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Age-related ranges of respiratory inductance plethysmography (RIP) reference values for infants and children
Accepted Manuscript Review Age-related ranges of respiratory inductance plethysmography (RIP) reference values for infants and children Sona Lakshme Balasubramaniam, Yanping Wang, Lauren Ryan, Jobayer Hossain, Tariq Rahman, Thomas H. Shaffer PII: DOI: Reference:
S1526-0542(18)30076-9 https://doi.org/10.1016/j.prrv.2018.03.010 YPRRV 1271
To appear in:
Paediatric Respiratory Reviews
Please cite this article as: S.L. Balasubramaniam, Y. Wang, L. Ryan, J. Hossain, T. Rahman, T.H. Shaffer, Agerelated ranges of respiratory inductance plethysmography (RIP) reference values for infants and children, Paediatric Respiratory Reviews (2018), doi: https://doi.org/10.1016/j.prrv.2018.03.010
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1 Review Article: Age-related ranges of respiratory inductance plethysmography (RIP) reference values for infants and children Sona Lakshme Balasubramaniam, PhD*; Yanping Wang, PhD*; Lauren Ryan, BS; Jobayer Hossain, PhD; Tariq Rahman, PhD; Thomas H. Shaffer, MSE, PhD Department of Biomedical Research and Center for Pediatric Lung Research, Nemours/Alfred I. duPont Hospital for Children, Wilmington, Delaware USA Short Title: A review of the age-dependent RIP literature *First authors Corresponding author: Thomas H. Shaffer, MSE, PhD Professor of Pediatrics and Physiology & Director, Center for Pediatric Lung Research Nemours/Alfred I. duPont Hospital for Children P.O. Box 269 Wilmington, DE 19899 USA Phone: 00-1-302-651-6837; Fax: 00-1-302-651-6888; Email: [email protected] Funding: This study was funded by The Nemours Foundation; The University of Delaware, Center for Advanced Technology (CAT) Program Grant #44058 (Tariq Rahman and Thomas H.
2 Shaffer, CO-principal investigators); and NIH COBRE Grant P30 GM114736 (Thomas H. Shaffer, PI, principal investigator). Conflicts of Interest: None Abbreviations: ABD = abdominal; AS = asthma score; BPM = breaths per minute; CI = confidence interval; ED = emergency department; HFNC = high-flow nasal cannula; LBI = labored breathing index; nCPAP = nasal continuous positive airway pressure; PFT = pulmonary function testing; RC = rib cage; RIP = respiratory inductance plethysmography; RR = respiratory rate; SD = standard deviation; SEM = standard error of mean; TAA = thoracoabdominal asynchrony; TAM = thoracoabdominal motion; Vt = tidal volume; WOB = work of breathing; Φ = phase angle; % RC = percent of rib cage contribution to breathing
3 Summary The current noninvasive method for respiratory monitoring is respiratory inductance plethysmography (RIP); two bands are connected, one each to the chest and the abdomen, to measure the breathing pattern. RIP requires post hoc analysis to calculate indices such as respiratory rate, phase angle, labored breathing index, and percent of rib cage contribution to breathing. Clinical studies have provided patient RIP values and age-matched normal values, but they lack global evaluation of normative data for a wide age range of pediatric subjects. Herein, we compiled normative RIP indices from numerous studies for a large range of pediatric ages. From these data, we derived regression equations useful for computing normal RIP parameters as a function of age. The presented review will provide caregivers the ability to compare RIP data of pediatric patients against the regression analysis. This comparison will help identify patients with pulmonary complications and aid in guiding respiratory therapy. Educational Aims: The reader will be able to provide: •
Respiratory caregivers an overview of noninvasive pulmonary function assessment.
•
Normative respiratory inductance plethysmography (RIP) data for pediatric subjects.
•
Ranges and regression analyses of RIP parameters.
•
An ability to compare RIP data of pediatric patients against the regression analysis.
Future research directions: The iPAD technology mentioned herein was developed in our Pediatric Lung Center as a research tool to assist clinical fellows interested in studying thoracoabdominal synchrony in
4 infants and children. Considering the limited pediatric data in the literature, the addition of realtime assessment of RIP parameters using iPAD technology and ease of use should expand the normative database. As this database expands, we plan to integrate these data into our regression analysis. In this regard, we are currently, studying thoracoabdominal synchrony in children with progressive neuromuscular diseases in our outpatient clinics compared with additional normal subjects. In addition, we have a prospective study in infants with respiratory distress on high flow respiratory support in order to evaluate optimum flow settings.
Keywords: Normal respiratory inductance plethysmography values, respiratory rate, phase angle, labored breathing index, percent of rib cage contribution to breathing
5 1.
Introduction Pulmonary function testing (PFT) is important in evaluating the respiratory system,
determining normal function, and identifying the risk of developing respiratory problems. Early methods of PFT involved spirometry to measure lung volume and lung function. Spirometry requires patient cooperation, effort dependence, and post hoc analysis of the volume-time and flow-volume loops. When properly performed, pulmonary function tests provide insight into lung pathophysiology after comparison of results with age-, sex-, and weight-matched normative values. Current spirometry practices involve a comparison of measured values as a percentage of predicted normal values, thereby determining the severity of the respiratory disease. Another valid alternative method of reporting normal lung function is to express results as Z-scores. Use of Z-scores solves many potential problems by taking into account age, height, sex, and ethnic group, as well as the age-dependent reference range [1]. A trained physician applies this approach to evaluate respiratory symptoms, stratify preoperative risk, and diagnose the disease. Continuous respiratory monitoring aids in early diagnosis of disease processes and improves patient safety by reducing the incidence of critical events. Furthermore, continuous, point-of-care respiratory monitoring is essential for observing a patient’s response to a particular treatment or for optimizing a patient’s ventilator settings [2]. Respiratory inductance plethysmography (RIP) has been used for more than three decades and enables noninvasive respiratory monitoring with minimal patient cooperation and effort. Respiratory inductance plethysmography measures the rib cage (RC) and abdominal (ABD) movements with elastic bands around the chest and abdomen [3-6]. The process involves no noticed discomfort, and tests can be performed over clothes, provided the bands do not slip over clothing. In a healthy subject, the movements of the RC and ABD are synchronous and occur concurrently during quiet tidal
6 breathing. Asynchronous movement of RC and ABD is known as thoracoabdominal asynchrony (TAA), a common incidence in respiratory distress indicated by increased work of breathing (WOB). This technique has been used to calculate respiratory indices such as respiratory rate (RR), phase angle (Φ) between RC and ABD, percent RC contribution (% RC) to tidal volume (Vt), and labored breathing index (LBI) calculated from the sum of excursions by RC and ABD over Vt. All of these parameters can indicate respiratory distress. Currently, these parameters are determined only by post hoc analysis. Literature indicates that the RIP method is employed to evaluate respiratory function in patients ranging from neonates to adults [3,7]. Since RIP does not directly evaluate airflow or respiratory volumes, this approach is useful for patients experiencing air leaks during high-flow nasal cannula (HFNC) and nasal continuous positive airway pressure (nCPAP) respiratory support. The RIP parameters also assist in identifying TAA, paradoxical breathing, and the onset of respiratory muscle fatigue, which are often observed in infants and children with respiratory disorders [8]. Various studies have used this method to evaluate pulmonary disorders such as parenchymal processes, obstructive airways (asthma, chronic obstructive pulmonary disease), and musculoskeletal and neuromuscular diseases [3,4,8-16]. The need for normative data from healthy subjects without pulmonary complications for WOB parameters such as RR, Φ, % RC, and LBI for all ages is critical for the usefulness of this method. Thus, the development of a database for normative RIP parameters in the pediatric population is the purpose of this extensive literature review and analysis. This review aims to establish normative WOB parameters for RR, Φ, % RC, and LBI as a function of age. Establishing the relationship between these parameters and age will aid in both research and clinical settings.
7
2.
Methods The PubMed database of the National Center for Biotechnology Information (NCBI,
https://www.ncbi.nlm.nih.gov/) was searched for the key words “respiratory inductance plethysmography,” “(RIP),” “respiratory rate,” “phase angle,” “percent rib cage contribution to tidal volume (Vt),” “% RC,” “labored breathing index,” and “LBI.” Journals, reviews, and book chapters were screened for the WOB index values measured using the RIP method in healthy individuals (inclusive of values measured from analog and digital graphs, values generated by inbuilt algorithms). Two authors (SB, YW) assessed eligibility of studies for inclusion, and further inspections (year of study, participants [age range, number], study setting, method of measurement, sleep state, and position) as well as disagreements were resolved by a third author (LR). When multiple results were reported for a single group of children at a particular age, we used both awake and asleep data in tabular form when both measurements were available and the baseline result where multiple baseline measurements were recorded in intervention studies. Finally, when descriptions of the behavioral state were not given, all data were used. Furthermore, corresponding standard error of mean (SEM) or standard deviation (SD) values are presented, along with number of subjects, age range (preterm infants to adolescents), sex, and body position (supine or sitting, typical position for clinical pulmonary evaluation in infants and children). Several clinical studies have provided WOB indices in healthy preterm infants at various post-conceptual ages [3,4,17], so to universally analyze age-dependent effects on RIP parameters, the age for all studies was adjusted in the current study to begin at conception age in years. In studies in which only SD or SEM was provided, the other value was calculated based on the number of test subjects. On the basis of our initial review of the literature, we could
8 not find any references concerning age that related to the effect of ethnicity or sex on all RIP parameters.
