[Correlation of nutritional status with clinical characteristics and lung function in children with cystic fibrosis]. / å›Šæ€§çº¤ç»´åŒ–å„¿ç«¥è¥å »çŠ¶å†µä¸Žä¸´åºŠç‰¹å¾å’Œè‚ºåŠŸèƒ½çš„ç›¸å ³æ€§åˆ†æž.

OBJECTIVES: To investigate the nutritional status of children with cystic fibrosis (CF) and understand the correlation between malnutrition and clinical characteristics as well as lung function. METHODS: A retrospective analysis was performed on clinical data of CF children admitted from January 2016 to June 2023. Clinical characteristics of CF children with different nutritional statuses were compared, and the correlation between malnutrition and lung function was analyzed. RESULTS: A total of 52 CF children were included, comprising 25 boys (48%) and 27 girls (52%), aged between 7 months and 17 years. Respiratory symptoms were the predominant clinical manifestations (96%, 50/52). The prevalence of malnutrition was 65% (34/52), with moderate/severe malnutrition being the most common (65%, 22/34). The malnutrition group had a longer duration of illness, higher proportion of digestive system symptoms, and lower levels of serum albumin (P<0.05). Pulmonary function parameters, including forced expiratory volume in one second as a percentage of the predicted value, ratio of forced expiratory volume in one second to forced vital capacity, forced expiratory flow at 25% of forced vital capacity exhaled, forced expiratory flow at 50% of forced vital capacity exhaled, forced expiratory flow at 75% of forced vital capacity exhaled, and maximum mid-expiratory flow as a percentage of the predicted value, were lower in the malnutrition group compared to the normal nutrition group (P<0.05). Correlation analysis showed body mass index Z-score was positively correlated with the above six pulmonary function parameters (P<0.05). CONCLUSIONS: The prevalence of malnutrition is high in CF children and is associated with decreased lung function. CF children with higher body mass index have better lung function. Therefore, screening and evaluation of nutritional status as well as appropriate nutritional intervention should be emphasized in CF children.

A retrospective study of resting energy expenditure in children hospitalized with different nutritional status.

Background: Resting energy expenditure (REE) refers to the energy consumption of the body in a resting state without skeletal muscle activity. This study aimed to examine the REE among children hospitalized with varying nutritional status. Methods: This was a retrospective study. We enrolled 109 pediatric cases that underwent indirect calorimetry (IC) and divided into four groups: mild malnutrition group (15 cases), moderate malnutrition group (30 cases), severe malnutrition group (32 cases), and obesity group (32 cases). We compared and analyzed the measured REE (mREE) using IC with the predicted REE (pREE) using five energy equations. The paired t-test was used to compare the results of two samples. Pearson analysis was used to assess the correlation between two values. The agreement analysis was performed using the Bland-Altman method. Results: There was no significant difference in mREE between the mild, moderate, and severe malnutrition groups, but each differed significantly from the obesity group. All populations exhibited significant correlation between the mREEs and all five energy equations, and the equation with the highest predictive accuracy was the Schofield equation, which achieved an accuracy of 47.7%. In subgroup analysis, there was no significant difference between mREE and pREE for each of the five equations in the mild, moderate malnutrition groups. Only the prediction result of the Liu equation was not significantly different from the mREE in the severe malnutrition group. The prediction accuracy of the Liu equation was relatively the highest (34.4%). However, in the obese group, there were significant differences in pREE and mREE between the Liu equation and Mifflin equation. Under different nutritional statuses, the results of the Bland-Altman analysis suggested that deviation values between REEs predicted by each equation and mREE were greater than ±10%. Conclusions: There were differences in REE among children with different nutritional status. The results obtained from the five predictive energy equations deviated from the IC results. When REE cannot be measured by IC, it is essential to choose an appropriate predictive energy equation based on the nutritional status of the individual.