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Akkaya Hocagil T, Hwang H, Jacobson JL, Jacobson SW, Ryan LM. Meta-analysis on studies with heterogeneous and partially observed covariates. JBI Evid Synth 2024; 22:413-433. [PMID: 38475899 DOI: 10.11124/jbies-23-00078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Individual participant data meta-analysis is a commonly used alternative to the traditional aggregate data meta-analysis. It is popular because it avoids relying on published results and enables direct adjustment for relevant covariates. However, a practical challenge is that the studies being combined often vary in terms of the potential confounders that were measured. Furthermore, it will inevitably be the case that some individuals have missing values for some of those covariates. In this paper, we demonstrate how these challenges can be resolved using a propensity score approach, combined with multiple imputation, as a strategy to adjust for covariates in the context of individual participant data meta-analysis. To illustrate, we analyze data from the Bill and Melinda Gates Foundation-funded Healthy Birth, Growth, and Development Knowledge Integration project to investigate the relationship between physical growth rate in the first year of life and cognition measured later during childhood. We found that the overall effect of average growth velocity on cognitive outcome is slightly, but significantly, positive with an estimated effect size of 0.36 (95% CI 0.18, 0.55).
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Affiliation(s)
- Tugba Akkaya Hocagil
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada
- Department of Biostatistics, Ankara University School of Medicine, Ankara, Turkiye
| | - Hon Hwang
- School of Mathematical and Physical Sciences, University of Technology Sydney, Sydney, NSW, Australia
| | - Joseph L Jacobson
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, USA
| | - Sandra W Jacobson
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University School of Medicine, Detroit, MI, USA
| | - Louise M Ryan
- School of Mathematical and Physical Sciences, University of Technology Sydney, Sydney, NSW, Australia
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Walters WA, Ley C, Hastie T, Ley RE, Parsonnet J. A modified Michaelis-Menten equation estimates growth from birth to 3 years in healthy babies in the USA. BMC Med Res Methodol 2024; 24:27. [PMID: 38302887 PMCID: PMC10832211 DOI: 10.1186/s12874-024-02145-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 01/08/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis-Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated whether this equation could be used to interpolate missing growth data in children in the first three years of life and compared this interpolation to several common interpolation methods and pediatric growth models. METHODS We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N = 97) then in a large, outpatient, pediatric sample (N = 14,695). RESULTS The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22 kg [IQR:0.19; 90% < 0.43]; girls: 0.20 kg [IQR:0.17; 90% < 0.39]) and height (median RMSE: boys: 0.93 cm [IQR:0.53; 90% < 1.0]; girls: 0.91 cm [IQR:0.50;90% < 1.0]). Growth data were modeled accurately with as few as four values from routine well-baby visits in year 1 and seven values in years 1-3; birth weight or length was essential for best fit. Interpolation with this equation had comparable (for weight) or lower (for height) mean RMSE compared to the best performing alternative models. CONCLUSIONS A modified Michaelis-Menten equation accurately describes growth in healthy babies aged 0-36 months, allowing interpolation of missing weight and height values in individual longitudinal measurement series. The growth pattern in healthy babies in resource-rich environments mirrors an enzymatic saturation curve.
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Affiliation(s)
- William A Walters
- Department of Microbiome Science, Max Planck Institute for Biology, Tübingen, Germany
| | - Catherine Ley
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305-5170, USA.
