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Original Studies |
Sektion Pädiatrische Endokrinologie, Universitätsklinikum Tübingen, Eberhard Karls Universität (M.B.R.), Tubingen, Germany D-7206; Pharmacia & Upjohn, Inc. (A.L., P.W.), Stockholm S-11287, Sweden; Service de Pédiatrie, Endocrinologie et Diabétologie Infantiles, Université Claude Bernard, Hôpital Debrousse (P.C.), Lyon F-69322, France; the Department of Pediatrics, University of Auckland (W.C.), Auckland, New Zealand; the Department of Pediatrics, East Hospital (K.A.-W.), Gothenburg S-41685, Sweden; and the Department of Pediatrics, St. Mary’s Hospital (D.A.P.), Manchester GB-M271HA, United Kingdom
Address all correspondence and requests for reprints to: Dr. Michael B. Ranke, Sektion Pädiatrische Endokrinologie, Universitätsklinikum Tübingen, Eberhard Karls Universität, Hoppe-Seyler Str. 1, 72076 Tubingen, Germany.
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Abstract |
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Introduction |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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By accessing the large databases accumulated in the postmarketing outcome surveys (pharmaco-epidemiological surveys) of recombinant human GH, it is now possible to analyze the factors that determine responsiveness to exogenous GH. This information can be used to develop disease-specific growth prediction models for GH interventions in different etiologies of short stature. These models can be used to assist the clinician in the individualization of a patient’s treatment from the start to the end of GH therapy. We sought to develop growth prediction models for use in prepubertal children with idiopathic GHD by analyzing data from the multicenter Kabi Pharmacia International Growth Study (KIGS) study (the Pharmacia & Upjohn, Inc. International Growth Database) (6). Importantly, we then sought to test the accuracy and practicality of the growth prediction models by prospective means, using cohorts of recently diagnosed prepubertal children with idiopathic GHD.
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Subjects and Methods |
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Patients
Data obtained from prepubertal patients with idiopathic GHD enrolled in KIGS by August 31, 1997, were analyzed for construction of the prediction models. These patients were treated exclusively with recombinant human GH (Genotropin, Pharmacia & Upjohn, Stockholm, Sweden), receiving 6 or 7 injections of GH/week. At this time the KIGS database included 593 children suitable for analysis. The diagnosis was made by the treating physician according to the KIGS etiology classification system Code 1 (7). Only patients with peak GH levels of 10 µg/L or less after 2 standard GH provocation tests (excluding testing with GHRH), as reported by the treating physician, were included in the analysis. The patients were prepubertal; the boys had a mean testes volume 3 mL or less, whereas the girls had a Tanner breast stage of B1. The age at onset of GH treatment was between 2–10 yr for the girls and between 2–12 yr for the boys. Height measurements, for the calculation of a full year’s height velocity, were made at intervals of 11–13 months during the first year of treatment and 9–15 months in subsequent years. Patients were excluded from the analysis if they missed their GH injections for a total of more than 14 days during 1 yr. All patients analyzed were an appropriate size for gestational age at birth. Patients born small for gestational age were excluded.
KIGS is an on-going study, and new patients are continually being recruited. Because many of the selected patients were recruited into KIGS in the years immediately preceding the analysis, progressively fewer data were available for analyses of subsequent yearly growth responses after the first year growth response. Data from all 593 patients (148 girls and 455 boys) were used for the analysis of the first year growth response. Of these patients, data were available from 573 (144 girls and 429 boys) for the analysis of the second year growth response, 335 (83 girls and 252 boys) for the third year growth response, and 180 (40 girls and 140 boys) for the fourth year response.
Statistical analysis
Growth responses (height velocities, centimeters per yr) were correlated with several patient variables by multiple regression analysis. These variables are reported as the median and range as well as the mean ± SD. SD scores were calculated as follows: SD score = (patient value - the mean value for age- and sex-matched normal subjects) ÷ SD of the value for age- and sex-matched normal subjects.
