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Biomedical subjects

Paul Rubel

Publications and source records attributed to Paul Rubel.

2 recordsLinked to original sources

QT dynamicity and sudden death after myocardial infarction: results of a long-term follow-up study.

INTRODUCTION: The aim of this study was to determine whether impaired adaptation of the QT interval to changes in heart rate predicts sudden death after an acute myocardial infarction. METHODS AND RESULTS: The Groupe d'Etude du Pronostic de l'Infarctus du Myocarde (GREPI) trial was a prospective multicenter study designed to evaluate the long-term outcome of myocardial infarction. QT dynamicity was evaluated in 265 patients by analyzing 24-hour Holter recordings obtained 9 to 14 days after myocardial infarction. The linear regression slope of QT intervals measured to the apex and to the end of the T wave (QTe) plotted against RR intervals was calculated using a dedicated Holter algorithm. The value of QT/RR in predicting sudden death and total mortality was compared with those of ejection fraction, heart rate variability, and late potentials. Mean follow-up was 81 +/- 27 months. There were 73 deaths, of which 23 were sudden. Of all the parameters, an increased diurnal QTe/RR slope (>0.18) was the strongest independent predictor of sudden death (relative risk 6.07, confidence interval 1.48-24.95, P = 0.01). CONCLUSION: Increased diurnal QTe dynamicity is independently predictive of sudden death among patients with myocardial infarction. This simple parameter may help to stratify risk and select patients who may benefit from antiarrhythmic prophylaxis.

Aged↗

Adaptive user interface customization through browsing knowledge capitalization.

Hypermedia data browsing is a mean for improving information access. However, the overload and the heterogeneity of medical information, as well as the multitude of possible navigational paths, turn the consultation of data into a difficult task. We present in this paper a solution for the development of adaptive user interfaces in a hypermedia data browsing environment. It is based on the capitalization of the users knowledge in the decision-making process, expressed in terms of navigational paths and of data presentation modes that are customized to the user's preferences and practice. This capitalization offers the user a way to automatically store and reuse the experience accumulated in browsing through patient records. We illustrate our approach with the implementation of HEMA, a clinical workstation prototype that we have specialized for the cardiology domain.

Artificial Intelligence↗