Near Adult Heights and Predictions of Prospective Adult Heights using Automated and Conventional Greulich-Pyle Bone Age Determinations in children with chronic endocrine diseases

DOI: https://doi.org/10.21203/rs.3.rs-383539/v1

Abstract

Calculation of prospective adult heights (PAH) is associated with considerable bone age interrater variability. Therefore, the new PAH method based on automated bone age (BA) determination (BoneXpert™) was compared to the conventional PAH method by Bayley- Pinneau (BP) based on BA determination according to Greulich and Pyle (GP) and to observed near adult heights. Heights and near adult heights were measured in 82 patients (48 females) with chronic endocrinopathies at age of 10.45 ± 2.12 years and at time of transition to adult care (17.98 ± 3.02 years). Further, BA were assessed according to conventional GP - by three experts- and by BoneXpert™. PAH were calculated using conventional BP tables and BoneXpert™. The conventional and the automated BA determinations revealed a mean difference of 0.25 ± 0.72 years (p = 0.0027). The automated PAH by BoneXpert™ were 156.96 ± 0.86 cm in females and 171.75 ± 1.6 cm in males compared to 153.95 ± 1.12 cm in females and 169.31 ± 1.6 cm in males by conventional BP, respectively, and in comparison to near adult heights 156.38 ± 5.84 cm in females and 168.94 ± 8.18 cm in males, respectively.

Conclusion: BA ratings and adult height predictions by BoneXpert™ in children with chronic endocrinopathies abolish rater dependent variability and enhance reproducibility of estimates thereby refining care in growth disorders. Conventional methods may outperform automated analyses in specific cases.

What is known

Bone age determination plays a pivotal role in growth disturbances, but calculation of prospective adult heights is associated with considerable bone age inter-rater variability. Recently, the fully automated bone age determination by BoneXpertTM has been validated for patients with different endocrine diseases.

What is new

BA ratings and adult height predictions by BoneXpert™ in children with chronic endocrinopathies abolish rater dependent variability and enhance reproducibility of estimates thereby refining care in growth disorders.

Introduction

Growth failure is very common in patients with chronic endocrinopathies, and the determination of skeletal maturity (bone age, BA) plays a pivotal role in endocrine diagnostic and is important to evaluate the individual growth potential [1], [2, 3]. The most widely used methods for BA assessment are the Tanner-Whitehouse (TW2, TW3) methods [4, 5] and Greulich and Pyle (GP) method. For the latter a trained rater with a background of paediatric endocrinology or paediatric radiology performs comparisons with the plates of the GP atlas from 1959 [5]. This manual procedure is time-consuming, requires experience, and is susceptible to considerable inter- and intra-rater variability [2]. These bone age ratings can therefore be biased and have large variability.

Recently, an automated image analysis method, BoneXpert™, has been introduced [6] and has been validated in patients with different endocrinopathies and with chronic renal failure [710]. BoneXpert™ uses an adopted prospective height prediction method (BX), which is an improvement and extension of the Bayley-Pinneau method (BP) [11]. So far it is validated for healthy children [12]. The objectives of this study were the validation of adult height predictions (BX) using automated Greulich-Pyle bone age determinations in children with chronic endocrine diseases. Therefore, these results were compared to prospective adult height calculations (BP), based on conventional assessments of GP performed by three independent experienced clinicians and to near adult heights at the time of transition from paediatric to adult care.

Patients And Methods

Study design/Patients

At the University´s Hospital Heidelberg 88 consecutive adolescents with chronic endocrine diseases were evaluated after achieving the primary paediatric treatment goals (girls: bone age ≥ 14 years and menarche; boys: bone ages ≥ 16 years and voice brake) when > 98% of final adult height was reached [13]. Written informed consent was obtained from each participant and their parents. The ethics committees of the University of Heidelberg approved the study which was performed according to the Declaration of Helsinki and conformed to legal and ethical norms (S-019/2011).

