Impact of autologous blood transfusion after bone marrow harvest on unrelated donor's health and outcome: a CIBMTR analysis.

Pre-harvest autologous blood collection from bone marrow (BM) donors is performed to meet potential post-operative transfusion needs. This study examines the impact of autologous blood transfusion on BM donor's health and safety. The study included first-time unrelated BM donors from the United States whose BM harvest was facilitated by the National Marrow Donor Program (NMDP) centers between 2006 and 2017. Examination of 7024 BM donors revealed that 60% received at least one unit of autologous blood. The donors who received autologous blood were older, had lower hemoglobin pre-harvest, underwent longer duration of anesthesia, and higher volume BM harvest. Only donors who underwent high-volume BM harvest, defined as a BM harvest volume >27% of donor's blood volume, benefited from autologous transfusion. After a high-volume BM harvest, autologous blood transfusion was shown to decrease grade 2 to 4 collection-associated toxicities within 48 h of BM donation (p = 0.010) and shorten the time to donor-reported "complete" recovery from donation-associated symptoms (p < 0.001). Therefore, autologous transfusion could be avoided as support of marrow donation in the majority of unrelated BM donors and should be limited to cases where the planned BM harvest volume is expected to exceed 27% of donor's blood volume.

Primary Immune Deficiency Treatment Consortium (PIDTC) report.

The Primary Immune Deficiency Treatment Consortium (PIDTC) is a network of 33 centers in North America that study the treatment of rare and severe primary immunodeficiency diseases. Current protocols address the natural history of patients treated for severe combined immunodeficiency (SCID), Wiskott-Aldrich syndrome, and chronic granulomatous disease through retrospective, prospective, and cross-sectional studies. The PIDTC additionally seeks to encourage training of junior investigators, establish partnerships with European and other International colleagues, work with patient advocacy groups to promote community awareness, and conduct pilot demonstration projects. Future goals include the conduct of prospective treatment studies to determine optimal therapies for primary immunodeficiency diseases. To date, the PIDTC has funded 2 pilot projects: newborn screening for SCID in Navajo Native Americans and B-cell reconstitution in patients with SCID after hematopoietic stem cell transplantation. Ten junior investigators have received grant awards. The PIDTC Annual Scientific Workshop has brought together consortium members, outside speakers, patient advocacy groups, and young investigators and trainees to report progress of the protocols and discuss common interests and goals, including new scientific developments and future directions of clinical research. Here we report the progress of the PIDTC to date, highlights of the first 2 PIDTC workshops, and consideration of future consortium objectives.

Accurate critical constants for the one-sided approximate likelihood ratio test of a normal mean vector when the covariance matrix is estimated.

Tang, Gnecco, and Geller (1989, Biometrika 76, 577-583) proposed an approximate likelihood ratio (ALR) test of the null hypothesis that a normal mean vector equals a null vector against the alternative that all of its components are nonnegative with at least one strictly positive. This test is useful for comparing a treatment group with a control group on multiple endpoints, and the data from the two groups are assumed to follow multivariate normal distributions with different mean vectors and a common covariance matrix (the homoscedastic case). Tang et al. derived the test statistic and its null distribution assuming a known covariance matrix. In practice, when the covariance matrix is estimated, the critical constants tabulated by Tang et al. result in a highly liberal test. To deal with this problem, we derive an accurate small-sample approximation to the null distribution of the ALR test statistic by using the moment matching method. The proposed approximation is then extended to the heteroscedastic case. The accuracy of both the approximations is verified by simulations. A real data example is given to illustrate the use of the approximations.