Methods, systems, and computer readable media for predicting cross-match compatibility using machine learning human leukocyte antigen (hla) antibody model

A system for predicting a virtual crossmatch outcome using a machine learning human leukocyte antigen (HLA) antibody model, the system comprising:

• a computing platform including at least one processor and a memory;
• a data preparation module implemented by the at least one processor for receiving, as input, donor specific antibody (DSA) mean fluorescence intensity (MFI) values obtained from mixing donor-specific single antigen beads with serum samples of a prospective transplant recipient and normalizing the DSA MFI values;
• and
• an HLA antibody model implemented by the at least one processor for receiving, as input, the normalized DSA MFI values and generating, as output, a predicted crossmatch probability.

A non-transitory computer readable medium having stored thereon executable instructions that when executed by a processor of a computer control the computer to perform steps comprising:

• receiving, as input, donor specific antibody (DSA) mean fluorescence intensity (MFI) values obtained from mixing donor-specific single antigen beads with serum samples of a prospective transplant recipient;
• normalizing the DSA MFI values;
• inputting the normalized DSA MFI values into a machine learning donor-specific human leukocyte antigen (HLA) antibody model;
• receiving, as output from the HLA antibody model, a predicted crossmatch probability;
• and
• using the predicted crossmatch probability to inform a transplant decision.