scholarly journals Establishment of a personalized fetal growth curve model

2021 ◽  
Vol 29 ◽  
pp. 311-317
Author(s):  
Xinyu Bao ◽  
Yan Wang ◽  
Song Zhang ◽  
Lin Yang ◽  
Guoli Liu ◽  
...  
Keyword(s):  
Birth Weight ◽  
Fetal Growth ◽  
Growth Curve ◽  
Growth And Development ◽  
Fetal Weight ◽  
Growth Curve Model ◽  
Normal Range ◽  
Normal Term ◽  
Curve Model ◽  
Gestational Week

BACKGROUND: Fetal weight is one of the important indicators for judging whether fetal growth and development are normal. Fetal weight exceeding the normal range may lead to poor delivery outcomes. OBJECTIVE: We aimed to establish a personalized fetal growth curve in order to effectively monitor fetal growth during pregnancy. Fetal weight can be monitored while fetal growth and development are assessed. METHODS: This study retrospectively analyzed the birth weight and ultrasound database of 3,093 newborns delivered at normal term. The personalized fetal growth curve model was generated based on the birth weight formula established by Gardosi combined with the proportional weight equation. RESULTS: (1) The average birth weight of the single fetus at normal term was 3,457g. (2) According to the regression results of the proportion of fetal weight in full-term pregnancy and gestational week, the proportional weight equation is Weight% = 500.9 - 51.60GA + 1.727GA2- 0.01718GA3 (GA is gestational week), R2 is 98%, P< 0.001. CONCLUSIONS: In this study, the normal birth weight of newborns and normal range of fetal weight can be estimated by using the personalized fetal growth curve model.

scholarly journals Fetal growth prediction: Establishing fetal growth prediction curves in the second trimester

2021 ◽  
Vol 29 ◽  
pp. 345-350
Author(s):  
Yan Wang ◽  
Xinyu Bao ◽  
Song Zhang ◽  
Lin Yang ◽  
Guoli Liu ◽  
...  
Keyword(s):  
Birth Weight ◽  
Fetal Growth ◽  
Growth Curve ◽  
Stepwise Regression ◽  
Fetal Weight ◽  
Maternal Height ◽  
Second Trimester ◽  
Growth Prediction ◽  
Curve Model ◽  
Input Model

BACKGROUND: Monitoring fetal weight during pregnancy has a guiding role in prenatal care. OBJECTIVE: To establish a personalized fetal growth curve for effectively monitoring fetal growth during pregnancy. METHODS: (1) This study retrospectively analyzed the birth weight database of 2,474 singleton newborns delivered normally at term. The personalized fetal growth curve model was formed by combining the estimating birth weight of newborns with the proportional weight formula. (2) Multiple linear stepwise regression method was used to estimate the birth weight of newborns. RESULTS: (1) Delivery gestational age, weight at first visit, maternal height, pre-pregnancy body mass index, fetal sex, parity had significant effects on birth weight. Based on these parameters, the formula for calculating term optimal weight was obtained (R2= 22.8%, P< 0.001). (2) The personalized fetal growth curve was obtained according to the epidemiological factors input model of each pregnant woman. CONCLUSIONS: A model of personalized fetal growth curve can be established, and be used to evaluate fetal growth and development through estimated fetal weight monitoring.

Problem gambling symptomatology and alcohol misuse among adolescents: A parallel-process latent growth curve model.

2017 ◽  
Vol 31 (4) ◽  
pp. 447-456 ◽  
Author(s):  
Seema Mutti-Packer ◽  
David C. Hodgins ◽  
Nady el-Guebaly ◽  
David M. Casey ◽  
Shawn R. Currie ◽  
...  
Keyword(s):  
Problem Gambling ◽  
Growth Curve ◽  
Alcohol Misuse ◽  
Parallel Process ◽  
Latent Growth Curve ◽  
Growth Curve Model ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Curve Model

Mental health outcomes in military veterans: A latent growth curve model.