The variables tested were 1) status at birth: sex, weight SD score, length SD score, ponderal index, mode of delivery, and Apgar score; 2) genetic background: height SD score of the mother, height SD score of the father, midparental height (MPH) SD score, and ethnic origin (the ethnic background of the patients was analyzed by adding dummy variables, e.g. 0/1 Asian/not Asian, to allow mathematical analysis within the multiple regression computer program); 3) treatment modality: GH dose [international units per kg BW and international units per kg ideal body weight (weight for height)], frequency of GH injections, and accumulated years of GH treatment; 4) patient variables at the beginning of the treatment period: age, bone age, height SD score, weight SD score, height SD score minus MPH SD score, peak GH level during provocative testing, and pituitary hormone deficiency status (i.e. isolated GHD or multiple pituitary hormone deficiencies).
The height standards used for normal children were those of Tanner et al. (8), and the weight standards were those of Freeman et al. (9). Birth weight for gestational age was transformed to SD score values based on the standards of Niklasson et al. (10). The MPH SD score was calculated as the (father’s height SD score + mother’s height SD score): 1.61 (11), based on the standards of Tanner et al. (8). Bone ages, calculated according to the method of Greulich and Pyle (12), were taken as reported by the treating physician.
The prediction models were developed by means of multiple linear regression analysis fitted by least squares and the REG procedure in the SAS computer program (Mainframe version 6.12). A hierarchy of predictive factors was derived by the all possible regression approach, using Mallow’s C(p) criterion for ordering predictive factors, as described by Weisberg (13, 14). Differences between observed and predicted height velocities were expressed in terms of Studentized residuals. The residual is calculated as the observed height velocity minus the predicted height velocity for each observation, and the Studentized residual is the residual divided by its SE.
Model validation
For the validation of the prediction models, data from three cohorts of patients were used. 1) A cohort of 237 prepubertal patients with idiopathic GHD from the KIGS database, who were treated with Genotropin, but who had not been included in the development of the prediction models, were studied. These patients were either registered in KIGS too recently for inclusion in analysis (n = 97) or else they had been excluded from the analysis because their first year growth response was assessed between 10–14 months of treatment, but not within the inclusion criterion of 11–13 months (n = 140). Data from all of these patients were available for analysis of first year growth responses, whereas the patient numbers available for analysis of prepubertal second, third, and fourth year growth responses were 188, 127, and 67, respectively. In 48 patients, information on growth and treatment for the fifth to eighth prepubertal years were also available. In these cases, observed vs. predicted growth was compared using the prediction models developed for the fourth treatment year.
2) A cohort of 29 prepubertal patients (9 girls and 20 boys) with idiopathic GHD from the Australian database, OZGROW (15), were studied. All data required for growth prediction within the models from this cohort were provided by Dr. Christopher Cowell (Sydney, Australia). Growth data from all of these patients were available for the first prepubertal treatment year. The number of patients available for analysis of growth during the second, third, and fourth prepubertal years of GH therapy were 24, 22, and 7, respectively. These patients were treated with various commercially available recombinant human GH products.
3) A cohort of 33 prepubertal children (16 girls and 17 boys) with idiopathic GHD diagnosed at the University Children’s Hospital (Tubingen, Germany) were studied. The diagnosis was made on the basis of 2 standard provocation tests for GH secretory capacity. All parameters required for the prediction models were available for these patients. The patients were treated with a variety of commercially available recombinant human GH products. Prepubertal growth data were available for 33, 27, 26, and 16 patients for the first, second, third and fourth years of GH replacement therapy, respectively.
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Results |
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The characteristics, at the onset of GH treatment, of the patients
who were treated longitudinally for 2 yr are listed in Table 1a
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Equivalent characteristics of the patients who were treated
longitudinally for 3 and 4 yr are listed in Tables 1b and 1c,
respectively. In the group treated longitudinally for 4 yr, the mean
age of the patients was slightly younger, and the severity of GHD was
slightly greater compared with those in the group treated for 2 yr.
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Growth predictors and growth prediction models
The variables found to be predictive of height velocity in the
first 4 yr of prepubertal treatment, their rank order as predictors,
the overall correlation coefficients of the prediction models, and the
error SD of their predictions are listed in Table 2. All single predictors were found to be
significant at a level of P < 0.001.
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The equation describing the predicted height velocity (PHV) for the
first year of GH therapy, when the parameter of maximum GH response to
provocation testing is included, is as follows: PHV (cm/yr) = 14.55 +
[-1.37 x maximum GH response [ln; µL)] + (-0.32 x
age at onset (yr)]) + (0.32 x birth weight SD score)
+ [1.62 x GH dose (ln; IU/kg·week)] + (-0.4 x height
SD score - MPH SD score) + (0.29 x
body weight SD score) [± 1.46] (Refer to Table 2
).
