查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Heart Disorders and Diseases - He art Disease is the subject of a report. According to news originating from Groni ngen, Netherlands, by NewsRx correspondents, research stated, “Prosthetic valve endocarditis (PVE) is a serious complication of prosthetic valve implantation, w ith an estimated yearly incidence of at least 0.4-1.0%. The Duke cr iteria and subsequent modifications have been developed as a diagnostic framewor k for infective endocarditis (IE) in clinical studies.” Our news journalists obtained a quote from the research from the University of G roningen, “However, their sensitivity and specificity are limited, especially fo r PVE. Furthermore, their most recent versions (ESC2015 and ESC2023) include adv anced imaging modalities, e.g., cardiac CTA and [F] FDG PET/CT as major criteria. However, despite these significant changes, the we ighing system using major and minor criteria has remained unchanged. This may ha ve introduced bias to the diagnostic set of criteria. Here, we aimed to evaluate and improve the predictive value of the modified Duke/ESC 2015 (MDE2015) criter ia by using machine learning algorithms. In this proof-of-concept study, we used data of a welldefined retrospective multicentre cohort of 160 patients evaluat ed for suspected PVE. Four machine learning algorithms were compared to the pred iction of the diagnosis according to the MDE2015 criteria: Lasso logistic regres sion, decision tree with gradient boosting (XGBoost), decision tree without grad ient boosting, and a model combining predictions of these (ensemble learning). A ll models used the same features that also constitute the MDE2015 criteria. The final diagnosis of PVE, based on endocarditis team consensus using all available clinical information, including surgical findings whenever performed, and with at least 1 year follow up, was used as the composite gold standard. The diagnost ic performance of the MDE2015 criteria varied depending on how the category of ‘ possible’ PVE cases were handled. Considering these cases as positive for PVE, s ensitivity and specificity were 0.96 and 0.60, respectively. Whereas treating th ese cases as negative, sensitivity and specificity were 0.74 and 0.98, respectiv ely. Combining the approaches of considering possible endocarditis as positive a nd as negative for ROCanalysis resulted in an excellent AUC of 0.917. For the m achine learning models, the sensitivity and specificity were as follows: logisti c regression, 0.92 and 0.85; XGBoost, 0.90 and 0.85; decision trees, 0.88 and 0. 86; and ensemble learning, 0.91 and 0.85, respectively. The resulting AUCs were, in the same order: 0.938, 0.937, 0.930, and 0.941, respectively. In this proof- of-concept study, machine learning algorithms achieved improved diagnostic perfo rmance compared to the major/minor weighing system as used in the MDE2015 criter ia. Moreover, these models provide quantifiable certainty levels of the diagnosi s, potentially enhancing interpretability for clinicians. Additionally, they all ow for easy incorporation of new and/or refined criteria, such as the individual weight of advanced imaging modalities such as CTA or [F] FDG PET/CT.”
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