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 Table of Contents  
ORIGINAL RESEARCH
Year : 2018  |  Volume : 2  |  Issue : 3  |  Page : 147-154

Estimation of Z-scores of cardiac structures in healthy Indian pediatric population


1 Department of Pediatric Cardiology, Fortis Child Heart Mission, Fortis Hospital, Mumbai, Maharashtra, India
2 Statistician, Independent Consultant, Mumbai, Maharashtra, India

Date of Web Publication10-Dec-2018

Correspondence Address:
Dr. Bhadra Trivedi
Department of Pediatric Cardiology, Fortis Child Heart Mission, Fortis Hospital, Mulund (w), Mumbai - 400 078, Maharashtra
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jiae.jiae_25_18

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  Abstract 

Introduction: Nomograms of pediatric cardiac structures are an effective tool to differentiate between normal and abnormal changes in dimensions of the heart. There is impending need for nomograms of Z-Scores of echocardiographic data derived from Indian children. Objective: The main objective of this study is to gather echocardiographic data from the healthy Indian pediatric population visiting the pediatric cardiology outpatient clinic and to derive the Z-Scores for various cardiac structures. Materials and Methods: All the echocardiographic studies from an eligible normal Indian population at a single centre were assessed. All the studies were performed on a single vendor echocardiography machine using weight appropriate neonatal, pediatric, and adult probes. Statistical Analysis: Body surface area (BSA) was used as an independent variable in a nonlinear regression analysis for the predicted mean value of each of the 19 echocardiographically measured structures. Results: The total number of children evaluated during the study period was 596, with age ranging from newborn to 16 years. The total parameters collected in the study were 8102. The correlation with Haycock's BSA and an individual parameter was found to be the most sensitive predictor of normal progression with age. Relationship of individual parameters with BSA was represented in the form of graphs. Conclusion: This study of normal Indian pediatric population is the largest Indian study to date. The regression formulae along with the graphs can be used to acquire the Z score of 19 individual echocardiographic parameters.

Keywords: Congenital heart disease, echocardiogram, nomograms, pediatric cardiology, Z-Scores


How to cite this article:
Trivedi B, Chokhandre M, Dhobe P, Garekar S. Estimation of Z-scores of cardiac structures in healthy Indian pediatric population. J Indian Acad Echocardiogr Cardiovasc Imaging 2018;2:147-54

How to cite this URL:
Trivedi B, Chokhandre M, Dhobe P, Garekar S. Estimation of Z-scores of cardiac structures in healthy Indian pediatric population. J Indian Acad Echocardiogr Cardiovasc Imaging [serial online] 2018 [cited 2019 Jan 16];2:147-54. Available from: http://www.jiaecho.org/text.asp?2018/2/3/147/247027




  Introduction Top


The echocardiogram forms the cornerstone for pediatric cardiac evaluation, an important aspect of which is a quantification of cardiac structure in terms of dimensions. The dimensions of a child's heart structure are affected by his or her hemodynamics and somatic growth.[1],[2] Nomograms are an effective tool to differentiate between normal and abnormal changes in dimensions of the heart. Most published nomograms are derived from the Caucasian population.[3],[4],[5] These have been included in the recent American Society of Echocardiography and the European Association of Echocardiography guidelines.[6]

The Indian population has so far been evaluated by Z-Scores derived from such studies and guidelines. It has been proven that the Caucasian anthropometric data are not representative of Indian children.[7] In this context, growth charts developed by the Indian Academy of Pediatrics[8] are widely used in the Indian subcontinent. The prevalence of the congenital heart disease (CHD) in India is not well studied but has estimated to be between 4.2/1000[9] and 19.1/1000 newborns.[10] Thus, there is an impending need for nomograms of Z-Scores of echocardiographic data derived from Indian children.

Objective

The objective of this study is to gather the echocardiographic data from the healthy pediatric population visiting the pediatric cardiology outpatient clinic situated in a tertiary care center in the metropolis of Mumbai and to derive the Z-Scores for various cardiac structures inclusive of the aortic arch.