This model explained 61% of the variability of the response, with an error SD of 1.46 cm. The parameter of the natural log (ln) of the maximum GH response to provocation testing was the most important predictor of the six identified. Thus, the greater the severity of the child’s GHD, the greater their first year growth response to GH therapy. In addition, the growth response was negatively correlated with chronological age and the distance between the child’s present height SD score and his MPH SD score. Therefore, the younger and smaller the child, the greater his first year growth response to GH therapy. First year growth response was positively correlated with body weight SD score, weekly GH dose (ln), and birth weight SD score. These findings imply that the heavier the child is at present, the heavier the child was at birth, and the more GH the child receives during the first year, the greater his growth response will be.
The model excluding the maximum GH response to provocative testing
explained 45% of the variability of the response, with a
SD of 1.72 cm (Table 2). In this model the difference
between present height SD score and MPH SD
score was the most important single predictor. Otherwise, the
predictors in the two models for first year growth response were
identical. Figure 1 shows for each
patient the difference in predicted first year growth response between
these two models. Overall, the two models gave similar results, but the
model excluding peak GH response during provocation testing tended to
underpredict the growth response in those patients who had a very low
GH secretory capacity.
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). For the
second and third years, height velocity during the previous year was
the most important predictor of response, followed by body weight
SD score, chronological age, and weekly GH dose (ln). The
model for the fourth year explained 30% of the variability in the
response, with a SD of 0.95 cm (Table 2). Body weight
SD score rather than height velocity in the previous year
was the most important predictor for fourth year growth. Validation of prediction models
The plots of the Studentized residuals (see Materials and
Methods) vs. predicted response in the original KIGS
cohort using the two models for the first year growth response are
illustrated in Fig. 2, a and b.
Studentized residual plots are used to diagnose outliers, nonlinearity,
and nonconstant error variance in prediction models and are a part of
their mathematical validation. The fact that the observations are
randomly clustered implies that there is no heterogeneity in the group
with respect to the relative importance of the different predictors.
Figure 3 shows the Studentized residuals
(mean, SD) for the predicted response during each year
calculated for each of the cohorts studied prospectively for
validation. The growth response in the validation groups was not
significantly different from that predicted in any of the treatment
years, although there was a tendency for greater than predicted growth
in most years in the validation cohorts (Fig. 3).
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).
There were no statistically significant differences between the
predicted and the observed growth responses in any of the 4 treatment
yr (Table 3a and Fig. 3).
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). Although
there were no overall differences in MPH, birth weight, and dose of GH
between the OZGROW and KIGS cohorts, the Australian patients were
younger, smaller, lighter, and had a lower maximum GH response to
provocation testing (P < 0.05) compared to the
original KIGS cohort. Despite these differences, there were no
statistically significant differences between the predicted and the
observed growth responses for each of the 4 yr (P >
0.1; Table 3b and Fig. 3).
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). The Tubingen patients were the youngest of all
the groups used for validation, although their age was not
significantly different from that of the OZGROW cohort. Once again,
there were no significant differences in the Tubingen cohort between
the predicted and observed growth responses in all 4 treatment yr
(P > 0.1; Table 3c and Fig. 3). The weekly dose of GH
did not differ significantly over the years of observation in any of
the cohorts used for validation (P > 0.1).
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Discussion |
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In contrast, it is difficult to define, in numerical terms, what the appropriate level of growth should be to reach the goals set. This problem applies when considering groups of patients as well as individual patients. The response to GH treatment is a function of treatment modalities, such as the dose of GH, the frequency of injections, and the substitution of other hormonal deficiencies as well as the responsiveness of the individual. The latter may be determined by parameters such as the severity of the hormonal deficit(s), the patient’s age, pubertal status, and other biological and genetic characteristics.
Other studies examining the growth response during the first year of GH replacement therapy in patients with GHD have shown positive correlations between height velocity and GH dose (18, 19, 20, 21, 22, 23), frequency of GH injections (23, 24), and skinfold thickness (18, 22, 25) and negative correlations between height velocity and age (20, 25), bone age (19, 25), height (22, 25), weight for height index (22, 25), and height velocity before treatment (22). The adult height of patients with GHD who were treated with GH has been shown to correlate positively with height SD score at the start of treatment (3), height at onset of puberty (2, 3), MPH SD score, and height velocity during the first year of treatment (26). Final height has been shown to negatively correlate with the age at the start of GH therapy (27). The predictive utility of these findings is limited because most of these results were obtained from relatively small heterogeneous groups of patients using simple analytical techniques.
