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

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.

We compared the performance of a recently developed formula SCS \(_{BTT}\) ${}_{\mathit{BTT}}$ 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.

MCDM methods revealed that the Shine & Lal and SCS \(_{BTT}\) ${}_{\mathit{BTT}}$ were the best-performing formulae. Further, a modification of the SCS \(_{BTT}\) ${}_{\mathit{BTT}}$ 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 \(_{BTT}\) ${}_{\mathit{BTT}}$ along with the condition MCV \(\le\) $\le$ 80 fl was recommended for a higher heterogeneous population set. It was found that SCS \(_{BTT}\) ${}_{\mathit{BTT}}$ can classify all BTT samples with 100% sensitivity when MCV \(\le\) $\le$ 80 fl.

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 \(_{BTT}\) ${}_{\mathit{BTT}}$ and its web application SUSOKA can provide 100% sensitivity when MCV \(\le\) $\le$ 80 fl.