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Positive Predictive Value of Respiratory Infection Diagnoses in The Danish National Patient Registry: a Post-hoc Validation Analysis of the DANFLU-1 Trial.

Christensen J., Johansen ND., Bernholm KF., Borchsenius JI-MH., Hyldekær Janstrup K., Modin D., Skaarup KG., Nealon J., Samson S., Loiacono M., Huang Y., Harris R., Larsen CS., Jensen AMR., Landler NE., Claggett BL., Solomon SD., Gislason GH., Køber L., Landray MJ., Sivapalan P., Stæhr Jensen JU., Biering-Sørensen T.

ObjectivesTo validate respiratory infection diagnosis codes in the Danish National Patient Registry (DNPR) in the DANFLU-1 feasibility trial, and to assess whether reperforming effectiveness analyses with validated events altered findings.MethodsHospitalizations for influenza or pneumonia (ICD-10 codes J09-18) were validated against medical chart review by two blinded physicians. Events were validated against 1) medical opinion and 2) diagnostic criteria of symptomatology combined with laboratory or radiological verification. Positive predictive values (PPV) were calculated along with relative vaccine effectiveness (rVE) of high-dose (HD) vs. standard-dose (SD) influenza vaccines.ResultsAmong 12,477 participants, we identified 43 events in the DNPR (influenza J09-11: n=3, pneumonia J12-18: n=40), which demonstrated a PPV of 86% (95% CI; 72%-95%) and 91% (78%-97%) against the medical opinion of the respective reviewers. Against diagnostic criteria, the PPV was 65% (49%-79%) and 67% (51%-81%). For influenza codes, the PPV was 100% according to both reviewers and references, and between 63% and 90% depending on reviewer and reference for pneumonia. The rVE consistently favored HD vs. SD against validated events.ConclusionHospitalizations for pneumonia registered in the DNPR may benefit from subsequent validation, warranting further studies. HD influenza vaccination was associated with fewer validated events compared with SD.

More information Original publication

DOI

10.1016/j.ijid.2026.108703

Type

Journal article

Publication Date

2026-04-01T00:00:00+00:00

Addresses

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