| | - Trevor Hastie
- Departments of Statistics and of Biomedical Data Sciences, Stanford University, Stanford, CA, USA
| | - Ruth E Ley
- Department of Microbiome Science, Max Planck Institute for Biology, Tübingen, Germany
| | - Julie Parsonnet
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305-5170, USA
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
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Rocha AS, Ribeiro-Silva RDC, Silva JFM, Pinto EJ, Silva NJ, Paixao ES, Fiaccone RL, Kac G, Rodrigues LC, Anderson C, Barreto ML. Postnatal growth in small vulnerable newborns: a longitudinal study of 2 million Brazilians using routine register-based linked data. Am J Clin Nutr 2024; 119:444-455. [PMID: 38128734 PMCID: PMC10884605 DOI: 10.1016/j.ajcnut.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/21/2023] [Accepted: 12/18/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Preterm, low-birth weight (LBW) and small-for-gestational age (SGA) newborns have a higher frequency of adverse health outcomes, including linear and ponderal growth impairment. OBJECTIVE To describe the growth trajectories and to estimate catch-up growth during the first 5 y of life of small newborns according to 3 vulnerability phenotypes (preterm, LBW, SGA). METHODS Longitudinal study using linked data from the 100 Million Brazilian Cohort baseline, the Brazilian National Live Birth System (SINASC), and the Food and Nutrition Surveillance System (SISVAN) from 2011 to 2017. We estimated the length/height-for-age (L/HAZ) and weight-for-age z-score (WAZ) trajectories from children of 6-59 mo using the linear mixed model for each vulnerable newborn phenotype. Growth velocity for both L/HAZ and WAZ was calculated considering the change (Δ) in the mean z-score between 2 time points. Catch-up growth was defined as a change in z-score > 0.67 at any time during follow-up. RESULTS We analyzed 2,021,998 live born children and 8,726,599 observations. The prevalence of at least one of the vulnerable phenotypes was 16.7% and 0.6% were simultaneously preterm, LBW, and SGA. For those born at term, all phenotypes had a period of growth recovery from 12 mo. For preterm infants, the onset of L/HAZ growth recovery started later at 24 mo and the growth trajectories appear to be lower than those born at term, a condition aggravated among children with the 3 phenotypes. Preterm and female infants seem to experience slower growth recovery than those born at term and males. The catch-up growth occurs at 24-59 mo for males preterm: preterm + AGA + NBW (Δ = 0.80), preterm + AGA + LBW (Δ = 0.88), and preterm + SGA + LBW (Δ = 1.08); and among females: term + SGA + NBW (Δ = 0.69), term + AGA + LBW (Δ = 0.72), term + SGA + LBW (Δ = 0.77), preterm + AGA + LBW (Δ = 0.68), and preterm + SGA + LBW (Δ = 0.83). CONCLUSIONS Children born preterm seem to reach L/HAZ and WAZ growth trajectories lower than those attained by children born at term, a condition aggravated among the most vulnerable.
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Affiliation(s)
- Aline S Rocha
- Center of Data and Knowledge Integration for Health (CIDACS), Oswaldo Cruz Foundation, Salvador, Brazil; School of Nutrition, Federal University of Bahia (UFBA), Salvador, Brazil.
| | - Rita de Cássia Ribeiro-Silva
- Center of Data and Knowledge Integration for Health (CIDACS), Oswaldo Cruz Foundation, Salvador, Brazil; School of Nutrition, Federal University of Bahia (UFBA), Salvador, Brazil; Institute of Collective Health, Federal University of Bahia (ISC/UFBA), Salvador, Brazil
| | - Juliana F M Silva
- Center of Data and Knowledge Integration for Health (CIDACS), Oswaldo Cruz Foundation, Salvador, Brazil
| | - Elizabete J Pinto
- Center of Data and Knowledge Integration for Health (CIDACS), Oswaldo Cruz Foundation, Salvador, Brazil; Health Sciences Center, Federal University of Recôncavo da Bahia, Santo Antônio de Jesus, Brazil
| | - Natanael J Silva
- Center of Data and Knowledge Integration for Health (CIDACS), Oswaldo Cruz Foundation, Salvador, Brazil; ISGlobal, Hospital Clínic. Universitat de Barcelona, Barcelona, Spain
| | - Enny S Paixao
- Center of Data and Knowledge Integration for Health (CIDACS), Oswaldo Cruz Foundation, Salvador, Brazil; Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
| | - Rosemeire L Fiaccone
- Center of Data and Knowledge Integration for Health (CIDACS), Oswaldo Cruz Foundation, Salvador, Brazil; Department of Statistics, Federal University of Bahia (UFBA), Salvador, Brazil
| | - Gilberto Kac
- Nutritional Epidemiology Observatory, Josué de Castro Nutrition Institute, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Laura C Rodrigues
- Center of Data and Knowledge Integration for Health (CIDACS), Oswaldo Cruz Foundation, Salvador, Brazil; Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Craig Anderson
- School of Mathematics and Statistics, University of Glasgow, Scotland, United Kingdom
| | - Mauricio L Barreto
- Center of Data and Knowledge Integration for Health (CIDACS), Oswaldo Cruz Foundation, Salvador, Brazil; Institute of Collective Health, Federal University of Bahia (ISC/UFBA), Salvador, Brazil
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Carrilho TRB, Silva NDJ, Paixão ES, Falcão IR, Fiaccone RL, Rodrigues LC, Katikireddi SV, Leyland AH, Dundas R, Pearce A, Velasquez-Melendez G, Kac G, Silva RDCR, Barreto ML. Maternal and child nutrition programme of investigation within the 100 Million Brazilian Cohort: study protocol. BMJ Open 2023; 13:e073479. [PMID: 37673446 PMCID: PMC10496662 DOI: 10.1136/bmjopen-2023-073479] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 08/18/2023] [Indexed: 09/08/2023] Open
Abstract
