Comparisons of the rate of acute myocardial infarction between COVID-19 patients and individuals received COVID-19 vaccines: a population-based study

BackgroundBoth Coronavirus Disease-2019 (COVID-19) infection and COVID-19 vaccination have been associated with the development of acute myocardial infarction (AMI). This study compared the rates of AMI after COVID-19 infection and among the COVID-19 vaccinated populations in Hong Kong. MethodsThis was a population-based cohort study from Hong Kong, China. Patients with positive real time-polymerase chain reaction (RT-PCR) test for COVID-19 between January 1st, 2020 and June 30th, 2021 were included. The data of the vaccinated and unvaccinated population was obtained from the "Reference Data of Adverse Events in Public Hospitals" published by the local government. The individuals who were vaccinated with COVID-19 vaccination prior the observed period (December 6th, 2021 to January 2nd, 2022) in Hong Kong were also included. The vaccination data of other countries were obtained by searching PubMed using the terms ["COVID-19 vaccine" AND "Myocardial infarction"] from its inception to February 1st, 2022. The main exposures were COVID-19 test positivity or previous COVID-19 vaccination. The primary outcome was the development of AMI within 28 days observed period. ResultsThis study included 11441 COVID-19 patients, of whom 25 suffered from AMI within 28 days of exposure (rate per million: 2185; 95% confidence interval [CI]: 1481-3224). The rates of AMI were much higher than those who were not vaccinated by the COVID-19 vaccine before December 6th, 2021 (rate per million: 162; 95% CI: 147-162) with a rate ratio of 13.5 (95% CI: 9.01-20.2). Meanwhile, the rate of AMI was lower amongst the vaccinated population (rate per million: 47; 95% CI: 41.3-53.5) than COVID-19 infection with a rate ratio of 0.02 (0.01, 0.03). Regarding post-vaccination AMI, COVID-19 infection was associated with a significantly higher rate of AMI than post-COVID-19 vaccination AMI in other countries. ConclusionsCOVID-19 infection was associated with a higher rate of AMI than the vaccinated general population, and those immediately after COVID-19 vaccination.

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques.This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice

Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography‑based diagnosis

Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of cur‑ rent ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges.

Comparisons of the risk of myopericarditis between COVID-19 patients and individuals receiving COVID-19 vaccines: a population-based study

BackgroundBoth COVID-19 infection and COVID-19 vaccines have been associated with the development of myopericarditis. The objective of this study is to 1) analyze the rates of myopericarditis after COVID-19 infection and COVID-19 vaccination in Hong Kong and 2) compare to the background rates, and 3) compare the rates of myopericarditis after COVID-19 vaccination to those reported in other countries. MethodsThis was a population-based cohort study from Hong Kong, China. Patients with positive RT-PCR test for COVID-19 between 1st January 2020 and 30th June 2021 or individuals who received COVID-19 vaccination until 31st August were included. The main exposures were COVID-19 positivity or COVID-19 vaccination. The primary outcome was myopericarditis. ResultsThis study included 11441 COVID-19 patients from Hong Kong, of whom four suffered from myopericarditis (rate per million: 350; 95% confidence interval [CI]: 140-900). The rate was higher than the pre-COVID-19 background rate in 2020 (rate per million: 61, 95% CI: 55-67) with a rate ratio of 5.73 (95% CI: 2.23-14.73. Compared to background rates, the rate of myopericarditis among vaccinated subjects in Hong Kong was substantially lower (rate per million: 8.6; 95% CI: 6.4-11.6) with a rate ratio of 0.14 (95% CI: 0.10-0.19). The rates of myocarditis after vaccination in Hong Kong are comparable to those vaccinated in the United States, Israel, and the United Kingdom. ConclusionsCOVID-19 infection is associated with a higher rate of myopericarditis whereas COVID-19 vaccination is associated with a lower rate of myopericarditis compared to the background.

