Pneumologie 2012; 66 - P432
DOI: 10.1055/s-0032-1302793

Can a composite analysis of autonomic and vascular signals predict cardiovascular risk? – The ASI approach

L Grote 1, D Sommermeyer 2, D Zou 1, DN Eder 1, JH Ficker 3, WJ Randerath 4, T Penzel 5, B Sanner 6, J Hedner 1
  • 1Dept. of Pulmonary Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
  • 2Dept. Of Pulmonary Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden, Karlsruhe Institute Of Technology (KIT)
  • 3Dept. of Pulmonary Medicine, Clinic Nürnberg Nord, Nürnberg
  • 4Dept. of Pulmonary Medicine, Bethanien Hospital, Solingen
  • 5Dept. of Cardiology, University Hospital Charité, Berlin
  • 6Dept. of Pulmonary Medicine, Bethesda Hospital, Wuppertal

Introduction: Analysis of multiple continuous physiological signals obtained during sleep may provide a novel method to assess cardiovascular (CV) risk. The novel autonomic state indicator (ASI) algorithm combines information from arterial oxygen saturation (SpO2) and a photoplethysmographic pulse wave signal and computes a CV risk index.

Methods: Subjects (n=327, 227 male, age 55,1±13,6yrs, BMI 30,1±6,4kg/m2) referred to five sleep centers in Germany and Sweden were studied. The occurrence of CV risk factors was assessed and subjects were classified by four established CV risk matrixes (Framingham, EU-SCORE, PROCAM and ESC/ESH). Peripheral pulse wave was measured by overnight digital photoplethysmography. The ASI algorithm extracted patterns of the peripheral pulse wave and SpO2 signal by amplitude and time/frequency analysis. Five derived parameters (hypoxic variation, vascular augmentation, cardio acceleration, cardio-respiratory coupling and pulse wave amplitude) were used to determine the final ASI score (range 0–1).

Results: The computed ASI CV risk index was significantly associated with the ESH/ESC risk matrix (r=0,48, p<0,0001), the Framingham risk score (r=0,42, p<0,001, the PROCAM score (r=0,45, p<0,001) and the EU-SCORE (r=0,36, p<0,001). Moreover, the ASI CV risk index was elevated in patients with an already established CV endpoint (MI and/or stroke, n=29) compared with the remaining patients (0,72±0,43 vs. 0,47±0,38, p=0,002).

Conclusions: The ASI technique appears to provide a possibility to recognize subjects with increased CV risk based on recording of physiological signals. Interestingly, the sleep period appears to be a particularly useful window for assessment. This technique – based on a modified pulse oximeter – may be useful in both sleep, pulmonary, and cardiovascular medicine.

The study was supported by Weinmann GMBH, the Swedish Heart and Lung Foundation and the University of Gothenburg.