Six of the consecutive 88 patients were excluded as one X-ray of the left hand was unavailable and Digital Imaging and Communication in Medicine (DICOM) datasets from five patients were ineligible. Therefore, 82 X-rays of 48 females and 34 males were analysed and corresponding heights were measured in an upright position, using a wall-mounted Harpenden stadiometer (Mentone Educational, Moorabbin, Victoria, Australia) at the chronological age of 10.45 \(\pm\) 2.12 years and at time of transition (17.98 ± 3.02 years). Reference percentiles of Brandt and Reinken were used for calculation of standard deviation scores (SDS) [14]. At time of transition the patients attained their near-adult heights rather than adult heights as > 98% of the final height potential is reached at bone ages > 14 years in girls and > 16 years in boys and negligible growth potential is left. [13].

Medications and information on potential osseous disorders were available. Patients with the following diagnoses were included: congenital adrenal hyperplasia (CAH), isolated growth hormone deficiency (iGHD), panhypopituitarism (MPHD), small for gestational age (SGA), and Ullrich-Turner syndrome (UTS). For details of patients characteristics see Table 1 and Table 2. All patients were treated according to international consensus guidelines [1518].

Table 1

Patient characteristics at time of transition from Paediatric to Adult care

Diagnosis

Gender

Total

CAH

iGHD

MPHD

SGA

UTS

Gender (f/m) n

 

82 (48/34)

15 (7/8)

22 (9/13)

10 (5/5)

17 (9/8)

18 (18/-)

Chronological Age (years)

 

17.98 ± 3.02

19.14 ± 2.63#+

17.45 ± 2.23+#

21.34 ± 4.67*+&

15.75 ± 1.31#*&#

18.18 ± 2.00#+

Near Adult Height (cm)

female

156.38 ± 5.84

159.89 ± 3.82+&

159.56 ± 5.77+

160.03 ± 7.23+&

152.87 ± 4.44*$

154.13 ± 4.93*$#

male

168.94 ± 8.18

173.33 ± 7.37+$

168.73 ± 6.42+*

170.36 ± 9.29+&

164.0 ± 4.75+$#

-

Target Height (cm)

female

161.87 ± 5.82

161.86 ± 5.46

162.69 ± 4.06

163.33 ± 6.54+

157.0 ± 7.13#&

163.46 ± 4.85+

male

175.34 ±

6.42

176.88 ± 5.81

174.34 ± 5.54

176.6 ± 10.34

174.63 ± 6.31

-

CAH congenital adrenal hyperplasia; iGHD isolated growth hormone deficiency; MPHD panhypopituitarism; SGA small for gestational age; UTS Ullrich-Turner syndrome; f female; m male; Data are given as mean ± standard deviation (SD). Statistics by ANOVA: *P < 0.05 vs. CAH; §P < 0.05 vs. GHD; #P < 0.05 vs. MPHD; +P < 0.05 vs. SGA; &P < 0.05 vs. UTS.

 

Table 2

Heights (SDS) at mean chronological age of 10.5 ± 2.1 years, near adult heights (SDS), target heights (SDS), and differences between respective target heights and near adult heights (SDS).

Diagnoses

Total

CAH

iGHD

MPHD

SGA

UTS

Target Height SDS

-0.88 ± 1.09

-0.72 ± 1.00

-0.95 ± 0.90

-0.42 ± 1.24

-1.41 ± 1.3

-0.70 ± 0.95

Height SDS

(at age CA of 10.5 ± 2.1 years)

