Evidence map›Paper›PMID 37986125›Full record

ArticleJournal of health, population, and nutrition2023

Validation study on a prediction formula to estimate the weight of children & adolescents with special needs aged 2-18 years old.

Nurul Huda Ibrahim, Norasimah Kassim, Salimah Othman, Azahadi Omar, Norsuhaila Shaari, Anis Aslah Awiskarni, Norafidza Ashiquin Abdul Patah, Nabila Mohamed Nezuri, Maizatul Naqiah Zulfifli, Mohd Nadzrul Anuar Awang and 3 more

Abstract read
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Article in Journal of health, population, and nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Nurul Huda IbrahimInstitute for Public Health, Ministry of Health Malaysia, 40170, Setia Alam, Selangor, Malaysia. nurul_hudai@moh.gov.my.
Norasimah KassimFamily Health Development Division, Ministry of Health Malaysia, 62590, Putrajaya, Malaysia.
Salimah OthmanFamily Health Development Division, Ministry of Health Malaysia, 62590, Putrajaya, Malaysia.
Azahadi OmarNational Institute of Health, Ministry of Health Malaysia, Setia Alam, Malaysia.
Norsuhaila ShaariHospital Kuala Lumpur, Kuala Lumpur, Malaysia.
Anis Aslah AwiskarniKlinik Kesihatan Bandar Botanik, Ministry of Health, 42000, Klang, Selangor, Malaysia.
Norafidza Ashiquin Abdul PatahKlinik Kesihatan Seremban, Seremban, Malaysia.
Nabila Mohamed NezuriKlinik Kesihatan Nilai, Nilai, Negeri Sembilan, Malaysia.
Maizatul Naqiah ZulfifliKlinik Kesihatan Seksyen 19, Shah Alam, Selangor, Malaysia.
Mohd Nadzrul Anuar AwangHospital Kuala Lumpur, Kuala Lumpur, Malaysia.
Muhamad Farid SaniKlinik Kesihatan Putrajaya, Persint 18, Putrajaya, Malaysia.
Noorfadzlina Abdul RashadKlinik Kesihatan Cheras, Kuala Lumpur, Malaysia.
Siti Farhana MesbahFamily Health Development Division, Ministry of Health Malaysia, 62590, Putrajaya, Malaysia.

Funding

National Institute of Health, Malaysia NMRR-17-2743-35970
6 · The paper itself

Abstract

backgroundThis study aims to validate two predictive formulas of weight estimating strategies in children with special needs, namely the Cattermole formula and the Mercy formula. METHODOLOGY: A cross-sectional study with a universal sampling of children and adolescents with special needs aged 2-18 years old, diagnosed with cerebral palsy, down syndrome, autism and attention-deficit/hyperactivity disorder was conducted at Community-Based Rehabilitation in Central Zone Malaysia. Socio-demographic data were obtained from files, and medical reports and anthropometric measurements (body weight, height, humeral length, and mid-upper arm circumference) were collected using standard procedures. Data were analysed using IBM SPSS version 26. The accuracy of the formula was determined by intraclass correlation, prediction at 20% of actual body weight, residual error (RE) and root mean square error (RMSE).

resultA total of 502 children with a median age of 7 (6) years were enrolled in this study. The results showed that the Mercy formula demonstrated a smaller degree of bias than the Cattermole formula (PE = 1.97 ± 15.99% and 21.13 ± 27.76%, respectively). The Mercy formula showed the highest intraclass correlation coefficient (0.936 vs. 0.858) and predicted weight within 20% of the actual value in the largest proportion of participants (84% vs. 48%). The Mercy formula also demonstrated lower RE (0.3 vs. 3.6) and RMSE (3.84 vs. 6.56) compared to the Cattermole formula. Mercy offered the best option for weight estimation in children with special needs in our study population.

Indexed as

Body HeightAdolescentAnthropometryBody WeightChildChild, PreschoolCross-Sectional StudiesHumansMalaysiaBody weight estimationBody weight formulaChildren with special needsMUAC

Identifiers

PMID37986125
PMCPMC10662488

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.