Population-adjusted cut-off: A new approach for enhancing the diagnostic efficacy of hematological discrimination formulae for screening β-Thalassemia trait
{"title":"Population-adjusted cut-off: A new approach for enhancing the diagnostic efficacy of hematological discrimination formulae for screening β-Thalassemia trait","authors":"Isuru Aravinda , Shashini Dharmasiri , Chathurika Sewwandi , Sinthu Karunaithas , Sonali Goonetilleke , Karunaithas Rasaratnam","doi":"10.1016/j.cca.2025.120592","DOIUrl":null,"url":null,"abstract":"<div><div>Screening for β-thalassemia trait (βTT) is crucial for preventing β-thalassemia major in offspring. Although hematological discrimination formulae (HDF), developed using complete blood count parameters, are cost-effective tools for screening βTT, their performance varies across different populations. This study evaluated the performance of 32 HDF for screening βTT in the Sri Lankan population. Data were retrieved from laboratory databases and categorized into confirmed βTT and non-βTT groups based on high-performance liquid chromatography results. The βTT screening performance of the HDF was assessed using accuracy measurements, the receiver operating characteristic (ROC) curve, and Youden’s index (YI). Furthermore, a population-adjusted cut-off was determined using the Index of Union (IU) method to optimize the predictive accuracy of HDF in screening βTT. Bordbar demonstrated excellent predictive performance in males (AUC = 0.908; YI = 0.815), while Shine & Lal, Kerman-I, Nishad, Sehgal, Bordbar, and Roth demonstrated high discriminative ability in females (AUC > 0.833; YI > 0.666). Applying a population-adjusted cut-off improved the βTT screening potential of Shine & Lal, Kerman-I, Nishad, Bordbar, and Roth in males (AUC > 0.911; YI > 0.822) and enhanced the performance of Kerman-II in females (AUC > 0.861; YI > 0.722). Notably, Shine & Lal (AUC = 0.937; YI > 0.873) and Nishad (AUC = 0.897; YI > 0.794) demonstrated the best performance for males and females, respectively, when a population-adjusted cut-off was applied for screening βTT. In conclusion, determining a population-adjusted cut-off is a new initiative to enhance the βTT screening performance of HDF across different populations.</div></div>","PeriodicalId":10205,"journal":{"name":"Clinica Chimica Acta","volume":"579 ","pages":"Article 120592"},"PeriodicalIF":2.9000,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinica Chimica Acta","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0009898125004711","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Screening for β-thalassemia trait (βTT) is crucial for preventing β-thalassemia major in offspring. Although hematological discrimination formulae (HDF), developed using complete blood count parameters, are cost-effective tools for screening βTT, their performance varies across different populations. This study evaluated the performance of 32 HDF for screening βTT in the Sri Lankan population. Data were retrieved from laboratory databases and categorized into confirmed βTT and non-βTT groups based on high-performance liquid chromatography results. The βTT screening performance of the HDF was assessed using accuracy measurements, the receiver operating characteristic (ROC) curve, and Youden’s index (YI). Furthermore, a population-adjusted cut-off was determined using the Index of Union (IU) method to optimize the predictive accuracy of HDF in screening βTT. Bordbar demonstrated excellent predictive performance in males (AUC = 0.908; YI = 0.815), while Shine & Lal, Kerman-I, Nishad, Sehgal, Bordbar, and Roth demonstrated high discriminative ability in females (AUC > 0.833; YI > 0.666). Applying a population-adjusted cut-off improved the βTT screening potential of Shine & Lal, Kerman-I, Nishad, Bordbar, and Roth in males (AUC > 0.911; YI > 0.822) and enhanced the performance of Kerman-II in females (AUC > 0.861; YI > 0.722). Notably, Shine & Lal (AUC = 0.937; YI > 0.873) and Nishad (AUC = 0.897; YI > 0.794) demonstrated the best performance for males and females, respectively, when a population-adjusted cut-off was applied for screening βTT. In conclusion, determining a population-adjusted cut-off is a new initiative to enhance the βTT screening performance of HDF across different populations.
期刊介绍:
The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC)
Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells.
The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.