-1.92 ± 1.87

0.63 ± 1.85§#+&

-2.0 ± 1.38*

-2.3 ± 2.12*

-2.7 ± 1.0*

-2.94 ± 0.69*

Differences

Height SDS –

Target Height SDS

-1.04 ±

1.63

1.35 ±

1.45

-1.05 ± 1.38#&

-1.88 ± 1.22§+

-1.29 ± 1.29#&

-2.24 ± 0.92§+

Near adult Heights SDS

-1.72 ± 1.2

-1.13 ± 0.96+&

-1.42 ± 1.03&

-1.25 ± 1.78&

-2.12 ± 0.89*

-2.51 ± 0.98*§#

Differences

Near adult Heights SDS –

Target Heights SDS

-0.84 ± 1.09

-0.41 ± 0.98&

-0.47 ± 0.79&

-0.83 ± 1.12&

-0.71 ± 1.16&

-1.81 ± 0.86*§#+

CAH congenital adrenal hyperplasia; iGHD isolated growth hormone deficiency; MPHD panhypopituitarism; SGA small for gestational age; UTS Ullrich-Turner syndrome; f female; m male; SDS standard deviation score; Data are given as mean ± standard deviation (SD). Statistics by ANOVA: *P < 0.05 vs. CAH; §P < 0.05 vs. GHD; #P < 0.05 vs. MPHD; +P < 0.05 vs. SGA; &P < 0.05 vs. UTS.

 

Determination of bone age

Two paediatric radiologists and one paediatric endocrinologist determined BA according to the atlas method of GP [5]. For further statistical analysis, the mean of these three independent determinations of BA was used; named conventional BA and interrater variability was calculated. In addition, the automated assessment of BA was performed by BoneXpert™ version 2.1 (named automated BA, BoneXpert™, Visiana, Holte, Denmark, www.boneXpert.com). A more detailed description of this method is published elsewhere [19]. In brief, the automated BA consists of three layers of computation [19]. Layer A reconstructs and validates the borders of 15 bones: radius, ulna, all metacarpals, and the phalanges of finger I, III, V. The bone will be rejected if it is not placed correctly or it is severely dysmorphic. Layer B determines a so called intrinsic BA for each bone of the RUS bones (except metacarpal II and IV). Bones with BA deviated more than 2.4 years of the average BA were rejected. Average BA will not be calculated if less than 8 bones out of 13 were accepted. Layer C transforms the intrinsic BA into the scale of GP or TW. At the time of this study the BoneXpert™ covered the BA ranges 2.5–17 years for boys and 2–15 years for girls.

Predicted adult height

Height predictions were calculated using the conventional BA according to the methods of Bayley-Pinneau for girls and boys (conventional BP) [20] calculated by the anthropometric software GrowthXP from PC PAL, Stockholm, Sweden. Prospective height predictions for girls and boys using the automated BA were calculated with BoneXpert™ (automated BX) according to Thodberg et al. [11] on www.bonexpert.com. Briefly, the growth potential (gp) was defined as: gp = (H - h)/H when H equals adult height and h represents actual height. The growth potential prediction is denoted gppred (BA, CA– BA), from this the so-called raw prediction of the adult height is derived: Hraw=h/(1-gppred). The new method constructs gppred (BA, CA - BA) as a nonlinear function of two variables. This is implemented as one neural network (a standard nonlinear regression method) for each gender as described in detail elsewhere [11]. In addition, target heights (cm; SDS) were calculated with the formula of Tanner (mid-parental height (+ 6.5 in boys or -6.5 in girls), respectively) [21].

Statistical analysis

Data are given as mean ± standard deviation (SD), if not indicated otherwise. The differences between the conventional and automated BA were described using a Bland-Altman plot. The overall bias and limits of agreement are provided. Interrater variability expressed as Fleiss´κ was also calculated. All data were assessed for normal distribution by the Kolmogorov-Smirnov test. Correlation coefficients were calculated according to Pearson. p < 0.05 was considered as statistically significant. Since this was an exploratory analysis, no adjustment for multiple testing was done. Stat View version 5.0 1998 and the software R in combination with the package psychometric were used for statistical analysis [22, 23].

Results

Comparison of automated and conventional bone age determination

None of the 82 analysed images were rejected by BoneXpert™. The interrater variability of the conventional BA determination according to Greulich and Pyle performed by three experienced raters was 0.88.

