Individual growth curve models for assessing evidence‐based referral criteria in growth monitoring

Abstract

SUMMARYThe goal of this study is to assess whether a growth curve model approach will lead to a more precise detection of Turner sydnrome (TS) than conventional referral criteria for growth monitoring. The JenssBayley growth curve model was used to describe the process of growth over time. A new screening rule is deÿned on the parameters of this growth curve model, parental height and gestational age. The rule is applied to longitudinal growth data of a group of children with TS (n = 777) and a reference (n = 487) group. The outcome measures are sensitivity, speciÿcity and median referral age. Growth curve parameters for TS children were di erent from reference children and can therefore be used for screening. The Jenss-Bayley growth model, which uses all longitudinal measurements from birth to a maximum age of 5 years with at least one measurement after the age of 2, together with parental height and gestational age can achieve a sensitivity of 85.2 per cent with a speciÿcity of 99.5 per cent and a median referral age of 4.2 (the last measurement between the age of 2 and 5 of each child is considered to be the moment of referral). Sensitivity increases by 2 percentage points when decreasing the speciÿcity to 99 per cent. The Jenss-Bayley growth model from birth to a maximum age of 8 years with at least one measurement after the age of 2, together with parental height results in a sensitivity of 89.0 per cent with a speciÿcity of 99.5 per cent and a median referral age of 6.1. For a speciÿcity of 98 per cent, we obtain a sensitivity of 92.3 per cent. In comparison to conventional rules applied to the same data, sensitivity is about 11-30 percentage points higher at the same level of speciÿcity for the Jenss-Bayley growth rule. We conclude that from the age of 4, growth curve models can improve the screening on TS to conventional screening rules.

Modelling individual longitudinal human growth from fetal to adult life − QEPS I

“…Childhood, the most stable growth period during the prepubertal years, may be the 'easiest' period during which to identify children with abnormal growth patterns [9]. However, variations in maturation and thereby tempo of growth in individual children during this phase need to be taken into account.…”

“…A second reason is to describe growth patterns during the different phases of growth in order to detect individuals growing abnormal, and to identify the negative influences that may impair growth, such as diseases and syndromes [9]. The detection of abnormal growth is usually the main goal when creating reference data for use in healthcare systems worldwide [2][3][4][5][10][11][12][13][14][15].…”

Multi-level modelling of longitudinal child growth data from the Birth-to-Twenty Cohort: a comparison of growth models

“…In a study of Indian children, Johnson et al (2012b) found that the Berkey-Reed 1st order model fitted better to infant weight and height data compared to other models such as the Count and 2nd order polynomial (quadratic) models. Studies that have modelled weight or height beyond 2 years have used models such as the Jenss-Bayley, Kouchi, adapted Jenss-Bayley and quadratic models and none of these did a comparative study on the fitness of the different models (Black & Krishnakumar, 1999; Botton et al, 2008; Dwyer et al, 1983; Martin-Gonzalez et al, 2012; van Dommelen et al, 2005). Some studies have used the quadratic model mainly for its simplicity and not necessarily because the model fits well to the data (Ehrenkranz et al, 1999; Grimm et al, 2011).…”

“…Thus, ecological and environmental influences that vary those monotonic functions are likely to lead to poorer fitting models, depending on the amount of variation that is driven by biological processes and the amount driven by ecological and environmental influences. Despite this, several studies have shown that the models can fit equally well to weight measurements (Botton et al, 2008; Dwyer et al, 1983; Johnson et al, 2012a, b; Pagezy & Hauspie, 1985; Simondon et al, 1992; van Dommelen et al, 2005). …”

Perinatal exposure to chlordecone and infant growth

“…The Jenss-Bayley non-linear mixed effects model (JB growth model) (Dommelen et al, 2005), which describes the growth of children from birth to eight years of age on the basis of four parameters, was fitted to individual weight and height curves. It was not possible to model BMI directly, because of the complex shape of its trajectory.…”