Improved risk assessment in parvovirus B19‐positive patients with heart failure by multiparametric analysis of endomyocardial biopsy using machine learning methods
Christian Baumeier, Johannes Starlinger, Ganna Aleshcheva, Gordon Wiegleb, Felicitas Escher, Philip Wenzel, Amin Polzin, Heinz‐Peter Schultheiss
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引用次数: 0
Abstract
AimsThe analysis of endomyocardial biopsies (EMB) is a prerequisite for a definitive diagnosis in patients with unexplained heart failure (HF). The use of machine learning (ML) methods may help to identify high‐risk patients and to initiate therapy. In this study, we develop ML models for risk stratification of parvovirus B19 (B19V) positive patients with HF based on key features from multiparametric EMB analyses.Methods and resultsWe retrospectively enrolled 263 B19V‐positive patients with HF (mean age 51 ± 15 years, 62% male) and followed them over a median period of 22 months (interquartile range 4–35 months). All‐cause mortality, left ventricular (LV) deterioration and persistent LV systolic dysfunction were used as clinical combined endpoint. EMB were analysed for a variety of inflammatory and infectious markers, and patient prognosis was assessed using ML methods (logistic regression, random forest, support vector machines and gradient boosting). Detection of intramyocardial inflammation and B19V viral activity was associated with poor clinical outcome (hazard ratio 3.50, 95% confidence interval 1.96–6.23, p < 0.001). Linear combination of demographic and clinical data with multiparametric EMB markers increased the prognostic performance (area under the curve [AUC] = 0.724) compared to using single features (AUC = 0.667). The use of gradient boosting ML methods significantly improved the accuracy of risk prediction (AUC = 0.926).ConclusionsIntramyocardial inflammation and B19V viral activity were detected more frequently in patients with poor clinical outcome, suggesting that they are risk factors for death and LV dysfunction. Using multiparametric EMB data, we present an ML‐based prognostic tool that can determine the clinical outcome of patients with a high degree of accuracy. This could be helpful in assessing the progression of the disease and making appropriate treatment decisions.
期刊介绍:
European Journal of Heart Failure is an international journal dedicated to advancing knowledge in the field of heart failure management. The journal publishes reviews and editorials aimed at improving understanding, prevention, investigation, and treatment of heart failure. It covers various disciplines such as molecular and cellular biology, pathology, physiology, electrophysiology, pharmacology, clinical sciences, social sciences, and population sciences. The journal welcomes submissions of manuscripts on basic, clinical, and population sciences, as well as original contributions on nursing, care of the elderly, primary care, health economics, and other related specialist fields. It is published monthly and has a readership that includes cardiologists, emergency room physicians, intensivists, internists, general physicians, cardiac nurses, diabetologists, epidemiologists, basic scientists focusing on cardiovascular research, and those working in rehabilitation. The journal is abstracted and indexed in various databases such as Academic Search, Embase, MEDLINE/PubMed, and Science Citation Index.