Displaying publications 1 - 20 of 209 in total

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  1. Gudigar A, Kadri NA, Raghavendra U, Samanth J, Maithri M, Inamdar MA, et al.
    Comput Biol Med, 2024 Apr;172:108207.
    PMID: 38489986 DOI: 10.1016/j.compbiomed.2024.108207
    Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.
    Matched MeSH terms: Electrocardiography
  2. Ferdowsi M, Kwan BH, Tan MP, Saedon NI, Subramaniam S, Abu Hashim NFI, et al.
    Biomed Eng Online, 2024 Mar 30;23(1):37.
    PMID: 38555421 DOI: 10.1186/s12938-024-01229-9
    BACKGROUND: The diagnostic test for vasovagal syncope (VVS), the most common cause of syncope is head-up tilt test (HUTT) assessment. During the test, subjects experienced clinical symptoms such as nausea, sweating, pallor, the feeling of palpitations, being on the verge of passing out, and fainting. The study's goal is to develop an algorithm to classify VVS patients based on physiological signals blood pressure (BP) and electrocardiography (ECG) obtained from the HUTT.

    METHODS: After 10 min of supine rest, the subject was tilted at a 70-degree angle on a tilt table for approximately a total of 35 min. 400 µg of glyceryl trinitrate (GTN) was administered sublingually after the first 20 min and monitoring continued for another 15 min. Mean imputation and K-nearest neighbors (KNN) imputation approaches to handle missing values. Next, feature selection techniques were implemented, including genetic algorithm, recursive feature elimination, and feature importance, to determine the crucial features. The Mann-Whitney U test was then performed to determine the statistical difference between two groups. Patients with VVS are categorized via machine learning models including Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Multinomial Naïve Bayes (MNB), KNN, Logistic Regression (LR), and Random Forest (RF). The developed model is interpreted using an explainable artificial intelligence (XAI) model known as partial dependence plot.

    RESULTS: A total of 137 subjects aged between 9 and 93 years were recruited for this study, 54 experienced clinical symptoms were considered positive tests, while the remaining 83 tested negative. Optimal results were obtained by combining the KNN imputation technique and three tilting features with SVM with 90.5% accuracy, 87.0% sensitivity, 92.7% specificity, 88.6% precision, 87.8% F1 score, and 95.4% ROC (receiver operating characteristics) AUC (area under curve).

    CONCLUSIONS: The proposed algorithm effectively classifies VVS patients with over 90% accuracy. However, the study was confined to a small sample size. More clinical datasets are required to ensure that our approach is generalizable.

    Matched MeSH terms: Electrocardiography
  3. Ibrahim NS, Rampal S, Lee WL, Pek EW, Suhaimi A
    Cardiovasc Eng Technol, 2024 Feb;15(1):12-21.
    PMID: 37973701 DOI: 10.1007/s13239-023-00693-z
    PURPOSE: Photoplethysmography measurement of heart rate with wrist-worn trackers has been introduced in healthy individuals. However, additional consideration is necessary for patients with ischemic heart disease, and the available evidence is limited. The study aims to evaluate the validity and reliability of heart rate measures by a wrist-worn photoplethysmography (PPG) tracker compared to an electrocardiogram (ECG) during incremental treadmill exercise among patients with ischemic heart disease.

    METHODS: Fifty-one participants performed the standard incremental treadmill exercise in a controlled laboratory setting with 12-lead ECG attached to the patient's body and wearing wrist-worn PPG trackers.

    RESULTS: At each stage, the absolute percentage error of the PPG was within 10% of the standard acceptable range. Further analysis using a linear mixed model, which accounts for individual variations, revealed that PPG yielded the best performance at the baseline low-intensity exercise. As the stages progressed, heart rate validity decreased but was regained during recovery. The reliability was moderate to excellent.

    CONCLUSIONS: Low-cost trackers AMAZFIT Cor and Bip validity and reliability were within acceptable ranges, especially during low-intensity exercise among patients with ischemic heart disease recovering from cardiac procedures. Though using the tracker as part of the diagnosis tool still requires more supporting studies, it can potentially be used as a self-monitoring tool with precautions.

