Biomedical subjects

Christian Perrey

Publications and source records attributed to Christian Perrey.

2 recordsLinked to original sources

PubMed2005

Optimizing revascularization strategies in patients with multivessel coronary disease: impact of intracoronary pressure measurements.

OBJECTIVES: In patients with multivessel coronary disease, the functional significance of each lesion is often unclear, and preinterventional stress tests may be inconclusive. In this setting, intracoronary pressure measurements may be helpful to define the optimal revascularization strategy. METHODS: Twenty-five consecutive patients (aged 64 +/- 11 years) with multivessel disease, inconclusive stress tests or not performed stress tests, and an angiographically intermediate coronary artery stenosis in at least 1 major vessel underwent intracoronary pressure measurements. Myocardial fractional flow reserve was measured for the intermediate lesions under the condition of maximum hyperemia induced by intravenous adenosine (140 microg x kg(-1) x min(-1). Revascularization strategies based on angiographic information alone were compared with treatment strategies based on fractional flow reserve results. RESULTS: The original recommendation of the revascularization procedure of choice (bypass operation or angioplasty) was changed in 9 patients (36%) on the basis of the results of fractional flow reserve measurements. In 6 more patients, pressure measurements led to a change in the recommended number of anastomoses to be aimed for during the operation. Within diffusely diseased vessels, fractional flow reserve provided an exact segmental resolution of pathologic vessel resistance for optimal graft placement. Significant left main disease was confirmed in 3 of 6 patients and was detected in 3 angiographically unsuspected cases. CONCLUSIONS: In patients with multivessel disease, coronary pressure-derived fractional flow reserve is a valuable tool to guide clinical decision making and support cardiologists and cardiovascular surgeons in the composition of optimal revascularization strategies.

Adenosine↗

PubMed2005

A tutorial on the use of ROC analysis for computer-aided diagnostic systems.

The application of the receiver operating characteristic (ROC) curve for computer-aided diagnostic systems is reviewed. A statistical framework is presented and different methods of evaluating the classification performance of computer-aided diagnostic systems, and, in particular, systems for ultrasonic tissue characterization, are derived. Most classifiers that are used today are dependent on a separation threshold, which can be chosen freely in many cases. The separation threshold separates the range of output values of the classification system into different target groups, thus conducting the actual classification process. In the first part of this paper, threshold specific performance measures, e.g., sensitivity and specificity, are presented. In the second part, a threshold-independent performance measure, the area under the ROC curve, is reviewed. Only the use of separation threshold-independent performance measures provides classification results that are overall representative for computer-aided diagnostic systems. The following text was motivated by the lack of a complete and definite discussion of the underlying subject in available textbooks, references and publications. Most manuscripts published so far address the theme of performance evaluation using ROC analysis in a manner too general to be practical for everyday use in the development of computer-aided diagnostic systems. Nowadays, the user of computer-aided diagnostic systems typically handles huge amounts of numerical data, not always distributed normally. Many assumptions made in more or less theoretical works on ROC analysis are no longer valid for real-life data. The paper aims at closing the gap between theoretical works and real-life data. The review provides the interested scientist with information needed to conduct ROC analysis and to integrate algorithms performing ROC analysis into classification systems while understanding the basic principles of classification.

Diagnosis, Computer-Assisted↗