Renin-angiotensin system blockers and mortality in COVID-19: a territory-wide study from Hong Kong

AimsRenin-angiotensin system blockers such as angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) may increase the risk of adverse outcomes in COVID-19. In this study, the relationships between ACEI/ARB use and COVID-19 related mortality were examined. MethodsConsecutive patients diagnosed with COVID-19 by RT-PCR at the Hong Kong Hospital Authority between 1st January and 28th July 2020 were included. ResultsThis study included 2774 patients. The mortality rate of the COVID-19 positive group was 1.5% (n=42). Those who died had a higher median age (82.3[76.5-89.5] vs. 42.9[28.2-59.5] years old; P<0.0001), more likely to have baseline comorbidities of cardiovascular disease, diabetes mellitus, hypertension, and chronic kidney disease (P<0.0001). They were more frequently prescribed ACEI/ARBs at baseline, and steroids, lopinavir/ritonavir, ribavirin and hydroxychloroquine during admission (P<0.0001). They also had a higher white cell count, higher neutrophil count, lower platelet count, prolonged prothrombin time and activated partial thromboplastin time, higher D-dimer, troponin, lactate dehydrogenase, creatinine, alanine transaminase, aspartate transaminase and alkaline phosphatase (P<0.0001). Multivariate Cox regression showed that age, cardiovascular disease, renal disease, diabetes mellitus, the use of ACEIs/ARBs and diuretics, and various laboratory tests remained significant predictors of mortality. ConclusionsWe report that an association between ACEIs/ARBs with COVID-19 related mortality even after adjusting for cardiovascular and other comorbidities, as well as medication use. Patients with greater comorbidity burden and laboratory markers reflecting deranged clotting, renal and liver function, and increased tissue inflammation, and ACEI/ARB use have a higher mortality risk. Key PointsO_LIWe report that an association between ACEIs/ARBs with COVID-19 related mortality even after adjusting for cardiovascular and other comorbidities, as well as medication use. C_LIO_LIPatients with greater comorbidity burden and laboratory markers reflecting deranged clotting, renal and liver function, and increased tissue inflammation, and ACEI/ARB use have a higher mortality risk. C_LI

Identifying Sequential Complication and Mortality Patterns in Diabetes Mellitus: Comparisons of Machine Learning Methodologies

BackgroundDiabetes mellitus-related complications adversely affect the quality of life. Better risk-stratified care through mining of sequential complication patterns is needed to enable early detection and prevention. MethodsUnivariable and multivariate logistic regression was used to identify significant variables that can predict mortality. A sequence analysis method termed Prefixspan was applied to identify the most common couple, triple, quadruple, quintuple and sextuple sequential complication patterns in the directed comorbidity pathology network. A knowledge enhanced CPT+ (KCPT+) sequence prediction model is developed to predict the next possible outcome along the progression trajectories of diabetes-related complications. FindingsA total of 14,144 diabetic patients (51% males) were included. Acute myocardial infarction (AMI) without known ischaemic heart disease (IHD) (odds ratio [OR]: 2.8, 95% CI: [2.3, 3.4]), peripheral vascular disease (OR: 2.3, 95% CI: [1.9, 2.8]), dementia (OR: 2.1, 95% CI: [1.8, 2.4]), and IHD with AMI (OR: 2.4, 95% CI: [2.1, 2.6]) are the most important multivariate predictors of mortality. KCPT+ shows high accuracy in predicting mortality (F1 score 0.90, ACU 0.88), osteoporosis (F1 score 0.86, AUC 0.82), ophthalmological complications (F1 score 0.82, AUC 0.82), IHD with AMI (F1 score 0.81, AUC 0.85) and neurological complications (F1 score 0.81, AUC 0.83) with a particular prior complication sequence. InterpretationSequence analysis identifies the most common pattern characteristics of disease-related complications efficiently. The proposed sequence prediction model is accurate and enables clinicians to diagnose the next complication earlier, provide better risk-stratified care, and devise efficient treatment strategies for diabetes mellitus patients.

