0
Original Research: SLEEP DISORDERS |

Oximeter-Based Autonomic State Indicator Algorithm for Cardiovascular Risk Assessment

Ludger Grote, MD, PhD; Dirk Sommermeyer, MSc; Ding Zou, MD, PhD; Derek N. Eder, PhD; Jan Hedner, MD, PhD
Author and Funding Information

From the Sleep Disorders Center (Drs Grote, Zou, Eder, and Hedner, and Mr Sommermeyer), Department of Pulmonary Medicine and Allergology, Sahlgrenska University Hospital, Gothenburg, Sweden; and Measure Check Control GmbH (Mr Sommermeyer) and the Institute of Biomedical Engineering, Karlsruhe Institute of Technology (Mr Sommermeyer), Karlsruhe, Germany.

Correspondence to: Ludger Grote, MD, PhD, Sleep Laboratory, Department of Pulmonary Medicine, Sahlgrenska University Hospital, SE 41345 Gothenburg, Sweden; e-mail: Ludger.grote@lungall.gu.se


Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/site/misc/reprints.xhtml).

Funding/Support: This study was supported by Weinmann GmbH, Measure Check and Control (MCC) GmbH, the German Ministry for Education and Science (BMBF), the Swedish Heart and Lung Foundation, the Göteborg Medical Society, and the Sahlgrenska Academy at the University of Gothenburg, Sweden.


© 2011 American College of Chest Physicians


Chest. 2011;139(2):253-259. doi:10.1378/chest.09-3029
Text Size: A A A
Published online

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.

Figures in this Article

Sign In to Access Full Content

MEMBER & INDIVIDUAL SUBSCRIBER

Want Access?

NEW TO CHEST?

Become a CHEST member and receive a FREE subscription as a benefit of membership.

Individuals can purchase this article on ScienceDirect.

Individuals can purchase a subscription to the journal.

Individuals can purchase a subscription to the journal or buy individual articles.

Learn more about membership or Purchase a Full Subscription.

INSTITUTIONAL ACCESS

Institutional access is now available through ScienceDirect and can be purchased at myelsevier.com.

Sign In to Access Full Content

MEMBER & INDIVIDUAL SUBSCRIBER

Want Access?

NEW TO CHEST?

Become a CHEST member and receive a FREE subscription as a benefit of membership.

Individuals can purchase this article on ScienceDirect.

Individuals can purchase a subscription to the journal.

Individuals can purchase a subscription to the journal or buy individual articles.

Learn more about membership or Purchase a Full Subscription.

INSTITUTIONAL ACCESS

Institutional access is now available through ScienceDirect and can be purchased at myelsevier.com.

Figures

Tables

References

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Sign In to Access Full Content

MEMBER & INDIVIDUAL SUBSCRIBER

Want Access?

NEW TO CHEST?

Become a CHEST member and receive a FREE subscription as a benefit of membership.

Individuals can purchase this article on ScienceDirect.

Individuals can purchase a subscription to the journal.

Individuals can purchase a subscription to the journal or buy individual articles.

Learn more about membership or Purchase a Full Subscription.

INSTITUTIONAL ACCESS

Institutional access is now available through ScienceDirect and can be purchased at myelsevier.com.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Find Similar Articles
CHEST Journal Articles
  • CHEST Journal
    Print ISSN: 0012-3692
    Online ISSN: 1931-3543