Differential cardiotoxic electrocardiographic response to doxorubicin treatment in conscious versus anesthetized mice

Warhol, Anna; George, Sharon A.; Obaid, Sofian N.; Efimova, Tatiana; Efimov, Igor R.

doi:10.14814/phy2.14987

Cited by 18 publications

(17 citation statements)

References 50 publications

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“…ECG is considered one of the sensitive tools that monitor this chemotherapy-induced cardiotoxicity [28]. Changes in the ECG parameters may be considered features that indicate the severity of dose-dependent adverse effects of DOX on the cardiac tissues [29]. Applications of machine learning as a type of artificial intelligence can be of great assistance to medical efficiency and diagnostic accuracy [30].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based ECG Signal Classification for Enhanced Early Detection of Doxorubicin-Induced Cardiotoxicity in Rats

Mohammed,

H.Hanafy,

abdo

et al. 2024

Preprint

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Cardiotoxicity, which leads to irreversible myocardial damage, is a major adverse effect associated with chemotherapy. Electrocardiogram (ECG) is an inexpensive, rapid, and simple tool that may provide valuable diagnostic information pertinent to cardiotoxicity. An automatic interpretation and classification of the ECG signals by machine learning algorithms is considered superior to human interpretation of the ECG which may not be able to early detect subtle alterations in the ECG and vary according to the experience of the specialist. The present work aimed at using different machine learning algorithms to classify ECG signals recorded from doxorubicin-injected rats. Rats were divided into four groups and each group was intraperitoneally injected with different cumulative doses of doxorubicin (0, 6, 12, and 18 mg/kg). ECG signal classification depended on multiple features that were extracted from the recorded signals under different conditions. K nearest neighbors’ algorithm achieved higher classification accuracy (99.83 %) than random forest (99.56 %), decision tree (99.54 %), artificial neural network (99.50 %), and support vector machine (99.38 %). Furthermore, the dose-dependent cardiotoxicity was validated via a histopathological examination of the left ventricle that indicated significant pathological alterations in the cardiac tissue. The present findings emphasized the potential of the machine learning-based enhanced detection of cardiotoxicity and validated the dose-dependent toxicity of doxorubicin in the cardiac left ventricle. This approach might be applicable clinically to avoid cardiotoxicity in chemotherapy-treated patients.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Based ECG Signal Classification for Enhanced Early Detection of Doxorubicin-Induced Cardiotoxicity in Rats

Mohammed,

H.Hanafy,

abdo

et al. 2024

Preprint

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“…Whether certain other anesthesia protocols may lead to less interference with murine cardiac EP remains elusive. However, available data on parameters such as hemodynamics and heart rate [34][35][36][53][54][55] suggest that all protocols have the potential to largely interfere with cardiac function and EP; therefore, further invasive studies are warranted to compare IF regimen with other isoflurane-based or injectable protocols, and identify the best compromise for invasive arrhythmia research in rodents.…”

Section: Discussionmentioning

confidence: 99%

“…The study focused on pentobarbital, and a combination of ketamine, xylazine and acepromazine, which to our knowledge are both not commonly used in murine electrophysiological studies. Given that even studies assessing cardiac EP noninvasively have shown the fundamental impact of different narcosis regimens on ECG 35,36 and given the increasing importance of transgenic mouse models in arrythmia research, a systematic assessment of narcosis-related effects in general, and specifically of MMF and IF, on cardiac EP in mice is highly needed.…”

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confidence: 99%

Medetomidine/midazolam/fentanyl narcosis alters cardiac autonomic tone leading to conduction disorders and arrhythmias in mice

et al. 2023

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Arrhythmias are critical contributors to cardiovascular morbidity and mortality. Therapies are mainly symptomatic and often insufficient, emphasizing the need for basic research to unveil the mechanisms underlying arrhythmias and to enable better and ideally causal therapies. In translational approaches, mice are commonly used to study arrhythmia mechanisms in vivo. Experimental electrophysiology studies in mice are performed under anesthesia with medetomidine/midazolam/fentanyl (MMF) and isoflurane/fentanyl (IF) as commonly used regimens. Despite evidence of adverse effects of individual components on cardiac function, few data are available regarding the specific effects of these regimens on cardiac electrophysiology in mice. Here we present a study investigating the effects of MMF and IF narcosis on cardiac electrophysiology in vivo in C57BL/6N wild-type mice. Telemetry transmitters were implanted in a group of mice, which served as controls for baseline parameters without narcosis. In two other groups of mice, electrocardiogram and invasive electrophysiology studies were performed under narcosis (with either MMF or IF). Basic electrocardiogram parameters, heart rate variability parameters, sinus node and atrioventricular node function, and susceptibility to arrhythmias were assessed. Experimental data suggest a remarkable influence of MMF on cardiac electrophysiology compared with IF and awake animals. While IF only moderately reduced heart rate, MMF led to significant bradycardia, spontaneous arrhythmias, heart rate variability alterations as well as sinus and AV node dysfunction, and increased inducibility of ventricular arrhythmias. On the basis of these observed effects, we suggest avoiding MMF in mice, specifically when studying cardiac electrophysiology, but also whenever a regular heartbeat is required for reliable results, such as in heart failure or imaging research.

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“…This finding is in agreement with an earlier study that demonstrated a wider RR and QT interval in anesthetized versus conscious guinea pigs ( Hamlin et al, 2003 ). The effect of anesthesia on ECG parameters has been documented in other experimental models, including mice ( Warhol et al, 2021 ), as well as the impact of alternative anesthetic agents ( Ho et al, 2011 ). Similarly, clinical studies ( Kazanci et al, 2009 ) have also reported that anesthesia impacts ECG measurement values.…”

Section: Discussionmentioning

confidence: 99%

Demographic and Methodological Heterogeneity in Electrocardiogram Signals From Guinea Pigs

Haq

Cooper

Berk³

et al. 2022

Front. Physiol.

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Electrocardiograms (ECG) are universally used to measure the electrical activity of the heart; however, variations in recording techniques and/or subject demographics can affect ECG interpretation. In this study, we investigated variables that are likely to influence ECG metric measurements in cardiovascular research, including recording technique, use of anesthesia, and animal model characteristics. Awake limb lead ECG recordings were collected in vivo from adult guinea pigs using a platform ECG system, while recordings in anesthetized animals were performed using both a platform and needle ECG system. We report significant heterogeneities in ECG metric values that are attributed to methodological differences (e.g., ECG lead configuration, ECG recording platform, presence or absence of anesthesia) that persist even within the same cohort of animals. Further, we report that variability in animal demographics is preserved in vivo ECG recordings—with animal age serving as a significant contributor, while sex-specific influences were less pronounced. Methodological approaches and subject demographics should be fully considered when interpreting ECG values in animal models, comparing datasets between studies, or developing artificial intelligence algorithms that utilize an ECG database.

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Differential cardiotoxic electrocardiographic response to doxorubicin treatment in conscious versus anesthetized mice

Cited by 18 publications

References 50 publications

Machine Learning-Based ECG Signal Classification for Enhanced Early Detection of Doxorubicin-Induced Cardiotoxicity in Rats

Machine Learning-Based ECG Signal Classification for Enhanced Early Detection of Doxorubicin-Induced Cardiotoxicity in Rats

Medetomidine/midazolam/fentanyl narcosis alters cardiac autonomic tone leading to conduction disorders and arrhythmias in mice

Demographic and Methodological Heterogeneity in Electrocardiogram Signals From Guinea Pigs

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