Multi-criteria decision making to validate performance of RBC-based formulae to screen $$\beta$$-thalassemia trait in heterogeneous haemoglobinopathies

Abstract

Background India has the most significant number of children with thalassemia major worldwide, and about 10,000-15,000 children with the disease are born yearly. Scaling up e-health initiatives in rural areas using a cost-effective digital tool to provide healthcare access for all sections of people remains a challenge for government or semi-governmental institutions and agencies. Methods We compared the performance of a recently developed formula SCS$$_{BTT}$$ BTT and its web application SUSOKA with 42 discrimination formulae presently available in the literature. 6,388 samples were collected from the Postgraduate Institute of Medical Education and Research, Chandigarh, in North-Western India. Performances of the formulae were evaluated by eight different measures: sensitivity, specificity, Youden’s Index, AUC-ROC, accuracy, positive predictive value, negative predictive value, and false omission rate. Three multi-criteria decision-making (MCDM) methods, TOPSIS, COPRAS, and SECA, were implemented to rank formulae by ensuring a trade-off among the eight measures. Results MCDM methods revealed that the Shine & Lal and SCS$$_{BTT}$$ BTT were the best-performing formulae. Further, a modification of the SCS$$_{BTT}$$ BTT formula was proposed, and validation was conducted with a data set containing 939 samples collected from Nil Ratan Sircar (NRS) Medical College and Hospital, Kolkata, in Eastern India. Our two-step approach emphasized the necessity of a molecular diagnosis for a lower number of the population. SCS$$_{BTT}$$ BTT along with the condition MCV$$\le$$ ≤ 80 fl was recommended for a higher heterogeneous population set. It was found that SCS$$_{BTT}$$ BTT can classify all BTT samples with 100% sensitivity when MCV$$\le$$ ≤ 80 fl. Conclusions We addressed the issue of how to integrate the higher-ranked formulae in mass screening to ensure higher performance through the MCDM approach. In real-life practice, it is sufficient for a screening algorithm to flag a particular sample as requiring or not requiring further specific confirmatory testing. Implementing discriminate functions in routine screening programs allows early identification; consequently, the cost will decrease, and the turnaround time in everyday workflows will also increase. Our proposed two-step procedure expedites such a process. It is concluded that for mass screening of BTT in a heterogeneous set of data, SCS$$_{BTT}$$ BTT and its web application SUSOKA can provide 100% sensitivity when MCV$$\le$$ ≤ 80 fl.