Browsing by Subject "Predictive value"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Open Access Application of the RIMARC algorithm to a large data set of action potentials and clinical parameters for risk prediction of atrial fibrillation(Springer, 2015) Ravens, U.; Katircioglu-Öztürk, D.; Wettwer, E.; Christ, T.; Dobrev, D.; Voigt, N.; Poulet, C.; Loose, S.; Simon, J.; Stein, A.; Matschke, K.; Knaut, M.; Oto, E.; Oto, A.; Güvenir, H. A.Ex vivo recorded action potentials (APs) in human right atrial tissue from patients in sinus rhythm (SR) or atrial fibrillation (AF) display a characteristic spike-and-dome or triangular shape, respectively, but variability is huge within each rhythm group. The aim of our study was to apply the machine-learning algorithm ranking instances by maximizing the area under the ROC curve (RIMARC) to a large data set of 480 APs combined with retrospectively collected general clinical parameters and to test whether the rules learned by the RIMARC algorithm can be used for accurately classifying the preoperative rhythm status. APs were included from 221 SR and 158 AF patients. During a learning phase, the RIMARC algorithm established a ranking order of 62 features by predictive value for SR or AF. The model was then challenged with an additional test set of features from 28 patients in whom rhythm status was blinded. The accuracy of the risk prediction for AF by the model was very good (0.93) when all features were used. Without the seven AP features, accuracy still reached 0.71. In conclusion, we have shown that training the machine-learning algorithm RIMARC with an experimental and clinical data set allows predicting a classification in a test data set with high accuracy. In a clinical setting, this approach may prove useful for finding hypothesis-generating associations between different parameters.Item Open Access High-resolution magic anglespinning ¹H nuclear magnetic resonance spectroscopy metabolomics of hyperfunctioning parathyroid glands(Mosby, Inc., 2016) Battini, S.; Imperiale, A.; Taïeb, D.; Elbayed, K.; Cicek, A. E.; Sebag, F.; Brunaud, L.; Namer, Izzie-JacquesBackground Primary hyperparathyroidism (PHPT) may be related to a single gland disease or multiglandular disease, which requires specific treatments. At present, an operation is the only curative treatment for PHPT. Currently, there are no biomarkers available to identify these 2 entities (single vs. multiple gland disease). The aims of the present study were to compare (1) the tissue metabolomics profiles between PHPT and renal hyperparathyroidism (secondary and tertiary) and (2) single gland disease with multiglandular disease in PHPT using metabolomics analysis. Methods The method used was 1H high-resolution magic angle spinning nuclear magnetic resonance spectroscopy. Forty-three samples from 32 patients suffering from hyperparathyroidism were included in this study. Results Significant differences in the metabolomics profile were assessed according to PHPT and renal hyperparathyroidism. A bicomponent orthogonal partial least square-discriminant analysis showed a clear distinction between PHPT and renal hyperparathyroidism (R2Y = 0.85, Q2 = 0.63). Interestingly, the model also distinguished single gland disease from multiglandular disease (R2Y = 0.96, Q2 = 0.55). A network analysis was also performed using the Algorithm to Determine Expected Metabolite Level Alterations Using Mutual Information (ADEMA). Single gland disease was accurately predicted by ADEMA and was associated with higher levels of phosphorylcholine, choline, glycerophosphocholine, fumarate, succinate, lactate, glucose, glutamine, and ascorbate compared with multiglandular disease. Conclusion This study shows for the first time that 1H high-resolution magic angle spinning nuclear magnetic resonance spectroscopy is a reliable and fast technique to distinguish single gland disease from multiglandular disease in patients with PHPT. The potential use of this method as an intraoperative tool requires specific further studies.