Optimizing single-chamber pacing in dogs Part 1: Rate determinations, rate interventions and hysteresis

Moı̈se, N. Sydney; Flanders, Wyatt H.; Flanders, Nicholas H.; Pariaut, Romain

doi:10.1016/j.tvjl.2021.105650

Cited by 2 publications

(2 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the group of dogs defined as HP/LSM was based on a 24‐hour heart rate <65 bpm, the cRMSSD was not different from the group of dogs defined as balanced autonomic modulation. A possible explanation for this finding rests in the all‐pervading sinus arrhythmia and slowing of heart rate with increase in parasympathetic modulation during sleep reflected in healthy dogs as a heart rate nocturnal dip 63 . That is, the dominance of parasympathetic modulation in the dog still prevails when the 24‐hour heart rate is between 74 and 96 bpm.…”

Section: Discussionmentioning

confidence: 99%

“…A possible explanation for this finding rests in the all-pervading sinus arrhythmia and slowing of heart rate with increase in parasympathetic modulation during sleep reflected in healthy dogs as a heart rate nocturnal dip. 63 That is, the dominance of parasympathetic modulation in the dog still prevails when the 24-hour heart rate is between 74 and 96 bpm. This assessment also is supported by the number of dogs in the latter group classified by machine learning and Poincaré density grid as HP/LSM during the stable/sleep hours.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Use of machine learning and Poincaré density grid in the diagnosis of sinus node dysfunction caused by sinoatrial conduction block in dogs

Flanders,

Moïse,

Otani

2024

Veterinary Internal Medicne

Self Cite

View full text Add to dashboard Cite

BackgroundSinus node dysfunction because of abnormal impulse generation or sinoatrial conduction block causes bradycardia that can be difficult to differentiate from high parasympathetic/low sympathetic modulation (HP/LSM).HypothesisBeat‐to‐beat relationships of sinus node dysfunction are quantifiably distinguishable by Poincaré plots, machine learning, and 3‐dimensional density grid analysis. Moreover, computer modeling establishes sinoatrial conduction block as a mechanism.AnimalsThree groups of dogs were studied with a diagnosis of: (1) balanced autonomic modulation (n = 26), (2) HP/LSM (n = 26), and (3) sinus node dysfunction (n = 21).MethodsHeart rate parameters and Poincaré plot data were determined [median (25%‐75%)]. Recordings were randomly assigned to training or testing. Supervised machine learning of the training data was evaluated with the testing data. The computer model included impulse rate, exit block probability, and HP/LSM.ResultsConfusion matrices illustrated the effectiveness in diagnosing by both machine learning and Poincaré density grid. Sinus pauses >2 s differentiated (P < .0001) HP/LSM (2340; 583‐3947 s) from sinus node dysfunction (8503; 7078‐10 050 s), but average heart rate did not. The shortest linear intervals were longer with sinus node dysfunction (315; 278‐323 ms) vs HP/LSM (260; 251‐292 ms; P = .008), but the longest linear intervals were shorter with sinus node dysfunction (620; 565‐698 ms) vs HP/LSM (843; 799‐888 ms; P < .0001).ConclusionsNumber and duration of pauses, not heart rate, differentiated sinus node dysfunction from HP/LSM. Machine learning and Poincaré density grid can accurately identify sinus node dysfunction. Computer modeling supports sinoatrial conduction block as a mechanism of sinus node dysfunction.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%