Background: The clinical use of angiotensin-converting enzyme inhibitors (ACEI) and angiotensin-receptor blockers (ARB) in patients with COVID-19 infection remains controversial. Therefore, we performed a meta-analysis on the effects of ACEI/ARB on disease symptoms and laboratory tests in hypertensive patients infected with COVID-19 virus and those who did not use ACEI/ARB. Methods: We systematically searched the relevant literatures from Pubmed, Embase, EuropePMC, CNKI, and other databases during the study period of 31 December 2019 (solstice, 15 March 2020), and analyzed the differences in symptoms and laboratory tests between patients with COVID-19 and hypertension who used ACEI/ARB drugs and those who did not. All statistical analyses were performed with REVMAN5.3. Results: We included a total of 1808 patients with hypertension diagnosed with COVID-19 in six studies. Analysis results show that ACEI/ARB drugs group D-dimer is lower (SMD = −0.22, 95%CI: −0.36 to −0.06), and the chances of getting fever is lower (OR = 0.74, 95%CI: 0.55 to 0.98). Meanwhile, laboratory data and symptoms were not statistical difference, but creatinine tends to rise (SMD = 0.22, 95% CI: 0.04 to 0.41). Conclusion: We found that the administration of ACEI/ARB drugs had positive effect on reducing D-dimer and the number of people with fever. Meanwhile it had no significant effect on other laboratory tests (creatinine excepted) or symptoms in patients with COVID-19, while special attention was still needed in patients with renal insufficiency.

Background Early identification of the occurrence of arrhythmia in patients with acute myocardial infarction plays an essential role in clinical decision-making. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial infarction (AMI). Methods A total of 2084 patients with acute myocardial infarction were enrolled in this study. (All data is available on Github: https://github.com/wangsuhuai/AMI-database1.git). The primary outcome is whether tachyarrhythmia occurred during admission containing atrial arrhythmia, ventricular arrhythmia, and supraventricular tachycardia. All data is randomly divided into a training set (80%) and an internal testing set (20%). Apply three machine learning algorithms: decision tree, random forest (RF), and artificial neural network (ANN) to learn the training set to build a model, then use the testing set to evaluate the prediction performance, and compare it with the model built by the Global Registry of Acute Coronary Events (GRACE) risk variable set. Results Three ML models predict the occurrence of tachyarrhythmias after AMI. After variable selection, the artificial neural network (ANN) model has reached the highest accuracy rate, which is better than the model constructed using the Grace variable set. After applying SHapley Additive exPlanations (SHAP) to make the model interpretable, the most important features are abnormal wall motion, lesion location, bundle branch block, age, and heart rate. Among them, RBBB (odds ratio [OR]: 4.21; 95% confidence interval [CI]: 2.42–7.02), ≥ 2 ventricular walls motion abnormal (OR: 3.26; 95% CI: 2.01–4.36) and right coronary artery occlusion (OR: 3.00; 95% CI: 1.98–4.56) are significant factors related to arrhythmia after AMI. Conclusions We used advanced machine learning methods to build prediction models for tachyarrhythmia after AMI for the first time (especially the ANN model that has the best performance). The current study can supplement the current AMI risk score, provide a reliable evaluation method for the clinic, and broaden the new horizons of ML and clinical research. Trial registration Clinical Trial Registry No.: ChiCTR2100041960.

Objectives Cardiac injury is associated with poor prognosis of 2019 novel coronavirus disease 2019 (COVID-19), but the risk factors for cardiac injury have not been fully studied. In this study, we carried out a systematic analysis of clinical characteristics in COVID-19 patients to determine potential risk factors for cardiac injury complicated COVID-19 virus infection. Methods We systematically searched relevant literature published in Pubmed, Embase, Europe PMC, CNKI and other databases. All statistical analyses were performed using STATA 16.0. Results We analysed 5726 confirmed cases from 17 studies. The results indicated that compared with non-cardiac-injured patients, patients with cardiac injury are older, with a greater proportion of male patients, with higher possibilities of existing comorbidities, with higher risks of clinical complications, need for mechanical ventilation, ICU transfer and mortality. Moreover, C-reactive protein, procalcitonin, D-dimer, NT-proBNP and blood creatinine in patients with cardiac injury are also higher while lymphocyte counts and platelet counts decreased. However, we fortuitously found that patients with cardiac injury did not present higher clinical specificity for chest distress (P = 0.304), chest pain (P = 0.334), palpitations (P = 0.793) and smoking (P = 0.234). Similarly, the risk of concomitant arrhythmia (P = 0.103) did not increase observably either. Conclusion Age, male gender and comorbidities are risk factors for cardiac injury complicated COVID-19 infection. Such patients are susceptible to complications and usually have abnormal results of laboratory tests, leading to poor outcomes. Contrary to common cardiac diseases, cardiac injury complicated COVID-19 infection did not significantly induce chest distress, chest pain, palpitations or arrhythmias. Our study indicates that early prevention should be applied to COVID-19 patients with cardiac injury to reduce adverse outcomes.

Background: Early identification of the occurrence of arrhythmia in patients with acute myocardial infarction plays an essential role in clinical decision-making. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial infarction (AMI).Methods: A total of 2084 patients with acute myocardial infarction were enrolled in this study. The primary outcome is whether tachyarrhythmia occurred during admission containing atrial arrhythmia, ventricular arrhythmia, and supraventricular tachycardia. All data is randomly divided into training set (80%) and internal testing set (20%). Three machine learning algorithms (including decision tree, random forest, and artificial neural network) learn from the training set to build a model, use the testing set to evaluate the prediction performance, and compare it with the model built by the variable set involved GRACE risk score.Results：Three ML models predict the occurrence of tachyarrhythmia after AMI. After variable selection, the artificial neural network (ANN) model achieves the highest accuracy of 0.654 (95% CI, 0.625--0.683). The area under the value of the curve (AUC) is 0.597 (95% CI, 0.568-0.626). The highest accuracy of the model built using the Grace variable set is 0.627 (95% CI, 0.598-0.656), and the AUC value is 0.574 (95% CI, 0.545-0.603).Conclusions：We used advanced machine learning methods to build prediction models for tachyarrhythmia after AMI for the first time (especially the ANN model has the best performance). The current study can supplement the current AMI risk score, provide a reliable evaluation method for the clinic, and broaden the new horizons of ML and clinical research.Trial registration：Clinical Trial Registry No.: ChiCTR2100041960.