Clinical observation of Suhexiang Pills in treatment of patients with tachycardia after SARS-CoV-2 infection.

Objective To explore the clinical effect and safety of Suhexiang Pills () in the treatment of patients with tachycardia after severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. Methods A total of 138 patients with tachycardia after SARS-CoV-2 infection admitted to eight hospitals such as 971st Hospital of the PLA Navy, Changzhou Second People's Hospital, Xuzhou First People's Hospital, Henan Provincial People's Hospital, Henan Chest Hospital from February 2023 to March 2023 were randomly divided into control group and treatment group, with 87 patients in the treatment group and 51 in the control group. Patients in the control group were po administered with betaloc, once a day, and the initial dose was 23.75 mg, adjusted in time according to the patient's heart rate. Patients in the treatment group were po administered with Suhexiang Pills, 1 pill/time, twice daily. Patients in two groups were treated for 7 d. The clinical efficacy of the two groups was observed, and the heart rate and cardiac function indexes, RR interval, blood oxygen saturation and adverse reactions were compared between the two groups before and after treatment. Results After treatment, the total effective rate of the treatment group was 98.85%, and the total effective rate of the control group was 90.20%, and the difference between the two groups was statistically significant (P < 0.05). After treatment, heart rates were significantly decreased in both groups (P < 0.05), and the heart rates of the treatment group were significantly better than those of the control group (P < 0.05) on the 7th day of treatment. After treatment, the level of left ventricular ejection fraction (LVEF) in both groups was significantly higher than that before treatment (P < 0.05), and there was statistical difference between the treatment group and the control group (P < 0.05). The levels of left ventricular end diastolic dimension (LVEDD) and left ventricular end-systolic diameter (LVESD) in the treatment group significantly decreased than that before treatment (P < 0.05), and there was no statistical difference compared with the control group (P > 0.05). After treatment, the maximum RR interval in both groups reached the normal range on the third day, and the treatment group was significantly better than the control group (P < 0.05). Blood oxygen saturation of the treatment group was significantly increased on the 7th day of treatment compared with before treatment (P < 0.05), but there was no statistical significance between the two groups (P > 0.05). There was no significant difference in the total incidence of adverse events between the two groups (P > 0.05). Conclusion Suhexiang Pills decrease heart rates in patients with tachycardia after SARS-CoV-2 infection, which was equivalent to the effect of western medicine, and can protect heart, improve heart function to a certain extent.Copyright © 2023 Editorial Office of Chinese Traditional and Herbal Drugs. All rights reserved.

Pseudo-V1 ECG lead recording with quality smartwatches and medical interpretation may be necessary for timely and accurate remote detection of atrial flutter/fibrillation

Background: Remote rhythm monitoring with wearable devices is increasingly used especially for early detection of atrial fibrillation/flutter (AF/Afl), being the access to hospital discouraged, especially for frail elderly patients, due to the burden and risk of COVID-19 pandemic. Whereas devices using photo plethysmography (PPG) may misinterpret as AF pulse irregularities due to extrasystoles, patient-directed recording of a single (usually wrist-to-wrist) lead ECG (LEAD I) with hand-held devices or smartwatches have been developed to increase accuracy in AF detection. However, although recent studies validating such devices single-lead ECG recording have shown high sensitivity and specificity, false negative findings such as those reported here are still possible and must be prevented [1]. Purpose(s): Given previous experience of diagnostic uncertainty or failure of the smartwatch ECG (SW-ECG) LEAD I to detect AF/Afl, we have tested if false negative diagnosis could be avoided by recording in addition at least one right precordial (pseudo-V1) lead analyzed by a trained healthcare professional. Method(s): Over one calendar year observation, five patients with previous history of ablated supraventricular arrhythmias suffering sudden palpitations suspected of paroxysmal AF/Afl were instructed to record with their smartwatch at least one precordial lead in addition to LEAD I, to monitor ECG until the termination of symptoms. The SW-ECG strips were sent by telephone for professional interpretation. Diagnostic accuracy based on LEAD I and pseudo-V1 were independently validated by two cardiologists (diagnostic goldstandard - DGS). Result(s): 22 AF/Afl events occurred. Pharmacological cardioversion to sinus rhythm (SR) was obtained in 64%. 192 ECG strips were transmitted. 43,7% of the strips were automatically classified as not significant (or not valid ). Compared to DGS, out of 108 valid strips, correct automatic identification of AF/Afl was obtained in 36,4% with LEAD I, in 33,3% with pseudo V1 and in 54,5% with combined leads, respectively. Interestingly, the SW algorithm has wrongly diagnosed as SR, not only LEAD I, but also 39,4% of pseudo-V1 strips, despite clear-cut evidence of typical flutter waves (Figure 1), when RR intervals were regular due to high degree (e.g., 4:1) A-V block. Conclusion(s): With simple instructions, patients (or their relatives) can easily record an additional precordial (pseudo-V1) SW-ECG lead, that may enhance sensitivity and specificity for remote detection of AF/Afl. However, at present, visual interpretation of SW-ECG by a trained healthcare professional is still needed to guarantee 100% correct diagnosis of AF/Afl, crucial to reduce thromboembolic risk and timely initiate the appropriate treatments. The automatic interpretation of SW's ECG could be improved by appropriate training of a machine learning approach to detect and analyze the atrial waveform provided by an additional pseudo-V1 lead.

