ABSTRACT
Successful allogeneic hematopoietic stem cell transplantation (alloHSCT) relies significantly on adequate allograft cell composition to achieve sustained engraftment, and a minimum of 2 × 108 total nucleated cells (TNCs) per kilogram of recipient body weight has been identified as the prerequisite cell dose for successful engraftment of marrow-derived products. To meet this minimum requirement, marrow harvest volumes are estimated based on anticipated TNC concentrations of 18.3 × 106/mL. However, there is considerable variability in marrow TNC concentrations. Thus, an algorithm that incorporates baseline donor characteristics to predict TNC concentrations could optimize outcomes for both donors and recipients. For this study, donor baseline characteristics and corresponding unstimulated marrow products harvested between 2004 and 2017 at a single large-volume donor center were collected. Multivariable analysis was used to identify significant predictors of TNC concentration. Two models-ordinary least squares (OLS) and least absolute shrinkage and selection operator (LASSO) regression-were compared for their fitness to the data and their utility in predicting TNCs. Donors with higher body mass index, younger age, male sex, white race/ethnicity, smaller harvest volumes, lower preharvest hematocrit, higher preharvest platelet count, and higher preharvest WBC count predicted significantly higher TNC concentrations in marrow products. When comparing predictive models that incorporate these characteristics, the cross-validated LASSO and bootstrapped OLS provided the best fit. We now supply these formulas to be validated in other datasets before clinical use. TNC concentration in marrow products can be predicted using donor characteristics, most of which are readily available during the donor clinical assessment. The ability to predict marrow allograft TNC concentrations can optimize collection volumes during a harvest.
Subject(s)
Bone Marrow Cells , Bone Marrow , Demography , Humans , Male , Tissue Donors , Transplantation, HomologousABSTRACT
BACKGROUND: Hematological cancer impacts both patients and their caregivers. Although only patients experience direct physical effects from cancer, both patients and caregivers experience psychological effects from cancer-related stressors. Theories suggest that patient-caregiver dyads, although experiencing individual effects from cancer, may also indirectly affect one another's health. This is called interdependence. OBJECTIVE: This study investigated physical and psychological health interdependence among patient-caregiver dyads facing hematological cancer, and whether their relationship quality was a moderator of interdependence. METHODS: Thirty patient-caregiver dyads were assessed for their self-reported physical function, anxiety, and depression using the Patient-Reported Outcomes Measurement Information System and relationship quality using the Mutuality Scale. Data were analyzed using the Actor Partner Interdependence Model, a dyadic data methodology for estimating interdependence. RESULTS: Individual physical and psychological health were related within both patients and caregivers. Patients and caregivers did not significantly differ in their reported depression, anxiety, or mutuality. Partner effects were detected among caregivers' anxiety and patients' physical function and depression. Mutuality did not moderate partner effects. CONCLUSIONS: Findings suggest that health interdependence is present among patients with hematological cancer and their caregivers. In addition, there is a strong link between physical and psychological health within individuals, regardless of role. IMPLICATIONS FOR PRACTICE: Healthcare teams should consider dyadic assessment in their cancer care plans to identify risks for health decline in patients with hematological cancer and their caregivers. Assessment of caregivers, especially their anxiety levels and physical health, may yield integral information about the patients' physical and psychological health status.
