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Lipoprotein(a) repeat testing patterns and clinical significance of changes between repeat measurements: a real-world
Beyazıt Semih Yeşil1, Meltem Boz1, Begüm Dokuzağaç1
1Prof Dr Cemil Taşcıoğlu Şehir Hastanesi, İstanbul, Türkiye.
Introduction:
We evaluated lipoprotein(a) [Lp(a)] repeat testing patterns, inter-measurement intervals, department-specific repeat testing rates, and the clinical significance of changes between repeat measurements using asymmetric reference change values (RCV) and risk-category reclassification.
Materials And Methods:
All Lp(a) results from 1 April 2024 to 1 April 2026 at a tertiary hospital laboratory in Istanbul, Türkiye, were retrospectively extracted. Results were classified as low (<0.3 g/L), intermediate (0.3-0.5 g/L), or high (>0.5 g/L). The asymmetric RCV, calculated using an individual biological variation coefficient of 10.2% (EFLM Biological Variation Database) and analytical variation coefficient of 3.6% (internal quality control), were + 34.9% and - 25.9%. Pearson chi-square tests and Wilson 95% confidence intervals were applied.
Results:
Among 1623 patients with 1730 results, 96 (5.9%) underwent repeat testing, generating 107 consecutive pairs. Median inter-measurement interval was 156 days (IQR 71-234). Repeat testing rates did not differ by initial category (low 5.7%, intermediate 8.1%, high 5.3%; χ2 = 1.693, p = 0.429). RCV exceedance occurred in 23.4% (95% CI 16.4-32.2%), clinical reclassification in 11.2% (95% CI 6.5-18.6%), and both in 5.6% (95% CI 2.6-11.7%). RCV exceedance by baseline category was 20.8%, 33.3%, and 25.0% in low, intermediate, and high pairs, respectively. RCV exceedance did not differ between <90-day and ≥ 90-day intervals (17.6% vs. 26.0%, p = 0.340).
Conclusions:
Most repeat measurements remained within the RCV, and clinical reclassification was uncommon. Initial Lp(a) category did not predict repeat testing behaviour, suggesting requesting decisions reflect clinical context rather than the biochemical result. These findings support selective, indication-driven repeat testing rather than routine serial monitoring.
