Niloy Kumar Das1, Shantanu Nandy1, Rakesh Mondal2, Somosri Ray2, Avijit Hazra3. 1. Department of Anatomy, Medical College Kolkata, Kolkata, India. 2. Department of Pediatrics, Medical College Kolkata, Kolkata, India. 3. Department of Pharmacology, Institute of Post Graduate Medical Education and Research, Kolkata, India.
Abstract
OBJECTIVES: We sought to evaluate the relationship between gestational age (GA) and neonatal anthropometric parameters, namely head circumference (HC) and crown-heel length (CHL). METHODS: We conducted a cross-sectional study in a tertiary care hospital with 530 consecutively live-born newborns of 28-41 weeks gestation. Anthropometric parameters were measured after three days of life. We summarized the variables using descriptive statistics, including percentile values, and the strength of association was determined through correlation analysis. The correlation was strong for HC and CHL, and linear regression analysis was done to develop predictive equations. RESULTS: HC and CHL correlated well with GA with r-values of 0.863 and 0.859, respectively. The regression equations derived were GA (week) = 9.2671 + [0.8616 × HC (cm)] and GA (weeks) = 7.2489 + [0.621 × CHL (cm)]. Multiple regression gave the relationship as GA (weeks) = 4.0244 + [0.4058 × HC (cm)] + [0.4249 × CHL (cm)]. Application of this multiple regression equation to a test cohort of 30 babies for prediction of GA gave a mean margin of error of 2.9%, indicating that it is a satisfactory tool for prediction. CONCLUSIONS: HC and CHL can be used as simple tools for predicting GA in babies when this is in doubt. This can help in identification of high-risk newborns at primary care level without recourse to imaging modalities.
OBJECTIVES: We sought to evaluate the relationship between gestational age (GA) and neonatal anthropometric parameters, namely head circumference (HC) and crown-heel length (CHL). METHODS: We conducted a cross-sectional study in a tertiary care hospital with 530 consecutively live-born newborns of 28-41 weeks gestation. Anthropometric parameters were measured after three days of life. We summarized the variables using descriptive statistics, including percentile values, and the strength of association was determined through correlation analysis. The correlation was strong for HC and CHL, and linear regression analysis was done to develop predictive equations. RESULTS: HC and CHL correlated well with GA with r-values of 0.863 and 0.859, respectively. The regression equations derived were GA (week) = 9.2671 + [0.8616 × HC (cm)] and GA (weeks) = 7.2489 + [0.621 × CHL (cm)]. Multiple regression gave the relationship as GA (weeks) = 4.0244 + [0.4058 × HC (cm)] + [0.4249 × CHL (cm)]. Application of this multiple regression equation to a test cohort of 30 babies for prediction of GA gave a mean margin of error of 2.9%, indicating that it is a satisfactory tool for prediction. CONCLUSIONS: HC and CHL can be used as simple tools for predicting GA in babies when this is in doubt. This can help in identification of high-risk newborns at primary care level without recourse to imaging modalities.
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