    Matched MeSH terms: Electrocardiography
  4. Mandala S, Rizal A, Adiwijaya, Nurmaini S, Suci Amini S, Almayda Sudarisman G, et al.
    PLoS One, 2024;19(4):e0297551.
    PMID: 38593145 DOI: 10.1371/journal.pone.0297551
    Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm. Early and accurate detection is crucial for effective treatment. However, single-lead electrocardiogram (ECG) methods have limited sensitivity and specificity. This study propose an improved ensemble learning approach for arrhythmia detection using multi-lead ECG data. Proposed method, based on a boosting algorithm, namely Fine Tuned Boosting (FTBO) model detects multiple arrhythmia classes. For the feature extraction, introduce a new technique that utilizes a sliding window with a window size of 5 R-peaks. This study compared it with other models, including bagging and stacking, and assessed the impact of parameter tuning. Rigorous experiments on the MIT-BIH arrhythmia database focused on Premature Ventricular Contraction (PVC), Atrial Premature Contraction (PAC), and Atrial Fibrillation (AF) have been performed. The results showed that the proposed method achieved high sensitivity, specificity, and accuracy for all three classes of arrhythmia. It accurately detected Atrial Fibrillation (AF) with 100% sensitivity and specificity. For Premature Ventricular Contraction (PVC) detection, it achieved 99% sensitivity and specificity in both leads. Similarly, for Atrial Premature Contraction (PAC) detection, proposed method achieved almost 96% sensitivity and specificity in both leads. The proposed method shows great potential for early arrhythmia detection using multi-lead ECG data.
    Matched MeSH terms: Electrocardiography/methods
  5. Kaisbain N, Khoo KKL, Lim WJ
    Am J Emerg Med, 2023 Dec;74:196.e1-196.e4.
    PMID: 37863804 DOI: 10.1016/j.ajem.2023.10.009
    BACKGROUND/AIMS: Electrocardiogram (ECG) is an inexpensive, fundamental screening tool used in daily clinical practice. It is essential in the diagnosis of life-threatening conditions, such as acute myocardial infarctions, ventricular arrhythmias etc. However, ECG lead misplacement is a common technical error, which may translate into wrong interpretations, unnecessary investigations, and improper treatments.

    METHODS/RESULTS: We report a case of a multiple ECG lead misplacement made across two different planes of the heart, resulting in a bizarre series of ECG, mimicking an acute high lateral myocardial infarction. Multiple ECGs were done as there were abrupt changes compared to previous ECGS. Patient was pain free and administration of potentially harmful procedures and treatments were prevented.

    CONCLUSION: Our case demonstrated the importance of high clinical suspicion in diagnosing ECG lead misplacement. It is the responsibility of both the healthcare workers who are performing and interpreting the ECG to be alert of a possible lead malposition, to prevent untoward consequences to the patient.

    Matched MeSH terms: Electrocardiography/methods
  6. Loh HW, Ooi CP, Oh SL, Barua PD, Tan YR, Molinari F, et al.
    Comput Methods Programs Biomed, 2023 Nov;241:107775.
    PMID: 37651817 DOI: 10.1016/j.cmpb.2023.107775
    BACKGROUND AND OBJECTIVE: Attention Deficit Hyperactivity problem (ADHD) is a common neurodevelopment problem in children and adolescents that can lead to long-term challenges in life outcomes if left untreated. Also, ADHD is frequently associated with Conduct Disorder (CD), and multiple research have found similarities in clinical signs and behavioral symptoms between both diseases, making differentiation between ADHD, ADHD comorbid with CD (ADHD+CD), and CD a subjective diagnosis. Therefore, the goal of this pilot study is to create the first explainable deep learning (DL) model for objective ECG-based ADHD/CD diagnosis as having an objective biomarker may improve diagnostic accuracy.

