Efficiency analysis of nutritional screening tools for children with congenital heart disease: a retrospective observational study
Notice bibliographique
Résumé
Objective This study aims to (1) determine the prevalence of malnutrition among hospitalized children with congenital heart disease (CHD), (2) evaluate the effectiveness of common pediatric nutritional screening tools across various age groups, and (3) specifically assess the tools’ efficacy in identifying severe malnutrition, thereby facilitating early nutritional intervention. Methods A retrospective observational analysis was performed using clinical data from 3,677 children (0–18 years) with congenital heart disease who underwent surgical intervention at the Cardiothoracic Surgery Center between January 2018 and December 2022. The World Health Organization growth curves were used as standards to compare the efficacy of four screening tools: Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP), Screening Tool Risk on Nutritional status and Growth (STRONGkids), Risk Adjustment in Congenital Heart Surgery-1 method (RACHS-1), and the combined STAMP + STRONGkids (SS) adjusted score across different age groups. Categorical data were expressed as percentages, and Chi-square tests were used for statistical analysis, with pairwise comparisons performed using Bonferroni correction. Receiver Operating Characteristic (ROC) curves were employed to calculate specificity, sensitivity, and optimal cutoff values. The consistency of screening results was further assessed using Youden’s index and Kappa values. Results The prevalence of malnutrition among CHD patients was 32.9% (1,208/3,667). Using World Health Organization (WHO) curves as the gold standard for diagnosing malnutrition, the AUC for the ROC curves of STAMP, STRONGkids, and SS were 0.841, 0.747, and 0.863 ( P < 0.01), respectively, for nutritional risk screening among CHD patients. Optimal Youden indices were achieved at a STAMP score of 3.5 (55.9%), a STRONGkids score of 2.5 (41.5%), and an SS score of 3.25 (64.5%). Age-based subgroup analysis revealed that STAMP had the best sensitivity of 70.1% (Negative Prediction Rate (NPV) 96.1) at a score of 3.5 for children aged 6–18 years, STRONGkids showed optimal sensitivity of 78.1% (NPV 77.3) at a score of 2.5 for infants aged 0–1 year, and SS demonstrated 74.7% sensitivity (NPV 96.6) at a score of 3.25 for children aged 6–18 years. Further analysis indicated that STAMP at a score of 2.5 and SS at a score of 2.75 showed balanced sensitivity and specificity across all age groups. Additionally, for different degrees of nutritional deficiencies, STAMP at 3.5 and SS at 3.25 demonstrated ideal specificity, with all Kappa values being P < 0.001. Conclusion For hospitalized CHD patients aged 0–18 years, the nutritional screening tool STAMP is more effective than STRONGkids, and SS combines the advantages of both tools as it demonstrates the best screening efficacy. However, the area under the ROC curve (AUC) for the RACHS-1 score was 0.525 ( P >0.01), indicating not suitable for nutritional risk screening. To enhance sensitivity in screening malnutrition in CHD patients, the recommended cutoff values are 3 for STAMP and 2.5 for SS. For identifying severe malnutrition, STAMP at a cutoff of 3.5 and SS at 3.25 show higher overall screening efficacy.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».