Statistical Approach The current literature review and analysis includes all normative data available from the resources outlined above in methods. Among the various trend lines tried, the power function provided the best fit for the data. It was used for regression and statistical evaluation of these graphs to derive a relationship between each RIP parameter and age. Based on the regression line of mean RIP data across age, the regression equation and R2 values were calculated. Depending on individual mean and SEM from each study, 95% (mean ± 1.96*SEM) and 85% (mean ± 1.44*SEM) confidence intervals (CI) were derived. The statistical software SPSS (version 19.0, Chicago, IL, USA) was used for analysis. Regression correlations were tested, and these equations provided a range of predicted normative values with 5% and 15% acceptable deviation from the mean values.
3. Results Analysis of the literature review on healthy subjects was challenging because of the numerous issues outlined in the methods section. Literature studies provided the mean, SD or SEM, and the number of subjects (in most cases). The 95% and 85% confidence intervals were calculated based on the mean and SEM for each study and plotted in an age-dependent manner. After data were tabulated and plotted, a trend was observed in some parameters.
9 Although a number of studies have used RIP and reported WOB indices data for different age groups, there were limited data available to differentiate these parameters as a function of all body positions or sex; therefore, the effect of these data was not considered in our analysis [36,18]. The mean data, SD or SEM, body position (supine or sitting), and number of subjects (in most cases) for RR, Φ, % RC, and LBI from the literature survey are reported in Tables 1 to 4 for the age range studied. In total, data from 109 to 3750 subjects, depending on the RIP parameter (i.e., RR, Φ, % RC, LBI), were used in the development of the tables, graphs, and regression correlations. Table 1 summarizes mean RR data and SEM from 3750 subjects from our literature survey. As shown in Figure 1, RR (breaths per minute [BPM]) decreases with increase in age, as characterized by the power function shown in the figure legend. The correlation (y=45.80x-0.358; R2=0.901) between RR and post-conceptual age was excellent (range: preterm infants to adolescents). In most studies, the mean RR values were within or close to the 95% confidence intervals, as shown. For phase angle results, the mean Φ data and SEM from 193 subjects from our literature survey are summarized in Table 2. Phase angle in degrees did not correlate well with advancing age as shown in Figure 2 and by the correlation (y=16.64 x0.071; R²=0.018), indicating a flat line over the older developmental ages reported. Although there was a higher degree of variation in Φ in subjects younger than 3 years of post-conceptual age, there was only a slight increase in mean Φ in subjects younger than 3 years of post-conceptual age. As shown by the regression analysis, Φ in the supine position in young subjects is higher when compared with that of the older population in the sitting position. Furthermore, one subject was not plotted in Figure 2 because
10 of wide variance associated with supine positioning in an older subject; however, the data are included in Table 2. The mean data and SEM for % RC are summarized for 231 subjects from our literature review and calculations as outlined in the methods section, as shown in Table 3. Unlike RR and Φ, % RC did not decrease with increase in age. Rather, % RC increased with an increase in age, as shown in Figure 3. There was a correlation (y=37.52x0.078; R2=0.176) between % RC and postconceptual age. In addition, one subject was not plotted in Figure 3 because of wide variance associated with active sleep; however, the data are included in Table 3. Data from 109 subjects obtained from the literature review are shown in Table 4. As shown in Figure 4, mean LBI data in normal healthy subjects range between 1.0 and 1.05, across all ages. There was a correlation (y=1.027x-0.002; R2 = 0.014) between LBI and post-conceptual age. Furthermore, one subject was not plotted in Figure 4 because of wide variance associated with active sleep; however, the data are included in Table 4.
4. Discussion This report is the first literature review to provide an overall compilation of normative RIP data from neonates to adolescents. This study has both research and clinical significance to further assist physicians and researchers in early and rapid diagnosis of pulmonary problems. More specifically, the data address the greater need to determine normative range for each WOB parameter for neonates and children. The regression graphs from the data analysis demonstrate that the greatest changes in RR with age occur during early development. This finding is
11 consistent with the data found in other studies [35,36]. Presumably, these changes are associated with rapid pulmonary function development in the neonatal and pediatric populations. The younger population (i.e., both neonates and children) is at higher risk for respiratory fatigue, which can be identified with WOB indices. For example, the asthma score (AS), which is used to triage patients with asthma exacerbations in the emergency department (ED), is determined as a function of age. Additionally, AS based on clinical scoring of RR and chest retractions can be assessed accurately from RR and Φ parameters measured using the RIP technique. These findings further support the use of RIP. The normative data presented herein will be useful in generating a better asthma-scoring approach to quickly and objectively provide data at point-of-care. Similarly, Giordano et al. [37] showed with an observational ED study that RIP technology provided feasible, objective data to determine patient status in the pediatric population that presented to the ED with acute asthma exacerbations. As shown in Figures 2, 3, and 4, both Φ and LBI remain relatively constant after 3 years of post-conception age, whereas % RC increases with age. These findings are supported by regression equations, as Φ and LBI are a negative function of age, essentially flat lines after 3 years of post-conception age, whereas % RC is a positive function of age. Of importance, RR and % RC lower or higher than 85% confidence interval can indicate respiratory dysfunction, but that is not the case with Φ and LBI. Normal Φ and LBI can be lower than the 85% confidence interval, as low values indicate that RC and ABD are in synchrony; however, Φ or LBI higher than the 85% confidence interval indicates that RC and ABD are asynchronous and are trending toward respiratory muscle fatigue. Unlike RR and % RC, only a deviation above normal range for Φ and LBI is an indication of pulmonary dysfunction.
12 There are several limitations to the present review such as the unavailability of a large pediatric database on the effect of consistent body position during testing. Not all studies discussed body position with respect to RR studies. Additionally, few studies have reported normal LBI values in pediatric and adolescent populations [18,22,28,31,38], limiting the significance of the normal LBI regression analysis provided herein. In those studies that reported body position, some studies were conducted in the sitting position, whereas others reported standing, supine, or prone positions; however, these studies were specifically designed to investigate the physiological effect of posture on thoracoabdominal motion (TAM) at a specific age, not the determination of normative reference values (for our review, we selected only the sitting and supine data). However, it is noteworthy that in the sitting and standing positions, the chest and abdomen motions are similar because of lower abdominal compliance, with a net effect of more synchronous TAM [31]. Furthermore, in the supine position, there is an increase in abdominal compliance because of outward abdominal motion, leading to asynchronous TAM and increased Φ. Additionally, the effect of position on Φ is greater in children. The effect of sitting or standing positions on Φ is significantly higher when compared with that of the older population [31,39]. Because of insufficient data for all positions in this wide age range, the effect of various body positions was not explored in our analysis. Instead, as noted above, only data regarding supine and sitting positions were taken for this review, to limit these discrepancies. For all RIP testing, infants should be in a quiet behavioral state and supine, with small to no movements, regular respiration, and open or closed eyes, to reduce movement while recording the RIP data [40]. As noted previously, one subject each from Tables 2, 3, and 4 was not plotted in the respective figures because of wide variance associated with active sleep or supine
13 positioning in an older subject. Older children to adolescents should be in a sitting position with minimal movement and quiet breathing. Of note is that these data discrepancies can be avoided if a common body position and behavioral state is used in all studies during the measurement of WOB indices. Consistent body position and behavioral state should be a mandate for all clinical research studies because it will simplify comparing results with normative values and with treatment interventions. In this review and on the basis of our experience, we believe the supine position for infants and the sitting position for adolescents would be preferred in a clinical setting for pulmonary evaluations. Nevertheless, there are some physical limitations to finding a common position, such as the inability to measure WOB indices in distressed preterm infants or in bedridden children in the sitting position. On the basis of our comprehensive review of the pediatric literature, we could not find any reference across age that related to the effect of sex, height, weight, musculature, or ethnicity on RIP parameters. However, it is noteworthy that these factors could affect certain WOB parameters. Because of the limited data available in literature studies, the aforementioned factors were not considered as a factor in our analysis. Ideally, these variables could be reviewed in the future for pediatric patients. One study, concerned with healthy subjects aged 10 to 60 years old, reported that there are no differences regarding sex in TAM during quiet breathing [7]. Finally, some discrepancies in data comparisons may exist because of the limitations of the RespiTrace instrumentation used for most of the studies. The RespiTrace PT (Sensormedics, Yorba Linda, CA, USA) is a noninvasive respiratory monitor that applies the currently used RIP technique. Many research studies that use RespiTrace PT have provided limited normal breathing RIP parameters for a small range of age groups. The limited data and possible discrepancies are most likely due to the required post hoc analysis, which is time consuming and
14 labor intensive and lacks provision of real-time results. Therefore, the existing RIP devices are seldom used in the clinic and are restricted to research use. With the limitations outlined for the existing system, our group has developed a novel noninvasive respiratory research monitor, pneuRIPTM, which could provide more consistent and rapid data collection over longer epochs in numerous clinical settings [28]. . Since the pneuRIPTM system displays the WOB parameter values instantaneously, it may develop as a useful instrument in several clinical settings to evaluate a patient’s pulmonary function, identify patients with pulmonary disorders, and determine the effectiveness of treatment interventions immediately. Data from our research study using the pneuRIPTM were included in this review [28]. In summary, the accessibility of the pneuRIPTM instrument, the real-time display of the data, and the ability to securely share the recordings may enable widespread research and future clinical uses of RIP technology.
