Cystic fibrosis newborn screening in Switzerland - evaluation and scenarios for improvement after 11 years of follow-up.

BACKGROUND: Newborn bloodspot screening (NBS) for cystic fibrosis (CF) is important for early diagnosis and treatment. However, screening can lead to false-positive results leading to unnecessary follow-up tests and distress. This study evaluated the 11-year performance of the Swiss CF-NBS programme, estimated optimal cut-offs for immunoreactive trypsinogen (IRT), and examined how simulated algorithms would change performance. METHODS: The Swiss CF-NBS is based on an IRT-DNA algorithm with a second IRT (IRT-2) as safety net. We analysed data from 2011 to 2021, covering 959,006 IRT-1 analyses and 282 children with CF. We studied performance based on European Cystic Fibrosis Society (ECFS) standards including sensitivity, specificity, positive predictive value (PPV), false negative rate, and second heel-prick tests; identified optimal IRT cut-offs using receiver operating characteristics (ROC) curves; and calculated performance for simulated algorithms with different cut-offs for IRT-1, IRT-2, and safety net. RESULTS: The Swiss CF-NBS showed excellent sensitivity (96 %, 10 false negative cases) but moderate PPV (25 %). Optimal IRT-1 and IRT-2 cut-offs were identified at 2.7 (>99th percentile) and 5.9 (>99.8th percentile) z-scores, respectively. Analysis of simulated algorithms showed that removing the safety net from the current algorithm could increase PPV to 30 % and eliminate >200 second heel-prick tests per year, while keeping sensitivity at 95 %. CONCLUSION: The Swiss CF-NBS program performed well over 11 years but did not achieve the ECFS standards for PPV (≥30 %). Modifying or removing the safety net could improve PPV and reduce unnecessary follow-up tests while maintaining the ECFS standards for sensitivity.

Multiomics tools for the diagnosis and treatment of rare neurological disease.

Conventional workup of rare neurological disease is frequently hampered by diagnostic delay or lack of diagnosis. While biomarkers have been established for many neurometabolic disorders, improved methods are required for diagnosis of previously unidentified or underreported causes of rare neurological disease. This would result in a higher diagnostic yield and increased patient numbers required for interventional studies. Recent studies using next-generation sequencing and metabolomics have led to identification of novel disease-causing genes and biomarkers. This combined approach can assist in overcoming challenges associated with analyzing and interpreting the large amount of data obtained from each technique. In particular, metabolomics can support the pathogenicity of sequence variants in genes encoding enzymes or transporters involved in metabolic pathways. Moreover, metabolomics can show the broader perturbation caused by inborn errors of metabolism and identify a metabolic fingerprint of metabolic disorders. As such, using "omics" has great potential to meet the current needs for improved diagnosis and elucidation of rare neurological disease.