Complement-Binding Donor-Specific Anti-HLA Antibodies and Risk of Primary Graft Failure in Hematopoietic Stem Cell Transplantation

AbstractDetection of donor-specific anti-HLA antibodies (DSA) has been associated with graft rejection in all forms of transplantation. The mechanism by which DSA increase the risk of graft failure remains unclear. We hypothesized that complement-binding DSA are associated with engraftment failure in hematopoietic stem cell transplantation (HSCT) and analyzed 122 haploidentical transplant recipients tested prospectively for DSA. Retrospective analysis to detect C1q binding DSA (C1q+DSA) was performed on 22 allosensitized recipients. Twenty-two of 122 patients (18%) had DSA, 19 of which were women (86%). Seven patients with DSA (32%) rejected the graft. Median DSA level at transplant for patients who failed to engraft was 10,055 mean fluorescence intensity (MFI) versus 2065 MFI for those who engrafted (P = .007). Nine patients with DSA were C1q positive in the initial samples with median DSA levels of 15,279 MFI (range, 1554 to 28,615), compared with 7 C1q-negative patients with median DSA levels of 2471 MFI (range, 665 to 12,254) (P = .016). Of 9 patients who were C1q positive in the initial samples, 5 patients remained C1q positive at time of transplant (all with high DSA levels [median, 15,279; range, 6487 to 22,944]) and experienced engraftment failure, whereas 4 patients became C1q negative pretransplant and all engrafted the donor cells (P = .008). In conclusion, patients with high DSA levels (>5000 MFI) and complement-binding DSA antibodies (C1q positive) appear to be at much higher risk of primary graft failure. The presence of C1q+DSA should be assessed in allosensitized patients before HSCT. Reduction of C1q+DSA levels might prevent engraftment failure in HSCT

Dichotomization of Multilevel Variables to Detect Hidden Associations

A test of independence is commonly used to determine differences (or associations) between samples in a nominal level measurement. Fisher’s exact test and Chi-square test are two of the most widely applied tests of independence used in the data analyses in different areas such as information technologies, biostatistics, psychology and health sciences. In some cases, contingency tables with null entries (also called random zeros) arise, particularly if the number of samples is small, and the variables analyzed are multilevel. This situation becomes a problem because if one or more entries in a contingency table are zero or have small values, then the tests of independence produce unreliable results. In this paper, we propose a method to address that issue. The method merges one or more levels of the variables analyzed to create contingency tables with only one degree of freedom, avoiding applying a test of independence on contingency tables with random zeros. The source code (Python) of the method is publicly available for use. The results obtained using our method give a complete panorama of the associations between the variables of a data set. To show the effectiveness of our approach to find dependencies between variables, we use four data sets publicly available on the Internet

Dichotomization of Multilevel Variables to Detect Hidden Associations

A test of independence is commonly used to determine differences (or associations) between samples in a nominal level measurement. Fisher’s exact test and Chi-square test are two of the most widely applied tests of independence used in the data analyses in different areas such as information technologies, biostatistics, psychology and health sciences. In some cases, contingency tables with null entries (also called random zeros) arise, particularly if the number of samples is small, and the variables analyzed are multilevel. This situation becomes a problem because if one or more entries in a contingency table are zero or have small values, then the tests of independence produce unreliable results. In this paper, we propose a method to address that issue. The method merges one or more levels of the variables analyzed to create contingency tables with only one degree of freedom, avoiding applying a test of independence on contingency tables with random zeros. The source code (Python) of the method is publicly available for use. The results obtained using our method give a complete panorama of the associations between the variables of a data set. To show the effectiveness of our approach to find dependencies between variables, we use four data sets publicly available on the Internet