    METHODS: The dataset used in this study consist of ECG data collected from 45 ADHD, 62 ADHD+CD, and 16 CD patients at the Child Guidance Clinic in Singapore. The ECG data were segmented into 2 s epochs and directly used to train our 1-dimensional (1D) convolutional neural network (CNN) model.

    RESULTS: The proposed model yielded 96.04% classification accuracy, 96.26% precision, 95.99% sensitivity, and 96.11% F1-score. The Gradient-weighted class activation mapping (Grad-CAM) function was also used to highlight the important ECG characteristics at specific time points that most impact the classification score.

    CONCLUSION: In addition to achieving model performance results with our suggested DL method, Grad-CAM's implementation also offers vital temporal data that clinicians and other mental healthcare professionals can use to make wise medical judgments. We hope that by conducting this pilot study, we will be able to encourage larger-scale research with a larger biosignal dataset. Hence allowing biosignal-based computer-aided diagnostic (CAD) tools to be implemented in healthcare and ambulatory settings, as ECG can be easily obtained via wearable devices such as smartwatches.

    Matched MeSH terms: Electrocardiography
  7. Jin C, Dai Q, Li P, Lam P, Cha YM
    J Cardiovasc Electrophysiol, 2023 Sep;34(9):1933-1943.
    PMID: 37548113 DOI: 10.1111/jce.16013
    INTRODUCTION: Left bundle branch area pacing (LBBP) is a novel conduction system pacing method to achieve effective physiological pacing and an alternative to cardiac resynchronization therapy (CRT) with biventricular pacing (BVP) for patients with heart failure with reduced ejection fraction (HFrEF). We conduted this meta-analysis and systemic review to review current data comparing BVP and LBBP in patients with HFrEF and indications for CRT.

    METHODS: We searched PubMed/Medline, Web of Science, and Cochrane Library from the inception of the database to November 2022. All studies that compared LBBP with BVP in patients with HFrEF and indications for CRT were included. Two reviewers performed study selection, data abstraction, and risk of bias assessment. We calculated risk ratios (RRs) with the Mantel-Haenszel method and mean difference (MD) with inverse variance using random effect models. We assessed heterogeneity using the I2 index, with I2  > 50% indicating significant heterogeneity.

    RESULTS: Ten studies (9 observational studies and 1 randomized controlled trial; 616 patients; 15 centers) published between 2020 and 2022 were included. We observed a shorter fluoroscopy time (MD: 9.68, 95% confidence interval [CI]: 4.49-14.87, I2  = 95%, p 

