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Development of a New Detection Algorithm to Identify Acute Coronary Syndrome Using Electrochemical Biosensors for Real-World Long-Term Monitoring

机译:一种新的检测算法用电化学生物传感器鉴定急性冠状动脉综合征以实现现实世界长期监测

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摘要

Electrochemically based technologies are rapidly moving from the laboratory to bedside applications and wearable devices, like in the field of cardiovascular disease. Major efforts have focused on the biosensor component in contrast with those employed in creating more suitable detection algorithms for long-term real-world monitoring solutions. The calibration curve procedure presents major limitations in this context. The objective is to propose a new algorithm, compliant with current clinical guidelines, which can overcome these limitations and contribute to the development of trustworthy wearable or telemonitoring solutions for home-based care. A total of 123 samples of phosphate buffer solution were spiked with different concentrations of troponin, the gold standard method for the diagnosis of the acute coronary syndrome. These were classified as normal or abnormal according to established clinical cut-off values. Off-the-shelf screen-printed electrochemical sensors and cyclic voltammetry measurements (sweep between −1 and 1 V in a 5 mV step) was performed to characterize the changes on the surface of the biosensor and to measure the concentration of troponin in each sample. A logistic regression model was developed to accurately classify these samples as normal or abnormal. The model presents high predictive performance according to specificity (94%), sensitivity (92%), precision (92%), recall (92%), negative predictive value (94%) and F-score (92%). The area under the curve of the precision-recall curve is 97% and the positive and negative likelihood ratios are 16.38 and 0.082, respectively. Moreover, high discriminative power is observed from the discriminate odd ratio (201) and the Youden index (0.866) values. The promising performance of the proposed algorithm suggests its capability to overcome the limitations of the calibration curve procedure and therefore its suitability for the development of trustworthy home-based care solutions.
机译:电化学技术正在从实验室迅速移动到床边应用和可穿戴设备,如在心血管疾病领域。与为长期现实世界监测解决方案创造更多合适的检测算法而采用的人侧重于生物传感器组件的主要努力。校准曲线过程在此上下文中具有主要限制。目的是提出一种新的算法,符合目前的临床指南,可以克服这些限制,并有助于为家庭护理提供可靠的可佩戴或远程监视解决方案的发展。用不同浓度的肌钙蛋白,总共123种磷酸盐缓冲溶液样品,致急性冠状动脉综合征的诊断。根据已建立的临床截止值,这些被归类为正常或异常。在现有的屏幕印刷的电化学传感器和循环伏安法测量(在5mV步骤中扫描-1和1V之间),以表征生物传感器表面的变化并测量每个样品中的肌钙蛋白的浓度。开发了一种逻辑回归模型,以准确地将这些样品作为正常或异常分类。该模型根据特异性(94%),灵敏度(92%),精度(92%),召回(92%),阴性预测值(94%)和F分(92%),对高预测性能。精密召回曲线的曲线下的面积分别为97%,阳性和负似然比分别为16.38和0.082。此外,从区分奇数比(201)和Youden指数(0.866)值观察到高鉴别力。所提出的算法的有希望的性能表明其能力克服校准曲线程序的局限性,从而适用于可靠的家庭护理解决方案的发展。

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