Development of a predictive risk model for severe COVID-19 disease using population-based administrative data

BackgroundRecent studies have reported numerous significant predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk score for prompt risk stratification. The objective is to develop a simple risk score for severe COVID-19 disease using territory-wide healthcare data based on simple clinical and laboratory variables. MethodsConsecutive patients admitted to Hong Kongs public hospitals between 1st January and 22nd August 2020 diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8th September 2020. ResultsCOVID-19 testing was performed in 237493 patients and 4445 patients (median age 44.8 years old, 95% CI: [28.9, 60.8]); 50% male) were tested positive. Of these, 212 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, hypertension, stroke, diabetes mellitus, ischemic heart disease/heart failure, respiratory disease, renal disease, increases in neutrophil count, monocyte count, sodium, potassium, urea, alanine transaminase, alkaline phosphatase, high sensitive troponin-I, prothrombin time, activated partial thromboplastin time, D-dimer and C-reactive protein, as well as decreases in lymphocyte count, base excess and bicarbonate levels. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. ConclusionsA simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.

Identifying main and interaction effects of risk factors to predict intensive care admission in patients hospitalized with COVID-19: a retrospective cohort study in Hong Kong

BackgroundThe coronavirus disease 2019 (COVID-19) has become a pandemic, placing significant burdens on the healthcare systems. In this study, we tested the hypothesis that a machine learning approach incorporating hidden nonlinear interactions can improve prediction for Intensive care unit (ICU) admission. MethodsConsecutive patients admitted to public hospitals between 1st January and 24th May 2020 in Hong Kong with COVID-19 diagnosed by RT-PCR were included. The primary endpoint was ICU admission. ResultsThis study included 1043 patients (median age 35 (IQR: 32-37; 54% male). Nineteen patients were admitted to ICU (median hospital length of stay (LOS): 30 days, median ICU LOS: 16 days). ICU patients were more likely to be prescribed angiotensin converting enzyme inhibitors/angiotensin receptor blockers, anti-retroviral drugs lopinavir/ritonavir and remdesivir, ribavirin, steroids, interferon-beta and hydroxychloroquine. Significant predictors of ICU admission were older age, male sex, prior coronary artery disease, respiratory diseases, diabetes, hypertension and chronic kidney disease, and activated partial thromboplastin time, red cell count, white cell count, albumin and serum sodium. A tree-based machine learning model identified most informative characteristics and hidden interactions that can predict ICU admission. These were: low red cells with 1) male, 2) older age, 3) low albumin, 4) low sodium or 5) prolonged APTT. A five-fold cross validation confirms superior performance of this model over baseline models including XGBoost, LightGBM, random forests, and multivariate logistic regression. ConclusionsA machine learning model including baseline risk factors and their hidden interactions can accurately predict ICU admission in COVID-19.

Impact of cardiovascular disease and cardiac injury on in-hospital mortality in patients with COVID-19: a systematic review and meta-analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has produced a significant health burden worldwide, especially in patients with cardiovascular comorbidities. The aim of this systematic review and meta-analysis was to assess the impact of underlying cardiovascular comorbidities and acute cardiac injury on in-hospital mortality risk. METHODS: PubMed, Embase and Web of Science were searched for publications that reported the relationship of underlying cardiovascular disease (CVD), hypertension and myocardial injury with in-hospital fatal outcomes in patients with COVID-19. The ORs were extracted and pooled. Subgroup and sensitivity analyses were performed to explore the potential sources of heterogeneity. RESULTS: A total of 10 studies were enrolled in this meta-analysis, including eight studies for CVD, seven for hypertension and eight for acute cardiac injury. The presence of CVD and hypertension was associated with higher odds of in-hospital mortality (unadjusted OR 4.85, 95% CI 3.07 to 7.70; I2=29%; unadjusted OR 3.67, 95% CI 2.31 to 5.83; I2=57%, respectively). Acute cardiac injury was also associated with a higher unadjusted odds of 21.15 (95% CI 10.19 to 43.94; I2=71%). CONCLUSION: COVID-19 patients with underlying cardiovascular comorbidities, including CVD and hypertension, may face a greater risk of fatal outcomes. Acute cardiac injury may act as a marker of mortality risk. Given the unadjusted results of our meta-analysis, future research are warranted.