    Matched MeSH terms: Electrocardiography
  8. Satyam SM, Bairy LK, Shetty P, Sainath P, Bharati S, Ahmed AZ, et al.
    Cardiovasc Toxicol, 2023 Feb;23(2):107-119.
    PMID: 36790727 DOI: 10.1007/s12012-023-09784-8
    Doxorubicin is a widely used anticancer drug whose efficacy is limited due to its cardiotoxicity. There is no ideal cardioprotection available against doxorubicin-induced cardiotoxicity. This study aimed to investigate the anticipated cardioprotective potential of metformin and dapagliflozin against doxorubicin-induced acute cardiotoxicity in Wistar rats. At the beginning of the experiment, cardiac screening of experimental animals was done by recording an electrocardiogram (ECG) before allocating them into the groups. Thereafter, a total of thirty healthy adult Wistar rats (150-200 g) were randomly divided into five groups (n = 6) and treated for eight days as follows: group I (normal control), group II (doxorubicin control), group III (metformin 250 mg/kg/day), group IV (metformin 180 mg/kg/day), and group V (dapagliflozin 0.9 mg/kg/day). On the 7th day of the treatment phase, doxorubicin 20 mg/kg was administered intraperitoneal to groups II, III, IV, and V. On the 9th day (immediately after 48 h of doxorubicin administration), blood was collected from anesthetized animals for glucose, lipid profile, CK-MB & AST estimation, and ECG was recorded. Later, animals were sacrificed, and the heart was dissected for histopathological examination. We found that compared to normal control rats, CK-MB, AST, and glucose were significantly increased in doxorubicin control rats. There was a significant reversal of doxorubicin-induced hyperglycemia in the rats treated with metformin 250 mg/kg compared to doxorubicin control rats. Both metformin (180 mg/kg and 250 mg/kg) and dapagliflozin (0.9 mg/kg) significantly altered doxorubicin-induced ECG changes and reduced the levels of cardiac injury biomarkers CK-MB and AST compared to doxorubicin control rats. Metformin and dapagliflozin protected the cellular architecture of the myocardium from doxorubicin-induced myocardial injury. Current study revealed that both metformin and dapagliflozin at the FDA-recommended antidiabetic doses mitigated doxorubicin-induced acute cardiotoxicity in Wistar rats. The obtained data have opened the perspective to perform chronic studies and then to clinical studies to precisely consider metformin and dapagliflozin as potential chemoprotection in the combination of chemotherapy with doxorubicin to limit its cardiotoxicity, especially in patients with comorbid conditions like type II diabetes mellitus.
    Matched MeSH terms: Electrocardiography
  9. Gul MU, Kamarul Azman MH, Kadir KA, Shah JA, Hussen S
    Comput Intell Neurosci, 2023;2023:8162325.
    PMID: 36909967 DOI: 10.1155/2023/8162325
    Atrial flutter (AFL) is a common arrhythmia with two significant mechanisms, namely, focal (FAFL) and macroreentry (MAFL). Discrimination of the AFL mechanism through noninvasive techniques can improve radiofrequency ablation efficacy. This study aims to differentiate the AFL mechanism using a 12-lead surface electrocardiogram. P-P interval series variability is hypothesized to be different in FAFL and MAFL and may be useful for discrimination. 12-lead ECG signals were collected from 46 patients with known AFL mechanisms. Features for a proposed classifier are extracted through descriptive statistics of the interval series. On the other hand, the class ratio of MAFL and FAFL was 41 : 5, respectively, which was highly imbalanced. To resolve this, different data augmentation techniques (SMOTE, modified-SMOTE, and smoothed-bootstrap) have been applied on the interval series to generate synthetic interval series and minimize imbalance. Modification is introduced in the classic SMOTE technique (modified-SMOTE) to properly produce data samples from the original distribution. The characteristics of modified-SMOTE are found closer to the original dataset than the other two techniques based on the four validation criteria. The performance of the proposed model has been evaluated by three linear classifiers, namely, linear discriminant analysis (LDA), logistic regression (LOG), and support vector machine (SVM). Filter and wrapper methods have been used for selecting relevant features. The best average performance was achieved at 400% augmentation of the FAFL interval series (90.24% sensitivity, 49.50% specificity, and 76.88% accuracy) in the LOG classifier. The variation of consecutive P-wave intervals has been shown as an effective concept that differentiates FAFL from MAFL through the 12-lead surface ECG.
    Matched MeSH terms: Electrocardiography/methods
  10. Borhanuddin BK, Abdul Latiff H, Mohamed Yusof AK
    Cardiol Young, 2022 Dec;32(12):1994-1998.
    PMID: 35707919 DOI: 10.1017/S1047951122000154
    BACKGROUND: CT is an accepted non-invasive imaging tool to assess the coronary arteries in adults; however, its utilisation in children is limited by high heart rate and lack of standardised protocol. We sought to assess diagnostic quality and factors that affect image quality of CT in assessing coronary artery lesions in Kawasaki patients less than 18 years of age.

    METHODOLOGY: CT coronary angiography was performed on patients with Kawasaki disease diagnosed with coronary aneurysm or suspected to have coronary stenosis. Studies were performed using electrocardiogram-gated protocols. General anaesthesia was used in patients who were not cooperative for breathing control. Heart rate, image quality, and effective radiation dose were documented.