2018 ◽  
Vol 24 (3) ◽  
pp. 228-235 ◽  
Author(s):  
Justin T. McDaniel ◽  
Kate H. Thomas ◽  
David L. Albright ◽  
Kari L. Fletcher ◽  
Margaret M. Shields
Keyword(s):  
Mental Health ◽  
Health Outcomes ◽  
Growth Curve ◽  
Military Veterans ◽  
Latent Growth Curve ◽  
Mental Health Outcomes ◽  
Growth Curve Model ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Curve Model

scholarly journals Swedish intrauterine growth reference ranges for estimated fetal weight

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Linda Lindström ◽  
Mårten Ageheim ◽  
Ove Axelsson ◽  
Laith Hussain-Alkhateeb ◽  
Alkistis Skalkidou ◽  
...  
Keyword(s):  
Fetal Growth ◽  
Postnatal Growth ◽  
Fetal Weight ◽  
Hierarchical Regression ◽  
Reference Ranges ◽  
Hierarchical Regression Model ◽  
Gestational Week ◽  
Prospective Longitudinal ◽  
Estimate Fetal Weight ◽  
Estimated Fetal Weight

AbstractFetal growth restriction is a strong risk factor for perinatal morbidity and mortality. Reliable standards are indispensable, both to assess fetal growth and to evaluate birthweight and early postnatal growth in infants born preterm. The aim of this study was to create updated Swedish reference ranges for estimated fetal weight (EFW) from gestational week 12–42. This prospective longitudinal multicentre study included 583 women without known conditions causing aberrant fetal growth. Each woman was assigned a randomly selected protocol of five ultrasound scans from gestational week 12 + 3 to 41 + 6. Hadlock’s 3rd formula was used to estimate fetal weight. A two-level hierarchical regression model was employed to calculate the expected median and variance, expressed in standard deviations and percentiles, for EFW. EFW was higher for males than females. The reference ranges were compared with the presently used Swedish, and international reference ranges. Our reference ranges had higher EFW than the presently used Swedish reference ranges from gestational week 33, and higher median, 2.5th and 97.5th percentiles from gestational week 24 compared with INTERGROWTH-21st. The new reference ranges can be used both for assessment of intrauterine fetal weight and growth, and early postnatal growth in children born preterm.

Decreased COVID-related adaptive behavior and increased negative affect: A multivariate latent growth curve model

2021 ◽  
pp. 135910532110216
Author(s):  
Hai-Ping Liao ◽  
Xiao-Fu Pan ◽  
Xue-Qin Yin ◽  
Ya-Fei Liu ◽  
Jie-Yang Li ◽  
...  
Keyword(s):  
Negative Affect ◽  
Adaptive Behavior ◽  
Growth Curve ◽  
Initial Level ◽  
Behavioral Changes ◽  
Latent Growth Curve ◽  
Growth Curve Model ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Curve Model

Data from a longitudinal questionnaire investigation of three time waves were used to investigate affective and behavioral changes and their covariant relationship among Chinese general population during the COVID-19 pandemic from March to May 2020. 145 participants aging from 15 to 63 completed three waves of survey. Latent growth curve analyses found that negative affect gradually increased as the pandemic continued. A faster increase in negative affect was related to a greater decrease in adaptive behavior and faster increase in non-adaptive behavior. A higher initial level of negative affect was related to a slower increase in non-adaptive behavior.

A Multi-Rater Latent Growth Curve Model

2021 ◽  
pp. 1-22
Author(s):  
James Soland ◽  
Megan Kuhfeld
Keyword(s):  
Growth Curve ◽  
Latent Growth Curve ◽  
Growth Curve Model ◽  
Latent Growth ◽  
Latent Growth Curve Model ◽  
Curve Model

scholarly journals Methods and Measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups

2009 ◽  
Vol 33 (6) ◽  
pp. 565-576 ◽  
Author(s):  
Nilam Ram ◽  
Kevin J. Grimm
Keyword(s):  
Stress Response ◽  
Growth Curve ◽  
Growth Modeling ◽  
Growth Mixture Modeling ◽  
Mixture Modeling ◽  
Longitudinal Change ◽  
Growth Curve Model ◽  
Growth Mixture ◽  
Multiple Group ◽  
Curve Model

Growth mixture modeling (GMM) is a method for identifying multiple unobserved sub-populations, describing longitudinal change within each unobserved sub-population, and examining differences in change among unobserved sub-populations. We provide a practical primer that may be useful for researchers beginning to incorporate GMM analysis into their research. We briefly review basic elements of the standard latent basis growth curve model, introduce GMM as an extension of multiple-group growth modeling, and describe a four-step approach to conducting a GMM analysis. Example data from a cortisol stress-response paradigm are used to illustrate the suggested procedures.

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