INTRODUCTION There is a limited understanding of the early nutrition and pregnancy determinants of short-term and long-term maternal and child health in ethnically diverse and socioeconomically vulnerable populations within low-income and middle-income countries. This investigation programme aims to: (1) describe maternal weight trajectories throughout the life course; (2) describe child weight, height and body mass index (BMI) trajectories; (3) create and validate models to predict childhood obesity at 5 years of age; (4) estimate the effects of prepregnancy BMI, gestational weight gain (GWG) and maternal weight trajectories on adverse maternal and neonatal outcomes and child growth trajectories; (5) estimate the effects of prepregnancy BMI, GWG, maternal weight and interpregnancy BMI changes on maternal and child outcomes in the subsequent pregnancy; and (6) estimate the effects of maternal food consumption and infant feeding practices on child nutritional status and growth trajectories. METHODS AND ANALYSIS Linked data from four different Brazilian databases will be used: the 100 Million Brazilian Cohort, the Live Births Information System, the Mortality Information System and the Food and Nutrition Surveillance System. To analyse trajectories, latent-growth, superimposition by translation and rotation and broken stick models will be used. To create prediction models for childhood obesity, machine learning techniques will be applied. For the association between the selected exposure and outcomes variables, generalised linear models will be considered. Directed acyclic graphs will be constructed to identify potential confounders for each analysis investigating potential causal relationships. ETHICS AND DISSEMINATION This protocol was approved by the Research Ethics Committees of the authors' institutions. The linkage will be carried out in a secure environment. After the linkage, the data will be de-identified, and pre-authorised researchers will access the data set via a virtual private network connection. Results will be reported in open-access journals and disseminated to policymakers and the broader public.
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Affiliation(s)
- Thais Rangel Bousquet Carrilho
- Nutritional Epidemiology Observatory, Josué de Castro Institute of Nutrition, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Natanael de Jesus Silva
- Centre for Data and Knowledge Integration for Health, Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil
- Barcelona Institute for Global Health, Hospital Clínic, University of Barcelona, Barcelona, Catalunya, Spain
| | - Enny Santos Paixão
- Centre for Data and Knowledge Integration for Health, Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil
- Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, London, UK
| | - Ila Rocha Falcão
- Centre for Data and Knowledge Integration for Health, Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil
- School of Nutrition, Federal University of Bahia, Salvador, BA, Brazil
| | - Rosemeire Leovigildo Fiaccone
- Centre for Data and Knowledge Integration for Health, Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil
- Institute of Mathematics and Statistics, Federal University of Bahia, Salvador, BA, Brazil
| | - Laura Cunha Rodrigues
- Centre for Data and Knowledge Integration for Health, Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, London, UK
| | | | - Alastair H Leyland
- MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, Scotland, UK
| | - Ruth Dundas
- MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, Scotland, UK
| | - Anna Pearce
- MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, Scotland, UK
| | - Gustavo Velasquez-Melendez
- Department of Maternal and Child Nursing and Public Health, Nursing School, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil
| | - Gilberto Kac
- Nutritional Epidemiology Observatory, Josué de Castro Institute of Nutrition, Federal University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
| | - Rita de Cássia Ribeiro Silva
- Centre for Data and Knowledge Integration for Health, Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil
- School of Nutrition, Federal University of Bahia, Salvador, BA, Brazil
| | - Mauricio L Barreto
- Centre for Data and Knowledge Integration for Health, Gonçalo Moniz Institute, Oswaldo Cruz Foundation, Salvador, BA, Brazil
- Institute of Collective Health, Federal University of Bahia, Salvador, BA, Brazil
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Walters W, Ley C, Hastie T, Ley R, Parsonnet J. A modified Michaelis-Menten equation estimates growth from birth to 3 years in healthy babies in the US. RESEARCH SQUARE 2023:rs.3.rs-2375831. [PMID: 36711501 PMCID: PMC9882604 DOI: 10.21203/rs.3.rs-2375831/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Background and Objectives Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis-Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated this equation could be used to interpolate missing growth data in children in the first three years of life. Methods We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N=97) then in a large, outpatient, pediatric sample (N=14,695). Results The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22kg [IQR:0.19; 90%<0.43]; girls: 0.20kg [IQR:0.17; 90%<0.39]) and height (median RMSE: boys: 0.93cm [IQR:0.53; 90%<1.0]; girls: 0.91cm [IQR:0.50;90%<1.0]). Growth data were modeled accurately with as few as four values from routine well-baby visits in year 1 and seven values in years 1-3; birth weight or length was essential for best fit. Conclusions A modified Michaelis-Menten equation accurately describes growth in healthy babies aged 0-36 months, allowing interpolation of missing weight and height values in individual longitudinal measurement series. The growth pattern in healthy babies in resource-rich environments mirrors an enzymatic saturation curve.
