title: Crossmatch prediction of highly sensitized patients.
abstract: 1. A subset of negative reactions of sera from highly sensitized patients to donor lymphocytes are predicted with high accuracy (96.5% negative correct). 2. The prediction is performed by a hybrid expert system (HES) which uses multiple knowledge of stochastic (SCORES), artificial neural net (ANN), and genetic algorithm (GA) techniques. 3. All knowledge for the T-cell predictions is derived from serological reactions of the investigated sera (93) to a large panel (284). 4. When analyzing 5 HLA Class I typing sera controls, HES performs better than a standard serum analysis method in 3 measurement categories: r value; percent correct; and percent negative correct. 5. SCORES and ANN produce the strongest complementary association. SCORES is the best method with low PRA sera, while ANN is better at predicting high PRA sera. GA performs very poorly with high PRA sera. 6. HES can acquire knowledge from any of the various methods used for serum screening and crossmatch testing. Therefore, there is no need for method standardization as each laboratory will produce its own program incorporating its patients' data. High standardization of HLA Class I typing is necessary. 7. Most recipients for whom donors are never selected by HES are in the PRA range of 97-100%. 8. Certainty level categorization of a crossmatch gives clinical flexibility in judgement of potential donors. 9. All programs are written in the C language and are portable to numerous platforms. HES is implemented on an inexpensive IBM-PC compatible computer and can calculate predictions quickly. 10. HES predicts negative crossmatches with enough accuracy to initiate an organ sharing protocol to increase the chance for highly sensitized patients to obtain a transplant.
authors.forename: B D
authors.initials: BD
authors.lastname: Clark
authors.forename: S W
authors.initials: SW
authors.lastname: Leong
mesh_terms: D000465:Algorithms; D005103:Expert Systems; D006084:Graft Rejection / Q000209:etiology / Q000276:immunology; D006680:HLA Antigens; D006650:Histocompatibility Testing / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D016030:Kidney Transplantation / Q000009:adverse effects / Q000276:immunology*; D016571:Neural Networks, Computer; D013269:Stochastic Processes
chemical_list: D006680:HLA Antigens