Biochemical testing for inborn errors of metabolism: experience from a large tertiary neonatal centre
Notice bibliographique
Résumé
Inborn errors of metabolism are an individually rare but collectively significant cause of mortality and morbidity in the neonatal period. They are identified by either newborn screening programmes or clinician-initiated targeted biochemical screening. This study examines the relative contribution of these two methods to the identification of inborn errors of metabolism and describes the incidence of these conditions in a large, tertiary, neonatal unit. We also examined which factors could impact the reliability of metabolic testing in this cohort. This is a retrospective, single-site study examining infants in whom a targeted metabolic investigation was performed from January 2018 to December 2020 inclusive. Data was also provided by the national newborn screening laboratory regarding newborn screening diagnoses. Two hundred and four newborns received a clinician-initiated metabolic screen during the time period examined with 5 newborns being diagnosed with an inborn error of metabolism (IEM) (2.4%). Of the 25,240 infants born in the hospital during the period examined, a further 11 newborns had an inborn error of metabolism diagnosed on newborn screening. This produced an incidence in our unit over the time described of 6.34 per 10,000 births. This number reflects a minimum estimate, given that the conditions diagnosed refer to early-onset disorders and distinctive categories of IEM only. Efficiency of the clinician-initiated metabolic screening process was also examined. The only statistically significant variable in requiring repeat metabolic screening was early day of life (z-score = - 2.58, p = 0.0098). A total of 28.4% was missing one of three key metabolic investigation parameters of blood glucose, ammonia or lactate concentration with ammonia the most common investigation missing. While hypoglycemia was the most common clinical rationale for a clinician-initiated metabolic test, it was a poor predictor of inborn error of metabolism with no newborns of 25 screened were diagnosed with a metabolic disorder. CONCLUSION: Clinician-targeted metabolic screening had a high diagnostic yield given the relatively low prevalence of inborn errors of metabolism in the general population. Thoughts should be given to the rationale behind each targeted metabolic test and what specific metabolic disease or category of inborn error of metabolism they are concerned along with commencing targeted testing. WHAT IS KNOWN: • Inborn errors of metabolism are a rare but potentially treatable cause of newborn mortality and morbidity. • A previous study conducted in a tertiary unit in an area with limited newborn screening demonstrated a diagnostic yield of 5.4%. WHAT IS NEW: • Clinician-initiated targeted metabolic screening has a good diagnostic performance even with a more expanded newborn screening programme. • Further optimisation could be achieved by examining the best timing and also the rationale of metabolic testing in the newborn period.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».