    RESULTS: Fifty-two Kawasaki patients underwent CT coronary angiography to assess coronary artery lesions. Median heart rate was 88 beats per minute (range 50-165 beats/minute). Image quality was graded as excellent in 34 (65%) patients, good in 17 (32%), satisfactory in 1, and poor in 1 patient. Coronary artery aneurysm was found in 25 (bilateral = 6, unilateral = 19, multiple = 11). Thrombus was found in 11 patients resulting in partial and total occlusion in 8 and 3 patients, respectively. Coronary stenosis was noted in 2 patients. The effective radiation dose was 1.296 millisievert (median 0.81 millisievert). Better diagnostic imaging quality was significantly related to lower heart rate (p = 0.007).

    CONCLUSION: Electrocardiogram-triggered CT coronary angiography provides a good diagnostic assessment of coronary artery lesions in children with Kawasaki disease.

    Matched MeSH terms: Electrocardiography/methods
  11. Mahmud S, Ibtehaz N, Khandakar A, Tahir AM, Rahman T, Islam KR, et al.
    Sensors (Basel), 2022 Jan 25;22(3).
    PMID: 35161664 DOI: 10.3390/s22030919
    Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most existing methods used in hospitals for continuous monitoring of BP are invasive. On the contrary, cuff-based BP monitoring methods, which can predict systolic blood pressure (SBP) and diastolic blood pressure (DBP), cannot be used for continuous monitoring. Several studies attempted to predict BP from non-invasively collectible signals such as photoplethysmograms (PPG) and electrocardiograms (ECG), which can be used for continuous monitoring. In this study, we explored the applicability of autoencoders in predicting BP from PPG and ECG signals. The investigation was carried out on 12,000 instances of 942 patients of the MIMIC-II dataset, and it was found that a very shallow, one-dimensional autoencoder can extract the relevant features to predict the SBP and DBP with state-of-the-art performance on a very large dataset. An independent test set from a portion of the MIMIC-II dataset provided a mean absolute error (MAE) of 2.333 and 0.713 for SBP and DBP, respectively. On an external dataset of 40 subjects, the model trained on the MIMIC-II dataset provided an MAE of 2.728 and 1.166 for SBP and DBP, respectively. For both the cases, the results met British Hypertension Society (BHS) Grade A and surpassed the studies from the current literature.
    Matched MeSH terms: Electrocardiography
  12. Chew KT, Raman V, Then PHH
    Sensors (Basel), 2021 Dec 08;21(24).
    PMID: 34960291 DOI: 10.3390/s21248197
    Cardiovascular disease continues to be one of the most prevalent medical conditions in modern society, especially among elderly citizens. As the leading cause of deaths worldwide, further improvements to the early detection and prevention of these cardiovascular diseases is of the utmost importance for reducing the death toll. In particular, the remote and continuous monitoring of vital signs such as electrocardiograms are critical for improving the detection rates and speed of abnormalities while improving accessibility for elderly individuals. In this paper, we consider the design and deployment characteristics of a remote patient monitoring system for arrhythmia detection in elderly individuals. Thus, we developed a scalable system architecture to support remote streaming of ECG signals at near real-time. Additionally, a two-phase classification scheme is proposed to improve the performance of existing ECG classification algorithms. A prototype of the system was deployed at the Sarawak General Hospital, remotely collecting data from 27 unique patients. Evaluations indicate that the two-phase classification scheme improves algorithm performance when applied to the MIT-BIH Arrhythmia Database and the remotely collected single-lead ECG recordings.
    Matched MeSH terms: Electrocardiography*
  13. Mutlag AA, Ghani MKA, Mohammed MA, Lakhan A, Mohd O, Abdulkareem KH, et al.
    Sensors (Basel), 2021 Oct 19;21(20).
    PMID: 34696135 DOI: 10.3390/s21206923
    In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%.
    Matched MeSH terms: Electrocardiography*
  14. Kaisbain N, Lim WJ, Kim HS
    BMJ Case Rep, 2021 Jul 27;14(7).
    PMID: 34315750 DOI: 10.1136/bcr-2021-244180
    Atrial septal defect (ASD) is the most common congenital heart disease observed in adult. Several ECG findings are considered sensitive for the diagnosis of ASD. We describe a 50 years old man who displayed Crochetage sign, incomplete right bundle branch block (IRBBB) and right ventricular strain pattern on ECG. Crochetage sign is highly specific for ASD and it correlates with shunt severity. The diagnostic specificity for ASD increases if the R waves have both Crochetage patterns and IRBBB. It is important not to confuse Crochetage signs with IRBBB abnormalities on ECG. Our patient was ultimately diagnosed with a large ASD measuring 3 cm with bidirectional shunt and concomitant pulmonary thrombosis. This illustrates that high suspicion of the ASD with the use of good-old ECG signs remains relevant in this modern era. This also reminds us that patients with Eisenmenger syndrome are at higher risk for pulmonary thrombosis.
    Matched MeSH terms: Electrocardiography
  15. Hasnul MA, Aziz NAA, Alelyani S, Mohana M, Aziz AA
    Sensors (Basel), 2021 Jul 23;21(15).
    PMID: 34372252 DOI: 10.3390/s21155015
    Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.
    Matched MeSH terms: Electrocardiography*
  16. Koh KT, Law WC, Zaw WM, Foo DHP, Tan CT, Steven A, et al.
    Europace, 2021 07 18;23(7):1016-1023.
    PMID: 33782701 DOI: 10.1093/europace/euab036
    AIMS: Atrial fibrillation (AF) is a preventable cause of ischaemic stroke but it is often undiagnosed and undertreated. The utility of smartphone electrocardiogram (ECG) for the detection of AF after ischaemic stroke is unknown. The aim of this study is to determine the diagnostic yield of 30-day smartphone ECG recording compared with 24-h Holter monitoring for detecting AF ≥30 s.