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Affiliation(s)
| | | | | | - Ruth Ley
- Max Plank Institute for Developmental Biology
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Walters W, Ley C, Hastie T, Ley R, Parsonnet J. A modified Michaelis-Menten equation estimates growth from birth to 3 years in healthy babies in the US. RESEARCH SQUARE 2023:rs.3.rs-2375831. [PMID: 36711501 PMCID: PMC9882604 DOI: 10.21203/rs.3.rs-2375831/v2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/13/2024]
Abstract
Background and Objectives Standard pediatric growth curves cannot be used to impute missing height or weight measurements in individual children. The Michaelis-Menten equation, used for characterizing substrate-enzyme saturation curves, has been shown to model growth in many organisms including nonhuman vertebrates. We investigated this equation could be used to interpolate missing growth data in children in the first three years of life. Methods We developed a modified Michaelis-Menten equation and compared expected to actual growth, first in a local birth cohort (N=97) then in a large, outpatient, pediatric sample (N=14,695). Results The modified Michaelis-Menten equation showed excellent fit for both infant weight (median RMSE: boys: 0.22kg [IQR:0.19; 90%<0.43]; girls: 0.20kg [IQR:0.17; 90%<0.39]) and height (median RMSE: boys: 0.93cm [IQR:0.53; 90%<1.0]; girls: 0.91cm [IQR:0.50;90%<1.0]). Growth data were modeled accurately with as few as four values from routine well-baby visits in year 1 and seven values in years 1-3; birth weight or length was essential for best fit. Conclusions A modified Michaelis-Menten equation accurately describes growth in healthy babies aged 0-36 months, allowing interpolation of missing weight and height values in individual longitudinal measurement series. The growth pattern in healthy babies in resource-rich environments mirrors an enzymatic saturation curve.
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Affiliation(s)
| | | | | | - Ruth Ley
- Max Plank Institute for Developmental Biology
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Affiliation(s)
- Nan M. Laird
- Department of Biostatistics Harvard T.H. Chan School of Public Health Boston Massachusetts USA
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8
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Karuppusami R, Antonisamy B, Premkumar PS. Functional principal component analysis for identifying the child growth pattern using longitudinal birth cohort data. BMC Med Res Methodol 2022; 22:76. [PMID: 35313828 PMCID: PMC8935724 DOI: 10.1186/s12874-022-01566-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 02/25/2022] [Indexed: 12/02/2022] Open
Abstract
Background Longitudinal studies are important to understand patterns of growth in children and limited in India. It is important to identify an approach for characterising growth trajectories to distinguish between children who have healthy growth and those growth is poor. Many statistical approaches are available to assess the longitudinal growth data and which are difficult to recognize the pattern. In this research study, we employed functional principal component analysis (FPCA) as a statistical method to find the pattern of growth data. The purpose of this study is to describe the longitudinal child growth trajectory pattern under 3 years of age using functional principal component method. Methods Children born between March 2002 and August 2003 (n = 290) were followed until their third birthday in three neighbouring slums in Vellore, South India. Field workers visited homes to collect details of morbidity twice a week. Height and weight were measured monthly from 1 month of age in a study-run clinic. Longitudinal child growth trajectory pattern were extracted using Functional Principal Component analysis using B-spline basis functions with smoothing parameters. Functional linear model was used to assess the factors association with the growth functions. Results We have obtained four FPCs explained by 86.5, 3.9, 3.1 and 2.2% of the variation respectively for the height functions. For height, 38% of the children’s had poor growth trajectories. Similarly, three FPCs explained 76.2, 8.8, and 4.7% respectively for the weight functions and 44% of the children’s had poor growth in their weight trajectories. Results show that gender, socio-economic status, parent’s education, breast feeding, and gravida are associated and, influence the growth pattern in children. Conclusions The FPC approach deals with subjects’ dynamics of growth and not with specific values at given times. FPC could be a better alternate approach for both dimension reduction and pattern detection. FPC may be used to offer greater insight for classification. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-022-01566-0.
