Instantaneous and averaged heart rate profiles: Developing strategies for programming pacing rates in dogs

Moı̈se, N. Sydney; Flanders, Nicholas H.; Gunzel, E.R.

doi:10.1016/j.tvjl.2021.105624

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…Although we did not identify a low meanHR Holter at which the HR reduction was no longer favorable within the range of meanHR Holter of our study population, a “trough” effect may exist (i.e., meanHR Holter <70 bpm could worsen survival or alternatively could further improve outcomes). Mean HR Holter in normal dogs with sinus rhythm (SR) ranges from 70 to 100 bpm 35 however, and thus even lower HR would not be expected to benefit dogs with AF. Our study suggests that a rate control strategy that aims for a target meanHR Holter ≤125 bpm and possibly as low as 70 bpm could be considered.…”

Section: Resultsmentioning

confidence: 99%

Optimal rate control in dogs with atrial fibrillation—ORCA study—Multicenter prospective observational study: Prognostic impact and predictors of rate control

Pedro

Mavropoulou²,

Oyama

et al. 2023

Veterinary Internal Medicne

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Background The optimal heart rate (HR) in dogs with atrial fibrillation (AF) is unknown. Impact of HR on survival needs elucidation. Hypothesis/Objectives Dogs with a 24 hours Holter‐derived meanHR ≤125 beats per minute (bpm; rate controlled) survive longer than dogs with higher meanHR. We further aimed to determine which variables predict ability to achieving rate control. Animals Sixty dogs with AF. Methods Holter‐derived meanHR, clinical, echocardiographic, and biomarker variables were analyzed prospectively. Survival was recorded from time of rate control, with all‐cause mortality as primary endpoint. Cox proportional hazards analysis identified variables independently associated with survival; Kaplan‐Meier survival analysis estimated the median survival time of dogs with meanHR ≤125 bpm vs >125 bpm. Logistic regression explored baseline variables associated with inability to achieve rate control. Results Structural heart disease was present in 56/60 dogs, 50/60 had congestive heart failure, and 45/60 died. Median time to all‐cause death was 160 days (range, 88‐303 days), dogs with meanHR >125 bpm (n = 27) lived 33 days (95% confidence interval [CI], 15‐141 days), dogs with meanHR ≤125 bpm (n = 33) lived 608 days (95% CI, 155‐880 days; P < .0001). Congenital heart disease and N‐terminal pro‐B‐type natriuretic peptide were independently associated with higher risk of death (P < .01 and <.0001, respectively) whereas meanHR ≤125 bpm decreased the risk of death (P < .001). Increased left atrial size, increased C‐reactive protein concentration and lower blood pressure at admission were associated with failure to achieve rate control. Conclusions and Clinical Importance Rate control affects survival; an optimal target meanHR <125 bpm should be sought in dogs with AF. Baseline patient variables can help predict if rate control is achievable.

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Section: Resultsmentioning

confidence: 99%

Optimal rate control in dogs with atrial fibrillation—ORCA study—Multicenter prospective observational study: Prognostic impact and predictors of rate control

Pedro

Mavropoulou²,

Oyama

et al. 2023

Veterinary Internal Medicne

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“…Sotalol was the only drug permitted in the HP/LSM dogs used for testing. A diagnosis of sinus node dysfunction included consideration of the following parameters: heart rate (<65 bpm), minimum heart rate (<35 bpm), pauses >2 s (>3000), pauses >4 s (>5), and longest pause ≥5.5 s. These characteristics were made based on reported values for normal dogs 23,43 . It was not required that all these features be met, instead that the diagnostic assessment included consideration of these values in the decision process (eg, a dog with sinus node dysfunction could have >200 pauses >5.5 s, but average heart rate >65 bpm because of tachy‐brady rhythm).…”

Section: Methodsmentioning

confidence: 99%

“…The goal was to obtain 24-hour recordings with 1 of 3 diagnoses: and longest pause ≥5.5 s. These characteristics were made based on reported values for normal dogs. 23,43 It was not required that all these features be met, instead that the diagnostic assessment included consideration of these values in the decision process (eg, a dog with sinus node dysfunction could have >200 pauses >5.5 s, but average heart rate >65 bpm because of tachy-brady rhythm). Careful attention was made to exclude any recordings that could represent examples of vasovagal response or accentuated antagonism.…”

Section: Selection Of 24-hour Ecg Datamentioning

confidence: 99%

“…Only recordings were included with sinus node dysfunction that exhibited the hypothesized exit block pattern of beat interval "clustering" as identified in the ECG, Poincaré plots, and tachograms. 25,43 Determination of whether or not the atrial depolarization was the result of initiation within the sinus node and exit from different sinoatrial pathways, macro-reentry involving the sinus node complex or atrial ectopic foci was not possible; however, the goal was to select beat-to-beat clustering that would be compatible with a pattern of hypothesized exit block. Consequently, the identified clustering was reflective of intervals that approximated multiples of the prevailing shortest intervals.…”

Section: Selection Of 24-hour Ecg Datamentioning

confidence: 99%

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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

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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.

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Fitness Conditioning for the Veterinary Technician and Owner

Rector,

Millis

2024

Physical Rehabilitation for Veterinary Technicians and Nurses

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Instantaneous and averaged heart rate profiles: Developing strategies for programming pacing rates in dogs

Cited by 9 publications

References 28 publications

Optimal rate control in dogs with atrial fibrillation—ORCA study—Multicenter prospective observational study: Prognostic impact and predictors of rate control

Optimal rate control in dogs with atrial fibrillation—ORCA study—Multicenter prospective observational study: Prognostic impact and predictors of rate control

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

Fitness Conditioning for the Veterinary Technician and Owner

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