    METHODS AND RESULTS: In this multicentre, open-label study, we randomly assigned 203 participants to undergo one additional 24-h Holter monitoring (control group, n = 98) vs. 30-day smartphone ECG monitoring (intervention group, n = 105) using KardiaMobile (AliveCor®, Mountain View, CA, USA). Major inclusion criteria included age ≥55 years old, without known AF, and ischaemic stroke or transient ischaemic attack (TIA) within the preceding 12 months. Baseline characteristics were similar between the two groups. The index event was ischaemic stroke in 88.5% in the intervention group and 88.8% in the control group (P = 0.852). AF lasting ≥30 s was detected in 10 of 105 patients in the intervention group and 2 of 98 patients in the control group (9.5% vs. 2.0%; absolute difference 7.5%; P = 0.024). The number needed to screen to detect one AF was 13. After the 30-day smartphone monitoring, there was a significantly higher proportion of patients on oral anticoagulation therapy at 3 months compared with baseline in the intervention group (9.5% vs. 0%, P = 0.002).

    CONCLUSIONS: Among patients ≥55 years of age with a recent cryptogenic stroke or TIA, 30-day smartphone ECG recording significantly improved the detection of AF when compared with the standard repeat 24-h Holter monitoring.

    Matched MeSH terms: Electrocardiography; Electrocardiography, Ambulatory
  17. Wong KC
    Med J Malaysia, 2021 07;76(4):565.
    PMID: 34305119
    No abstract provided.
    Matched MeSH terms: Electrocardiography*
  18. Ahmed AZ, Mumbrekar KD, Satyam SM, Shetty P, D'Souza MR, Singh VK
    Cardiovasc Toxicol, 2021 Jul;21(7):533-542.
    PMID: 33740233 DOI: 10.1007/s12012-021-09644-3
    Doxorubicin (DOX) is a potent anti-cancer antibiotic that was widely used for treatment of various cancers. It produces free radicals which result in extreme dose-limiting cardiotoxicity. This study investigated the cardioprotective potential of chia seed oil, an active polyphenolic nutraceutical against doxorubicin-induced cardiotoxicity in Wistar rats. Twenty-four female Wistar rats were divided into four groups (n = 6) which consist of normal control, DOX control, test-A and test-B group. Animals were prophylactically treated with two different doses of test drug, i.e. chia seed oil 2.5 ml/kg/day and 5 ml/kg/day in test-A and test-B groups orally for 7 days. Doxorubicin (25 mg/kg; single dose) was administered intraperitoneally to DOX control, Test-A and Test-B animals on the seventh day to induce cardiotoxicity. ECG analysis was done before and after treatment. Besides ECG, CK, CK-MB, LDH, AST, MDA and GSH were analyzed. DOX had significantly altered ECG, CK, CK-MB, LDH, AST, MDA and GSH. Pre-treatment with chia seed oil significantly alleviated DOX-induced ECG changes and also guarded against DOX-induced rise of serum CK, CK-MB and AST levels. Chia seed oil alleviated histopathological alteration in DOX-treated rats. It also significantly inhibited DOX-induced GSH depletion and elevation of MDA. The present study revealed that chia seed oil exerts cardioprotection against doxorubicin-induced cardiotoxicity in female Wistar rats. Our study opens the perspective to clinical studies to precisely consider chia seed oil as a potential chemoprotectant nutraceutical in the combination chemotherapy with doxorubicin to limit its cardiotoxicity.
