Enhanced Cardiovascular Assessment in Rats Using a Wireless Data Logger
Authors: Brockway Bob , Stonerook Michael , Delahanty Tom , Laszczyk Logan 
Published: May 2024

Study objectives and hypothesis: Cardiovascular assessment in animals can be complicated by signal loss of real-time telemetry. We evaluated the benefits of using a wireless data logger to capture 18 hours of blood pressure (BP) and electrocardiogram (ECG) data. This approach eliminates data loss, simplifies analysis and improves results. Methods: One male Sprague Dawley rat was implanted with a PBTA-L implant (Stellar, TSE Systems) using established procedures to collect BP and ECG. All procedures were approved by the AmplifyBio IACUC. Memory and real-time (RT) BP and ECG were recorded at 500Hz for 18+ hours in the acquisition software (Notocord-hem, Instem) on separate days. ECG RR intervals and BP intervals were compared for memory versus RT recordings. Time-based (RMSSD, SDNN, SDANN) and frequency-based (LF, HF) HRV assessment was performed in 5-minute increments. HRV from ECG vs BP were compared for the memory recording. Data and Results: High quality BP and ECG waveforms were recorded to memory and retrieved into the software with no manual intervention or loss of data. The RT recording included 0.48% signal loss. Interval outliers were removed for HRV assessment using thresholds to limit valid RR/beat intervals. This worked well for BP but was less effective for ECG as there were more erroneous intervals due to movement artifact. Time- and frequency-based HRV parameters were compared for memory vs RT recordings for BP and ECG intervals. HRV from BP showed lower variability for RT vs memory (LF↓24.3%, HR↓6.3%, RMSSD↓0.1%, SDNN↓15.4%, SDANN↓6.1%), while HRV from ECG generally produced higher variability for RT vs memory (LF↑111.8%, HF↑175.1%, RMSSD↑93.9%, SDNN↑16.7%, SDANN↓9.5%). These findings can be explained as follows: BP intervals produced less variability with the RT recording because the long intervals caused by signal loss are distinct and consistently removed by threshold criteria. The lost intervals result in lower HRV. ECG intervals produced more variability with the RT recording because the recordings were 1 week apart, and the later RT recording had more movement artifact on the ECG resulting in inaccurate QRS detection. The erroneous intervals were not as effectively removed by thresholds, resulting in higher HRV. We also compared HRV from the BP and ECG signals in the memory recording. The LF and HF powers were 5.4 and 4.5 times as high respectively for the RR (ECG) vs beat (BP) intervals. The time-based HRV parameters for RMSSD and SDNN were higher by 84% and 14%, while SDANN was very similar with ECG results 2% less than BP results. Conclusions: HRV analysis highlights the benefits of using a wireless data logger to record cardiovascular data. Our assessment demonstrated improvements in the effciency and accuracy of HRV analysis. As a secondary outcome, we demonstrated potential benefits of performing HRV analysis on BP vs ECG signals.

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This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.