Aims
Clinical differentiation of acute myocardial infarction (MI) from unstable angina and other presentations mimicking acute coronary syndromes (ACS) is critical for implementing time-sensitive interventions and optimizing outcomes. However, the diagnostic steps are dependent on blood draws and laboratory turn-around-times. We tested the clinical feasibility of a wrist-worn transdermal infra-red spectrophotometric sensor (Transdermal-ISS) in clinical practice and assessed the performance of a machine-learning algorithm for identifying elevated high-sensitivity cardiac Troponin-I (hs-cTnI) levels in patients hospitalized with ACS.
Methods and Results
We enrolled 238 patients hospitalized with ACS at five sites. The final diagnosis of MI (with or without ST-elevation) and unstable angina was adjudicated using ECG, cTn, echocardiography (regional wall motion abnormality), or coronary angiography. A Transdermal-ISS-derived deep-learning model was trained (three sites) and externally validated with hs-cTnI (one site), and echocardiography and angiography (two sites), respectively. The Transdermal-ISS model predicted elevated hs-cTnI with the area under the receiver operator characteristics of 0.90 (95% confidence interval [CI], 0.84-0.94; sensitivity, 0.86; specificity, 0.82) and 0.92 (95% CI, 0.80-0.98; sensitivity, 0.94; specificity, 0.64), for internal and external validation cohorts, respectively. In addition, the model predictions were associated with regional wall motion abnormalities (Odds Ratio [OR], 3.37; CI, 1.02-11.15; p=0.046) and significant coronary stenosis (OR, 4.69; CI, 1.27- 17.26; p = 0.019).
Conclusion
A wrist-worn transdermal infrared spectrophotometric sensor is clinically feasible for rapid, bloodless prediction of elevated hs-cTnI in real-world settings. It may have a role in establishing a point-of-care biomarker diagnosis of myocardial infarction and impact triaging patients with suspected ACS.