5. Conclusion In conclusion, this review and analysis of RIP parameters demonstrates several significant correlations between some RIP parameters and age. Respiratory rate decreases with age and % RC increases with age, whereas Φ and LBI show little change after 3 years of postconception age. The 95% and 85% confidence intervals from the mean provide acceptable deviation from normal expected value. Clinically, a deviation within ± 15% from normal value is considered acceptable depending on the parameter. The figures and regression curves in this
15 review present acceptable ranges (i.e., mean ± confidence intervals) of normal value for each WOB parameter. These findings will be useful in intensive care units, outpatient clinics, and research studies. By comparing WOB parameter values against age-matched normative values using the database and analysis of RIP values presented, clinicians and investigators can further refer to predictive values within 5% or 15% deviation from normal depending on the WOB parameter. Knowledge of normal values for WOB parameters is particularly important in the neonatal and younger pediatric populations who show greater variability than the adolescent population. Understanding the RIP parameter differences between normal and measured values in preterm and full-term babies will aid in rapid diagnosis of any pulmonary complications in newborns, particularly in the babies on newer forms of noninvasive respiratory support. Furthermore, a normative database will assist in a more rapid and user-friendly assessment of the infant’s response to treatment. Research respiratory monitors, like pneuRIPTM, with graphic displays and central processing units, provide instantaneous point-of-care WOB indices that may enable easy application of these regression equations to test if a patient is within the normative range for his or her age.
16 Acknowledgements Sources of Funding: This study was funded by The Nemours Foundation; The University of Delaware, Center for Advanced Technology (CAT) Program Grant #44058 (Tariq Rahman and Thomas H. Shaffer, CO-principal investigators); and NIH COBRE Grant P30 GM114736 (Thomas H. Shaffer, PI, principal investigator).
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20 [24] Iliff A, Lee VA. Pulse rate, respiratory rate, and body temperature of children between two months and eighteen years of age. Child Dev 1952;23:237-45. [25] França DC, Camargos PA, Martins JA, Abreu MC, Avelar e Araujo GH, Parreira VF. Feasibility and reproducibility of spirometry and inductance plethysmography in healthy Brazilian preschoolers. Pediatr Pulmonol 2013;48:716–24. doi: 10.1002/ppul.22743. [26] Rosenthal M, Bush A. Ventilatory variables in normal children during rest and exercise. Eur Respir J 2000;16:1075–83. [27] Tabachnik E, Muller N, Toye B, Levison H. Measurement of ventilation in children using the respiratory inductive plethysmograph. J Pediatr 1981;99:895–9. [28] Rahman T, Page R, Page C, Bonnefoy JR, Cox T, Shaffer TH. pneuRIPTM: a novel respiratory inductance plethysmography monitor. J Med Device 2017;11:0110101-110106. doi: 10.1115/1.4035546. [29] Tabachnik E, Muller NL, Bryan AC, Levison H. Changes in ventilation and chest wall mechanics during sleep in normal adolescents. J Appl Physiol Respir Environ Exerc Physiol 1981;51:557–64. [30] Sivan Y, Ward SD, Deakers T, Keens TG, Newth CJ. Rib cage to abdominal asynchrony in children undergoing polygraphic sleep studies. Pediatr Pulmonol 1991;11:141–6. [31] Mayer OH, Clayton RG Sr, Jawad AF, McDonough JM, Allen JL. Respiratory inductance plethysmography in healthy 3- to 5-year-old children. Chest 2003;124:1812–9. [32] Hershenson MB, Colin AA, Wohl ME, Stark AR. Changes in the contribution of the rib cage to tidal breathing during infancy. Am Rev Respir Dis 1990;141:922–5.
21 [33] Poole KA, Thompson JR, Hallinan HM, Beardsmore CS. Respiratory inductance plethysmography in healthy infants: a comparison of three calibration methods. Eur Respir J 2000;16:1084–90. [34] Brown K, Aun C, Jackson E, Mackersie A, Hatch D, Stocks J. Validation of respiratory inductive plethysmography using the Qualitative Diagnostic Calibration method in anaesthetized infants. Eur Respir J 1998;12:935–43. [35] Fleming S, Thompson M, Stevens R, Heneghan C, Pluddemann A, Maconochie I, et al. Normal ranges of heart rate and respiratory rate in children from birth to 18 years of age: a systematic review of observational studies. Lancet 2011;377:1011–8. doi: 10.1016/S01406736(10)62226-X. [36] Wallis LA, Maconochie I. Age related reference ranges of respiratory rate and heart rate for children in South Africa. Arch Dis Child 2006;91:330–3. [37] Giordano K, Rodriguez E, Green N, Armani M, Richards J, Shaffer TH, et al. Pulmonary function tests in emergency department pediatric patients with acute wheezing/asthma exacerbation. Pulm Med 2012;2012:724139. doi: 10.1155/2012/724139. [38] Warren RH, Horan SM, Robertson PK. Chest wall motion in preterm infants using respiratory inductive plethysmography. Eur Respir J 1997;10:2295–300. [39] Sackner MA, Gonzalez H, Rodriguez M, Belsito A, Sackner DR, Grenvik S. Assessment of asynchronous and paradoxic motion between rib cage and abdomen in normal subjects and in patients with chronic obstructive pulmonary disease. Am Rev Respir Dis 1984;130:588–93.
22 [40] Prechtl HF. The behavioural states of the newborn infant (a review). Brain Res 1974;76:185–212.
23
Table 1. Respiratory rate database from the literature review. Age in
RR
CI
CI
CI
CI
Sample
Years
(BPM)
<95%
>95%
<85%
>85%
SEM
Size
Ref No.
0.65
60.0
50.6
69.4
53.1
66.9
4.82
19
Levy et al. [19]
0.65
53.0
46.3
59.7
48.0
58.0
3.44
19
Levy et al. [19] Locke et al.
0.65
73.0
66.5
79.5
68.2
77.8
3.33
13
[20] Deoras et al.
0.68
77.0
69.2
84.8
71.2
82.8
4.00
10
[21] Warren &
0.76
53.1
49.8
56.4
50.7
55.5
1.67
46
Alderson [22] Warren &
0.76
55.2
52.3
58.1
53.1
57.3
1.49
46
Alderson [] Adams et al.
0.77
51.4
46.7
56.1
48.0
54.8
2.39
20
[18] Calder et al.
0.77
52.0
46.1
57.9
47.7
56.3
3.00
13
0.77
56.0
44.2
67.8
47.4
64.6
6.00
8
[17]
Deoras et al.
24 [21] Hershenson et 0.77
56.0
47.3
64.7
49.6
62.4
4.44
13
al. [23] Adams et al.
0.77
55.2
49.6
60.8
51.1
59.3
2.86
20
[18] Calder et al.
0.9
48.0
43.3
52.7
44.5
51.5
2.40
13
[17] Iliff & Lee
1.27
31.0
28.5
33.5
29.1
32.9
1.30
38
[24] Iliff & Lee
1.27
30.0
28.4
31.6
28.8
31.2
0.80
55
[24] Iliff & Lee
2.27
26.0
25.0
27.0
25.3
26.7
0.50
69
[24] Iliff & Lee
2.27
27.0
26.0
28.0
26.3
27.7
0.50
79
[24] Iliff & Lee
3.27
25.0
24.2
25.8
24.4
25.6
0.40
118
[24] Iliff & Lee
3.27
25.0
24.4
25.6
24.6
25.4
0.30
134
[24]
25 Iliff & Lee 4.27
24.0
23.6
24.4
23.7
24.3
0.20
131
[24] Iliff & Lee
4.27
24.0
23.6
24.4
23.7
24.3
0.20
119
[24] França et al.
4.77
24.0
21.7
26.3
22.3
25.7
1.15
12
[25] Iliff & Lee
5.27
23.0
22.6
23.4
22.7
23.3
0.20
122
[24] Iliff & Lee
5.27
22.0
21.6
22.4
21.7
22.3
0.20
113
[24] França et al.
5.77
25.0
23.7
26.3
24.0
26.0
0.69
19
[25] Iliff & Lee
6.27
22.0
21.6
22.4
21.7
22.3
0.20
110
[24] Iliff & Lee
6.27
21.0
20.6
21.4
20.7
21.3
0.20
100
[24] França et al.
6.77
23.0
21.5
24.5
21.9
24.1
0.75
16
7.27
21.0
20.6
21.4
20.7
21.3
0.20
128
[25]
Iliff & Lee
26 [24] Iliff & Lee 7.27
21.0
20.4
21.6
20.6
21.4
0.30
97
[24] Iliff & Lee
8.27
20.0
19.6
20.4
19.7
20.3
0.20
119
[24] Iliff & Lee
8.27
20.0
19.6
20.4
19.7
20.3
0.20
97
[24] Iliff & Lee
9.27
20.0
19.6
20.4
19.7
20.3
0.20
113
[24] Iliff A & Lee
9.27
20.0
19.6
20.4
19.7
20.3
0.20
101
[24] Rosenthal &
10.02
22.5
19.1
25.9
20.0
25.0
1.75
11
Bush [26] Rosenthal &
10.02
26.1
21.1
31.1
22.4
29.8
2.57
12
Bush [26] Iliff & Lee
10.27
19.0
18.6
19.4
18.7
19.3
0.20
141
[24] Iliff & Lee
10.27
19.0
18.6
19.4
18.7
19.3
0.20
98
[24]
27 Iliff & Lee 11.27
19.0
18.6
19.4
18.7
19.3
0.20
141
[24] Iliff & Lee
11.27
19.0
18.6
19.4
18.7
19.3
0.20
90
[24] Iliff & Lee
12.27
19.0
18.6
19.4
18.7
19.3
0.20
123
[24] Iliff & Lee
12.27
19.0
18.4
19.6
18.6
19.4
0.30
82
[24] Rosenthal &
12.27
20.2
17.3
23.1
18.1
22.3
1.47
13
Bush [26] Rosenthal &
12.27
21.2
17.8
24.6
18.7
23.7
1.75
11
Bush [26] Tabachnik et
12.77
19.7
17.0
22.4
17.7
21.7
1.39
20
al. [27] Iliff & Lee
13.27
19.0
18.6
19.4
18.7
19.3
0.20
131
[24] Iliff & Lee
13.27
19.0
18.4
19.6
18.6
19.4
0.30
72
14.27
13.2
11.3
15.0
11.8
14.5
0.96
10
[24]
Rahman et al.
