TY  - JOUR
A1  - Hahn-Klimroth, Maximilian Grischa
A1  - Loick, Philipp
A1  - Kim-Wanner, Soo-Zin
A1  - Seifried, Erhard
A1  - Bönig, Halvard-Björn
T1  - Generation and validation of a formula to calculate hemoglobin loss on a cohort of healthy adults subjected to controlled blood loss
T2  - Journal of translational medicine
N2  - 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.
KW  - Surgical blood loss
KW  - Blood management
KW  - Blood loss calculator
KW  - Blood loss formula
KW  - Anemia management
KW  - Machine learning
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/74542
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-745422
SN  - 1479-5876
N1  - Open Access funding enabled and organized by Projekt DEAL.
VL  - 19
IS  - art. 116
SP  - 1
EP  - 9
PB  - BioMed Central
CY  - London
ER  -