COVID-19 and the autonomic nervous system

Background: COVID-19 infection has been shown to have an adverse impact on the cardiovascular system. Cardiac injury, as indicated by elevated cardiac troponin and NT-proBNP levels have been confirmed in COVID-19 cases. There is still ambivalent data on the effect of left ventricular function. Cases of left ventricular impairment, persistent hypotension, acute myopericarditis, myocarditis, arrhythmia and heart failure have been reported in the short term, but there is a significant lacuna when it comes to medium and long-term follow-up of subjects previously infected with COVID-19. Purpose(s): To assess any residual myocardial and autonomic injury in subjects previously infected with COVID-19 at a median follow-up of 5 months. Method(s): A case-control study was performed. Cases were randomly selected subjects who were previously diagnosed with COVID-19 infection following nasopharyngeal swabbing. Controls were subjects who had not been found to be infected with COVID-19 following swabbing and were negative for COVID-19 IgG antibodies. All participants were submitted a standardised questionnaire regarding past medical history. Blood investigations were taken including NT-proBNP and troponin I levels. In addition, all participants underwent 24-hour ambulatory blood pressure monitoring (ABPM) and 24-hour ECG monitoring. The latter was used to assess both for underlying arrhythmias as well as heart rate variability (HRV), a measure of autonomic regulation of the heart. All data was analysed using SPSS version 23.0. Result(s): The study comprised 259 subjects, whereby cases included 174 participants while 75 subjects were age- and gender-matched controls. The study cohort was relatively young with a mean age of 46.1+/-13.8 years. The median follow-up was of approximately 5 months (median 173.5 days, IQR 129-193.25 days). There was no statistically significant difference between cases and controls with regards cardiovascular risk factors and underlying medical conditions. Likewise, there was no difference in blood investigations, including troponin I and NT-proBNP levels at 5-months followup. No difference was noted between the two groups in both awake and asleep blood pressure (BP) readings, as well as dipping BP status. No significant arrhythmias were noted in both groups on 24-hour ECG monitoring. However, when assessing for heart rate variability, it was shown that subjects who had been previously infected with COVID-19 exhibited lower root-mean square differences of successive R-R intervals (RMSSD), p=0.028. This indicates poor vagus nerve-mediated autonomic control of the heart. Conclusion(s): Subjects previously infected with COVID-19 exhibited lower HRV as exhibited by low RMSSD as compared to controls. Reduced HRV is a known biomarker for mortality and sudden death in cardiac disease. The possible long-term implications of reduced HRV in subjects previously infected with COVID-19 merits further investigation.

Heart rate variability in post-covid-19 recovered subjects using machine learning

Introduction: A significant proportion of patients recovering from COVID-19 infection experience symptoms attributable to autonomic cardiovascular dysregulation. Heart rate variability (HRV) is a non-invasive marker of cardiovascular dysautonomia. Machine learning (ML) models based on HRV can be used to identify post COVID-19 patients with autonomic dysfunction. Methods: We evaluated HRV and blood pressure (BP) responses to orthostatic stress (3-min active standing) in 92 patients within 30-45 days of recovery from COVID-19 infection and 120 healthy controls. HRV was evaluated based on 12-lead electrocardiogram over a 60 second period during supine paced breathing. Lead II was used to extract ECG features including (a) average RR interval, (b) R wave height, (c) Heart Rate (HR) standard deviation and (d) HRV root mean square [HRVRMS]. We also assed for (1) orthostatic hypotension (OH;>20/10 mmHg fall in BP) and (2) postural orthostatic tachycardia syndrome (POTS;HR increase >30 bpm without OH). Using ML, eleven candidate features were tested with eight algorithms (logistic regression, RandomForests, CatBoost, XGBoost, Extra-tree classifier, Multiple Perceptron (ANN), Support Vector Machines and AdaBoost Classifier) to distinguish between COVID-19 recovered and healthy controls. Results: HRV was significantly lower in post COVID-19 recovered subjects as compared to healthy controls (6.25+4.9 ms vs 9.8+8.9 ms;P<0.001). OH was reported in 12 patients (13.1%) while two patients (2.2%) had POTS. Patients with OH had a significantly lower HRV as compared to those without OH (3.29+3.16 ms vs 6.69+5.01 ms;P=0.025). Accuracy of various ML models varied between 67-80% with multiple perceptron being top model [80% weighted accuracy, AUC: 79.8%, Matthews's correlation coefficient: 0.59]. Permutation importance feature ranking showed HRV, Average RR and HR to be top feature that distinguish between COVID-19 recovered and healthy controls. Conclusions: A significant proportion of COVID-19 recovered patients experienced autonomic dysfunction as evident by lower HRV and presence of OH and POTS. ML model can help in early identification of autonomic dysfunction thereby leading to proper management in these patients.