Article Text

other Versions

PDF
Utility of a bedside acoustic cardiographic model to predict elevated left ventricular filling pressure
  1. Sean P Collins1,
  2. Michael C Kontos2,
  3. Andrew D Michaels3,
  4. Michel Zuber4,
  5. Peter Kipfer5,
  6. Christine Attenhofer Jost6,
  7. Marcus Roos4,
  8. Paul Jamshidi4,
  9. Paul Erne4,
  10. Christopher J Lindsell4
  1. 1Department of Emergency Medicine, University of Cincinnati, Cincinnati, Ohio, USA
  2. 2Division of Cardiology, Virginia Commonwealth University, Richmond, Virginia, USA
  3. 3Division of Cardiology, University of Utah, Salt Lake City, Utah, USA
  4. 4Division of Cardiology, Kantonsspital, Lucerne, Switzerland
  5. 5Cardiology Outpatient Clinic, Frauenfeld, Switzerland
  6. 6Cardiovascular Center Klinik Im Park, Zurich, Switzerland
  1. Correspondence to Dr Sean P Collins, University of Cincinnati, Department of Emergency Medicine, 231 Albert Sabin Way, Cincinnati, OH 45267-0769, USA; sean.collins{at}uc.edu

Abstract

Background The authors previously described an acoustic cardiographic model that predicted echocardiographic correlates of elevated left ventricular (LV) filling pressure. This study evaluated this bedside acoustic cardiographic model against invasive measurements of LV filling pressure.

Methods and Results Data were prospectively obtained from 68 adults referred for right heart catheterisation. Acoustic cardiographic measurements were obtained during right heart catheterisation. Elevated LV filling pressure was defined as a pulmonary capillary wedge pressure (PCWP) ≥15 mm Hg. Parameters generated from a previous dataset used for the current analysis were measures of LV systolic time, maximum negative area of the P wave, QTc interval and third heart sound (S3) score. Logistic regression was used to calculate area under the curve (AUC). Of the 66 patients included, 39 had elevated PCWP. Estimating the probability of an elevated PCWP from the derived model resulted in an AUC of 0.72 (95% CI 0.60 to 0.85). When the regression model's parameters were held constant but the parameter estimates were allowed to vary, the AUC in the validated model was 0.76 (95% CI 0.64 to 0.88). At a specificity of 90% the positive likelihood ratio (LR+) was 5.0 (1.7 to 15.3) and the negative likelihood ratio was 0.49 (0.34 to 0.71).

Conclusion These data demonstrate that the four-variable model predicts elevated filling pressure at the bedside with high specificity and an intermediate LR+. With improvements in sensitivity and further prospective validation of this model in a cohort of emergency department patients with undifferentiated dyspnoea this may be a useful bedside diagnostic modality.

Statistics from Altmetric.com

Footnotes

  • Competing interests SPC and MCK have been consultants for Inovise Medical, Inc. SPC has also received research support from Inovise Medical, Inc. ADM has received an unrestricted educational grant and research support from Inovise Medical, Inc. There was no financial support for the current paper.

  • Patient consent Obtained.

  • Ethics approval This study was conducted with the approval of all of the institutions where data were collected.

  • Provenance and peer review Not commissioned; externally peer reviewed.

Request permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.