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Affiliation(s)
- Reka Karuppusami
- Department of Biostatistics, Christian Medical College, Vellore, 632002, India
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Personalised Outcomes Forecasts of Supervised Exercise Therapy in Intermittent Claudication: An Application of Neighbours Based Prediction Methods with Routinely Collected Clinical Data. Eur J Vasc Endovasc Surg 2022; 63:594-601. [PMID: 35210160 DOI: 10.1016/j.ejvs.2021.12.040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 12/08/2021] [Accepted: 12/29/2021] [Indexed: 11/21/2022]
Abstract
OBJECTIVE Insights regarding individual patient prognosis may improve exercise therapy by informing patient expectations, promoting exercise adherence, and facilitating tailored care. Therefore, the aim was to develop and evaluate personalised outcomes forecasts for functional claudication distance over six months of supervised exercise therapy for patients with intermittent claudication. METHODS Data of 5 940 patients were eligible for analysis. Neighbours based predictions were generated via an adaptation of predictive mean matching. Data from the nearest 223 matches (a.k.a. neighbours) for an index patient were modelled via Generalised Additive Model for Location Scale and Shape (GAMLSS). The realised outcome measures were then evaluated against the GAMLSS model, and the average bias, coverage, and precision were calculated. Model calibration was analysed via within sample and of sample analyses. RESULTS Neighbours based predictions demonstrated small average bias (- 0.04 standard deviations; ideal = 0) and accurate average coverage (48.7% of realised data within 50% prediction interval; ideal = 50%). Moreover, neighbours based predictions improved prediction precision by 24%, compared with estimates derived from the whole sample. Both within sample and of sample testing showed predictions to be well calibrated. CONCLUSION Neighbours based prediction is a method for generating accurate personalised outcomes forecasts for patients with intermittent claudication undertaking supervised exercise therapy. Future work should examine the influence of personalised outcomes forecasts on clinical decisions and patient outcomes.
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Chen LZ, Holmes AJ, Zuo XN, Dong Q. Neuroimaging brain growth charts: A road to mental health. PSYCHORADIOLOGY 2021; 1:272-286. [PMID: 35028568 PMCID: PMC8739332 DOI: 10.1093/psyrad/kkab022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/03/2021] [Accepted: 12/17/2021] [Indexed: 12/30/2022]
Abstract
Mental disorders are common health concerns and contribute to a heavy global burden on our modern society. It is challenging to identify and treat them timely. Neuroimaging evidence suggests the incidence of various psychiatric and behavioral disorders is closely related to the atypical development of brain structure and function. The identification and understanding of atypical brain development provide chances for clinicians to detect mental disorders earlier, perhaps even prior to onset, and treat them more precisely. An invaluable and necessary method in identifying and monitoring atypical brain development are growth charts of typically developing individuals in the population. The brain growth charts can offer a series of standard references on typical neurodevelopment, representing an important resource for the scientific and medical communities. In the present paper, we review the relationship between mental disorders and atypical brain development from a perspective of normative brain development by surveying the recent progress in the development of brain growth charts, including four aspects on growth chart utility: 1) cohorts, 2) measures, 3) mechanisms, and 4) clinical translations. In doing so, we seek to clarify the challenges and opportunities in charting brain growth, and to promote the application of brain growth charts in clinical practice.
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Affiliation(s)
- Li-Zhen Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT 06511, USA
- Department of Psychiatry, Yale University, New Haven, CT 06511, USA
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- National Basic Science Data Center, Beijing 100190, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
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Neighbors-based prediction of physical function after total knee arthroplasty. Sci Rep 2021; 11:16719. [PMID: 34408167 PMCID: PMC8373960 DOI: 10.1038/s41598-021-94838-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 07/05/2021] [Indexed: 11/08/2022] Open
Abstract
The purpose of this study was to develop and test personalized predictions for functional recovery after Total Knee Arthroplasty (TKA) surgery, using a novel neighbors-based prediction approach. We used data from 397 patients with TKA to develop the prediction methodology and then tested the predictions in a temporally distinct sample of 202 patients. The Timed Up and Go (TUG) Test was used to assess physical function. Neighbors-based predictions were generated by estimating an index patient's prognosis from the observed recovery data of previous similar patients (a.k.a., the index patient's "matches"). Matches were determined by an adaptation of predictive mean matching. Matching characteristics included preoperative TUG time, age, sex and Body Mass Index. The optimal number of matches was determined to be m = 35, based on low bias (- 0.005 standard deviations), accurate coverage (50% of the realized observations within the 50% prediction interval), and acceptable precision (the average width of the 50% prediction interval was 2.33 s). Predictions were well-calibrated in out-of-sample testing. These predictions have the potential to inform care decisions both prior to and following TKA surgery.
