Conventional methods for cardiovascular disease risk stratification are based on quantification of recognized risk factors or assessment of biomarkers during the wake period. We evaluated an algorithm on the basis of a photoplethysmographic pulse wave recording during sleep for cardiovascular risk assessment.
Five hundred and twenty individuals (346 men, age 55.0 ±13.4 years, BMI 29.9 ± 6.1 kg/m2) with suspected sleep apnoea were randomly recruited at five sleep centres. Individual cardiovascular risk scores were calculated in accordance with established cardiovascular risk matrixes (ESH/ESC, Framingham, SCORE, PROCAM scores). A digital photoplethysmographic pulse wave signal was continuously recorded during the night using an oximeter sensor. An algorithm based on eight separate hypoxic and pulse wave derived parameters was trained in 130 individuals and validated in 390 individuals for low/high cardiovascular risk classification.
All derived parameters were associated with elevated ESH/ESC risk in univariate analysis and five in the multiple logistic regression model [discrimination index C = 0.8, Chi-square (7) = 69, P <0.0001]. The combined algorithm detected high-risk patients (validation set, ESH/ESC risk classes 4 and 5) with a sensitivity, specificity, positive predictive value and negative predictive value of 74.5, 76.4, 69.0 and 81.0%, respectively. Significant associations were also found for the Framingham, SCORE and PROCAM scores. The computed risk scores in individuals with/without (n = 34/356) a previous history of cardiovascular event (myocardial infarction, transitory ischemic attack or stroke) were 0.71 ± 0.27 and 0.42 ± 0.34 (P <0.001), respectively.
Parameters derived from modified pulse oximetry during sleep may provide information on cardiovascular function. Combined signal analysis may be used for recognition of individuals with established cardiovascular risk in a sleep laboratory cohort.