A novel curve fitting method for AV optimisation of biventricular pacemakers

Physiol Meas. 2015 Sep;36(9):1889-900. doi: 10.1088/0967-3334/36/9/1889. Epub 2015 Aug 6.

Abstract

In this study, we designed and tested a new algorithm, which we call the 'restricted parabola', to identify the optimum atrioventricular (AV) delay in patients with biventricular pacemakers. This algorithm automatically restricts the hemodynamic data used for curve fitting to the parabolic zone in order to avoid inadvertently selecting an AV optimum that is too long.We used R, a programming language and software environment for statistical computing, to create an algorithm which applies multiple different cut-offs to partition curve fitting of a dataset into a parabolic and a plateau region and then selects the best cut-off using a least squares method. In 82 patients, AV delay was adjusted and beat-to-beat systolic blood pressure (SBP) was measured non-invasively using our multiple-repetition protocol. The novel algorithm was compared to fitting a parabola across the whole dataset to identify how many patients had a plateau region, and whether a higher hemodynamic response was achieved with one method.In 9/82 patients, the restricted parabola algorithm detected that the pattern was not parabolic at longer AV delays. For these patients, the optimal AV delay predicted by the restricted parabola algorithm increased SBP by 1.36 mmHg above that predicted by the conventional parabolic algorithm (95% confidence interval: 0.65 to 2.07 mmHg, p-value = 0.002).AV optima selected using our novel restricted parabola algorithm give a greater improvement in acute hemodynamics than fitting a parabola across all tested AV delays. Such an algorithm may assist the development of automated methods for biventricular pacemaker optimisation.

Publication types

  • Randomized Controlled Trial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms*
  • Blood Pressure
  • Cardiac Resynchronization Therapy Devices*
  • Datasets as Topic
  • Hemodynamics
  • Humans
  • Least-Squares Analysis
  • Middle Aged
  • Pattern Recognition, Automated / methods*
  • Software