Abstract
BackgroundThe 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.MethodsRoutinely 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.ResultsOf 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.ConclusionsThe 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.
Highlights
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
Once the typical blood loss during a certain intervention has been established with some robustness, use of that value and a specific patient’s predicted individual anemia tolerance can be used to make decisions with respect to perioperative anemia management–will type and cross be required, will it be sufficient, or should red blood cell (RBC) products be immediately available in the operating room–and can aid blood bank inventory management
We identify a minimum dataset required for intraoperative hemoglobin loss calculation across a wide range equivalent to 0.5–4.5 RBC units, demonstrate insufficiency of established formulas with respect to accuracy and linearity, and propose a mathematically relatively complex new formula which is applied by entering the minimal data into a software application or tabular calculation datasheet
Summary
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. A number of simple formulas drawing on pre- and post-operative hemoglobin or hematocrit (Hct), typically incorporating total blood volume/ RBC volume/ hemoglobin mass of the respective patient, have been proposed since [16,17,18,19,20,21,22] None of these caught on, presumably because the former is cumbersome and the latter poorly validated, and a need to provide techniques for more accurate blood loss quantification was identified [14, 23]. We identify a minimum dataset required for intraoperative hemoglobin loss calculation across a wide range equivalent to 0.5–4.5 RBC units, demonstrate insufficiency of established formulas with respect to accuracy and linearity, and propose a mathematically relatively complex new formula which is applied by entering the minimal data into a software application (web-app) or tabular calculation datasheet
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