Background: Cardiovascular (CV) risk assessment is important in clinical practice. An autonomic state indicator (ASI) algorithm based on pulse oximetry was developed and validated for CV risk assessment.
Methods: One hundred forty-eight sleep clinic patients (98 men, mean age 50 ± 13 years) underwent an overnight study using a novel photoplethysmographic sensor. CV risk was classified according to the European Society of Hypertension/European Society of Cardiology (ESH/ESC) risk factor matrix. Five signal components reflecting cardiac and vascular activity (pulse wave attenuation, pulse rate acceleration, pulse propagation time, respiration-related pulse oscillation, and oxygen desaturation) extracted from 99 randomly selected subjects were used to train the classification algorithm. The capacity of the algorithm for CV risk prediction was validated in 49 additional patients.
Results: Each signal component contributed independently to CV risk prediction. The sensitivity and specificity of the algorithm to distinguish high/low CV risk in the validation group were 80% and 77%, respectively. The area under the receiver operating characteristic curve for high CV risk classification was 0.84. β-Blocker treatment was identified as an important factor for classification that was not in line with the ESH/ESC reference matrix.
Conclusions: Signals derived from overnight oximetry recording provide a novel potential tool for CV risk classification. Prospective studies are warranted to establish the value of the ASI algorithm for prediction of outcome in CV disease.