A Wearable Infrared Sensor for Detecting Non-ST Segment Elevation Acute Coronary Syndromes

Area of Science:

• Cardiology
• Biomedical Engineering
• Medical Diagnostics

Background:

• Conventional diagnosis of non-ST segment elevation acute coronary syndrome (NSTE-ACS) relies on electrocardiography and blood biomarkers.
• Current diagnostic methods can be invasive and time-consuming.
• There is a need for rapid, noninvasive diagnostic tools for early risk stratification.

Purpose of the Study:

• To investigate a noninvasive, bloodless, and electrode-free diagnostic strategy for NSTE-ACS using a wrist-worn infrared spectrophotometric biosensor (Infrasensor).
• To evaluate the diagnostic accuracy of the Infrasensor in detecting high-grade coronary obstruction and predicting 30-day adverse outcomes.

Main Methods:

• Prospective multicenter study involving 595 patients with suspected NSTE-ACS across 13 sites.
• Utilized a wrist-worn infrared spectrophotometric biosensor (Infrasensor).
• Employed machine learning models for data analysis, including external validation with 200 multi-ethnic controls.

Main Results:

• A machine learning model detected high-grade coronary obstruction with an AUC of 0.87 (95% CI: 0.84-0.90), 90% specificity, and 84% positive predictive value.
• A secondary model predicted 30-day adverse outcomes with an AUC of 0.89 (95% CI: 0.87-0.92), 99% sensitivity, and 96% negative predictive value.
• Performance surpassed conventional risk scores.

Conclusions:

• The Infrasensor demonstrated high diagnostic accuracy for high-grade coronary obstruction and 30-day adverse outcome prediction.
• The device showed superior performance compared to conventional risk scores in a diverse, multicenter cohort.
• The Infrasensor holds potential as a rapid, noninvasive point-of-care tool for early NSTE-ACS risk stratification.