USE OF PYTHON AND COMPLETE BLOOD COUNT PARAMETERS FOR COST-EFFECTIVE THALASSEMIA SCREENING IN RESOURCE-LIMITED SETTINGS: DEVELOPMENT AND VALIDATION OF A SCREENING PROGRAM
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| Název: | USE OF PYTHON AND COMPLETE BLOOD COUNT PARAMETERS FOR COST-EFFECTIVE THALASSEMIA SCREENING IN RESOURCE-LIMITED SETTINGS: DEVELOPMENT AND VALIDATION OF A SCREENING PROGRAM |
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| Autoři: | Abhishek Samanta, Nandan Bhattacharyya |
| Zdroj: | Asian Journal of Pharmaceutical and Clinical Research. :38-41 |
| Informace o vydavateli: | Innovare Academic Sciences Pvt Ltd, 2023. |
| Rok vydání: | 2023 |
| Témata: | 0301 basic medicine, 0303 health sciences, 03 medical and health sciences, 3. Good health |
| Popis: | Thalassemia screening is typically done 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 has the potential to improve the efficiency of screening programs, particularly in settings with limited resources. A Python program, using the study by Samanta et al. (2021) as a basis, to screen for thalassemia using CBC parameters was created. The program was developed in Python 3.8 using Spyder IDE (Integrated development environment), and it takes in CBC parameters, such as hemoglobin, RBC (Red blood Corpuscles), MCV (mean corpuscular volume), MCH (Mean corpuscular hemoglobin), and HCT (Hematocrit), to determine an individual's thalassemia status. We validated the program using a dataset of 3,000 students who had undergone CBC testing at a local clinic. The dataset was anonymized to ensure privacy protection Our study showed that the Python program for thalassemia screening based on CBC parameters accurately identified individuals with thalassemia. We validated its performance on a large dataset of students and found that it has the potential to improve screening efficiency and accuracy, particularly in resource-limited settings. However, additional validation studies are necessary to confirm its generalizability and usefulness in diverse populations. |
| Druh dokumentu: | Article |
| ISSN: | 2455-3891 0974-2441 |
| DOI: | 10.22159/ajpcr.2023.v16i10.48392 |
| Rights: | CC BY |
| Přístupové číslo: | edsair.doi...........7312b4b817e23681d0fb0cbb3ca9d26e |
| Databáze: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstrakt: | Thalassemia screening is typically done 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 has the potential to improve the efficiency of screening programs, particularly in settings with limited resources. A Python program, using the study by Samanta et al. (2021) as a basis, to screen for thalassemia using CBC parameters was created. The program was developed in Python 3.8 using Spyder IDE (Integrated development environment), and it takes in CBC parameters, such as hemoglobin, RBC (Red blood Corpuscles), MCV (mean corpuscular volume), MCH (Mean corpuscular hemoglobin), and HCT (Hematocrit), to determine an individual's thalassemia status. We validated the program using a dataset of 3,000 students who had undergone CBC testing at a local clinic. The dataset was anonymized to ensure privacy protection Our study showed that the Python program for thalassemia screening based on CBC parameters accurately identified individuals with thalassemia. We validated its performance on a large dataset of students and found that it has the potential to improve screening efficiency and accuracy, particularly in resource-limited settings. However, additional validation studies are necessary to confirm its generalizability and usefulness in diverse populations. |
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| ISSN: | 24553891 09742441 |
| DOI: | 10.22159/ajpcr.2023.v16i10.48392 |