18
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Development and validation of a prediction model for fat mass in children and adolescents: meta-analysis using individual participant data

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Objectives

          To develop and validate a prediction model for fat mass in children aged 4-15 years using routinely available risk factors of height, weight, and demographic information without the need for more complex forms of assessment.

          Design

          Individual participant data meta-analysis.

          Setting

          Four population based cross sectional studies and a fifth study for external validation, United Kingdom.

          Participants

          A pooled derivation dataset (four studies) of 2375 children and an external validation dataset of 176 children with complete data on anthropometric measurements and deuterium dilution assessments of fat mass.

          Main outcome measure

          Multivariable linear regression analysis, using backwards selection for inclusion of predictor variables and allowing non-linear relations, was used to develop a prediction model for fat-free mass (and subsequently fat mass by subtracting resulting estimates from weight) based on the four studies. Internal validation and then internal-external cross validation were used to examine overfitting and generalisability of the model’s predictive performance within the four development studies; external validation followed using the fifth dataset.

          Results

          Model derivation was based on a multi-ethnic population of 2375 children (47.8% boys, n=1136) aged 4-15 years. The final model containing predictor variables of height, weight, age, sex, and ethnicity had extremely high predictive ability (optimism adjusted R 2: 94.8%, 95% confidence interval 94.4% to 95.2%) with excellent calibration of observed and predicted values. The internal validation showed minimal overfitting and good model generalisability, with excellent calibration and predictive performance. External validation in 176 children aged 11-12 years showed promising generalisability of the model (R 2: 90.0%, 95% confidence interval 87.2% to 92.8%) with good calibration of observed and predicted fat mass (slope: 1.02, 95% confidence interval 0.97 to 1.07). The mean difference between observed and predicted fat mass was −1.29 kg (95% confidence interval −1.62 to −0.96 kg).

          Conclusion

          The developed model accurately predicted levels of fat mass in children aged 4-15 years. The prediction model is based on simple anthropometric measures without the need for more complex forms of assessment and could improve the accuracy of assessments for body fatness in children (compared with those provided by body mass index) for effective surveillance, prevention, and management of clinical and public health obesity.

          Related collections

          Most cited references29

          • Record: found
          • Abstract: found
          • Article: not found

          Beyond body mass index.

          Body mass index (BMI) is the cornerstone of the current classification system for obesity and its advantages are widely exploited across disciplines ranging from international surveillance to individual patient assessment. However, like all anthropometric measurements, it is only a surrogate measure of body fatness. Obesity is defined as an excess accumulation of body fat, and it is the amount of this excess fat that correlates with ill-health. We propose therefore that much greater attention should be paid to the development of databases and standards based on the direct measurement of body fat in populations, rather than on surrogate measures. In support of this argument we illustrate a wide range of conditions in which surrogate anthropometric measures (especially BMI) provide misleading information about body fat content. These include: infancy and childhood; ageing; racial differences; athletes; military and civil forces personnel; weight loss with and without exercise; physical training; and special clinical circumstances. We argue that BMI continues to serve well for many purposes, but that the time is now right to initiate a gradual evolution beyond BMI towards standards based on actual measurements of body fat mass.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Regression modelling strategies for improved prognostic prediction.

            Regression models such as the Cox proportional hazards model have had increasing use in modelling and estimating the prognosis of patients with a variety of diseases. Many applications involve a large number of variables to be modelled using a relatively small patient sample. Problems of overfitting and of identifying important covariates are exacerbated in analysing prognosis because the accuracy of a model is more a function of the number of events than of the sample size. We used a general index of predictive discrimination to measure the ability of a model developed on training samples of varying sizes to predict survival in an independent test sample of patients suspected of having coronary artery disease. We compared three methods of model fitting: (1) standard 'step-up' variable selection, (2) incomplete principal components regression, and (3) Cox model regression after developing clinical indices from variable clusters. We found regression using principal components to offer superior predictions in the test sample, whereas regression using indices offers easily interpretable models nearly as good as the principal components models. Standard variable selection has a number of deficiencies.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Regression modelling strategies for improved prognostic prediction

              Regression models such as the Cox proportional hazards model have had increasing use in modelling and estimating the prognosis of patients with a variety of diseases. Many applications involve a large number of variables to be modelled using a relatively small patient sample. Problems of overfitting and of identifying important covariates are exacerbated in analysing prognosis because the accuracy of a model is more a function of the number of events than of the sample size. We used a general index of predictive discrimination to measure the ability of a model developed on training samples of varying sizes to predict survival in an independent test sample of patients suspected of having coronary artery disease. We compared three methods of model fitting: (1) standard 'step-up' variable selection, (2) incomplete principal components regression, and (3) Cox model regression after developing clinical indices from variable clusters. We found regression using principal components to offer superior predictions in the test sample, whereas regression using indices offers easily interpretable models nearly as good as the principal components models. Standard variable selection has a number of deficiencies.
                Bookmark

                Author and article information

                Contributors
                Role: PhD student
                Role: professor of paediatric nutrition
                Role: associate professor in nutrition
                Role: honorary senior research associate
                Role: research nurse
                Role: professor of anthropology and paediatric nutrition
                Role: professor of biostatistics
                Role: professor of epidemiology
                Role: professor of epidemiology
                Role: professor of statistical epidemiology
                Role: professor of cardiovascular epidemiology
                Role: lecturer in medical statistics and epidemiology
                Journal
                BMJ
                BMJ
                BMJ-UK
                bmj
                The BMJ
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2019
                24 July 2019
                : 366
                : l4293
                Affiliations
                [1 ]Population Health Research Institute, St George’s, University of London, London SW17 0RE, UK
                [2 ]Population, Policy and Practice Programme, UCL Great Ormond Street Institute of Child Health, London, UK
                [3 ]College of Natural and Health Sciences, Department of Public Health and Nutrition, Zayed University, Dubai, UAE
                [4 ]Respiratory, Critical Care and Anaesthesia section of III Programme, UCL Great Ormond Street Institute of Child Health, London, UK
                [5 ]Centre for Prognosis Research, Research Institute for Primary Care and Health Sciences, Keele University, Staffordshire, UK
                Author notes
                Correspondence to: C M Nightingale cnightin@ 123456sgul.ac.uk (or @mohammedhudda on Twitter)
                Author information
                http://orcid.org/0000-0002-4803-7617
                Article
                hudm049262
                10.1136/bmj.l4293
                6650932
                31340931
                ee5c729b-13dd-4eb5-bdbf-32c76abc86d5
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 06 June 2019
                Categories
                Research
                1778

                Medicine
                Medicine

                Comments

                Comment on this article