Aims

Accurate skeletal age and final adult height prediction methods in paediatric orthopaedics are crucial for determining optimal timing of growth-guiding interventions and minimizing complications in treatments of various conditions. This study aimed to evaluate the accuracy of final adult height predictions using the central peak height (CPH) method with long leg X-rays and four different multiplier tables.

Background:

Paley et al has developed the multiplier method for predicting leg length. It is a tool that is used clinically to predict leg length discrepancy. The method is also a way of comparing different populations, to identify differences in growth trajectory. This has been done by identifying the differing multipliers for girls and boys. However it has not been used to identify trends in populations separated by time. Tanner showed that in the first half of the twentieth century girls went from an average age of menarche of 15 in 1900, to 13 in 1970, how this has affected growth trajectory over the last 50 years has not been studied.

Introduction:

Paley et al has developed a multiplier method for calculating both leg length and total height. In the development of this algorithm, they evaluated the effect of factors including bone age and sex. They established that sex had a significant impact, but adjusting for bone age did not improve accuracy. Bone age and menarche have been shown to improve other height prediction models.

Predictions of lower limb growth are based upon historical data, collected from patients who had coexistent poliomyelitis. By utilising standardised longitudinal prospective European data, our objective was to generate superior estimates for the age and rate at which lower limb skeletal maturity is reached; thus improving the timing of epiphysiodesis, for the management of leg length discrepancy. The Avon Longitudinal Study of Parents and Children of the 90s (ALSPAC) is a longitudinal cohort study of children recruited antenatally 2. Using a previously validated Multiplier Method, a sequence of leg length multipliers were calculated for each child. 15,458 individuals were recruited to the ALSPAC study; and of those whose growth was measured, 52% were boys and 48% girls, each with an average of eight recording episodes. 25,828 leg length multiplier (LLM) values were calculated with final recordings taken at a mean age of 15.5 years. From this data, the age at which girls reach skeletal maturity (LLM=1) is 11 months later than previously calculated and for boys nearly 9 months earlier. With nearly 4000 more children recruited in this cohort than preceding studies, this study brings increased power to future leg length calculations

Paley et al developed a mathematical model to predict height, using age, sex and current height. His predictions were based on growth charts from epidemiological databases, and then validated using 52 children. We looked at a recent large, local database, to assess whether the height multiplier is a reliable tool that can be used in clinical practice.

The Avon Longitudinal Study of Parents and Children of the 90s (ALSPAC) is a population based cohort study of 14, 000 contemporary British families. 5363 children had final height measured with an average of 10.5 additional height measurements. The height multiplier equation was defined as height at specific age divided by height at skeletal maturity.

No significant difference was observed between the mean results from Paley et al and the ALSPAC data. There was a significant range of results in the ALSPAC data, with a standard deviation of the multiplier of 0.08 for ages 7–15.

This large population study shows no significant difference between the historical databases Paley used and the more current European databases. The large range of results shown by the ALSPAC cast doubt on the clinical usefulness of individual results.

We compared the accuracy of the growth remaining method of assessing leg-length discrepancy (LLD) with the straight-line graph method, the multiplier method and their variants. We retrospectively reviewed the records of 44 patients treated by percutaneous epiphysiodesis for LLD. All were followed up until maturity. We used the modified Green–Anderson growth-remaining method (Method 1) to plan the timing of epiphysiodesis. Then we presumed that the other four methods described below were used pre-operatively for calculating the timing of epiphysiodesis. We then assumed that these four methods were used pre-operatively. Method 2 was the original Green–Anderson growth-remaining method; Method 3, Paley’s multiplier method using bone age; Method 4, Paley’s multiplier method using chronological age; and Method 5, Moseley’s straight-line graph method. We compared ‘Expected LLD at maturity with surgery’ with ‘Final LLD at maturity with surgery’ for each method. Statistical analysis revealed that ‘Expected LLD at maturity with surgery’ was significantly different from ‘Final LLD at maturity with surgery’. Method 2 was the most accurate. There was a significant correlation between ‘Expected LLD at maturity with surgery’ and ‘Final LLD at maturity with surgery’, the greatest correlation being with Method 2. Generally all the methods generated an overcorrected value. No method generates the precise ‘Expected LLD at maturity with surgery’. It is essential that an analysis of the pattern of growth is taken into account when predicting final LLD. As many additional data as possible are required.

Cite this article: Bone Joint J 2013;95-B:993–1000.

Introduction and Aims: We propose a new, simple, and universal method to predict adult height: the Height Multiplier Method. Our aim was to calculate height multipliers from various databases and validate their use for height prediction. Method: Standard growth charts, based on a diverse population, were published by the Centres for Disease Control and Prevention (CDC) in 2000. Height multipliers (M) for boys and girls were calculated by dividing the height at skeletal maturity (Htm) by the present height (Ht) (M = Htm/Ht) for each age, gender, and height percentile using CDC data. These multipliers were compared with multipliers derived from various height databases of 28 boys and 24 girls. The accuracy of the multipliers was tested on individual longitudinal data sets from 52 normal children. Results: The average CDC-derived multipliers were significantly different at each age for boys and girls, but within gender, different percentiles at each age were very similar. These multipliers were very similar to multipliers derived from each of the databases. For predictions based on individual data sets from 52 children, the median, 90%, and standard deviation of absolute error prediction (AEP) were calculated. Boys’ median AEP ranged from 1.4–4.3cm; 90% AEP ranged from 1.8–8.3cm. Girls’ median AEP ranged from 0.68–4.38cm; 90% AEP ranged from 1.5–10.6cm. Conclusion: The Height Multiplier Method of stature prediction is as accurate as CDC growth charts when based on single-height measurements and is similar in accuracy to other methods. The Height Multiplier Method has the advantage of percentile, race, nationality, and generation independence. Growth charts have the advantage of showing trends over time