Assessment of the Gold Standard Test for Fetal Malnutrition Using Statistical and Boolean Analyses
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: Fetal malnutrition (FM), defined by inadequate fat and muscle deposition in utero, poses serious risks to neonatal and long-term health. Early identification is vital for prompt intervention. This study aimed to compare the diagnostic accuracy of Body Mass Index (BMI), Ponderal Index (PI), and Mid-Upper Arm Circumference to Head Circumference ratio (MUAC/HC) against the Clinical Assessment of Nutritional Status Score (CANSCORE) for detecting FM. Methods: A cross-sectional study was conducted at CMH Rawalakot, AJK, from December 2022 to July 2023. The study included 320 live singleton neonates between 28–42 weeks of gestation. Neonates with congenital anomalies, macrosomia from diabetic mothers, or multiple pregnancies were excluded. Within 48 hours of birth, each neonate underwent anthropometric measurements (weight, length, HC, MUAC) by a single observer. Derived indices included BMI, PI, and MUAC/HC. CANSCORE (<25 indicating FM) was also recorded. Statistical analyses, including sensitivity, specificity, PPV, and NPV of FM assessment tools, were performed using MATLAB, with significance set at p < 0.05. Results: Using CANSCORE as the reference, BMI showed the highest sensitivity (92.9%) and NPV (95.1%), while PI demonstrated the highest specificity (81.6%) and PPV (70.9%). Error analysis revealed that BMI had the lowest total error and strongest consistency with other indices. A comparative literature review confirmed the superior diagnostic value of BMI and PI. All comparisons were statistically significant (p < 0.05) Conclusion: BMI outperformed other measures in detecting FM, making it a practical and reliable tool for clinical screening. It offers a valuable alternative to CANSCORE, especially in resource-limited settings.
Authors: Irum Javed, Waleed Ahmad, Nazish Fayaz, Tayyaba Khaliq, Asbah Fayyaz, Hissan Hafeez
Institution: University of Poonch Rawalakot
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7105695393
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