USE OF PYTHON AND COMPLETE BLOOD COUNT PARAMETERS FOR COST-EFFECTIVE THALASSEMIA SCREENING IN RESOURCE-LIMITED SETTINGS: DEVELOPMENT AND VALIDATION OF A SCREENING PROGRAM.

Abstract

Objective: Thalassemia screening is typically performed using high-performance liquid chromatography (HPLC), which is an accurate but expensive method that is not widely available. To overcome this issue, researchers have looked into alternative screening methods such as using erythrocytic indices obtained from a complete blood count (CBC) test. This approach has proven to be highly sensitive and specific, making it an attractive and cost-effective solution for excluding normal populations from thalassemia screening programs. Consequently, it can improve the efficiency of screening programs, particularly in settings with limited resources.

Methods: We have developed a Python program based on a novel methodology aimed at effectively excluding normal populations from chromatography-based screening programs for thalassemia mutation screening. The program was implemented in Python 3.8 using the Spider Integrated Development Environment. It takes input parameters such as hemoglobin, red blood corpuscles, mean corpuscular volume, and hematocrit from CBC tests to determine an individual’s thalassemia status. To validate the program, we utilized a dataset of 3,000 students who had undergone CBC testing at a local clinic. To ensure privacy protection, the dataset was anonymized.

Results: Our study showed that CBC parameters accurately identified individuals with thalassemia through the Python program-based thalassemia screening approach with no false-positive samples. We validated its performance on a large dataset of students and found that it can improve screening efficiency and accuracy, particularly in resource-limited settings.

Conclusion: However, additional validation studies are necessary to confirm its generalizability and usefulness in diverse populations