Lower Risks of New-Onset Hepatocellular Carcinoma in Patients With Type 2 Diabetes Mellitus Treated With SGLT2 Inhibitors Versus DPP4 Inhibitors.

BACKGROUND: Type 2 diabetes mellitus (T2DM) may be a risk factor for development of hepatocellular carcinoma (HCC). The association between risk of developing HCC and treatment with sodium-glucose cotransporter-2 inhibitors (SGLT2i) versus dipeptidyl peptidase-4 inhibitors (DPP4i) is currently unknown. This study aimed to compare the risk of new-onset HCC in patients treated with SGLT2i versus DPP4i. METHODS: This was a retrospective cohort study of patients with T2DM in Hong Kong receiving either SGLT2i or DPP4i between January 1, 2015, and December 31, 2020. Patients with concurrent DPP4i and SGLT2i use were excluded. Propensity score matching (1:1 ratio) was performed by using the nearest neighbor search. Multivariable Cox regression was applied to identify significant predictors. RESULTS: A total of 62,699 patients were included (SGLT2i, n=22,154; DPP4i, n=40,545). After matching (n=44,308), 166 patients (0.37%) developed HCC: 36 in the SGLT2i group and 130 in the DPP4i group over 240,269 person-years. Overall, SGLT2i use was associated with lower risks of HCC (hazard ratio [HR], 0.42; 95% CI, 0.28-0.79) compared with DPP4i after adjustments. The association between SGLT2i and HCC development remained significant in patients with cirrhosis or advanced fibrosis (HR, 0.12; 95% CI, 0.04-0.41), hepatitis B virus (HBV) infection (HR, 0.32; 95% CI, 0.17-0.59), or hepatitis C virus (HCV) infection (HR, 0.41; 95% CI, 0.22-0.80). The results were consistent in different risk models, propensity score approaches, and sensitivity analyses. CONCLUSIONS: SGLT2i use was associated with a lower risk of HCC compared with DPP4i use after adjustments, and in the context of cirrhosis, advanced fibrosis, HBV infection, and HCV infection.

Comparative effectiveness of sodium-glucose cotransporter-2 inhibitors for new-onset gastric cancer and gastric diseases in patients with type 2 diabetes mellitus: a population-based cohort study.

OBJECTIVE: To compare the risks of gastric cancer and other gastric diseases in patients with type-2 diabetes mellitus (T2DM) exposed to sodium-glucose cotransporter 2 inhibitors (SGLT2I), dipeptidyl peptidase-4 inhibitors (DPP4I) or glucagon-like peptide-1 receptor agonists (GLP1a). DESIGN: This was a population-based cohort study of prospectively collected data on patients with T2DM prescribed SGLT2I, DPP4I or GLP1a between January 1st 2015 and December 31st 2020 from Hong Kong. The outcomes were new-onset gastric cancer, peptic ulcer (PU), acute gastritis, non-acute gastritis, and gastroesophageal reflux disease (GERD). Propensity score matching (1:1) using the nearest neighbour search was performed, and multivariable Cox regression was applied. A three-arm comparison between SGLT2I, DPP4I and GLP1a was conducted using propensity scores with inverse probability of treatment weighting. RESULTS: A total of 62,858 patients (median age: 62.2 years old [SD: 12.8]; 55.93% males; SGLT2I: n = 23,442; DPP4I: n = 39,416) were included. In the matched cohort, the incidence of gastric cancer was lower in SGLT2I (Incidence rate per 1000 person-year, IR: 0.32; 95% confidence interval, CI 0.23-0.43) than in DPP4I (IR per 1000 person-year: 1.22; CI 1.03-1.42) users. Multivariable Cox regression found that SGLT2I use was associated with lower risks of gastric cancer (HR 0.30; 95% CI 0.19-0.48), PU, acute gastritis, non-acute gastritis, and GERD (p < 0.05) compared to DPP4I use. In the three-arm analysis, GLP1a use was associated with higher risks of gastric cancer and GERD compared to SGLT2I use. CONCLUSIONS: The use of SGLT2I was associated with lower risks of new-onset gastric cancer, PU, acute gastritis, non-acute gastritis, and GERD after matching and adjustments compared to DPP4I use. SGLT2I use was associated with lower risks of GERD and gastric cancer compared to GLP1a use.

Predictors of fatal arrhythmic events in patients with non-compaction cardiomyopathy: a systematic review.

Left ventricular non-compaction cardiomyopathy (LVNC) is a congenital heart disease with autosomal dominant inheritance. This review aims to summarize the existing data about the predictors of fatal arrhythmias in patients with LVNC. Medline and Cochrane library databases were searched from inception to November 2021 for articles on LVNC. The reference lists of the relevant research studies as well as the relevant review studies and meta-analyses were also searched. Clinical symptoms and electrocardiogram findings such as left bundle branch block are significantly associated with ventricular arrhythmias. Other non-invasive tools such as Holter monitoring, echocardiography, and cardiac magnetic resonance (CMR) can provide additional value for risk stratification. CMR-derived left and right ventricular ejection fraction, left ventricular end-diastolic diameter, late gadolinium enhancement, and non-compacted to compacted myocardium ratio are predictive of ventricular arrhythmias. An electrophysiological study can provide additional prognostic data in patients with LVNC who are at moderate risk of ventricular arrhythmias. Risk stratification of LVNC patients with no prior history of a fatal arrhythmic event remains challenging. Symptoms assessment, electrocardiogram, Holter monitoring, and cardiac imaging should be performed on every patient, while an electrophysiological study should be performed for moderate-risk patients. Large cohort studies are needed for the construction of score models for arrhythmic risk stratification purposes.

