DR.R. NAVEENA,DR. YOGALAKSHMI,DR.V. RAMYA
DOI: https://doi.org/Qualitative platelet disorders, whether intrinsic or extrinsic, present significant diagnostic challenges due to their diverse manifestations and complex underlying mechanisms. Conventional aggregation testing methods are labor-intensive, require skilled personnel, and are time-consuming, thereby, delaying diagnosis and limiting timely intervention. To address these challenges, automated hemostasis analyzers have emerged as a valuable solution, enabling faster diagnostics and promoting quicker recovery through early ascertainment and holistic management.
Objective: This investigation aims to evaluate the diagnostic precision and clinical utility of an automated platelet aggregation platform in identifying intrinsic and extrinsic thrombocyte abnormalities.
Methods: Using Hospital Based Diagnostic Study, Cases with suspected bleeding disorders and Cases who were already on Anti-Platelet Treatment (APT) (n-50) were analyzed using an automated aggregometer, Automated LTA method has been developed by Sysmex (Kobe, Japan) on a routine coagulation analyzer (CS-2400). Comparative assessment was performed against manual light transmission aggregometry and clinical history to establish concordance and sensitivity.
Results: Individuals with suspected bleeding disorders (n=25) were younger (mean age 42.3 ± 11.2) and more likely female (52%) compared to those on antiplatelet therapy (n=25; mean age 64.7 ± 8.9, 72% male). Group A showed more mucocutaneous (68% vs. 12%) and surgical bleeding (36% vs. 8%). Both groups had normal platelet counts (210 ± 35 vs. 198 ± 29 ×10⁹/L).Diagnostic agreement between Lumi-LTA and CS-2400 was high (overall 90%), with perfect concordance for aspirin effect and normal function (100%), and slightly lower for PSD (83.3%) and δ-SPD (87.5%). On the CS-2400, aggregation amplitudes and detection rates were highest for ristocetin (70 ± 9%, 98%) and collagen (67 ± 10%, 96%), and lower for ADP (55 ± 12%, 94%), epinephrine (43 ± 15%, 88%), and arachidonic acid (32 ± 18%, 76%). APAL and CPAL scores in healthy controls (n=19) were 9.7 (8.8–10.0) and 10.0 (10.0–10.0). Patients on antiplatelet drugs (n=28) had lower scores: APAL 6.4 (5.9–8.0), CPAL 7.1 (5.7–8.5), both p<0.001. ASA-only users had APAL 8.9 (8.0–9.7, p=0.362), CPAL 6.7 (6.2–7.2, p<0.001); combined ASA+Plavix showed the largest drop (APAL 6.2, CPAL 4.7, both p<0.001). Congenital PFD (n=18) had lower aggregation with collagen (38% vs. 72%), U46619 (0.5 μM: 12% vs. 58%), TRAP (22% vs. 45%), and arachidonic acid (28% vs. 66%), all p<0.05 relative to acquired PFD (n=32). ATP release and granule content (serotonin 0.18 vs. 0.36; ADP 0.62 vs. 2.12) were significantly lower, and ATP/ADP higher (7.06 vs. 1.98, p=0.002)
Conclusion: Automated platelet aggregation analysis provides a robust, standardized alternative. Its adoption can enhance diagnostic consistency and support timely clinical decision-making, especially in high-throughput laboratory environments.