There was a good correlation between the conventional and the automated bone age ratings (r = 0.843; p < 0.001). The automated and the conventional BA determinations differed by -0.25 ± 0.72 years (p = 0.0027) (Table 3) ranging from − 1.67 to 1.19 years. The conventional method tended to rate bone ages slightly more mature. The exact distribution is shown in Fig. 1 using a Bland-Altman plot. Further, the mean differences between the automated and conventional method for female and male patients were significantly different (females, -0.16 ± 0.68 years; males, -0.37 ± 0.77 years; p = 0.001).

Table 3

Comparison of conventional bone age determination according to Greulich and Pyle (conventional BA) and automated bone age determination (automated BA)

Diagnosis

Total

CAH

iGHD

MPHD

SGA

UTS

Chron. Age (CA; years)

10.45 ± 2.12

8.75 ± 1.60§#&

11.16 ± 1.85*+

12.12 ± 2.02*+

9.66 ± 1.53§#

10.82 ± 2.22*

automated BA (years)

9.59 ± 1.86

10.78 ± 1.85+

9.63 ± 2.08+

9.60 ± 1.6

8.43 ± 1.45*§&

9.67 ± 1.51+

Difference

automated BA - CA (years)

-0.86 ± 2.26

2.04 ± 2.67§#+&

-1.53 ± 1.67*

-2.52 ± 2.01*

-1.23 ± 1.05*

-1.15 ± 1.5*

conventional BA (years)

9.84 ± 1.78

11.06 ± 1.79#+&

9.94 ± 1.99

9.80 ± 1.57*

8.98 ± 1.43*

9.66 ± 1.52*

Differences conventional BA - CA (years)

-0.61 ± 2.20

2.32 ± 2.57§#+&

-1.22 ± 1.55*

-2.32 ± 1.8*+

-0.68 ± 0.97*#

-1.16 ± 1.46*

Differences automated BA - conventional BA (years)

-0.25 ± 0.72

-0.28 ± 0.69

-0.31 ± 0.57

-0.2 ± 0.97

-0.54 ± 0.77&

0.01 ± 0.68

CAH congenital adrenal hyperplasia; iGHD isolated growth hormone deficiency; MPHD panhypopituitarism; SGA small for gestational age; UTS Ullrich-Turner syndrome; f female; m male; BA bone age; CA chronological age. Data are given as mean ± standard deviation (SD). Statistics by ANOVA: *P < 0.05 vs. CAH; §P < 0.05 vs. GHD; #P < 0.05 vs. MPHD; +P < 0.05 vs. SGA; &P < 0.05 vs. UTS.

 

Overall the automated BA was retarded by 0.86 ± 2.26 years (Table 3). The respective bone ages were accelerated by 2.04 years in CAH, but retarded in MPHD by -2.52 years, and moderately retarded in SGA, UTS, and iGHD (Table 3). Conventional and automated bone age determinations were similar in all patient groups (p = ns) except in patients with SGA (p < 0.05) (Table 3).

Prediction of Adult Height

The mean near adult heights (females 156.38 ± 5.84 cm, males 168.94 ± 8.18 cm) were significantly lower than target heights in females (161.87 ± 5.82 cm) and in males (175.34 ± 6.42 cm), respectively (p < 0.001) (Table 1).

The predicted adult heights using the method of BoneXpert™ (automated BX) were 156.96 ± 0.86 cm in girls matching near adult heights (Δ 0.58 ± 0.84 cm; p = ns) (Fig. 2). In contrast, the predicted adult heights using conventional BP were 153.95 ± 1.12 cm in girls underestimating near adult heights by 2.43 ± 0.84 cm (p < 0.01) (Fig. 2). The automated BX were 171.75 ± 1.6 cm in boys, overestimating near adult heights by 2.81 ± 2 cm (p < 0.05) (Fig. 2). Conventional BP in boys corresponded to near adult heights in boys (169.31 ± 1.6 cm; Δ 0.37 ± 1.4 cm; p = ns) (Fig. 2).

