ArticleBMC medical informatics and decision making2021
Establishing an expert consensus for the operational definitions of asthma-associated infectious and inflammatory multimorbidities for computational algorithms through a modified Delphi technique.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Artificial intelligence biomarker detects high-risk childhood asthma subgroup for respiratory infections and exacerbations.The Journal of allergy and clinical immunology · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundA subgroup of patients with asthma has been reported to have an increased risk for asthma-associated infectious and inflammatory multimorbidities (AIMs). To systematically investigate the association of asthma with AIMs using a large patient cohort, it is desired to leverage a broad range of electronic health record (EHR) data sources to automatically identify AIMs accurately and efficiently.
methodsWe established an expert consensus for an operational definition for each AIM from EHR through a modified Delphi technique. A series of questions about the operational definition of 19 AIMS (11 infectious diseases and 8 inflammatory diseases) was generated by a core team of experts who considered feasibility, balance between sensitivity and specificity, and generalizability. Eight internal and 5 external expert panelists were invited to individually complete a series of online questionnaires and provide judgement and feedback throughout three sequential internal rounds and two external rounds. Panelists' responses were collected, descriptive statistics tabulated, and results reported back to the entire group. Following each round the core team of experts made iterative edits to the operational definitions until a moderate (≥ 60%) or strong (≥ 80%) level of consensus among the panel was achieved.
resultsResponse rates for each Delphi round were 100% in all 5 rounds with the achievement of the following consensus levels: (1) Internal panel consensus: 100% for 8 definitions, 88% for 10 definitions, and 75% for 1 definition, (2) External panel consensus: 100% for 12 definitions and 80% for 7 definitions.
conclusionsThe final operational definitions of AIMs established through a modified Delphi technique can serve as a foundation for developing computational algorithms to automatically identify AIMs from EHRs to enable large scale research studies on patient's multimorbidities associated with asthma.
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