Comparison of the additive, logistic european system for cardiac operative risk (EuroSCORE) with the EuroSCORE 2 to predict mortality in high-risk cardiac surgery

Objective The introduction of new clinical risk scores (e.g. European System for Cardiac Operative Risk Evaluation (EuroSCORE) II) superseding original scores (e.g. EuroSCORE I) with different variable sets typically result in disparate datasets due to high levels of missingness for new score variables prior to time of adoption. Little is known about the use of ensemble learning to incorporate disparate data from legacy scores. We tested the hypothesised that Homogenenous and Heterogeneous Machine Learning (ML) ensembles will have better performance than ensembles of Dynamic Model Averaging (DMA) for combining knowledge from EuroSCORE I legacy data with EuroSCORE II data to predict cardiac surgery risk. Methods Using the National Adult Cardiac Surgery Audit dataset, we trained 12 different base learner models, based on two different variable sets from either EuroSCORE I (LogES) or EuroScore II (ES II), partitioned by the time of score adoption (1996–2016 or 2012–2016) and evaluated on holdout set (2017–2019). These base learner models were ensembled using nine different combinations of six ML algorithms to produce homogeneous or heterogeneous ensembles. Performance was assessed using a consensus metric. Results Xgboost homogenous ensemble (HE) was the highest performing model (clinical effectiveness metric (CEM) 0.725) with area under the curve (AUC) (0.8327; 95% confidence interval (CI) 0.8323–0.8329) followed by Random Forest HE (CEM 0.723; AUC 0.8325; 95%CI 0.8320–0.8326). Across different heterogenous ensembles, significantly better performance was obtained by combining siloed datasets across time (CEM 0.720) than building ensembles of either 1996–2011 ( t-test adjusted, p = 1.67×10−6) or 2012–2019 ( t-test adjusted, p = 1.35×10−193) datasets alone. Conclusions Both homogenous and heterogenous ML ensembles performed significantly better than DMA ensemble of Bayesian Update models. Time-dependent ensemble combination of variables, having differing qualities according to time of score adoption, enabled previously siloed data to be combined, leading to increased power, clinical interpretability of variables and usage of data.

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Intraoperative hemoadsorption in high-risk patients with infective endocarditis

Haidari

Demircioğlu

Boss

et al. 2022

PLoS ONE

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Background Postoperative sepsis is an important cause of morbidity and mortality in patients with infective endocarditis undergoing surgical therapy. Blood purification using hemoadsorption therapy shows promising results in the treatment of sepsis. In this study, the clinical effects of intraoperative hemoadsorption in high-risk patients with infective endocarditis were evaluated. Methods Eligible candidates were high-risk patients with infective endocarditis undergoing cardiac surgery between January 2014 and December 2019. Patients with intraoperative hemoadsorption (hemoadsorption) were compared to patients without hemoadsorption (control). The endpoints were the incidence of postoperative sepsis, sepsis-associated death and in-hospital mortality. Additionally, postoperative vasopressor need, systemic vascular resistance indices and Sequential Organ Failure Assessment (SOFA) scores were compared. Results After propensity score matching, 70 high-risk patients were included. Postoperative sepsis occurred in 14 patients in the hemoadsorption group and in 16 patients in the control group, p = 0.629. Four patients died due to postoperative sepsis in the hemoadsorption group, while 11 postoperative septic patients died in the control group, p = 0.041. In-hospital mortality was 34% in the hemoadsorption group versus 43% in the control group, p = 0.461. On ICU-admission and the first postoperative day, the cumulative vasopressor need was 0.17 versus 0.25 μg/kgBW/min, p = 0.123 and 0.06 versus 0.11 μg/kgBW/min, p = 0.037, and the systemic vascular resistance index was 1448 versus 941 dyn·s·cm-5, p = 0.013 and 1156 versus 858 dyn·s·cm-5, p = 0.110 in the hemoadsorption versus control group, respectively. Postoperative course of SOFA score normalized significantly (p = 0.01) faster in the hemoadsorption group. Conclusions In high-risk cardiac surgical patients with infective endocarditis, intraoperative hemoadsorption significantly reduced sepsis-associated mortality. Furthermore, intraoperative hemoadsorption resulted in significant faster recovery of hemodynamics and organ function. Intraoperative hemoadsorption seems to attenuate the severity of postoperative sepsis.

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Comparison of the additive, logistic european system for cardiac operative risk (EuroSCORE) with the EuroSCORE 2 to predict mortality in high-risk cardiac surgery

Cited by 5 publications

References 28 publications

Validation of the European system for cardiac operative risk evaluation II in a large Taiwan cardiac surgical centre

Validation of the European system for cardiac operative risk evaluation II in a large Taiwan cardiac surgical centre

Cardiac surgery risk prediction using ensemble machine learning to incorporate legacy risk scores: A benchmarking study

Intraoperative hemoadsorption in high-risk patients with infective endocarditis

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