Allison Johnson1, Claudia Gambrah-Sampaney1, Esha Khurana1, James Baier1, Esther Baranov1, Baphaleng Monokwane2, David R Bearden3. 1. University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania. 2. Department of Pediatrics, University of Botswana, Gaborone, Botswana. 3. Department of Pediatrics, University of Botswana, Gaborone, Botswana; Division of Neurology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Division of Child Neurology, Department of Neurology, University of Rochester School of Medicine, Rochester, New York. Electronic address: david_bearden@urmc.rochester.edu.
Abstract
BACKGROUND: Children with cerebral palsy in low-resource settings are at high risk of malnutrition, which further increases their risk of poor health outcomes. However, there are few available data on specific risk factors for malnutrition among children with cerebral palsy in the developing world. METHODS: We performed a case-control study among children with cerebral palsy receiving care at a tertiary care hospital in Gaborone, Botswana. Children with cerebral palsy and malnutrition were identified according to World Health Organization growth curves and compared with subjects with cerebral palsy without malnutrition. Risk factors for malnutrition were identified using multivariable logistic regression models. These risk factors were then used to generate a Malnutrition Risk Score, and Receiver Operating Characteristic curves were used to identify optimal cutoffs to identify subjects at high risk of malnutrition. RESULTS: We identified 61 children with cerebral palsy, 26 of whom (43%) met criteria for malnutrition. Nonambulatory status (odds ratio 13.8, 95% confidence interval [CI] 3.8-50.1, P < 0.001) and a composite measure of socioeconomic status (odds ratio 1.6, 95% CI 1.0-2.5, P = 0.03) were the strongest risk factors for malnutrition. A Malnutrition Risk Score was constructed based on these risk factors, and receiver operating characteristic curve analysis demonstrated excellent performance characteristics of this score (area under the curve 0.92, 95% CI 0.89-0.94). CONCLUSIONS: Malnutrition is common among children with cerebral palsy in Botswana, and a simple risk score may help identify children with the highest risk. Further studies are needed to validate this screening tool and to determine optimal nutritional interventions in this population.
BACKGROUND:Children with cerebral palsy in low-resource settings are at high risk of malnutrition, which further increases their risk of poor health outcomes. However, there are few available data on specific risk factors for malnutrition among children with cerebral palsy in the developing world. METHODS: We performed a case-control study among children with cerebral palsy receiving care at a tertiary care hospital in Gaborone, Botswana. Children with cerebral palsy and malnutrition were identified according to World Health Organization growth curves and compared with subjects with cerebral palsy without malnutrition. Risk factors for malnutrition were identified using multivariable logistic regression models. These risk factors were then used to generate a Malnutrition Risk Score, and Receiver Operating Characteristic curves were used to identify optimal cutoffs to identify subjects at high risk of malnutrition. RESULTS: We identified 61 children with cerebral palsy, 26 of whom (43%) met criteria for malnutrition. Nonambulatory status (odds ratio 13.8, 95% confidence interval [CI] 3.8-50.1, P < 0.001) and a composite measure of socioeconomic status (odds ratio 1.6, 95% CI 1.0-2.5, P = 0.03) were the strongest risk factors for malnutrition. A Malnutrition Risk Score was constructed based on these risk factors, and receiver operating characteristic curve analysis demonstrated excellent performance characteristics of this score (area under the curve 0.92, 95% CI 0.89-0.94). CONCLUSIONS:Malnutrition is common among children with cerebral palsy in Botswana, and a simple risk score may help identify children with the highest risk. Further studies are needed to validate this screening tool and to determine optimal nutritional interventions in this population.
Authors: Steven J Korzeniewski; Elizabeth N Allred; Robert M Joseph; Tim Heeren; Karl C K Kuban; T Michael O'Shea; Alan Leviton Journal: Pediatrics Date: 2017-10-13 Impact factor: 7.124
Authors: Kyle K Obana; Bensen B Fan; James T Bennett; Adrian Lin; Rachel Y Goldstein; Lindsay M Andras; Robert M Kay Journal: Medicine (Baltimore) Date: 2021-11-24 Impact factor: 1.817