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| Year:
2025 |
Month:
April
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Volume:
14 |
Issue:
2 |
Page:
PO01 - PO03 |
Analysis of Manual and Automated Platelet Count Estimation Methods: A Cross-sectional Study from a Rural Tertiary Care Centre, Maharashtra, India
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Correspondence
Aditya Sureshkumar Keswani, Dnyaneshwar Shivajirao Jadhav, Arvind Namdeorao Bagate, Sheela Lakshmanrao Gaikwad,
Dr. Aditya Sureshkumar Keswani,
Dean Office Building, First Floor, Department of Pathology, Swami Ramanand Teerth
Rural Government Medical College, Ambajogai-431517, Maharashtra, India.
E-mail: adityakeswani@hotmail.com :
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Introduction: Platelet count estimation is a critical diagnostic tool in various haematological disorders. In resource-limited settings, manual methods are often employed due to cost constraints. However, their accuracy compared to automated haematology analysers requires validation.
Aim: To assess the accuracy, reliability and agreement of manual and automated platelet count estimation methods.
Materials and Methods: A cross-sectional study was conducted at Swami Ramanand Teerth Rural Government Medical College, Ambajogai, Maharashtra, India from January to March 2024, involving 250 blood samples. Peripheral venous blood was collected in Dipotassium Ethylenediaminetetraacetic Acid (K2EDTA) tubes. Platelet counts of 250 patients were estimated using both manual methods (peripheral blood smear microscopy with Leishman stain) and an automated haematology analyser (Erba Manheim Elite 580). Data were analysed using Statistical Package for the Social Sciences (SPSS) version 29.0. Descriptive statistics, paired t-tests, Pearson correlation coefficient, Bland-Altman analysis, Intraclass Correlation Coefficient (ICC), and One-way Analysis of Variance (ANOVA) were employed. Statistical significance was set at p-value <0.05.
Results: Of the 250 cases analysed, a strong positive correlation (r=0.98, p-value <0.001) was observed between manual and automated platelet counts. Bland-Altman analysis, which assesses agreement between two methods by plotting the difference against the average of the methods, revealed a mean bias of 5.72×10³/μL (95% limits of agreement: -0.01 to 11.45×10³/μL), indicating clinically acceptable agreement. Although a statistically significant difference (p-value=0.03) was found between mean counts, its clinical relevance was minor. Agreement remained consistent across age and sex subgroups, with an ICC of 0.98 (95% CI), reflecting excellent reliability.
Conclusion: This study validates the manual platelet count estimation method as a reliable and cost-effective alternative to automated analysers in resource-constrained settings. However, rigorous training and adherence to standardised protocols are essential for accurate results. Further research is recommended to validate these findings in diverse populations and clinical scenarios, enhancing the applicability of manual methods in rural healthcare settings.
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