In recent years, however, the factors determining the growth response to exogenous GH in patients with GHD have been analyzed in better defined, larger cohorts followed within multicenter studies using multiple regression analysis (17, 26, 28, 29, 30). For example, Blethen and colleagues (28) studied a cohort of 523 patients with idiopathic GHD from the National Cooperative Growth Study, and showed that the first year height velocity SD score in prepubertal children given GH replacement therapy was a function of age, body weight SD score, maximum GH response to provocative testing (ln), MPH, injection frequency, and GH dose (ln). Overall, 40% of the variability of the response was explained by a descriptive model based on these variables, but the error SD was not given.
A previous study from KIGS analyzed a cohort of 472 patients with idiopathic GHD (17). In this study, the first year height velocity in prepubertal children was a function of age, height SD score minus MPH SD score, GH injection frequency, birth weight SD score, dose of GH, and weight for height index. These parameters explained 56% of the variability of the response, with an error SD of 1.79 cm. The present analysis of KIGS data differed from this earlier analysis in that stricter inclusion criteria were employed. The present analysis was restricted to patients receiving six or seven injections per week, and data were only admitted if a year’s height velocity had been assessed between 11–13 months (the previous KIGS analysis allowed assessments to be made between 9–15 months). These differences account for the rather modest increase in the number of patients assessed and differences in the explained variability and precision between the previous and present models.
Nevertheless, the present first year model that excludes the maximum GH response to provocation testing confirmed the parameters of importance identified in the previous KIGS study. The present model explained 45% of the variability of the response with an error SD of 1.72 cm. Height SD score minus MPH SD score (i.e. the difference between present height and target height) was the most important of the predictors in the present model, and body weight SD score was a better predictor than weight for height index. The present model also found the GH dose (ln) to be a better predictor than the numerical GH dose. The frequency of GH injections was no longer a variable in the present model because only patients receiving 6 or 7 injections per week were included in the analysis; this regimen has now become common practice. Indeed, it is possible that the observation in this study of nonsignificant, slightly greater growth responses in the validation groups compared with those in the original KIGS cohort may reflect a greater percentage of patients in the validation groups receiving 7 injections of GH per week. About 50% of the original cohort of 593 KIGS patients were receiving GH in 6 injections/week.
When the maximum GH level (ln) during provocation testing was included in the present analysis, this parameter proved to be the most important of all the predictors. The differences in the predicted first year height velocity between the two present models only became relevant in cases where the maximum GH level during provocative testing was below 5 µg/L, that is in patients with severe GHD. In these individuals, the model excluding the parameter of maximum GH response to provocative testing tended to underpredict the growth response to GH replacement therapy. This finding indicates that the severity of a patient’s GHD is of major significance in determining his responsiveness to GH therapy regardless of his height deviation.
It is noteworthy that there was considerable heterogeneity between the centers participating in KIGS with regard to the methods used for provocation testing and assaying GH. It is therefore remarkable that the model that included the maximum GH response to testing predicted a growth response with a higher precision than the model excluding this parameter. It is important to note, however, that although GH response to testing is the most important predictor of the first year growth response, it is also the most difficult parameter to measure accurately and consistently. The effects of intercenter or interindividual differences in this value are likely to be attenuated when considering the mean growth responses in large cohorts. However, when individual patients are considered, inconsistencies in this variable could introduce a source of error into the prediction. It is therefore important that standardization is achieved in the measurement of this variable if it is to be used in the prediction of an individual’s growth. For this reason, we recommend that the highest peak GH level recorded from two separate provocation tests is used.
That height deficit, age, severity of GHD, and GH dose were found to be important predictors of the first year growth response to GH therapy is compatible with our current understanding. However, there is no simple explanation for why the parameters of weight at birth and current weight are independent predictors in these models. It is possible that birth weight reflects the overall responsiveness of the organism to growth-promoting factors, whereas body weight SD score may reflect the eating habits and metabolic handling of nutrition by the organism.
