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Published on: October 17, 2025
Impact of Structured Data Validation on Case Completeness and Key Quality Indicators in the SWISS Implant Registry
Emin Aghayev1,2,3, Mazda Farshad4, Thorsten Jentzsch1,4
1Scientific Advisory Board (SSAB) SIRIS Spine, Foundation for Quality Assurance in Implant Surgery (SIRIS), Bern, Switzerland.
Abstract:
Study designRetrospective single-center registry validation audit.ObjectiveTo quantify the impact of semi-automated, chart-adjudicated data validation workflow on case completeness and key quality indicators in the Swiss Implant Registry (SIRIS Spine).MethodsAll Swiss procedure (CHOP)-coded spine surgeries meeting SIRIS Spine inclusion criteria (01/2021-07/2025) were extracted from the hospital information system at a tertiary spine center and reconciled with SIRIS Spine entries. We screened for duplicate patient identification number entries to identify all revision surgeries, and adjudicated discrepancies by operative report review. Reoperation/revision rate analyses focused on the first recorded event. Pre-versus post-validation comparisons used chi-square tests; between-year variation and linear trend were assessed using logistic regression.ResultsValidation for data completeness increased the number of registered surgeries by 21% (N=1,425 to 1,726; p<0.001). Overall, 32% of surgery records required modification during content validation, while 68% remained unchanged (p<0.001). Of modified records, 76% were added and 24% were deleted (p<0.001). Added surgeries were most commonly reoperations/revisions (57%), followed by surgeries for degenerative disease (33%) (p<0.001). Deletions were mainly due to not meeting inclusion criteria (65%) or inaccurate case information 22%) (p<0.001). Dural tear rates changed from 9% to 10% and first-reoperation/revision rates from 10% to 17% after data validation (both p<0.001).ConclusionsAt a single-center, structured data validation improved registry completeness, particularly for revision surgery, and changed benchmark-relevant indicators. Ongoing local validation is needed to ensure reliable quality assurance, benchmarking, and research.