28 [28] Iliff & Lee 14.27
19.0
18.6
19.4
18.7
19.3
0.20
110
[24] Iliff & Lee
14.27
18.0
17.4
18.6
17.6
18.4
0.30
68
[24] Rosenthal &
14.27
20.7
17.7
23.7
18.5
22.9
1.53
18
Bush [26] Rosenthal &
14.27
18.0
16.3
19.7
16.7
19.3
0.87
19
Bush [26] Iliff & Lee
15.27
18.0
17.6
18.4
17.7
18.3
0.20
106
[24] Iliff & Lee
15.27
18.0
17.2
18.8
17.4
18.6
0.40
57
[24] Tabachnik et
15.77
17.4
16.6
18.2
16.8
18.0
0.43
9
al. [29] Iliff & Lee
16.27
17.0
16.4
17.6
16.6
17.4
0.30
76
[24] Iliff & Lee
16.27
18.0
17.2
18.8
17.4
18.6
0.40
47
[24]
29 Rosenthal & 16.47
21.4
19.2
23.6
19.8
23.0
1.14
13
Bush [26] Rosenthal &
16.47
19.8
15.9
23.7
16.9
22.7
2.00
9
Bush [26]] Iliff & Lee
17.27
17.0
16.2
17.8
16.4
17.6
0.40
45
[24] Iliff & Lee
17.27
17.0
16.0
18.0
16.3
17.7
0.50
30
[24] Iliff & Lee
18.27
16.0
15.0
17.0
15.3
16.7
0.50
38
[24] Iliff & Lee
18.27
17.0
15.6
18.4
16.0
18.0
0.70
20
[24]
RR = respiratory rate; BPM = breaths per minute; SEM = standard error of mean; CI = confidence interval
30
Table 2. Phase angle database from the literature review. Age in Years
Φ (°)
CI
CI
CI
CI
<95%
>95%
<85%
>85%
SEM
Sample Size
Position
Ref No. Levy et al.
0.65
55.0
36.1
73.9
41.1
68.9
9.64
19
supine
[19] Deoras et al.
0.68
38.0
20.9
55.1
25.5
50.5
8.7
10
supine
[21] Allen et al.
0.75
8.0
2.1
13.9
3.7
12.3
3
6
supine
[3] Deoras et al.
0.77
9.3
4.2
14.4
5.6
13.0
2.6
8
supine
[21] Allen et al.
0.96
8.0
2.1
13.9
3.7
12.3
3
6
supine
[3] Sivan et al.
2.94
11.8
10.2
13.4
10.6
13.0
0.8
45
supine
[30] França et al.
4.77
16.0
10.9
21.1
12.3
19.7
2.6
12
sitting
[25] Mayer et al.
5.19
15.7
7.9
23.5
9.9
21.5
4
42
sitting
[31]
31 Mayer et al. 5.19
56.1
47.7
64.5
49.9
62.3
4.3
42
supine
[31] França et al.
5.77
18.0
13.5
22.5
14.7
21.3
2.29
19
sitting
[25] França et al.
6.77
15.0
10.1
19.9
11.4
18.6
2.5
16
sitting
[25] Rahman et
14.27
14.1
9.2
19.0
10.5
17.7
2.48
10
Φ = phase angle; SEM = standard error of mean; CI = confidence interval
sitting
al. [28]
32
Table 3. % RC database from the literature review and from the presented derivation equations. Age in
%
CI
CI
CI
CI
Years
RC
<95%
>95%
<85%
>85%
Sample SEM
Size
Position
Ref No. Warren &
0.76
28.3
24.3
32.3
25.3
31.3
2.06
46
supine
Alderson [22] Adams et al.
0.77
16.3
11.7
20.9
12.9
19.7
2.37
9
supine*
[18] Adams et al.
0.77
28.8
23.4
34.2
24.8
32.8
2.75
11
supine
[18] Hershenson et
0.77
34.0
29.1
38.9
30.4
37.6
2.5
13
supine
al. [23] Hershenson et
1.15
40.0
35.3
44.7
36.5
43.5
2.41
14
supine
al. [32]
1.22
49.8
42.7
56.9
44.6
55.0
3.63
12
supine
Poole et al. [33] Brown et al.
1.94
50.2
39.4
61.0
42.2
58.2
5.53
23
supine
[34] Hershenson et
2.31
59.0
45.4
72.6
49.0
69.0
6.94
6
supine
al. [32]
33 França et al. 4.77
39.0
31.2
46.8
33.2
44.8
4
12
sitting
[25] França et al.
5.77
35.0
30.1
39.9
31.4
38.6
2.52
19
sitting
[25] França et al.
6.77
43.0
35.2
50.8
37.2
48.8
4
16
sitting
[25] Rahman et al.
14.27
51.4
46.0
56.8
47.4
55.3
2.76
10
sitting
[28] Tabachnik et al.
15.77
40.0
34.1
45.9
35.7
44.3
3
9
supine
[29] Verschakelen &
16.52
55.2
43.1
67.4
46.3
64.2
6.2
20
sitting
Demedts [7] Verschakelen &
16.52
38.9
26.5
51.3
29.8
48.0
6.32
20
supine
Demedts [7]
% RC = percent of rib cage contribution to breathing; SEM = standard error of mean; CI = confidence interval; * = active sleep
34
Table 4. LBI database from the literature review. Age in Years
LBI
CI
CI
CI
CI
<95%
>95%
<85%
>85%
Sample SEM
Size
Position
Ref No. Warren & Alderson
0.76
1.06
1.04
1.08
1.05
1.07
0.01
46
supine
[22]
0.77
2.00
1.67
2.33
1.76
2.24
0.17
9
supine*
Adams et al. [18]
0.77
1.00
0.95
1.05
0.97
1.03
0.02
11
supine
Adams et al. [18]
5.19
1.01
0.99
1.03
1
1.02
0.01
42
sitting
Mayer et al. [31]
14.27
1.03
1
1.06
1.01
1.05
0.01
10
sitting
Rahman et al. [28]
LBI = labored breathing index; SEM = standard error of mean; CI = confidence interval; * = active sleep
35 Figure Legends Figure 1. Regression correlation between respiratory rate in breaths per minute (BPM) and postconceptual age in years (number of mean data points, n=62). The regression equation is y=45.80x-0.358 with R2 value of 0.901, indicating good correlation. The diameter of the data point reflects the relative sample size of the reference study.
Figure 2. Regression correlation between phase angle (Φ) in degrees and post-conceptual age in years (number of mean data points, n=11). The regression equation is y=16.64x0.071 with R2value of 0.018. The diameter of the data point reflects the relative sample size of the reference study.
Figure 3. Regression correlation between percent of rib cage contribution to breathing (% RC) and post-conceptual age in years (number of mean data points, n=14). The regression equation is y=37.52x0.078 with R2 value of 0.176. The diameter of the data point reflects the relative sample size of the reference study.
Figure 4. Regression correlation between labored breathing index (LBI) and post-conceptual age in years (number of mean data points, n=4). The regression equation is y=1.027x-0.002 with R2 value of 0.014. The diameter of the data point reflects the relative sample size of the reference study.
36 Figure 1
37 Figure 2
38 Figure 3
39 Figure 4
S1526-0542(18)30076-9 https://doi.org/10.1016/j.prrv.2018.03.010 YPRRV 1271
To appear in:
Paediatric Respiratory Reviews
Please cite this article as: S.L. Balasubramaniam, Y. Wang, L. Ryan, J. Hossain, T. Rahman, T.H. Shaffer, Agerelated ranges of respiratory inductance plethysmography (RIP) reference values for infants and children, Paediatric Respiratory Reviews (2018), doi: https://doi.org/10.1016/j.prrv.2018.03.010
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1 Review Article: Age-related ranges of respiratory inductance plethysmography (RIP) reference values for infants and children Sona Lakshme Balasubramaniam, PhD*; Yanping Wang, PhD*; Lauren Ryan, BS; Jobayer Hossain, PhD; Tariq Rahman, PhD; Thomas H. Shaffer, MSE, PhD Department of Biomedical Research and Center for Pediatric Lung Research, Nemours/Alfred I. duPont Hospital for Children, Wilmington, Delaware USA Short Title: A review of the age-dependent RIP literature *First authors Corresponding author: Thomas H. Shaffer, MSE, PhD Professor of Pediatrics and Physiology & Director, Center for Pediatric Lung Research Nemours/Alfred I. duPont Hospital for Children P.O. Box 269 Wilmington, DE 19899 USA Phone: 00-1-302-651-6837; Fax: 00-1-302-651-6888; Email: [email protected] Funding: This study was funded by The Nemours Foundation; The University of Delaware, Center for Advanced Technology (CAT) Program Grant #44058 (Tariq Rahman and Thomas H.
