METHODS: Blood donation data of 4120 donors, spanning from January to December 2020, were retrospectively reviewed. The blood were screened for TTI markers, including hepatitis B surface antigen (HBsAg), anti-hepatitis B core (anti-HBc), anti-hepatitis C virus (anti-HCV), anti-human immunodeficiency viruses 1 and 2 (anti-HIV1&2), anti-human T-lymphotropic virus types 1 and 2 (anti-HTLV-1&2), and syphilis antigen.

RESULTS: Positive TTI markers were detected in 10.9% of the donors. The most detected TTI marker was anti-HBc (8.9%), followed by HBsAg (0.7%). Other markers were individually detected in <1% of the donors. Anti-HBc-positive was significantly elevated among non-Saudi blood donors. There was an association between age groups and anti-HCV (p=0.002), anti-HTLV (p=0.004) and syphilis antigen (p=0.02) markers positivity. The AB positive blood group exhibited the most positivity for TTI markers, followed by O positive blood group. Similarly, association was found between ABO group and HBsAg (p=0.01), anti-HBc (p=0.001), and anti-HCV (p<0.001) markers positivity.

OBJECTIVE: This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods.

METHOD: The proposed framework is illustrated on the basis of two distinct and consecutive phases (i.e. testing and development). In testing, ABO compatibility is assessed after classifying donors into the four blood types, namely, A, B, AB and O, to indicate the suitability and safety of plasma for administration in order to refine the CP tested list repository. The development phase includes patient and donor sides. In the patient side, prioritisation is performed using a contracted patient decision matrix constructed between 'serological/protein biomarkers and the ratio of the partial pressure of oxygen in arterial blood to fractional inspired oxygen criteria' and 'patient list based on novel MCDM method known as subjective and objective decision by opinion score method'. Then, the patients with the most urgent need are classified into the four blood types and matched with a tested CP list from the test phase in the donor side. Thereafter, the prioritisation of CP tested list is performed using the contracted CP decision matrix.

RESULT: An intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19 to help doctors hasten treatments.