Biochemical testing for inborn errors of metabolism: experience from a large tertiary neonatal centre
Bibliographic record
Abstract
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".