  Materials and Methods Top


All the stored echocardiographic studies of a normal echocardiogram from the eligible Indian population at the Fortis Hospital Mulund, Mumbai, between January 2014 and August 2016 were studied. The study was approved by the Institute Review Board.

Echocardiographic examination

All the echocardiographic studies were performed on a Philips iE33 Machine (Philips Medical Systems, Bothell, WA) using weight appropriate neonatal, pediatric, and adult probes, which include S12–S4, S8–S3, or S5–S1 MHz sector array transducers. All the echocardiograms were performed by single echocardiographer. All echocardiograms in the laboratory followed a rigorous quantitative protocol.[2] All echocardiograms were electrocardiography gated, with a minimum of 2 heartbeats recorded per loop. All measurements were made live (online) as they are considered essential elements of our report. Infants were sedated with oral trichlophos if required. The echocardiography planes were standard and the two-dimensional and M-mode measurements were made following prescribed methods. Measurements of aortic arch were taken as described by Kaine et al.[11] Echocardiography data were stored in digital imaging and communications in medicine format, on DVDs. Measurements were stored electronically after stripping off personal identifiers.

Inclusion and exclusion criteria

The details of all the children visited the pediatric cardiology outpatient department (OPD) were reviewed. Only those children aged 0 days to 16 years with a normal echocardiogram report, normal physical examination and clinical history and absent systemic illness were included in the study. Normal echocardiograms included those with a tiny patent ductus arteriosus and/or a patent foramen ovale. Exclusion criteria were presence or suspicion of a syndrome, systemic illness, any structural heart defect (other than that mentioned above), and any hemodynamic abnormality like systemic or pulmonary hypertension. Incomplete studies in terms of nonstandard acquisitions were excluded. The protocol for obtaining measurements is detailed in [Table 1] and [Figure 1].
Table 1: Details about measurement of an individual parameter employed in the study

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Figure 1: Location of echocardiographic measurements of the parameters. LV: Left ventricle, RV: Right ventricle, RPA: Right pulmonary artery, Ao Ann: Aortic annulus, STJ: ST junction, A Ao: Ascending aorta, TV: Tricuspid valve, MV: Mitral valve

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Statistical analysis

The parameters were indexed with the body surface area (BSA). There have been various studies which have established the relationship between growing cardiac structures and height, weight, BSA of an individual.[12],[13],[14],[15],[16]

Body surface area was used as an independent variable in a non-linear regression analysis for the predicted mean value of each of the 19 echocardiographically measured structures. Because of the problem of heterogeneous variances with these measurements across the range of BSA, a logarithmic transformation using the natural logarithm was performed on 19 structures before the fitting of the regression models for the purpose of stabilizing the variances.

A polynomial model of the third power was selected as the final regression model. Studentized error residuals were used to detect the outliers to be excluded from analysis.

The first step was to transform the measurement of each structure by computing its natural logarithm (ln), which is represented as:

y = ln(measurement)(1)

The transformed echocardiographic measurements were entered into a nonlinear regression model as the dependent variable and BSA, BSA2, BSA3 as predictors (independent variables).

Expectedy = β01* BSA+β2* BSA2+ β3* BSA3(2)

Once the regression coefficients (β0, β1, β2, and β3) were obtained, the equation (2) was then transformed back to measurement's original scale by exponentiating y as explained in the following equation (3).



The Mean Y obtained from the equation (3) is used to standardize the observed value of the parameter using the equation number (4) as shown below, which in turn will produce Z score as a final product.



To plot a graph between echocardiographic measurements and BSA, the regression equations were considered in original units and 6 curves corresponding to the z score = ±1, ±2, ±3 were plotted for each echocardiographic measurement.

Data analysis were performed using SAS version 9.1 (SAS Institute, Cary, NC, USA).


