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Background and Objectives: Red blood cell (RBC) transfusions are needed by almost every acute myeloid leukaemia (AML) patient undergoing induction chemotherapy and constitute a cornerstone in supportive measures for cancer patients in general. Randomized controlled trials have shown non‐inferiority or even superiority of restrictive transfusion guidelines over liberal transfusion guidelines in specific clinical situations outside of medical oncology. In this study, we analysed whether more restrictive RBC transfusion reduces blood use without affecting hard outcomes.
Materials and Methods: A total of 352 AML patients diagnosed between 2007 and 2018 and undergoing intensive induction chemotherapy were included in this retrospective analysis. In the less restrictive transfusion group, patients received RBC transfusion for haemoglobin levels below 8 g/dl (2007–2014). In the restrictive transfusion group, patients received RBC transfusion for haemoglobin levels below 7 g/dl (2016–2018). Liberal transfusion triggers were never endorsed.
Results: A total of 268 (76·1%) and 84 (23·9%) AML patients fell into the less restrictive and restrictive transfusion groups, respectively. The less restrictive transfusion group had 1 g/dl higher mean haemoglobin levels, received their first RBC transfusions earlier and needed 1·5 more units of RBC during the hospital stay of induction chemotherapy. Febrile episodes, C‐reactive protein levels, admission to the intensive care unit, length of hospital stay as well as response and survival rates did not differ between the two cohorts.
Conclusion: From our retrospective analysis, we conclude that a more restrictive transfusion trigger does not affect important outcomes of AML patients. The opportunity to test possible effects of the more severe anaemia in the restrictive transfusion group on quality of life was missed.
Background: The ability to approximate intra-operative hemoglobin loss with reasonable precision and linearity is prerequisite for determination of a relevant surgical outcome parameter: This information enables comparison of surgical procedures between different techniques, surgeons or hospitals, and supports anticipation of transfusion needs. Different formulas have been proposed, but none of them were validated for accuracy, precision and linearity against a cohort with precisely measured hemoglobin loss and, possibly for that reason, neither has established itself as gold standard. We sought to identify the minimal dataset needed to generate reasonably precise and accurate hemoglobin loss prediction tools and to derive and validate an estimation formula.
Methods: Routinely available clinical and laboratory data from a cohort of 401 healthy individuals with controlled hemoglobin loss between 29 and 233 g were extracted from medical charts. Supervised learning algorithms were applied to identify a minimal data set and to generate and validate a formula for calculation of hemoglobin loss.
Results: Of the classical supervised learning algorithms applied, the linear and Ridge regression models performed at least as well as the more complex models. Most straightforward to analyze and check for robustness, we proceeded with linear regression. Weight, height, sex and hemoglobin concentration before and on the morning after the intervention were sufficient to generate a formula for estimation of hemoglobin loss. The resulting model yields an outstanding R2 of 53.2% with similar precision throughout the entire range of volumes or donor sizes, thereby meaningfully outperforming previously proposed medical models.
Conclusions: The resulting formula will allow objective benchmarking of surgical blood loss, enabling informed decision making as to the need for pre-operative type-and-cross only vs. reservation of packed red cell units, depending on a patient’s anemia tolerance, and thus contributing to resource management.