2 Shaffer, CO-principal investigators); and NIH COBRE Grant P30 GM114736 (Thomas H. Shaffer, PI, principal investigator). Conflicts of Interest: None Abbreviations: ABD = abdominal; AS = asthma score; BPM = breaths per minute; CI = confidence interval; ED = emergency department; HFNC = high-flow nasal cannula; LBI = labored breathing index; nCPAP = nasal continuous positive airway pressure; PFT = pulmonary function testing; RC = rib cage; RIP = respiratory inductance plethysmography; RR = respiratory rate; SD = standard deviation; SEM = standard error of mean; TAA = thoracoabdominal asynchrony; TAM = thoracoabdominal motion; Vt = tidal volume; WOB = work of breathing; Φ = phase angle; % RC = percent of rib cage contribution to breathing
3 Summary The current noninvasive method for respiratory monitoring is respiratory inductance plethysmography (RIP); two bands are connected, one each to the chest and the abdomen, to measure the breathing pattern. RIP requires post hoc analysis to calculate indices such as respiratory rate, phase angle, labored breathing index, and percent of rib cage contribution to breathing. Clinical studies have provided patient RIP values and age-matched normal values, but they lack global evaluation of normative data for a wide age range of pediatric subjects. Herein, we compiled normative RIP indices from numerous studies for a large range of pediatric ages. From these data, we derived regression equations useful for computing normal RIP parameters as a function of age. The presented review will provide caregivers the ability to compare RIP data of pediatric patients against the regression analysis. This comparison will help identify patients with pulmonary complications and aid in guiding respiratory therapy. Educational Aims: The reader will be able to provide: •
Respiratory caregivers an overview of noninvasive pulmonary function assessment.
•
Normative respiratory inductance plethysmography (RIP) data for pediatric subjects.
•
Ranges and regression analyses of RIP parameters.
•
An ability to compare RIP data of pediatric patients against the regression analysis.
Future research directions: The iPAD technology mentioned herein was developed in our Pediatric Lung Center as a research tool to assist clinical fellows interested in studying thoracoabdominal synchrony in
4 infants and children. Considering the limited pediatric data in the literature, the addition of realtime assessment of RIP parameters using iPAD technology and ease of use should expand the normative database. As this database expands, we plan to integrate these data into our regression analysis. In this regard, we are currently, studying thoracoabdominal synchrony in children with progressive neuromuscular diseases in our outpatient clinics compared with additional normal subjects. In addition, we have a prospective study in infants with respiratory distress on high flow respiratory support in order to evaluate optimum flow settings.
Keywords: Normal respiratory inductance plethysmography values, respiratory rate, phase angle, labored breathing index, percent of rib cage contribution to breathing
5 1.
Introduction Pulmonary function testing (PFT) is important in evaluating the respiratory system,
determining normal function, and identifying the risk of developing respiratory problems. Early methods of PFT involved spirometry to measure lung volume and lung function. Spirometry requires patient cooperation, effort dependence, and post hoc analysis of the volume-time and flow-volume loops. When properly performed, pulmonary function tests provide insight into lung pathophysiology after comparison of results with age-, sex-, and weight-matched normative values. Current spirometry practices involve a comparison of measured values as a percentage of predicted normal values, thereby determining the severity of the respiratory disease. Another valid alternative method of reporting normal lung function is to express results as Z-scores. Use of Z-scores solves many potential problems by taking into account age, height, sex, and ethnic group, as well as the age-dependent reference range [1]. A trained physician applies this approach to evaluate respiratory symptoms, stratify preoperative risk, and diagnose the disease. Continuous respiratory monitoring aids in early diagnosis of disease processes and improves patient safety by reducing the incidence of critical events. Furthermore, continuous, point-of-care respiratory monitoring is essential for observing a patient’s response to a particular treatment or for optimizing a patient’s ventilator settings [2]. Respiratory inductance plethysmography (RIP) has been used for more than three decades and enables noninvasive respiratory monitoring with minimal patient cooperation and effort. Respiratory inductance plethysmography measures the rib cage (RC) and abdominal (ABD) movements with elastic bands around the chest and abdomen [3-6]. The process involves no noticed discomfort, and tests can be performed over clothes, provided the bands do not slip over clothing. In a healthy subject, the movements of the RC and ABD are synchronous and occur concurrently during quiet tidal
6 breathing. Asynchronous movement of RC and ABD is known as thoracoabdominal asynchrony (TAA), a common incidence in respiratory distress indicated by increased work of breathing (WOB). This technique has been used to calculate respiratory indices such as respiratory rate (RR), phase angle (Φ) between RC and ABD, percent RC contribution (% RC) to tidal volume (Vt), and labored breathing index (LBI) calculated from the sum of excursions by RC and ABD over Vt. All of these parameters can indicate respiratory distress. Currently, these parameters are determined only by post hoc analysis. Literature indicates that the RIP method is employed to evaluate respiratory function in patients ranging from neonates to adults [3,7]. Since RIP does not directly evaluate airflow or respiratory volumes, this approach is useful for patients experiencing air leaks during high-flow nasal cannula (HFNC) and nasal continuous positive airway pressure (nCPAP) respiratory support. The RIP parameters also assist in identifying TAA, paradoxical breathing, and the onset of respiratory muscle fatigue, which are often observed in infants and children with respiratory disorders [8]. Various studies have used this method to evaluate pulmonary disorders such as parenchymal processes, obstructive airways (asthma, chronic obstructive pulmonary disease), and musculoskeletal and neuromuscular diseases [3,4,8-16]. The need for normative data from healthy subjects without pulmonary complications for WOB parameters such as RR, Φ, % RC, and LBI for all ages is critical for the usefulness of this method. Thus, the development of a database for normative RIP parameters in the pediatric population is the purpose of this extensive literature review and analysis. This review aims to establish normative WOB parameters for RR, Φ, % RC, and LBI as a function of age. Establishing the relationship between these parameters and age will aid in both research and clinical settings.
7
2.
Methods The PubMed database of the National Center for Biotechnology Information (NCBI,
https://www.ncbi.nlm.nih.gov/) was searched for the key words “respiratory inductance plethysmography,” “(RIP),” “respiratory rate,” “phase angle,” “percent rib cage contribution to tidal volume (Vt),” “% RC,” “labored breathing index,” and “LBI.” Journals, reviews, and book chapters were screened for the WOB index values measured using the RIP method in healthy individuals (inclusive of values measured from analog and digital graphs, values generated by inbuilt algorithms). Two authors (SB, YW) assessed eligibility of studies for inclusion, and further inspections (year of study, participants [age range, number], study setting, method of measurement, sleep state, and position) as well as disagreements were resolved by a third author (LR). When multiple results were reported for a single group of children at a particular age, we used both awake and asleep data in tabular form when both measurements were available and the baseline result where multiple baseline measurements were recorded in intervention studies. Finally, when descriptions of the behavioral state were not given, all data were used. Furthermore, corresponding standard error of mean (SEM) or standard deviation (SD) values are presented, along with number of subjects, age range (preterm infants to adolescents), sex, and body position (supine or sitting, typical position for clinical pulmonary evaluation in infants and children). Several clinical studies have provided WOB indices in healthy preterm infants at various post-conceptual ages [3,4,17], so to universally analyze age-dependent effects on RIP parameters, the age for all studies was adjusted in the current study to begin at conception age in years. In studies in which only SD or SEM was provided, the other value was calculated based on the number of test subjects. On the basis of our initial review of the literature, we could
8 not find any references concerning age that related to the effect of ethnicity or sex on all RIP parameters.
Statistical Approach The current literature review and analysis includes all normative data available from the resources outlined above in methods. Among the various trend lines tried, the power function provided the best fit for the data. It was used for regression and statistical evaluation of these graphs to derive a relationship between each RIP parameter and age. Based on the regression line of mean RIP data across age, the regression equation and R2 values were calculated. Depending on individual mean and SEM from each study, 95% (mean ± 1.96*SEM) and 85% (mean ± 1.44*SEM) confidence intervals (CI) were derived. The statistical software SPSS (version 19.0, Chicago, IL, USA) was used for analysis. Regression correlations were tested, and these equations provided a range of predicted normative values with 5% and 15% acceptable deviation from the mean values.
3. Results Analysis of the literature review on healthy subjects was challenging because of the numerous issues outlined in the methods section. Literature studies provided the mean, SD or SEM, and the number of subjects (in most cases). The 95% and 85% confidence intervals were calculated based on the mean and SEM for each study and plotted in an age-dependent manner. After data were tabulated and plotted, a trend was observed in some parameters.