Height velocities during the second, third, and fourth prepubertal years of GH therapy were predicted by the same four variables. These were age, body weight SD score, GH dose (ln), and height velocity during the previous year. Height velocity during the previous year was the most important predictor for second and third year growth responses, suggesting that the eventual height outcome for a patient may be indicated by his initial response to GH. The overall variabilities of response in the second to fourth year models were lower than those for the first year model, and the error SDs were smaller. These findings indicate that the second to fourth year models give more precise predictions, perhaps reflecting a more steady growth pattern after an initial phase of catch-up growth.
Not only were our prediction models selected for their ability to give a high degree of predictive power (high R), they were also required to have the ability to predict growth with high accuracy (low error SD). A low error SD is an important prerequisite if the model is to have practical utility as a predictive tool for individual patients. Our models do provide high accuracy, although their predictive power is relatively low, with the exception of those concerning first year growth response. This means that further parameters, explaining more of the variability of the response, may be missing from the models. Such parameters need to be identified in the future, but if they are to have practical utility, it is mandatory that their measurements should be standardized. An advantage of our present models is that they are based on robust and easily accessible parameters (31, 32). A recent study by Kriström et al. (33), in short prepubertal children with various capacities for GH secretion illustrated how greater predictive power can be achieved when further variables concerning the responsiveness of the GH axis are included in the regression analysis. It was possible to explain 58% of the variability of first year growth response to GH therapy by including parameters such as baseline serum levels of insulin-like growth factor I (IGF-I) and IGF-binding protein-3, as well as changes in IGF-I levels in response to GH. However, omitting measures of GH-IGF axis responsiveness, and hence restricting the model to more easily accessible parameters, resulted in the regression algorithms predicting only 40–43% of the variance.
Previously, prediction models have not been validated on the basis of independent data sources. The attempt to validate the prediction models developed in this study was made with two relatively small cohorts of patients (from OZGROW and one center in Tubingen) and a larger cohort from KIGS. These populations reflect the wide variety of patients with idiopathic GHD at the onset of GH therapy, and, in the case of the OZGROW and Tubingen cohorts, the different brands of recombinant human GH that can be used as treatment. Nevertheless, there was no statistically significant difference between the observed and predicted height velocities in the different groups and years. This observation confirms the predictive usefulness of the algorithms, and suggests equivalent efficacy for the different recombinant human GH products used in the validation cohorts. The range of Studentized residuals, however, did tend to be slightly greater in the groups used for validation. Remarkably, the prediction model for the fourth prepubertal year was also found to be useful for predicting growth in the fifth to eighth prepubertal years in the KIGS cohort. This finding appears to support the clinical observation of steady growth after an initial phase of catch-up growth.
It is intended that these prediction models will become widely available to endocrinologists through the development of user-friendly computer programs. Other models dealing with pubertal growth and the growth responses in patients with other causes of short stature are also in development. The introduction of these may aid clinicians in several ways. For example, prediction models could be used to calculate expected height velocities (mean, range) at the onset of GH treatment on the basis of the characteristics of the patients and putative treatment modalities. Differences (particularly negative) between the observed and predicted height velocities will be apparent if precise predictions have been made. Potential explanations for height velocities below predicted values include poor compliance, the presence of other hormonal deficiencies, misdiagnosis, primarily impaired responsiveness such as GH insensitivity, poor nutrition, and other concurrent diseases. These possibilities can be investigated and managed appropriately. Prediction models for prepubertal and pubertal growth (34) could also aid in the design of individual treatment regimens from the time of diagnosis, helping to plan a child’s treatment in advance from the start of their therapy until the end of growth. The amount of GH given per yr can be varied hypothetically within the models to estimate the effects at different phases of growth. This information can be used together with defined treatment goals (e.g. achievement of early catch-up growth and final height within the normal range or within the familial target range) for planning treatment. By including the empirical information stepwise during the process of growth, prediction models can be used to aid the optimization of treatment in pursuit of these goals. Prediction models may also help to provide patients, their families, treating physicians, and health providers with realistic expectations of the short term (yearly) and long term growth outcomes of treatment. Realistic expectations, rather than unspecific hopes, are a better basis on which to establish patient compliance. Furthermore, if the treating physician has a realistic expectation of the patient’s likely response, then unnecessary dose changes or inappropriate cessation of GH treatment may also be avoided.
Received September 10, 1998.
Revised January 4, 1999.
Accepted January 19, 1999.
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