    Matched MeSH terms: Electrocardiography*
  19. Koh HM, Chong PF, Tan JN, Chidambaram SK, Chua HJ
    J Clin Pharm Ther, 2021 Jun;46(3):800-806.
    PMID: 33768612 DOI: 10.1111/jcpt.13356
    WHAT IS KNOWN AND OBJECTIVE: Hydroxychloroquine and protease inhibitors were widely used as off-label treatment options for COVID-19 but the safety data of these drugs among the COVID-19 population are largely lacking. Drug-induced QTc prolongation is a known adverse reaction of hydroxychloroquine, especially during chronic treatment. However, when administered concurrently with potential pro-arrhythmic drugs such as protease inhibitors, the risk of QTc prolongation imposed on these patients is not known. We aim to investigate the incidence of QTc prolongation events and potential factors associated with its occurrence in COVID-19 population.

    METHODS: We included 446 SARS-CoV-2 RT-PCR-positive patients taking at least one treatment drug for COVID-19 within a period of one month (March-April 2020). In addition to COVID-19-related treatment (HCQ/PI), concomitant drugs with risks of QTc prolongation were considered. We defined QTc prolongation as QTc interval of ≥470 ms in postpubertal males, and ≥480 ms in postpubertal females.

    RESULTS AND DISCUSSION: QTc prolongation events occurred in 28/446 (6.3%) patients with an incidence rate of 1 case per 100 person-days. A total of 26/28 (93%) patients who had prolonged QTc intervals received at least two pro-QT drugs. Multivariate analysis showed that HCQ and PI combination therapy had five times higher odds of QTc prolongation as compared to HCQ-only therapy after controlling for age, cardiovascular disease, SIRS and the use of concurrent QTc-prolonging agents besides HCQ and/or PI (OR 5.2; 95% CI, 1.11-24.49; p = 0.036). Independent of drug therapy, presence of SIRS resulted in four times higher odds of QTc prolongation (OR 4.3; 95% CI, 1.66-11.06; p = 0.003). In HCQ-PI combination group, having concomitant pro-QT drugs led to four times higher odds of QTc prolongation (OR 3.8; 95% CI, 1.53-9.73; p = 0.004). Four patients who had prolonged QTc intervals died but none were cardiac-related deaths.

    WHAT IS NEW AND CONCLUSION: In our cohort, hydroxychloroquine monotherapy had low potential to increase QTc intervals. However, when given concurrently with protease inhibitors which have possible or conditional risk, the odds of QTc prolongation increased fivefold. Interestingly, independent of drug therapy, the presence of systemic inflammatory response syndrome (SIRS) resulted in four times higher odds of QTc prolongation, leading to the postulation that some QTc events seen in COVID-19 patients may be due to the disease itself. ECG monitoring should be continued for at least a week from the initiation of treatment.

    Matched MeSH terms: Electrocardiography
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