Development of a new detection algorithm to identify acute coronary syndrome using electrochemical biosensors for real-world long-term monitoring

Research output: Contribution to journalArticlepeer-review

Authors

Colleges, School and Institutes

Abstract

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 dis-criminative 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.

Bibliographic note

Funding Information: Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 713690 as well as from the Science Foundation Ireland (SFI) and the European Regional Development Fund under the Grant Numbers 13/RC/2073 and 15/RP/2765. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Details

Original languageEnglish
Article number28
Number of pages11
JournalBioengineering
Volume8
Issue number2
Publication statusPublished - 20 Feb 2021

Keywords

  • Cardiac biomarkers, Electrochemical sensor, Long-time monitoring, Risk stratification algorithm, Wearable device

ASJC Scopus subject areas

Sustainable Development Goals