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Anderson CB, Wurdeman SR, Miller MJ, Christiansen CL, Kittelson AJ. Development of a physical mobility prediction model to guide prosthetic rehabilitation. Prosthet Orthot Int 2021; 45:268-275. [PMID: 33840752 PMCID: PMC8422855 DOI: 10.1097/pxr.0000000000000001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 12/06/2020] [Indexed: 02/03/2023]
Abstract
BACKGROUND Prosthetic rehabilitation decisions depend on estimating a patient's mobility potential. However, no validated prediction models of mobility outcomes exist for people with lower-limb amputation (LLA). OBJECTIVES To develop and test predictions for self-reported mobility after LLA, using the Prosthetic Limb Users Survey of Mobility (PLUS-M). STUDY DESIGN This is a retrospective cohort analysis. METHODS Eight hundred thirty-one patient records (1,860 PLUS-M observations) were used to develop and test a neighbors-based prediction model, using previous patient data to predict the 6-month PLUS-M T-score trajectory for a new patient (based on matching characteristics). The prediction model was developed in a training data set (n = 552 patients) and tested in an out-of-sample data set of 279 patients with later visit dates. Prediction performance was assessed using bias, coverage, and precision. Prediction calibration was also assessed. RESULTS The average prediction bias for the model was 0.01 SDs, average coverage was 0.498 (ideal proportion within the 50% prediction interval = 0.5), and prediction interval was 8.4 PLUS-M T-score points (40% improvement over population-level estimates). Predictions were well calibrated, with the median predicted scores falling within the standard error of the median of observed scores, across all deciles of the data. CONCLUSIONS This neighbors-based prediction approach allows for accurate estimates of PLUS-M T-score trajectories for people with LLA.
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Affiliation(s)
- Chelsey B. Anderson
- Department of Physical Medicine and Rehabilitation, Physical Therapy Program, University of Colorado, Aurora, CO
| | - Shane R. Wurdeman
- Department of Clinical and Scientific Affairs, Hanger Clinic, Austin, TX
- Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE
| | - Matthew J. Miller
- Department of Physical Therapy and Rehabilitation Science, University of California, San Francisco, San Francisco, CA
- Division of Geriatrics, University of California, San Francisco, San Francisco, CA
| | - Cory L. Christiansen
- Department of Physical Medicine and Rehabilitation, Physical Therapy Program, University of Colorado, Aurora, CO
- Department of Geriatrics, Geriatric Research, Education, and Clinical Center, VA Eastern Colorado Healthcare System, Aurora, CO
| | - Andrew J. Kittelson
- Department of Physical Therapy and Rehabilitation Science, University of Montana, Missoula, MT
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Harrison E, Syed S, Ehsan L, Iqbal NT, Sadiq K, Umrani F, Ahmed S, Rahman N, Jakhro S, Ma JZ, Hughes M, Ali SA. Machine learning model demonstrates stunting at birth and systemic inflammatory biomarkers as predictors of subsequent infant growth - a four-year prospective study. BMC Pediatr 2020; 20:498. [PMID: 33126871 PMCID: PMC7597024 DOI: 10.1186/s12887-020-02392-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 10/15/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Stunting affects up to one-third of the children in low-to-middle income countries (LMICs) and has been correlated with decline in cognitive capacity and vaccine immunogenicity. Early identification of infants at risk is critical for early intervention and prevention of morbidity. The aim of this study was to investigate patterns of growth in infants up through 48 months of age to assess whether the growth of infants with stunting eventually improved as well as the potential predictors of growth. METHODS Height-for-age z-scores (HAZ) of children from Matiari (rural site, Pakistan) at birth, 18 months, and 48 months were obtained. Results of serum-based biomarkers collected at 6 and 9 months were recorded. A descriptive analysis of the population was followed by assessment of growth predictors via traditional machine learning random forest models. RESULTS Of the 107 children who were followed up till 48 months of age, 51% were stunted (HAZ < - 2) at birth which increased to 54% by 48 months of age. Stunting status for the majority of children at 48 months was found to be the same as at 18 months. Most children with large gains started off stunted or severely stunted, while all of those with notably large losses were not stunted at birth. Random forest models identified HAZ at birth as the most important feature in predicting HAZ at 18 months. Of the biomarkers, AGP (Alpha- 1-acid Glycoprotein), CRP (C-Reactive Protein), and IL1 (interleukin-1) were identified as strong subsequent growth predictors across both the classification and regressor models. CONCLUSION We demonstrated that children most children with stunting at birth remained stunted at 48 months of age. Value was added for predicting growth outcomes with the use of traditional machine learning random forest models. HAZ at birth was found to be a strong predictor of subsequent growth in infants up through 48 months of age. Biomarkers of systemic inflammation, AGP, CRP, IL1, were also strong predictors of growth outcomes. These findings provide support for continued focus on interventions prenatally, at birth, and early infancy in children at risk for stunting who live in resource-constrained regions of the world.
