title: Oximeter-based autonomic state indicator algorithm for cardiovascular risk assessment.
abstract: 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.
authors.affiliation: Sleep Disorders Center, Department of Pulmonary Medicine and Allergology, Sahlgrenska University Hospital, Gothenburg, Sweden. Electronic address: Ludger.grote@lungall.gu.se.
authors.forename: Ludger
authors.initials: L
authors.lastname: Grote
authors.affiliation: Sleep Disorders Center, Department of Pulmonary Medicine and Allergology, Sahlgrenska University Hospital, Gothenburg, Sweden; Measure Check Control GmbH, Karlsruhe, Germany; Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Karlsruhe, Germany.
authors.forename: Dirk
authors.initials: D
authors.lastname: Sommermeyer
authors.affiliation: Sleep Disorders Center, Department of Pulmonary Medicine and Allergology, Sahlgrenska University Hospital, Gothenburg, Sweden.
authors.forename: Ding
authors.initials: D
authors.lastname: Zou
authors.affiliation: Sleep Disorders Center, Department of Pulmonary Medicine and Allergology, Sahlgrenska University Hospital, Gothenburg, Sweden.
authors.forename: Derek N
authors.initials: DN
authors.lastname: Eder
authors.affiliation: Sleep Disorders Center, Department of Pulmonary Medicine and Allergology, Sahlgrenska University Hospital, Gothenburg, Sweden.
authors.forename: Jan
authors.initials: J
authors.lastname: Hedner
mesh_terms: D000465:Algorithms; D001341:Autonomic Nervous System / Q000503:physiopathology*; D002318:Cardiovascular Diseases / Q000503:physiopathology*; D016009:Chi-Square Distribution; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010092:Oximetry; D017286:Polysomnography; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D011795:Surveys and Questionnaires