9 Although a number of studies have used RIP and reported WOB indices data for different age groups, there were limited data available to differentiate these parameters as a function of all body positions or sex; therefore, the effect of these data was not considered in our analysis [36,18]. The mean data, SD or SEM, body position (supine or sitting), and number of subjects (in most cases) for RR, Φ, % RC, and LBI from the literature survey are reported in Tables 1 to 4 for the age range studied. In total, data from 109 to 3750 subjects, depending on the RIP parameter (i.e., RR, Φ, % RC, LBI), were used in the development of the tables, graphs, and regression correlations. Table 1 summarizes mean RR data and SEM from 3750 subjects from our literature survey. As shown in Figure 1, RR (breaths per minute [BPM]) decreases with increase in age, as characterized by the power function shown in the figure legend. The correlation (y=45.80x-0.358; R2=0.901) between RR and post-conceptual age was excellent (range: preterm infants to adolescents). In most studies, the mean RR values were within or close to the 95% confidence intervals, as shown. For phase angle results, the mean Φ data and SEM from 193 subjects from our literature survey are summarized in Table 2. Phase angle in degrees did not correlate well with advancing age as shown in Figure 2 and by the correlation (y=16.64 x0.071; R²=0.018), indicating a flat line over the older developmental ages reported. Although there was a higher degree of variation in Φ in subjects younger than 3 years of post-conceptual age, there was only a slight increase in mean Φ in subjects younger than 3 years of post-conceptual age. As shown by the regression analysis, Φ in the supine position in young subjects is higher when compared with that of the older population in the sitting position. Furthermore, one subject was not plotted in Figure 2 because
10 of wide variance associated with supine positioning in an older subject; however, the data are included in Table 2. The mean data and SEM for % RC are summarized for 231 subjects from our literature review and calculations as outlined in the methods section, as shown in Table 3. Unlike RR and Φ, % RC did not decrease with increase in age. Rather, % RC increased with an increase in age, as shown in Figure 3. There was a correlation (y=37.52x0.078; R2=0.176) between % RC and postconceptual age. In addition, one subject was not plotted in Figure 3 because of wide variance associated with active sleep; however, the data are included in Table 3. Data from 109 subjects obtained from the literature review are shown in Table 4. As shown in Figure 4, mean LBI data in normal healthy subjects range between 1.0 and 1.05, across all ages. There was a correlation (y=1.027x-0.002; R2 = 0.014) between LBI and post-conceptual age. Furthermore, one subject was not plotted in Figure 4 because of wide variance associated with active sleep; however, the data are included in Table 4.
4. Discussion This report is the first literature review to provide an overall compilation of normative RIP data from neonates to adolescents. This study has both research and clinical significance to further assist physicians and researchers in early and rapid diagnosis of pulmonary problems. More specifically, the data address the greater need to determine normative range for each WOB parameter for neonates and children. The regression graphs from the data analysis demonstrate that the greatest changes in RR with age occur during early development. This finding is
11 consistent with the data found in other studies [35,36]. Presumably, these changes are associated with rapid pulmonary function development in the neonatal and pediatric populations. The younger population (i.e., both neonates and children) is at higher risk for respiratory fatigue, which can be identified with WOB indices. For example, the asthma score (AS), which is used to triage patients with asthma exacerbations in the emergency department (ED), is determined as a function of age. Additionally, AS based on clinical scoring of RR and chest retractions can be assessed accurately from RR and Φ parameters measured using the RIP technique. These findings further support the use of RIP. The normative data presented herein will be useful in generating a better asthma-scoring approach to quickly and objectively provide data at point-of-care. Similarly, Giordano et al. [37] showed with an observational ED study that RIP technology provided feasible, objective data to determine patient status in the pediatric population that presented to the ED with acute asthma exacerbations. As shown in Figures 2, 3, and 4, both Φ and LBI remain relatively constant after 3 years of post-conception age, whereas % RC increases with age. These findings are supported by regression equations, as Φ and LBI are a negative function of age, essentially flat lines after 3 years of post-conception age, whereas % RC is a positive function of age. Of importance, RR and % RC lower or higher than 85% confidence interval can indicate respiratory dysfunction, but that is not the case with Φ and LBI. Normal Φ and LBI can be lower than the 85% confidence interval, as low values indicate that RC and ABD are in synchrony; however, Φ or LBI higher than the 85% confidence interval indicates that RC and ABD are asynchronous and are trending toward respiratory muscle fatigue. Unlike RR and % RC, only a deviation above normal range for Φ and LBI is an indication of pulmonary dysfunction.
12 There are several limitations to the present review such as the unavailability of a large pediatric database on the effect of consistent body position during testing. Not all studies discussed body position with respect to RR studies. Additionally, few studies have reported normal LBI values in pediatric and adolescent populations [18,22,28,31,38], limiting the significance of the normal LBI regression analysis provided herein. In those studies that reported body position, some studies were conducted in the sitting position, whereas others reported standing, supine, or prone positions; however, these studies were specifically designed to investigate the physiological effect of posture on thoracoabdominal motion (TAM) at a specific age, not the determination of normative reference values (for our review, we selected only the sitting and supine data). However, it is noteworthy that in the sitting and standing positions, the chest and abdomen motions are similar because of lower abdominal compliance, with a net effect of more synchronous TAM [31]. Furthermore, in the supine position, there is an increase in abdominal compliance because of outward abdominal motion, leading to asynchronous TAM and increased Φ. Additionally, the effect of position on Φ is greater in children. The effect of sitting or standing positions on Φ is significantly higher when compared with that of the older population [31,39]. Because of insufficient data for all positions in this wide age range, the effect of various body positions was not explored in our analysis. Instead, as noted above, only data regarding supine and sitting positions were taken for this review, to limit these discrepancies. For all RIP testing, infants should be in a quiet behavioral state and supine, with small to no movements, regular respiration, and open or closed eyes, to reduce movement while recording the RIP data [40]. As noted previously, one subject each from Tables 2, 3, and 4 was not plotted in the respective figures because of wide variance associated with active sleep or supine
13 positioning in an older subject. Older children to adolescents should be in a sitting position with minimal movement and quiet breathing. Of note is that these data discrepancies can be avoided if a common body position and behavioral state is used in all studies during the measurement of WOB indices. Consistent body position and behavioral state should be a mandate for all clinical research studies because it will simplify comparing results with normative values and with treatment interventions. In this review and on the basis of our experience, we believe the supine position for infants and the sitting position for adolescents would be preferred in a clinical setting for pulmonary evaluations. Nevertheless, there are some physical limitations to finding a common position, such as the inability to measure WOB indices in distressed preterm infants or in bedridden children in the sitting position. On the basis of our comprehensive review of the pediatric literature, we could not find any reference across age that related to the effect of sex, height, weight, musculature, or ethnicity on RIP parameters. However, it is noteworthy that these factors could affect certain WOB parameters. Because of the limited data available in literature studies, the aforementioned factors were not considered as a factor in our analysis. Ideally, these variables could be reviewed in the future for pediatric patients. One study, concerned with healthy subjects aged 10 to 60 years old, reported that there are no differences regarding sex in TAM during quiet breathing [7]. Finally, some discrepancies in data comparisons may exist because of the limitations of the RespiTrace instrumentation used for most of the studies. The RespiTrace PT (Sensormedics, Yorba Linda, CA, USA) is a noninvasive respiratory monitor that applies the currently used RIP technique. Many research studies that use RespiTrace PT have provided limited normal breathing RIP parameters for a small range of age groups. The limited data and possible discrepancies are most likely due to the required post hoc analysis, which is time consuming and
14 labor intensive and lacks provision of real-time results. Therefore, the existing RIP devices are seldom used in the clinic and are restricted to research use. With the limitations outlined for the existing system, our group has developed a novel noninvasive respiratory research monitor, pneuRIPTM, which could provide more consistent and rapid data collection over longer epochs in numerous clinical settings [28]. . Since the pneuRIPTM system displays the WOB parameter values instantaneously, it may develop as a useful instrument in several clinical settings to evaluate a patient’s pulmonary function, identify patients with pulmonary disorders, and determine the effectiveness of treatment interventions immediately. Data from our research study using the pneuRIPTM were included in this review [28]. In summary, the accessibility of the pneuRIPTM instrument, the real-time display of the data, and the ability to securely share the recordings may enable widespread research and future clinical uses of RIP technology.
5. Conclusion In conclusion, this review and analysis of RIP parameters demonstrates several significant correlations between some RIP parameters and age. Respiratory rate decreases with age and % RC increases with age, whereas Φ and LBI show little change after 3 years of postconception age. The 95% and 85% confidence intervals from the mean provide acceptable deviation from normal expected value. Clinically, a deviation within ± 15% from normal value is considered acceptable depending on the parameter. The figures and regression curves in this
15 review present acceptable ranges (i.e., mean ± confidence intervals) of normal value for each WOB parameter. These findings will be useful in intensive care units, outpatient clinics, and research studies. By comparing WOB parameter values against age-matched normative values using the database and analysis of RIP values presented, clinicians and investigators can further refer to predictive values within 5% or 15% deviation from normal depending on the WOB parameter. Knowledge of normal values for WOB parameters is particularly important in the neonatal and younger pediatric populations who show greater variability than the adolescent population. Understanding the RIP parameter differences between normal and measured values in preterm and full-term babies will aid in rapid diagnosis of any pulmonary complications in newborns, particularly in the babies on newer forms of noninvasive respiratory support. Furthermore, a normative database will assist in a more rapid and user-friendly assessment of the infant’s response to treatment. Research respiratory monitors, like pneuRIPTM, with graphic displays and central processing units, provide instantaneous point-of-care WOB indices that may enable easy application of these regression equations to test if a patient is within the normative range for his or her age.
16 Acknowledgements Sources of Funding: This study was funded by The Nemours Foundation; The University of Delaware, Center for Advanced Technology (CAT) Program Grant #44058 (Tariq Rahman and Thomas H. Shaffer, CO-principal investigators); and NIH COBRE Grant P30 GM114736 (Thomas H. Shaffer, PI, principal investigator).