  Results Top


The total number of children evaluated during the study period was 596, with age ranging from newborn to 16 years. A maximum number of children were within their 1st year (n = 349), followed by 1–5 years of age (n = 119). The total parameters collected in the study are 8102. The distribution is shown in [Table 2]. [Table 3] and [Table 4] show the distribution of the study population in relation to weight and height respectively.
Table 2: Frequency of all the parameters studied

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Table 3: Distribution of the study population in relation to weight

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Table 4: Distribution of the study population in relation to height

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Out of the total 596 study participants, there was a slight preponderance of males (59%). The most common reason for referral among all age groups was the detection of a murmur, which was almost 50%. The other common referral reasons were apparent cyanosis and tachycardia.

The relation between each parameter and the BSA, height, age, and weight was studied using the regression equations. Among the various possibilities, the correlation with Haycock's BSA and an individual parameter was found to be the most sensitive predictor of normal progression with age. The coefficients for each equation are given in [Table 5].
Table 5: Coefficients of regression formulas for the parameters

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R2 is the standard statistic used to measure how well data fits the selected regression model (goodness of fit) and has a value ranging from 0 to 1, with 1 representing a perfect fit and 0 representing a total lack of fit. In [Table 5] R2 values for most of the parameters are above 0.60 which indicates that there is a strong relationship between BSA and the measurement. The interventricular septum thickness varies throughout the cardiac cycle. The errors in accurate measurement of interventricular septal thickness (in systole and diastole) may be a contributing factor for lower R2 value. The lower R2 value of the aortic isthmus can be attributed to the less number of the observations.

[Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10], [Figure 11], [Figure 12], [Figure 13], [Figure 14], [Figure 15], [Figure 16], [Figure 17], [Figure 18], [Figure 19], [Figure 20] show the graphical presentation of the relationship of individual parameters with BSA. The graphs also show estimated regression lines corresponding to the Z-Scores of 0 (black line), while the red and green lines correspond to a Z score of ± 1 and 2, respectively. The graphs representing the cardiac valves, the branch pulmonary arteries, left ventricle internal dimensions, intersinus distance, sinotubular junction, ascending aorta, and the distal transverse arch is more “compact” as compared to other parameters. This is due to higher regression coefficient of these parameters.
Figure 2: Scatter plot of the mitral valve annulus (mm) versus body surface area (m2)

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Figure 3: Scatter plot of the tricuspid valve annulus (mm) versus body surface area (m2)

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Figure 4: Scatter plot of the aortic valve annulus (mm) versus body surface area (m2)

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Figure 5: Scatter plot of the pulmonary valve annulus (mm) versus body surface area (m2)

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Figure 6: Scatter plot of the right pulmonary artery (mm) versus body surface area (m2)

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Figure 7: Scatter plot of the left pulmonary artery (mm) versus body surface area (m2)

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Figure 8: Scatter plot of the interventricular septal thickness in diastole (mm) versus body surface area (m2)

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Figure 9: Scatter plot of the interventricular septal thickness – systole (mm) versus body surface area (m2)

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Figure 10: Scatter plot of the left ventricle internal diameter – diastole (mm) versus body surface area (m2)

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Figure 11: Scatter plot of the left ventricle internal dimension – systole (mm) versus body surface area (m2)

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Figure 12: Scatter plot of the posterior thickness in diastole (mm) versus body surface area (m2)

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Figure 13: Scatter plot of the posterior wall thickness in systole (mm) versus body surface area (m2)

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Figure 14: Scatter plot of the intersinus distance (mm) versus body surface area (m2)

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Figure 15: Scatter plot of the sinotubular junction (mm) versus body surface area (m2)

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Figure 16: Scatter plot of the diameter of ascending aorta (mm) versus body surface area (m2)

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Figure 17: Scatter plot of the diameter of proximal transverse arch (mm) versus body surface area (m2)

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Figure 18: Scatter plot of the diameter of the distal transverse arch (mm) versus body surface area (m2)

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Figure 19: Scatter plot of the diameter of the isthmus (mm) versus body surface area (m2)

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Figure 20: Scatter plot of the diameter of the descending aorta versus body surface area (m2)

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How to calculate Z-Scores

The Z score for an individual parameter can be calculated in two different ways.

