Dataset Information

Brazilian pediatric reference data for quantitative ultrasound of phalanges according to gender, age, height and weight.

ABSTRACT: To establish normative data for phalangeal quantitative ultrasound (QUS) measures in Brazilian students.The sample was composed of 6870 students (3688 females and 3182 males), aged 6 to 17 years. The bone status parameter, Amplitude Dependent Speed of Sound (AD-SoS) was assessed by QUS of the phalanges using DBM Sonic BP (IGEA, Carpi, Italy) equipment. Skin color was obtained by self-evaluation. The LMS method was used to derive smoothed percentiles reference charts for AD-SoS according to sex, age, height and weight and to generate the L, M, and S parameters.Girls showed higher AD-SoS values than boys in the age groups 7-16 (p<0.001). There were no differences on AD-SoS Z-scores according to skin color. In both sexes, the obese group showed lower values of AD-SoS Z-scores compared with subjects classified as thin or normal weight. Age (r2 = 0.48) and height (r2 = 0.35) were independent predictors of AD-SoS in females and males, respectively.AD-SoS values in Brazilian children and adolescents were influenced by sex, age and weight status, but not by skin color. Our normative data could be used for monitoring AD-SoS in children or adolescents aged 6-17 years.

SUBMITTER: Goncalves EM

PROVIDER: S-EPMC4456168 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Brazilian pediatric reference data for quantitative ultrasound of phalanges according to gender, age, height and weight.

Gonçalves Ezequiel Moreira EM Ribeiro Roberto Regis RR de Carvalho Wellington Roberto Gomes WR de Moraes Anderson Marques AM Roman Everton Paulo EP Santos Keila Donassolo KD Medaets Pedro Augusto Rodrigues PA Veiga-Junior Nélio Neves NN Coelho Adrielle Caroline Lace de Moraes AC Krahenbühl Tathyane T Sewaybricker Leticia Esposito LE Barros-Filho Antonio de Azevedo Ade A Morcillo Andre Moreno AM Guerra-Júnior Gil G

PloS one 20150604 6

<h4>Aims</h4>To establish normative data for phalangeal quantitative ultrasound (QUS) measures in Brazilian students.<h4>Methods</h4>The sample was composed of 6870 students (3688 females and 3182 males), aged 6 to 17 years. The bone status parameter, Amplitude Dependent Speed of Sound (AD-SoS) was assessed by QUS of the phalanges using DBM Sonic BP (IGEA, Carpi, Italy) equipment. Skin color was obtained by self-evaluation. The LMS method was used to derive smoothed percentiles reference charts ...[more]

PMID: 26043082

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Project description:There has been no machine learning study with a rich collection of clinical, sonographic markers to compare the performance measures for a variety of newborns' weight-for-height indicators. This study compared the performance measures for a variety of newborns' weight-for-height indicators based on machine learning, ultrasonographic data and maternal/delivery information. The source of data for this study was a multi-center retrospective study with 2949 mother-newborn pairs. The mean-squared-error-over-variance measures of five machine learning approaches were compared for newborn's weight, newborn's weight/height, newborn's weight/height2 and newborn's weight/hieght3. Random forest variable importance, the influence of a variable over average node impurity, was used to identify major predictors of these newborns' weight-for-height indicators among ultrasonographic data and maternal/delivery information. Regarding ultrasonographic fetal biometry, newborn's weight, newborn's weight/height and newborn's weight/height2 were better indicators with smaller mean-squared-error-over-variance measures than newborn's weight/height3. Based on random forest variable importance, the top six predictors of newborn's weight were the same as those of newborn's weight/height and those of newborn's weight/height2: gestational age at delivery time, the first estimated fetal weight and abdominal circumference in week 36 or later, maternal weight and body mass index at delivery time, and the first biparietal diameter in week 36 or later. These six predictors also ranked within the top seven for large-for-gestational-age and the top eight for small-for-gestational-age. In conclusion, newborn's weight, newborn's weight/height and newborn's weight/height2 are more suitable for ultrasonographic fetal biometry with smaller mean-squared-error-over-variance measures than newborn's weight/height3. Machine learning with ultrasonographic data would be an effective noninvasive approach for predicting newborn's weight, weight/height and weight/height2.

Dataset Information

Brazilian pediatric reference data for quantitative ultrasound of phalanges according to gender, age, height and weight.

Publications

Brazilian pediatric reference data for quantitative ultrasound of phalanges according to gender, age, height and weight.

Similar Datasets

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