., “Autonomic nervous system maturation in the premature extrauterine milieu,” Pediatric Research, 2020.

S. L. Smith, “Heart period variability of intubated very-low-birth-weight infants during incubator care and maternal holding,” American Journal of Critical Care, vol. 12, no. 1, pp. 54–64, 2003.

K. Javorka, Z. Lehotska, M. Kozar et al., “Heart rate variability in newborns,” Physiological Research, vol. 66, Suppl. 2, pp. S203–S214, 2017.

F. A. Selig, E. R. Tonolli, E. V. C. M. Silva, and M. F. Godoy, “Variabilidade da frequência cardíaca em neonatos prematuros e de termo,” Arquivos Brasileiros de Cardiologia, vol. 96, no. 6, pp. 443–449, 2011.

M. Filippa, P. Poisbeau, J. Mairesse et al., “Pain, parental involvement, and oxytocin in the neonatal intensive care unit,” Frontiers in Psychology, vol. 10, 2019.

N. O’Callaghan, A. Dee, and R. K. Philip, “Evidence-based design for neonatal units: a systematic review,” Maternal Health, Neonatology and Perinatology, vol. 5, no. 1, 2019.

X. W. Zhu, Y. Shi, and L. P. Shi, “Non-invasive high-frequency oscillatory ventilation versus nasal continuous positive airway pressure in preterm infants with respiratory distress syndrome: study protocol for a multi-center prospective randomized controlled trial,” Trials, vol. 19, no. 1, 2018.

X. Chen, L. Huitao, O. Xiaomei, Y. Chuanzhong, and F. J. Walther, “Neonatal hematological parameters and the risk of moderate-severe bronchopulmonary dysplasia in extremely premature infants,” BMC Pediatrics, vol. 19, no. 1, p. 138, 2019.

A. Sohaila, S. S. Tikmani, I. A. Khan et al., “Frequency of retinopathy of prematurity in premature neonates with a birth weight below 1500 grams and a gestational age less than 32 weeks: a study from a tertiary care hospital in a lower-middle income country,” PLoS One, vol. 9, no. 7, article e100785, 2014.

R. Pineda, R. Guth, A. Herring, L. Reynolds, S. Oberle, and J. Smith, “Enhancing sensory experiences for very preterm infants in the NICU: an integrative review,” Journal of Perinatology, vol. 37, no. 4, pp. 323–332, 2017.

C. Fontana, A. De Carli, D. Ricci et al., “Effects of early intervention on visual function in preterm infants: a randomized controlled trial,” Frontiers in Pediatrics, vol. 8, 2020.

J. S. Christensen, H. Wild, E. S. Kenzie, W. Wakeland, D. Budding, and C. Lillas, “Diverse autonomic nervous system stress response patterns in childhood sensory modulation,” Frontiers in Integrative Neuroscience, vol. 14, 2020.

S. Ziylan, V. Ozturk, O. Yabaş-Kızıloğlu, and F. Çiftçi, “Myopia, visual acuity and strabismus in the long term following treatment of retinopathy of prematurity,” The Turkish Journal Pediatrics, vol. 56, pp. 518–523, 2014.

A. C. Guyton, Tratado de Fisiologia Médica, Guanabara, 8ª edition, 1992.

J. L. Ballard, J. C. Khoury, K. Wedig, L. Wang, B. L. Eileis-Walsman, and R. Lipp, “New Ballard Score, expanded to include extremely premature infants,” The Journal of Pediatrics, vol. 119, no. 3, pp. 417–423, 1991.

A. Chandna, “Natural history of the development of visual acuity in infants,” Eye, vol. 5, no. 1, pp. 20–26, 1991.

H. L. Hall, M. L. Courage, and R. J. Adams, “The predictive utility of the Teller acuity cards for assessing visual outcome in children with preterm birth and associated perinatal risks,” Vision Research, vol. 40, no. 15, pp. 2067–2076, 2000.

S. R. Salomão, F. Ejzenbaum, A. Berezovsky, P. A. Sacai, and J. M. Pereira, “Age norms for monocular grating acuity measured by sweep-VEP in the first three years of age,” Arquivos Brasileiros de Oftalmologia, vol. 71, no. 4, pp. 475–479, 2008.

S. R. Salomão and D. F. Ventura, “Large sample population age norms for visual acuities obtained with vistech – Teller acuity cards,” Investigative Ophthalmology & Visual Science, vol. 36, no. 3, pp. 657–670, 1995.

G. C. McCain, S. M. Ludington-Hoe, J. Y. Swinth, and A. J. Hadeed, “Heart rate variability responses of a preterm infant to kangaroo care,” Journal of Obstetric, Gynecologic, and Neonatal Nursing, vol. 34, no. 6, pp. 689–694, 2005.

A. G. F. Oliveira, M. F. Costa, J. M. de Souza, and D. F. Ventura, “Contrast sensitivity threshold measured by sweep-visual evoked potential in term and preterm infants at 3 and 10 months of age,” Brazilian Journal of Medical and Biological Research, vol. 37, no. 9, pp. 1389–1396, 2004.

R. Spencer, “Long-term visual outcomes in extremely low-birth-weight children (an American ophthalmological society thesis),” Transactions of the American Ophthalmological Society, vol. 104, pp. 493–516, 2006.

G. Mirabella, P. K. Kjaer, A. M. Norcia, W. V. Good, and A. Madan, “Visual development in very low birth weight infants,” Pediatric Research, vol. 60, no. 4, pp. 435–439, 2006.

E. Alberman, J. Benson, and S. Evans, “Visual defects in children of low birthweight,” Archives of Disease in Childhood, vol. 57, no. 11, pp. 818–822, 1982.

A. R. O'Connor, “Visual function in low birthweight children,” The British Journal of Ophthalmology, vol. 88, no. 9, pp. 1149–1153, 2004.

C. Krueger, J. H. van Oostrom, and J. Shuster, “A longitudinal description of heart rate variability in 28-34 week-old preterm infants,” Biological Research for Nursing, vol. 11, no. 3, pp. 261–268, 2009.

G. S. Barreto, F. M. Vanderlei, L. C. Vanderlei, and Á. J. Leite, “Impact of malnutrition on cardiac autonomic modulation in children,” Jornal de Pediatria, vol. 92, no. 6, pp. 638–644, 2016.

S. Eventov-Friedman, H. Liba, O. Flidel-Rimon, A. Juster-Reicher, and E. S. Shinwell, “The red reflex examination in neonates: an efficient tool for early diagnosis of congenital ocular diseases,” IMAJ, vol. 12, no. 5, pp. 259–261, 2010.

R. M. Graziano and C. R. Leone, “Frequent ophthalmologic problems and visual development of preterm newborn infants,” The Journal of Pediatrics, vol. 81, 1 suppl, pp. S95–S100, 2005.

S. Cardoso, M. J. Silva, and H. Guimarães, “Autonomic nervous system in newborns: a review based on heart rate variability,” Child's Nervous System, vol. 33, no. 7, pp. 1053–1063, 2017.

G. E. Billman, “The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance,” Frontiers in Physiology, vol. 4, no. 26, 2013.