B-149 Establishing Pediatric Reference Intervals in a US Population using Refine R
Notice bibliographique
Résumé
Abstract Background Establishing reference intervals (RIs) is essential for accurately interpreting laboratory test results, as they help define the range of values expected in a healthy population. There are two primary methods for establishing RIs: the direct method, which involves prospectively recruiting healthy individuals, and the indirect method, which uses retrospective data from a presumed healthy population. Established studies such as CALIPER, a direct RI-derivation study conducted in Canada, aimed to address gaps in pediatric reference intervals using thousands of prospectively recruited healthy pediatric patients. However, such studies are very costly to perform and their RIs may not translate to US hospitals serving different or more diverse populations. In this study, our objective was to establish RIs using an indirect approach using RefineR and to compare our results to those of CALIPER RIs. RefineR uses advanced algorithms to adjust for biases and variables such as age, sex, and other factors, ensuring that RIs are more context-specific and accurate for clinical decision-making. A secondary objective was to also compare our findings to RIs derived per CLSI guidelines (non-parametric central 95%, with Tukey outlier elimination). Methods To evaluate, we retrieved data from 5,064 unique patients under 19 years of age who visited our outpatient clinic between 1/1/23 and 12/31/24. These data were based on results obtained on the Roche Cobas instruments for a comprehensive metabolite panel (CMP). We compared the CALIPER-derived RIs with those generated using RefineR and the non-parametric RI (NPRI) derived per CLSI guideline EP28-A3c. Results There was good agreement between CALIPER, Refine R and NPRI when the distribution of results were Gaussian, as was the case for sodium, calcium, albumin, BUN, total bilirubin, and CO2. However, discrepancies were observed for other tests. For example, our ALP levels were higher than those of CALIPER RIs for children aged 1-<10 years (CALIPER: 142-335 U/L, NPRI: 123-397 U/L, RefineR: 137-381 U/L). This discrepancy may be explained by factors such as ethnicity, geographic location, and socioeconomic status, which can all influence ALP levels, making comparisons challenging. For total protein, our population*s levels were approximately 0.5 g/dL higher on the upper end (NPRI: 5.8-7.8 g/dL, Refine R: 6.1-7.8 g/dL) compared to CALIPER RIs (5.9-7.3 g/dL). This difference is likely due to the use of both serum and plasma samples in our analysis, while CALIPER only used serum, which is known to have lower total protein. We also observed higher ALT levels in our population for the 13-19 age group (CLSI: 9-45 U/L, RefineR: 9-41 U/L) compared to CALIPER RIs (12-27 U/L). This difference may be due to unaccounted differences in important variables, like body mass index or alcohol consumption, affecting each population. Conclusion When implementing pediatric RIs, it is crucial to ensure alignment with the correct methodology and preanalytical factors (such as sample type). Discrepancies may arise if variations in patient populations or preanalytical factors are not carefully considered. It is appropriate to adopt CALIPER RIs for US pediatric population for most tests in a CMP, but not all.
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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 | 0,010 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».