17 References [1] Pellegrino R, Viegi G, Brusasco V, Crapo RO, Burgos F, Casaburi R, et al. Interpretative strategies for lung function tests. Eur Respir J 2005;26:948–68. [2] van Loon K, van Zaane B, Bosch EJ, Kalkman CJ, Peelen LM. Non-invasive continuous respiratory monitoring on general hospital wards: a systematic review. PLoS One 2015;10:e0144626. doi: 10.1371/journal.pone.0144626. [3] Allen JL, Greenspan JS, Deoras KS, Keklikian E, Wolfson MR, Shaffer TH. Interaction between chest wall motion and lung mechanics in normal infants and infants with bronchopulmonary dysplasia. Pediatr Pulmonol 1991;11:37–43. [4] Allen JL, Wolfson MR, McDowell K, Shaffer TH. Thoracoabdominal asynchrony in infants with airflow obstruction. Am Rev Respir Dis 1990;141:337–42. [5] Petrovic MD, Petrovic J, Danicic A, Vukcevic M, Bojovic B, Hadzievski Lj, et al. Noninvasive respiratory monitoring using long-period fiber grating sensors. Biomed Opt Express 2014;5:1136–44. doi: 10.1364/BOE.5.001136. [6] Shaffer TH, Alapati D, Greenspan JS, Wolfson MR. Neonatal non-invasive respiratory support: physiological implications. Pediatr Pulmonol 2012;47:837–47. doi: 10.1002/ppul.22610. [7] Verschakelen JA, Demedts MG. Normal thoracoabdominal motions. Influence of sex, age, posture, and breath size. Am J Respir Crit Care Med 1995;151:399–405. [8] Hammer J, Newth CJ. Assessment of thoraco-abdominal asynchrony. Paediatr Respir Rev 2009;10:75–80. doi: 10.1016/j.prrv.2009.02.004.
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19 [16] Rodriguez ME, Mackenzie WG, Ditro C, Miller TL, Chidekel A, Shaffer TH. Skeletal dysplasias: evaluation with impulse oscillometry and thoracoabdominal motion analysis. Pediatr Pulmonol 2010;45:679–86. doi: 10.1002/ppul.21246. [17] Calder NA, Williams BA, Kumar P, Hanson MA. The respiratory response of healthy term infants to breath-by-breath alternations in inspired oxygen at two postnatal ages. Pediatr Res 1994;35:321–4. [18] Adams JA, Zabaleta IA, Sackner MA. Comparison of supine and prone noninvasive measurements of breathing patterns in fullterm newborns. Pediatr Pulmonol 1994;18:8–12. [19] Levy J, Habib RH, Liptsen E, Singh R, Kahn D, Steele AM, et al. Prone versus supine positioning in the well preterm infant: effects on work of breathing and breathing patterns. Pediatr Pulmonol 2006;41:754–8. [20] Locke RG, Wolfson MR, Shaffer TH, Rubenstein SD, Greenspan JS. Inadvertent administration of positive end-distending pressure during nasal cannula flow. Pediatrics 1993;91:135–8. [21] Deoras KS, Greenspan JS, Wolfson MR, Keklikian EN, Shaffer TH, Allen JL. Effects of inspiratory resistive loading on chest wall motion and ventilation: differences between preterm and full-term infants. Pediatr Res 1992;32:589–94. [22] Warren RH, Alderson SH. Breathing patterns in infants utilizing respiratory inductive plethysmography. Chest 1986;89:717–22. [23] Hershenson MB, Stark AR, Mead J. Action of the inspiratory muscles of the rib cage during breathing in newborns. Am Rev Respir Dis 1989;139:1207–12.
20 [24] Iliff A, Lee VA. Pulse rate, respiratory rate, and body temperature of children between two months and eighteen years of age. Child Dev 1952;23:237-45. [25] França DC, Camargos PA, Martins JA, Abreu MC, Avelar e Araujo GH, Parreira VF. Feasibility and reproducibility of spirometry and inductance plethysmography in healthy Brazilian preschoolers. Pediatr Pulmonol 2013;48:716–24. doi: 10.1002/ppul.22743. [26] Rosenthal M, Bush A. Ventilatory variables in normal children during rest and exercise. Eur Respir J 2000;16:1075–83. [27] Tabachnik E, Muller N, Toye B, Levison H. Measurement of ventilation in children using the respiratory inductive plethysmograph. J Pediatr 1981;99:895–9. [28] Rahman T, Page R, Page C, Bonnefoy JR, Cox T, Shaffer TH. pneuRIPTM: a novel respiratory inductance plethysmography monitor. J Med Device 2017;11:0110101-110106. doi: 10.1115/1.4035546. [29] Tabachnik E, Muller NL, Bryan AC, Levison H. Changes in ventilation and chest wall mechanics during sleep in normal adolescents. J Appl Physiol Respir Environ Exerc Physiol 1981;51:557–64. [30] Sivan Y, Ward SD, Deakers T, Keens TG, Newth CJ. Rib cage to abdominal asynchrony in children undergoing polygraphic sleep studies. Pediatr Pulmonol 1991;11:141–6. [31] Mayer OH, Clayton RG Sr, Jawad AF, McDonough JM, Allen JL. Respiratory inductance plethysmography in healthy 3- to 5-year-old children. Chest 2003;124:1812–9. [32] Hershenson MB, Colin AA, Wohl ME, Stark AR. Changes in the contribution of the rib cage to tidal breathing during infancy. Am Rev Respir Dis 1990;141:922–5.
21 [33] Poole KA, Thompson JR, Hallinan HM, Beardsmore CS. Respiratory inductance plethysmography in healthy infants: a comparison of three calibration methods. Eur Respir J 2000;16:1084–90. [34] Brown K, Aun C, Jackson E, Mackersie A, Hatch D, Stocks J. Validation of respiratory inductive plethysmography using the Qualitative Diagnostic Calibration method in anaesthetized infants. Eur Respir J 1998;12:935–43. [35] Fleming S, Thompson M, Stevens R, Heneghan C, Pluddemann A, Maconochie I, et al. Normal ranges of heart rate and respiratory rate in children from birth to 18 years of age: a systematic review of observational studies. Lancet 2011;377:1011–8. doi: 10.1016/S01406736(10)62226-X. [36] Wallis LA, Maconochie I. Age related reference ranges of respiratory rate and heart rate for children in South Africa. Arch Dis Child 2006;91:330–3. [37] Giordano K, Rodriguez E, Green N, Armani M, Richards J, Shaffer TH, et al. Pulmonary function tests in emergency department pediatric patients with acute wheezing/asthma exacerbation. Pulm Med 2012;2012:724139. doi: 10.1155/2012/724139. [38] Warren RH, Horan SM, Robertson PK. Chest wall motion in preterm infants using respiratory inductive plethysmography. Eur Respir J 1997;10:2295–300. [39] Sackner MA, Gonzalez H, Rodriguez M, Belsito A, Sackner DR, Grenvik S. Assessment of asynchronous and paradoxic motion between rib cage and abdomen in normal subjects and in patients with chronic obstructive pulmonary disease. Am Rev Respir Dis 1984;130:588–93.
22 [40] Prechtl HF. The behavioural states of the newborn infant (a review). Brain Res 1974;76:185–212.
23
Table 1. Respiratory rate database from the literature review. Age in
RR
CI
CI
CI
CI
Sample
Years
(BPM)
<95%
>95%
<85%
>85%
SEM
Size
Ref No.
0.65
60.0
50.6
69.4
53.1
66.9
4.82
19
Levy et al. [19]
0.65
53.0
46.3
59.7
48.0
58.0
3.44
19
Levy et al. [19] Locke et al.
0.65
73.0
66.5
79.5
68.2
77.8
3.33
13
[20] Deoras et al.
0.68
77.0
69.2
84.8
71.2
82.8
4.00
10
[21] Warren &
0.76
53.1
49.8
56.4
50.7
55.5
1.67
46
Alderson [22] Warren &
0.76
55.2
52.3
58.1
53.1
57.3
1.49
46
Alderson [] Adams et al.
0.77
51.4
46.7
56.1
48.0
54.8
2.39
20
[18] Calder et al.
0.77
52.0
46.1
57.9
47.7
56.3
3.00
13
0.77
56.0
44.2
67.8
47.4
64.6
6.00
8
[17]
Deoras et al.
24 [21] Hershenson et 0.77
56.0
47.3
64.7
49.6
62.4
4.44
13
al. [23] Adams et al.
0.77
55.2
49.6
60.8
51.1
59.3
2.86
20
[18] Calder et al.
0.9
48.0
43.3
52.7
44.5
51.5
2.40
13
[17] Iliff & Lee
1.27
31.0
28.5
33.5
29.1
32.9
1.30
38
[24] Iliff & Lee
1.27
30.0
28.4
31.6
28.8
31.2
0.80
55
[24] Iliff & Lee
2.27
26.0
25.0
27.0
25.3
26.7
0.50
69
[24] Iliff & Lee
2.27
27.0
26.0
28.0
26.3
27.7
0.50
79
[24] Iliff & Lee
3.27
25.0
24.2
25.8
24.4
25.6
0.40
118
[24] Iliff & Lee
3.27
25.0
24.4
25.6
24.6
25.4
0.30
134
[24]
25 Iliff & Lee 4.27
24.0
23.6
24.4
23.7
24.3
0.20
131
[24] Iliff & Lee
4.27
24.0
23.6
24.4
23.7
24.3
0.20
119
[24] França et al.
4.77
24.0
21.7
26.3
22.3
25.7
1.15
12
[25] Iliff & Lee
5.27
23.0
22.6
23.4
22.7
23.3
0.20
122
[24] Iliff & Lee
5.27
22.0
21.6
22.4
21.7
22.3
0.20
113
[24] França et al.
