Search

Background Invasive fractional flow reserve (FFR) is a standard tool for identifying ischemia-producing coronary stenosis. However, in clinical practice, over 70% of treatment decisions still rely on visual estimation of angiographic stenosis, which has limited accuracy (about 60%-65%) for the prediction of FFR < 0.80. One of the reasons for the visual-functional mismatch is that myocardial ischemia can be affected by the supplied myocardial size, which is not always evident by coronary angiography. The aims of this study were to develop an angiography-based machine learning (ML) algorithm for predicting the supplied myocardial volume for a stenosis, as measured using coronary computed tomography angiography (CCTA), and then to build an angiography-based classifier for the lesions with an FFR < 0.80 versus [greater than or equal to] 0.80. Methods and findings A retrospective study was conducted using data from 1,132 stable and unstable angina patients with 1,132 intermediate lesions who underwent invasive coronary angiography, FFR, and CCTA at the Asan Medical Center, Seoul, Korea, between 1 May 2012 and 30 November 2015. The mean age was 63 ± 10 years, 76% were men, and 72% of the patients presented with stable angina. Of these, 932 patients (assessed before 31 January 2015) constituted the training set for the algorithm, and 200 patients (assessed after 1 February 2015) served as a test cohort to validate its diagnostic performance. Additionally, external validation with 79 patients from two centers (CHA University, Seongnam, Korea, and Ajou University, Suwon, Korea) was conducted. After automatic contour calibration using the caliber of guiding catheter, quantitative coronary angiography was performed using the edge-detection algorithms (CAAS-5, Pie-Medical). Clinical information was provided by the Asan BiomedicaL Research Environment (ABLE) system. The CCTA-based myocardial segmentation (CAMS)-derived myocardial volume supplied by each vessel (right coronary artery [RCA], left anterior descending [LAD], left circumflex [LCX]) and the myocardial volume subtended to a stenotic segment (CAMS-%V.sub.sub) were measured for labeling. The ML for (1) predicting vessel territories (CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA) and CAMS-%V.sub.sub and (2) identifying the lesions with an FFR < 0.80 was constructed. Angiography-based ML, employing a light gradient boosting machine (GBM), showed mean absolute errors (MAEs) of 5.42%, 8.57%, and 4.54% for predicting CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA, respectively. The percent myocardial volumes predicted by ML were used to predict the CAMS-%V.sub.sub . With 5-fold cross validation, the MAEs between ML-predicted percent myocardial volume subtended to a stenotic segment (ML-%V.sub.sub) and CAMS-%V.sub.sub were minimized by the elastic net (6.26% ± 0.55% for LAD, 5.79% ± 0.68% for LCX, and 2.95% ± 0.14% for RCA lesions). Using all attributes (age, sex, involved vessel segment, and angiographic features affecting the myocardial territory and stenosis degree), the ML classifiers (L2 penalized logistic regression, support vector machine, and random forest) predicted an FFR 53% (66%, AUC = 0.71, 95% confidence intervals 0.65-0.78). The external validation showed 84% accuracy (AUC = 0.89, 95% confidence intervals 0.83-0.95). The retrospective design, single ethnicity, and the lack of clinical outcomes may limit this prediction model's generalized application. Conclusion We found that angiography-based ML is useful to predict subtended myocardial territories and ischemia-producing lesions by mitigating the visual-functional mismatch between angiographic and FFR. Assessment of clinical utility requires further validation in a large, prospective cohort study., Author(s): Hyeonyong Hae 1, Soo-Jin Kang 1,*, Won-Jang Kim 2, So-Yeon Choi 3, June-Goo Lee 4, Youngoh Bae 1, Hyungjoo Cho 1, Dong Hyun Yang 5, Joon-Won Kang 5, Tae-Hwan [...]

This paper presents an intra-body power transfer (IBPT) system that can deliver power greater than 1 mW across an on-body distance of 160 cm. A system simulation model is built for the characterization of the channel and optimization of the power transfer. Our system analysis and experimental validation demonstrate that 1 MHz is an optimal carrier frequency for IBPT in terms of power delivered to the load (PDL) and power efficiency (PE). Prototype TX and RX boards were built, and an IC was fabricated in a 180-nm CMOS process for the RX. The proposed RX IC consists of a voltage doubler (VD) and a charge pump (CP) to obtain a sufficiently high voltage conversion ratio (VCR). Among various rectifier topologies, the VD is the optimal topology for the power receiver front-end because the parasitic ground coupling capacitances, which inevitably exist in the IBPT system, act as an inherent input-coupling capacitance for the VD. The implemented VD utilizes a dynamic $V_{TH}$ compensation (DVC) for its diode components. Compared to the conventional static $V_{TH}$ compensation (SVC), DVC in the VD reduces the reverse leakage current of the diode, thus maximizing the power conversion efficiency (PCE) and VCR. In addition, the PDL is enhanced by inserting an inductor on the TX board. It reduces the backward-path impedance without increasing the RX volume, boosting the PDL by up to 9.9 times compared to the PDL without the inductor insertion. The proposed IBPT system delivers up to 178.8 $\mu$ W of power at 11.7% of maximum power efficiency with 3.3-V DC output voltage and even 1.385 mW of power with the inductor insertion, supporting various biomedical wearable sensors, such as ECG sensor modules. [ABSTRACT FROM AUTHOR]