TRACKing or TRUSTing transfusion prediction:Validation of Red blood cell transfusion prediction models for low transfusion rate cardiac surgery and high transfusion rate post-cardiotomy veno-arterial extracorporeal life support

Abstract bodyPreoperative identification of patients at risk of red blood cell (RBC) transfusion is necessary to prevent adverse outcomes. Several models can determine this risk. Models like TRACK, TRUST and ACTA-PORT differ in complexity and performance. Some models outperform TRACK, but their complexity limits clinical application. In 2009, the TRACK model was developed with criteria for everyday practice, simplicity and easy clinical implementation. Advances in hemodilution management in Europe has reduced transfusion rates in adult cardiac surgery, necessitating re-evaluation of the TRACK model in low transfusion rate populations.MethodsThe TRACK model was validated using 4053 adult patients who underwent cardiac surgery between 2015 and 2022. Subsequently, the database was divided at random into a derivation and validation data set. Original coefficients of the TRACK model were updated in the derivation data set and validated in a validation data set on accuracy and discriminative ability. Model calibration and discriminative ability were assessed as measures of model performance. Further, the TRACK model will be validated and updated in the same way for predicting blood transfusion in post-cardiotomy ECLS patients.ResultsAll variables but age remained significant in the external validation of the TRACK model. The odds ratio of female sex on blood transfusion increased from 1.42 to 2.42 (95% CI, 1.94 – 3.02). The original TRACK model demonstrated an area under the curve (AUC) of 0.76 (95% CI, 0.74 – 0.78) while showing poor calibration indicating overoptimistic estimation of RBC transfusion risk (p &lt; 0.05). The updated TRACK model demonstrated a slightly higher AUC of 0.78 (95% CI,0.75 – 0.81) and showed good calibration over all risk strata (p = 0.19).ConclusionsRefining the TRACK coefficients improved preoperative at-risk identification. The updated TRACK model improves predicted accuracy and may help clinicians make better discissions, especially in low-transfusion adult cardiac surgery. This study demonstrates the feasibility of RBC transfusion prediction models for adult cardiac surgery. Our ongoing study is evaluating RBC transfusion prediction models for post-cardiotomy ECLS. These results will also be presented at the conference.<br/

Towards Meta Model Provenance: a Goal-Driven Approach to Document the Provenance of Meta Models

Part 1: Regular PapersInternational audienceThis paper introduces the notion of meta model provenance. Meta model provenance helps to understand the origins of meta model elements, such as language concepts, attributes, or constraints. Thus, it should answer questions such as: where did this language concept come from? under which assumptions was it introduced? Among others, meta model provenance is intended to support the controlled evolution of languages and informed language (re-)design. In this paper, we focus on a goal-driven meta model provenance approach. This is one specific operationalization of the meta model provenance concept, which shows how goal models help to understand the origins of the elements of a conceptual modeling language. To illustrate our goal-driven provenance approach, we use a scenario from the electricity domain