First is using the statistical equations attached to the study.

Example with our data:

Weight: 8 Kg; Height: 110 cm; BSA: 0.5, Measured mitral valve annulus – 15 mm.

For mitral valve: β0 = 1.875, β1 = 2.865, β2 = -2.29, and β3 = 0.684 and insert these values in equation (2) to obtain the mean for mitral valve annulus size for BSA = 0.5.

Mean y = 1.875 + 2.865 × 0.5–2.29 × 0.52 + 0.684 × 0.53

= 2.821

Next, take the natural log of the observed mitral valve annulus size (15 mm) of a patient using equation (1) and standardize it using the following formula:



= − 0.68

The formulae for calculation of Z-Scores (of all 19 parameters) using this study are given in Appendix 1 (attached as an add-on file).

The second method employs the graphs. Plotting the individual parameter on the graph against the BSA will give an estimated Z score. [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10], [Figure 11], [Figure 12], [Figure 13], [Figure 14], [Figure 15], [Figure 16], [Figure 17], [Figure 18], [Figure 19], [Figure 20] show the scatter plot of the measurement of an individual parameter versus BSA.


  Discussion Top


The measurements of cardiac structures and their comparison with nomograms are essential for preoperative planning for most congenital heart defects[17],[18],[19]Z-Scores are essential to monitor the disease progression for the management of various acquired heart diseases such as Kawasaki disease or rheumatic heart disease.[2],[20] Major pediatric cardiac centers across the world have developed their own nomograms.[21],[22],[23]

The Z scores of cardiac structures of the Indian pediatric population remains a challenge. The only Indian study is a recent one that excluded the population below 4 years.[24] Our study, on the other hand, focuses on the younger population with the majority (74%) <4 years and including 132 neonates. With the majority of CHD being detecting at the preschool age group, our study is likely to be of more relevance.

The demographic profile of our study in terms of the number of neonates we studied, matches a study by Tacy et al. However, this study included only neonates (n = 129) and reported on atrioventricular valve annuli sizes exclusively.[25]

The mean BSA of Indian versus Caucasian children in various age groups differed significantly. This agrees with the reported demographic differences between normal Indian and Caucasian children and has been attributed to racial differences.[26] The nomograms, therefore, should be derived from regional population and ideally, one cannot be substituted for another.

It is imperative to note that nomograms for cardiac structures in the pediatric population are derived from regression formulae which are unique to each study's demographic profile. Because of this, the Z scores derived from the different studies have variations between them.[27] Our study has 177 children under the age of 3 months (BSA of 0.25 m2); the number being significantly higher than in the Detroit study (n = 82).[22] The data spread provides a more accurate estimation of the Z-Scores for the age and the BSA group in discussion.

Rogé et al. showed that the either of height, weight, BSA, or cube root of weight can be used for regression as they are all strongly associated with the growth of cardiac structure.[28] BSA was chosen here to make the results comparable to various published studies on the same subject.

There are no representative data of Indian children for aortic root and arch measurements. It has been observed that the Western nomograms tend to underestimate the Z score of Indian children and overestimate the severity of the arch hypoplasia, which does not concur with intraoperative findings. This study is a first attempt to derive root and arch nomograms for Indian children in an age <4 years


  Conclusion Top


This study of normal Indian pediatric population is the largest to date. The regression formulae along with the graphs can be used to acquire the Z-Scores of 19 individual echocardiographic parameters. The ready to use calculator is provided in the online version of this article.

Limitations

As with other studies reporting Z-Scores in pediatric echocardiography, the z scores are derived from extrapolations using regression formulae rather than an actual measurement. Only those children with a normal echocardiogram, history, and physical examination who visited pediatric cardiology OPD were included, this could be potential for selection bias. Any children with preexisting illness or syndromes were excluded as mentioned earlier.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10], [Figure 11], [Figure 12], [Figure 13], [Figure 14], [Figure 15], [Figure 16], [Figure 17], [Figure 18], [Figure 19], [Figure 20]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

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