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Affiliation(s)
- Elizabeth Harrison
- School of Medicine, University of Virginia, Charlottesville, VA, USA.,Children's Hospital of Pittsburgh, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Sana Syed
- School of Medicine, University of Virginia, Charlottesville, VA, USA. .,Department of Paediatrics and Child Health, Aga Khan University, Stadium Road, P. O. Box 3500, Karachi, 74800, Pakistan.
| | - Lubaina Ehsan
- School of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Najeeha T Iqbal
- Department of Paediatrics and Child Health, Aga Khan University, Stadium Road, P. O. Box 3500, Karachi, 74800, Pakistan
| | - Kamran Sadiq
- Department of Paediatrics and Child Health, Aga Khan University, Stadium Road, P. O. Box 3500, Karachi, 74800, Pakistan
| | - Fayyaz Umrani
- Department of Paediatrics and Child Health, Aga Khan University, Stadium Road, P. O. Box 3500, Karachi, 74800, Pakistan
| | - Sheraz Ahmed
- Department of Paediatrics and Child Health, Aga Khan University, Stadium Road, P. O. Box 3500, Karachi, 74800, Pakistan
| | - Najeeb Rahman
- Department of Paediatrics and Child Health, Aga Khan University, Stadium Road, P. O. Box 3500, Karachi, 74800, Pakistan
| | - Sadaf Jakhro
- Department of Paediatrics and Child Health, Aga Khan University, Stadium Road, P. O. Box 3500, Karachi, 74800, Pakistan
| | - Jennie Z Ma
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Molly Hughes
- Department of Medicine, University of Virginia, Charlottesville, VA, USA
| | - S Asad Ali
- Department of Paediatrics and Child Health, Aga Khan University, Stadium Road, P. O. Box 3500, Karachi, 74800, Pakistan.
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14
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Ahmadi S, Bodeau-Livinec F, Zoumenou R, Garcia A, Courtin D, Alao J, Fievet N, Cot M, Massougbodji A, Botton J. Comparison of growth models to describe growth from birth to 6 years in a Beninese cohort of children with repeated measurements. BMJ Open 2020; 10:e035785. [PMID: 32948547 PMCID: PMC7511607 DOI: 10.1136/bmjopen-2019-035785] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
OBJECTIVE To select a growth model that best describes individual growth trajectories of children and to present some growth characteristics of this population. SETTINGS Participants were selected from a prospective cohort conducted in three health centres (Allada, Sekou and Attogon) in a semirural region of Benin, sub-Saharan Africa. PARTICIPANTS Children aged 0 to 6 years were recruited in a cohort study with at least two valid height and weight measurements included (n=961). PRIMARY AND SECONDARY OUTCOME MEASURES This study compared the goodness-of-fit of three structural growth models (Jenss-Bayley, Reed and a newly adapted version of the Gompertz growth model) on longitudinal weight and height growth data of boys and girls. The goodness-of-fit of the models was assessed using residual distribution over age and compared with the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The best-fitting model allowed estimating mean weight and height growth trajectories, individual growth and growth velocities. Underweight, stunting and wasting were also estimated at age 6 years. RESULTS The three models were able to fit well both weight and height data. The Jenss-Bayley model presented the best fit for weight and height, both in boys and girls. Mean height growth trajectories were identical in shape and direction for boys and girls while the mean weight growth curve of girls fell slightly below the curve of boys after neonatal life. Finally, 35%, 27.7% and 8% of boys; and 34%, 38.4% and 4% of girls were estimated to be underweight, wasted and stunted at age 6 years, respectively. CONCLUSION The growth parameters of the best-fitting Jenss-Bayley model can be used to describe growth trajectories and study their determinants.
