SwissPedLung – a data stream enabling retrospective analysis of guideline changes through large-scale data

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INTRODUCTION: SwissPedLung, nested in the pediatric data stream of the Swiss Personalized Health Network (SPHN), will enable large-scale analysis of lung function (LF) and clinical data from Swiss hospitals for research. Recent guidelines on asthma diagnosis in children suggest to change the post bronchodilation (BD) cut-off for FEV1 from >12% [1] to >10% [2]. How this impacts asthma prevalence and diagnostic pathways is yet unknown. AIM: This pilot study assessed the feasibility of analyzing such large-scale LF data and examined the impact of lowering the BD FEV1 cut-off from 12% to 10%. METHODS: We created a pipeline to transfer raw LF data from the LF software to a clinical data warehouse, where we merged it with clinical data from electronic health records. This exemplary analysis used all spirometry measurements done in 2024 in the University Children’s hospital in Bern, Switzerland. RESULTS: The export of 2923 spirometry measurements from 1880 children aged 3–17 years from the data warehouse was feasible. 1471 (50%) LF tests were followed by a BD test. Mean (SD) FEV1 difference pre to post BD was 8.5% (±8.1). 527 (37%) tests had an FEV1 difference >10% and 388 (27%) >12%, resulting in 10% of tests being regarded as positive only with the new cut-off. CONCLUSIONS: Compared to the old one, the new cut-off would have allowed avoiding bronchoprovocation in 139 cases, simplifying the diagnostic process. Analyzing large-scale data including raw spirometry data is feasible within SwissPedLung and allows assessing the impact of policy changes on real-life diagnostic decisions. [1] Gaillard, E.A., et al. Eur Respir J, 2021. 58(5). [2] Möller, A., et al., Paediatrica, 2023. 34(5).

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