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.

Purpose of the Study:

• To investigate a noninvasive, bloodless, and electrode-free diagnostic strategy for NSTE-ACS.
• To evaluate a wrist-worn infrared spectrophotometric biosensor (Infrasensor) for early risk stratification.

Main Methods:

• Prospective multicenter study involving 595 patients with suspected NSTE-ACS.
• Utilized a machine learning model with data from a wrist-worn infrared biosensor.
• External validation using a leave-one-cohort-out approach with 200 multi-ethnic controls.

Main Results:

• The primary model detected high-grade coronary obstruction with an AUC of 0.87, 90% specificity, and 84% positive predictive value.
• A secondary model predicted 30-day adverse outcomes with an AUC of 0.89, 99% sensitivity, and 96% negative predictive value.
• Performance surpassed conventional risk scores.

Conclusions:

• The Infrasensor demonstrates high diagnostic accuracy for coronary obstruction and adverse outcome prediction in NSTE-ACS.
• The device shows potential as a rapid, noninvasive point-of-care tool for NSTE-ACS risk stratification.
• Findings support the Infrasensor's utility in a diverse, multicenter setting.