Conventional BP and automated BX were analysed separately in CAH and MPHD because of distinct differences in bone ages and chronological ages (CAH: 2.04 ± 2.67 years; MPHD: -2.52 ± 2.01 years, respectively). Predicted adult heights using both methods were similar as near adult heights in females regardless of bone age acceleration and retardation (Table 4). Automated BX overestimated significantly predicted adult heights in males with MPHD when bone ages were retarded (9.3 ± 3.02 cm; p < 0.05) (Table 4).

Table 4

Predicted adult heights using the method of Bayley Pinneau (conventional BP) and BoneXpert™ (automated BX) in patients with accelerated bone age (BA) (congenital adrenal hyperplasia (CAH)) and delayed BA (panhypopituitarism (MPHD)).

   

CAH

MPHD

female

Near adult Heights

(cm)

160.0 ± 3.8

161.5 ± 7.1

conventional BP

(cm)

160.0 ± 8.3

162.5 ± 6.6

automated BX (cm)

162.2 ± 6.0

162.6 ± 2.8

male

Near adult Heights

(cm)

173.3 ± 7.4

170.4 ± 6.4

conventional BP

(cm)

170.0 ± 9.6

173.6 ± 7.9

automated BX (cm)

177.5 ± 6.5

179.7 ± 4.9*

CAH congenital adrenal hyperplasia; MPHD panhypopituitarism. The difference of conventional BA and chronological age (CA) in patients with CAH was 2.32 ± 2.57 years and was 2.04 ± 2.67 years when BA was estimated by Bone BoneXpert™ (automated BA). The difference of conventional BA and CA in patients with MPHD was − 2.32 ± 1.8 years and was − 2.52 ± 2.01 years when automated BA was utilized (see Table 3). Data are given as mean ± standard deviation (SD). Statistics by t-test: *P < 0.05 vs. near adult height.

Discussion

This study represents to our best knowledge the first comparison of observed near final heights and the recent adult height calculation method based on automated bone age determination (BoneXpert™) with the conventional PAH method by Bayley-Pinneau based on bone age determination according to Greulich and Pyle in children with various chronic endocrinopathies. In general, there was a good agreement of automated BX in girls, regardless of bone age deviations from chronological age. The recent method of automate BX implements a nonlinear growth potential function [11]. This is more graduated and therefore more precise than the conventional BP using the tables of Bayley-Pinneau which rates only for three ranges namely advanced ( BA > CA > 1 year), normal BA(CA = BA ± 1 year) and retarded ( BA < CA < -1 year). Our results applying automated BX in girls are in accordance with the results of Unrath et al., [12] and Thodberg et al. [24], who investigated healthy children with short stature. Martin et al. reported a slight underprediction of automated BX in girls with short stature by 0,8 cm when bone age was younger than 12 years [25]. This observation was explained by an individual, unpredictable growth pattern of six included girls.

Remarkably, automated BX overestimated adult heights in boys, especially, when bone ages were severely retarded. This observation was confirmed by Thodberg et al. [24]. In their study, height predictions in boys using automated BX did not outperform the conventional method in boys indicating an inherent weak spot. It is known, that conventional BP systematically overestimates adult height in boys, especially when bone age is retarded [26, 27] or in constitutional tall stature [28].

These observations cannot be explained by a systematic error in bone age determination of automated BA, because our study and all other studies reported a good accordance with the conventional BA assessment (Fig. 1 and Table 2) [12, 19]. This difference is similar to the deviation between two manual raters [19]. Further, this remarked effect is not caused by incorrect bone age determination due to dysmorphic bones, because none of the images were rejected by BoneXpert™.