5.77
25.0
23.7
26.3
24.0
26.0
0.69
19
[25] Iliff & Lee
6.27
22.0
21.6
22.4
21.7
22.3
0.20
110
[24] Iliff & Lee
6.27
21.0
20.6
21.4
20.7
21.3
0.20
100
[24] França et al.
6.77
23.0
21.5
24.5
21.9
24.1
0.75
16
7.27
21.0
20.6
21.4
20.7
21.3
0.20
128
[25]
Iliff & Lee
26 [24] Iliff & Lee 7.27
21.0
20.4
21.6
20.6
21.4
0.30
97
[24] Iliff & Lee
8.27
20.0
19.6
20.4
19.7
20.3
0.20
119
[24] Iliff & Lee
8.27
20.0
19.6
20.4
19.7
20.3
0.20
97
[24] Iliff & Lee
9.27
20.0
19.6
20.4
19.7
20.3
0.20
113
[24] Iliff A & Lee
9.27
20.0
19.6
20.4
19.7
20.3
0.20
101
[24] Rosenthal &
10.02
22.5
19.1
25.9
20.0
25.0
1.75
11
Bush [26] Rosenthal &
10.02
26.1
21.1
31.1
22.4
29.8
2.57
12
Bush [26] Iliff & Lee
10.27
19.0
18.6
19.4
18.7
19.3
0.20
141
[24] Iliff & Lee
10.27
19.0
18.6
19.4
18.7
19.3
0.20
98
[24]
27 Iliff & Lee 11.27
19.0
18.6
19.4
18.7
19.3
0.20
141
[24] Iliff & Lee
11.27
19.0
18.6
19.4
18.7
19.3
0.20
90
[24] Iliff & Lee
12.27
19.0
18.6
19.4
18.7
19.3
0.20
123
[24] Iliff & Lee
12.27
19.0
18.4
19.6
18.6
19.4
0.30
82
[24] Rosenthal &
12.27
20.2
17.3
23.1
18.1
22.3
1.47
13
Bush [26] Rosenthal &
12.27
21.2
17.8
24.6
18.7
23.7
1.75
11
Bush [26] Tabachnik et
12.77
19.7
17.0
22.4
17.7
21.7
1.39
20
al. [27] Iliff & Lee
13.27
19.0
18.6
19.4
18.7
19.3
0.20
131
[24] Iliff & Lee
13.27
19.0
18.4
19.6
18.6
19.4
0.30
72
14.27
13.2
11.3
15.0
11.8
14.5
0.96
10
[24]
Rahman et al.
28 [28] Iliff & Lee 14.27
19.0
18.6
19.4
18.7
19.3
0.20
110
[24] Iliff & Lee
14.27
18.0
17.4
18.6
17.6
18.4
0.30
68
[24] Rosenthal &
14.27
20.7
17.7
23.7
18.5
22.9
1.53
18
Bush [26] Rosenthal &
14.27
18.0
16.3
19.7
16.7
19.3
0.87
19
Bush [26] Iliff & Lee
15.27
18.0
17.6
18.4
17.7
18.3
0.20
106
[24] Iliff & Lee
15.27
18.0
17.2
18.8
17.4
18.6
0.40
57
[24] Tabachnik et
15.77
17.4
16.6
18.2
16.8
18.0
0.43
9
al. [29] Iliff & Lee
16.27
17.0
16.4
17.6
16.6
17.4
0.30
76
[24] Iliff & Lee
16.27
18.0
17.2
18.8
17.4
18.6
0.40
47
[24]
29 Rosenthal & 16.47
21.4
19.2
23.6
19.8
23.0
1.14
13
Bush [26] Rosenthal &
16.47
19.8
15.9
23.7
16.9
22.7
2.00
9
Bush [26]] Iliff & Lee
17.27
17.0
16.2
17.8
16.4
17.6
0.40
45
[24] Iliff & Lee
17.27
17.0
16.0
18.0
16.3
17.7
0.50
30
[24] Iliff & Lee
18.27
16.0
15.0
17.0
15.3
16.7
0.50
38
[24] Iliff & Lee
18.27
17.0
15.6
18.4
16.0
18.0
0.70
20
[24]
RR = respiratory rate; BPM = breaths per minute; SEM = standard error of mean; CI = confidence interval
30
Table 2. Phase angle database from the literature review. Age in Years
Φ (°)
CI
CI
CI
CI
<95%
>95%
<85%
>85%
SEM
Sample Size
Position
Ref No. Levy et al.
0.65
55.0
36.1
73.9
41.1
68.9
9.64
19
supine
[19] Deoras et al.
0.68
38.0
20.9
55.1
25.5
50.5
8.7
10
supine
[21] Allen et al.
0.75
8.0
2.1
13.9
3.7
12.3
3
6
supine
[3] Deoras et al.
0.77
9.3
4.2
14.4
5.6
13.0
2.6
8
supine
[21] Allen et al.
0.96
8.0
2.1
13.9
3.7
12.3
3
6
supine
[3] Sivan et al.
2.94
11.8
10.2
13.4
10.6
13.0
0.8
45
supine
[30] França et al.
4.77
16.0
10.9
21.1
12.3
19.7
2.6
12
sitting
[25] Mayer et al.
5.19
15.7
7.9
23.5
9.9
21.5
4
42
sitting
[31]
31 Mayer et al. 5.19
56.1
47.7
64.5
49.9
62.3
4.3
42
supine
[31] França et al.
5.77
18.0
13.5
22.5
14.7
21.3
2.29
19
sitting
[25] França et al.
6.77
15.0
10.1
19.9
11.4
18.6
2.5
16
sitting
[25] Rahman et
14.27
14.1
9.2
19.0
10.5
17.7
2.48
10
Φ = phase angle; SEM = standard error of mean; CI = confidence interval
sitting
al. [28]
32
Table 3. % RC database from the literature review and from the presented derivation equations. Age in
%
CI
CI
CI
CI
Years
RC
<95%
>95%
<85%
>85%
Sample SEM
Size
Position
Ref No. Warren &
0.76
28.3
24.3
32.3
25.3
31.3
2.06
46
supine
Alderson [22] Adams et al.
0.77
16.3
11.7
20.9
12.9
19.7
2.37
9
supine*
[18] Adams et al.
0.77
28.8
23.4
34.2
24.8
32.8
2.75
11
supine
[18] Hershenson et
0.77
34.0
29.1
38.9
30.4
37.6
2.5
13
supine
al. [23] Hershenson et
1.15
40.0
35.3
44.7
36.5
43.5
2.41
14
supine
al. [32]
1.22
49.8
42.7
56.9
44.6
55.0
3.63
12
supine
Poole et al. [33] Brown et al.
1.94
50.2
39.4
61.0
42.2
58.2
5.53
23
supine
[34] Hershenson et
2.31
59.0
45.4
72.6
49.0
69.0
6.94
6
supine
al. [32]
33 França et al. 4.77
39.0
31.2
46.8
33.2
44.8
4
12
sitting
[25] França et al.
5.77
35.0
30.1
39.9
31.4
38.6
2.52
19
sitting
[25] França et al.
6.77
43.0
35.2
50.8
37.2
48.8
4
16
sitting
[25] Rahman et al.
14.27
51.4
46.0
56.8
47.4
55.3
2.76
10
sitting
[28] Tabachnik et al.
15.77
40.0
34.1
45.9
35.7
44.3
3
9
supine
[29] Verschakelen &
16.52
55.2
43.1
67.4
46.3
64.2
6.2
20
sitting
Demedts [7] Verschakelen &
16.52
38.9
26.5
51.3
29.8
48.0
6.32
20
supine
Demedts [7]
% RC = percent of rib cage contribution to breathing; SEM = standard error of mean; CI = confidence interval; * = active sleep
34
Table 4. LBI database from the literature review. Age in Years
LBI
CI
CI
CI
CI
<95%
>95%
<85%
>85%
Sample SEM
Size
Position
Ref No. Warren & Alderson
0.76
1.06
1.04
1.08
1.05
1.07
0.01
46
supine
[22]
0.77
2.00
1.67
2.33
1.76
2.24
0.17
9
supine*
Adams et al. [18]
0.77
1.00
0.95
1.05
0.97
1.03
0.02
11
supine
Adams et al. [18]
5.19
1.01
0.99
1.03
1
1.02
0.01
42
sitting
Mayer et al. [31]
14.27
1.03
1
1.06
1.01
1.05
0.01
10
sitting
Rahman et al. [28]
LBI = labored breathing index; SEM = standard error of mean; CI = confidence interval; * = active sleep
35 Figure Legends Figure 1. Regression correlation between respiratory rate in breaths per minute (BPM) and postconceptual age in years (number of mean data points, n=62). The regression equation is y=45.80x-0.358 with R2 value of 0.901, indicating good correlation. The diameter of the data point reflects the relative sample size of the reference study.
Figure 2. Regression correlation between phase angle (Φ) in degrees and post-conceptual age in years (number of mean data points, n=11). The regression equation is y=16.64x0.071 with R2value of 0.018. The diameter of the data point reflects the relative sample size of the reference study.
Figure 3. Regression correlation between percent of rib cage contribution to breathing (% RC) and post-conceptual age in years (number of mean data points, n=14). The regression equation is y=37.52x0.078 with R2 value of 0.176. The diameter of the data point reflects the relative sample size of the reference study.
Figure 4. Regression correlation between labored breathing index (LBI) and post-conceptual age in years (number of mean data points, n=4). The regression equation is y=1.027x-0.002 with R2 value of 0.014. The diameter of the data point reflects the relative sample size of the reference study.
36 Figure 1
37 Figure 2
38 Figure 3
39 Figure 4