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Affiliation(s)
- Shukrullah Ahmadi
- Université de Paris, Centre of Research in Epidemiology and Statistics /CRESS, INSERM, INRA, Paris, France
| | - Florence Bodeau-Livinec
- Université de Paris, Centre of Research in Epidemiology and Statistics /CRESS, INSERM, INRA, Paris, France
- EHESP, F-35000 Rennes, France
| | - Roméo Zoumenou
- Institut de Recherche pour le Développement (IRD), Cotonou, Benin
| | - André Garcia
- MERIT (Mère et Enfant Face aux Infections Tropicales)-UMR 216, Institut de Recherche pour le Développement (IRD), Université Paris Descartes, Paris, France
| | - David Courtin
- MERIT (Mère et Enfant Face aux Infections Tropicales)-UMR 216, Institut de Recherche pour le Développement (IRD), Université Paris Descartes, Paris, France
| | - Jules Alao
- Paediatric Department, Mother and Child University and Hospital Center (CHU-MEL), Cotonou, Benin
| | - Nadine Fievet
- MERIT (Mère et Enfant Face aux Infections Tropicales)-UMR 216, Institut de Recherche pour le Développement (IRD), Université Paris Descartes, Paris, France
| | - Michel Cot
- MERIT (Mère et Enfant Face aux Infections Tropicales)-UMR 216, Institut de Recherche pour le Développement (IRD), Université Paris Descartes, Paris, France
| | - Achille Massougbodji
- Faculté des Sciences de la Santé, Université d'Abomey-Calavi, Cotonou, Littoral, Benin
| | - Jérémie Botton
- EPI-PHARE Scientific Interest Group in Epidemiology of Health Products, French National Agency for the Safety of Medicines and Health Products and the French National Health Insurance, Saint-Denis, Ile-de-France, France
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Affiliation(s)
- Assaf Rabinowicz
- Department of Statistics and Operations Research, Tel-Aviv University, Tel-Aviv, Israel
| | - Saharon Rosset
- Department of Statistics and Operations Research, Tel-Aviv University, Tel-Aviv, Israel
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Anderson C, Hafen R, Sofrygin O, Ryan L. Comparing predictive abilities of longitudinal child growth models. Stat Med 2019; 38:3555-3570. [PMID: 30094965 PMCID: PMC6767565 DOI: 10.1002/sim.7693] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Revised: 12/15/2017] [Accepted: 04/02/2018] [Indexed: 12/20/2022]
Abstract
The Bill and Melinda Gates Foundation's Healthy Birth, Growth and Development knowledge integration project aims to improve the overall health and well-being of children across the world. The project aims to integrate information from multiple child growth studies to allow health professionals and policy makers to make informed decisions about interventions in lower and middle income countries. To achieve this goal, we must first understand the conditions that impact on the growth and development of children, and this requires sensible models for characterising different growth patterns. The contribution of this paper is to provide a quantitative comparison of the predictive abilities of various statistical growth modelling techniques based on a novel leave-one-out validation approach. The majority of existing studies have used raw growth data for modelling, but we show that fitting models to standardised data provide more accurate estimation and prediction. Our work is illustrated with an example from a study into child development in a middle income country in South America.
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Affiliation(s)
- Craig Anderson
- School of Mathematical and Physical SciencesUniversity of Technology Sydney
- ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
- School of Mathematics & StatisticsUniversity of Glasgow, University PlaceGlasgowG12 8QQUnited Kingdom
| | - Ryan Hafen
- Department of StatisticsPurdue University
| | - Oleg Sofrygin
- Division of BiostatisticsUniversity of CaliforniaBerkeleyCAUSA
| | - Louise Ryan
- School of Mathematical and Physical SciencesUniversity of Technology Sydney
- ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
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Ryan L. Four papers on child growth modelling. Stat Med 2019; 38:3505-3506. [PMID: 31184773 PMCID: PMC6771552 DOI: 10.1002/sim.8180] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 04/04/2019] [Accepted: 04/04/2019] [Indexed: 01/04/2023]
Affiliation(s)
- Louise Ryan
- University of Technology SydneyUltimoAustralia
- Australian Research Council Centre of Excellence in Mathematical and Statistical FrontiersParkvilleAustralia
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