Hahn-Klimroth Max, Loick Philipp, Kim-Wanner Soo-Zin, Seifried Erhard, Bonig Halvard
Goethe University Mathematics Institute, Frankfurt, Germany.
German Red Cross Blood Service BaWüHe, Institute Frankfurt, Sandhofstraße 1, 60528, Frankfurt, Germany.
J Transl Med. 2021 Mar 20;19(1):116. doi: 10.1186/s12967-021-02783-9.
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.
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.
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 R of 53.2% with similar precision throughout the entire range of volumes or donor sizes, thereby meaningfully outperforming previously proposed medical models.
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.
以合理的精度和线性度近似术中血红蛋白损失的能力是确定一个相关手术结果参数的先决条件：该信息能够比较不同技术、外科医生或医院之间的手术程序，并有助于预测输血需求。已经提出了不同的公式，但没有一个针对精确测量血红蛋白损失的队列验证其准确性、精度和线性度，可能正因如此，也没有一个公式成为金标准。我们试图确定生成合理精确和准确的血红蛋白损失预测工具所需的最小数据集，并推导和验证一个估计公式。
从401名健康个体的病历中提取常规可用的临床和实验室数据，这些个体的血红蛋白损失在29至233克之间且受到控制。应用监督学习算法来识别最小数据集，并生成和验证计算血红蛋白损失的公式。
在所应用的经典监督学习算法中，线性回归模型和岭回归模型的表现至少与更复杂的模型一样好。由于最易于分析和检查稳健性，我们采用了线性回归。体重、身高、性别以及干预前和干预后早晨的血红蛋白浓度足以生成一个估计血红蛋白损失的公式。所得模型的R值高达53.2%，在整个体积或供体大小范围内具有相似的精度，从而明显优于先前提出的医学模型。
所得公式将允许对外科手术失血进行客观的基准测试，根据患者的贫血耐受性，就术前仅进行血型交叉配血与储备红细胞单位的必要性做出明智决策，从而有助于资源管理。
