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Multi-criteria decision making to validate performance of RBC-based formulae to screen -thalassemia trait in
Atul Kumar Jain1, Prashant Sharma1, Sarkaft Saleh2
1Department of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
Insights
A new formula, SCS, and its web app SUSOKA show high accuracy in screening for Beta-thalassemia trait (BTT) in India. This cost-effective method can improve early detection and reduce healthcare costs for BTT.
Area of Science:
- Medical Informatics
- Genetics
- Public Health
Background:
- India faces a high burden of childhood thalassemia major, with 10,000-15,000 new cases annually.
- Expanding e-health in rural India for equitable healthcare access is a significant challenge for public health institutions.
Purpose of the Study:
- To evaluate a new formula, SCS, and its web application, SUSOKA, for Beta-thalassemia trait (BTT) screening.
- To compare SCS with existing discrimination formulae using multi-criteria decision-making (MCDM) methods.
Main Methods:
- Compared SCS and SUSOKA against 42 existing formulae using 6,388 samples from North-Western India.
- Evaluated performance using sensitivity, specificity, accuracy, and other metrics.
- Employed MCDM methods (TOPSIS, COPRAS, SECA) to rank formulae.
Main Results:
- SCS and Shine & Lal formulae demonstrated superior performance.
- A modified SCS formula was validated on 939 samples from Eastern India.
- SCS achieved 100% sensitivity for BTT detection when MCV was ≤80 fl.
Conclusions:
- The study proposes an integrated, two-step screening approach using SCS and SUSOKA for mass BTT screening.
- This method enhances early identification, reduces costs, and improves workflow efficiency.
- SCS with MCV ≤80 fl offers 100% sensitivity for BTT screening in diverse populations.
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 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 SCSwere the best-performing formulae. Further, a modification of the SCS 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 along with the condition MCV 80 fl was recommended for a higher heterogeneous population set. It was found that SCS can classify all BTT samples with 100% sensitivity when MCV 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 and its web application SUSOKA can provide 100% sensitivity when MCV80 fl.
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