Efficiency analysis of nutritional screening tools for children with congenital heart disease: a retrospective observational study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".