The residual errors of growth prediction in both methods arise from various sources: a) conventional growth prediction models presumed a linear dependence between adult height and BA, but puberty and its growth spurt are dynamic processes and not concordant with bone age advancement. Each child experiences an individual growth pattern. The tables of Bayley and Pinneau were initially evaluated in healthy children but thereafter adapted as benchmark to children with various growth disorders and treatments. Implementation in clinical routine of growth disorders revealed a valuable tool in treatment control and expectations ([29, 30]), b) incorrect measurements of height and (near) adult heights, c) unpredictable influences on growth and pubertal development, such as nutrition, genetic, and environmental factors. But a clear advantage using automated BX is that the bone age determination is not impaired by rater variability and can be therefore easily used for clinical studies. Automated bone age determination by BoneXpert™ can be used as a reliable tool and efficient method, because it is time- and cost saving. It is reliable also when bone age is accelerated or retarded [7, 8, 12, 9]. Apart from children with chronic kidney disease, in which automated BA tended to underestimate acceleration or retardation of bone age [10], this difference can be probably explained by the renal osteodystrophy in this cohort.

For evaluation of bone morphology a rating of the x-ray by an expert remains mandatory and cannot be replaced by computerization. A limitation of this study is the small numbers of patients in each group. Therefore, future multi-centre-studies with a larger sample size are needed to evaluate accuracy of automated BX.

Taking the described limitations into account, the new prediction method is superior to the conventional BP in girls, regardless of bone age deviation from chronological age. Automated BX tends to overestimate PAH in boys, especially, when bone age is retarded.

Bone age determination is a standard investigation in the work up of growth disturbances in children with various paediatric diseases [31], but also in orthodontics and paediatric orthopaedics [32]. Further it is used for legal issues, especially to determine a person's age based on skeletal radiographs. However, according to the statement of the European Society of Paediatric Radiology musculoskeletal task force group exact determination of chronological age of a person cannot be done with sufficient accuracy with existing methods [33]. Based on the bone age determination several prediction models for adult heights were established. The accuracy of the predicted adult heights is limited due to the growth pattern that can be influenced by medication, nutrition, individual variation in pubertal height gain and environmental factors [34]. This biological variation is not possible to overcome with mathematical prediction models. Therefore the accuracy of bone age assessment should be optimized to improve the accuracy of height prediction methods. Using automated bone age determination an inter- and intrarater error is eliminated and consecutively improves reproducibility of adult height prediction.

Abbreviations

BA: (automated) bone age

BP: conventional PAH method by Bayley-Pinneau

BX: adopted prospective height prediction method by BoneXpertTM

CAH: congenital adrenal hyperplasia

DICOM: Digital Imaging and Communication in Medicine

GP: Greulich and Pyle

 gp: growth potential

iGHD: isolated growth hormone deficiency

MPHD: panhypopituitarism

PAH: prospective adult heights

SD: standard deviation

SDS: standard deviation score

SGA: small for gestational age

TW: Tanner-Whitehouse

UTS: Ullrich-Turner syndrome

Declarations

Acknowledgments

The authors wish to thank Dr. Hans-Henrik Thodberg, Holte, Denmark for the fruitful support. We would like to thank the Joachim Siebeneicher Stiftung for financial support. This study was supported by an unrestricted grant of Pfizer, Germany.

Funding:

Janna Mittnacht has received a Research Grant from Pfizer, Germany. Jens Peter Schenk received a Research Grant from the Joachim Siebeneicher Stiftung, Germany.

Conflicts of interest/Competing interests:

The funding organizations played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

Availability of data and material

All relevant data to this study are included in the manuscript

Code availability

Stat View version 5.0 1998

Author contributions:

Daniela Choukair and Markus Bettendorf contributed to the study concept and design, the analysis and interpretation of data and the preparation of the manuscript. Annette Hückmann, Janna Mittnacht, Thomas Breil, Jens Peter Schenk, Abdulsattar Alrajab, and Lorenz Uhlmann contributed to the analysis and interpretation of data and to the revision of the manuscript. All authors participated in acquisition of data and approved the final version of the manuscript.

Ethics approval

The ethics committees of the University of Heidelberg approved the study which was performed according to the Declaration of Helsinki and conformed to legal and ethical norms (S-019/2011)

Consent to participate

Written consent has been obtained from the parents/ caregiver of each patient after full explanation of the purpose and nature of all procedures used.

Consent for publication

not applicable

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