title: The use of overnight pulse wave analysis for recognition of cardiovascular risk factors and risk: a multicentric evaluation.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  Five hundred and twenty individuals (346 men, age 55.0 ± 13.4 years, BMI 29.9 ± 6.  kg/m) 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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: aDepartment of Pulmonary Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden bSchool of Engineering and Architecture, SRH University, Heidelberg cDepartment of Respiratory Medicine, Allergology and Sleep Medicine, Klinikum Nuernberg, Nuernberg dDepartment of Pulmonary Medicine, Bethanien Hospital, Solingen eDepartment of Cardiology, University Hospital Charité, Berlin fDepartment of Pulmonary Medicine, Agaplesion Bethesda Krankenhaus Wuppertal, Wuppertal, Germany.
authors.forename: Dirk
authors.initials: D
authors.lastname: Sommermeyer
authors.forename: Ding
authors.initials: D
authors.lastname: Zou
authors.forename: Derek N
authors.initials: DN
authors.lastname: Eder
authors.forename: Jan
authors.initials: J
authors.lastname: Hedner
authors.forename: Joachim H
authors.initials: JH
authors.lastname: Ficker
authors.forename: Winfried
authors.initials: W
authors.lastname: Randerath
authors.forename: Christina
authors.initials: C
authors.lastname: Priegnitz
authors.forename: Thomas
authors.initials: T
authors.lastname: Penzel
authors.forename: Ingo
authors.initials: I
authors.lastname: Fietze
authors.forename: Bernd
authors.initials: B
authors.lastname: Sanner
authors.forename: Ludger
authors.initials: L
authors.lastname: Grote
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000209:etiology* / Q000503:physiopathology*; D015331:Cohort Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010092:Oximetry; D017156:Photoplethysmography; D063177:Pulse Wave Analysis; D012307:Risk Factors; D012890:Sleep / Q000502:physiology; D059289:Vascular Stiffness / Q000502:physiology