Predictive risk models for forecasting arrhythmic outcomes in Brugada syndrome: A focused review.

Brugada syndrome (BrS) is a rare disorder characterized by coved or saddle-shaped ST-segment elevation in the right precordial leads on the electrocardiogram. Risk stratification in BrS remains challenging. A number of clinical, electrocardiographic, programmed ventricular stimulation and genetic risk factors have been identified as important predictors of future major arrhythmic events. There is a positive association between the number of risk factors and arrhythmic events. Hence, a multi-parametric approach would provide comprehensive risk assessment and more accurate risk stratification, assisting in therapeutic decisions making, including implantable cardioverter-defibrillator placement or identification of low-risk individuals. However, the extent to which each variable influences the risk and non-linear interactions between the different risk variables make risk stratification challenging. This paper aims to provide a focused review of the multi-parametric risk models for BrS risk stratification published in the literature.

Healthcare Big Data in Hong Kong: Development and Implementation of Artificial Intelligence-Enhanced Predictive Models for Risk Stratification.

Routinely collected electronic health records (EHRs) data contain a vast amount of valuable information for conducting epidemiological studies. With the right tools, we can gain insights into disease processes and development, identify the best treatment and develop accurate models for predicting outcomes. Our recent systematic review has found that the number of big data studies from Hong Kong has rapidly increased since 2015, with an increasingly common application of artificial intelligence (AI). The advantages of big data are that i) the models developed are highly generalisable to the population, ii) multiple outcomes can be determined simultaneously, iii) ease of cross-validation by for model training, development and calibration, iv) huge numbers of useful variables can be analyzed, v) static and dynamic variables can be analyzed, vi) non-linear and latent interactions between variables can be captured, vii) artificial intelligence approaches can enhance the performance of prediction models. In this paper, we will provide several examples (cardiovascular disease, diabetes mellitus, Brugada syndrome, long QT syndrome) to illustrate efforts from a multi-disciplinary team to identify data from different modalities to develop models using territory-wide datasets, with the possibility of real-time risk updates by using new data captured from patients. The benefit is that only routinely collected data are required for developing highly accurate and high-performance models. AI-driven models outperform traditional models in terms of sensitivity, specificity, accuracy, area under the receiver operating characteristic and precision-recall curve, and F1 score. Web and/or mobile versions of the risk models allow clinicians to risk stratify patients quickly in clinical settings, thereby enabling clinical decision-making. Efforts are required to identify the best ways of implementing AI algorithms on the web and mobile apps.

Application of artificial intelligence in the diagnosis of sleep apnea.

STUDY OBJECTIVES: Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders. METHODS: A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed. RESULTS: Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models. CONCLUSIONS: The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently. CITATION: Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.

Metformin use and hospital attendance-related resources utilization among diabetic patients with prostate cancer on androgen deprivation therapy: A population-based cohort study.

BACKGROUND: Androgen deprivation therapy (ADT), used increasingly in the treatment of prostate cancer (PCa), negatively influences glycemic control in diabetes and is associated with an increased risk of diabetes complications where hospitalization commonly ensues. Metformin could decrease the metabolic consequences of ADT and enhance its effect. This study examined the association of metformin use with healthcare resources utilization among diabetic, PCa patients receiving ADT. METHODS: Diabetic adults with PCa on ADT in Hong Kong between December 1999 and March 2021 were identified. Patients with <6 months of concurrent metformin and ADT use were excluded. All included patients were followed up until September 2021. The outcomes were hospital attendances and related costs. RESULTS: In total, 1,284 metformin users and 687 non-users were studied. Over 8,045 person-years, 9,049 accident and emergency (A&E), and 21,262 inpatient attendances, with 11,2781 days of hospitalization were observed. Metformin users had significantly fewer A&E attendances (incidence rate ratio (IRR): 0.61 [95% confidence interval 0.54-0.69], p < 0.001), inpatient attendances (IRR: 0.57 [0.48-0.67], p < 0.001), and days of hospitalization (IRR: 0.55 [0.42-0.72], p < 0.001). Annual attendance costs were lower for metformin users than non-users (cost ratio: 0.28 [0.10-0.80], p = 0.017). CONCLUSIONS: Metformin use was associated with decreased hospital attendances, days of hospitalization, and associated costs, which could help reduce healthcare resource utilization following ADT in the treatment of PCa.

Attendance-related Healthcare Resource Utilisation and Costs in Patients With Brugada Syndrome in Hong Kong: A Retrospective Cohort Study.

Understanding health care resource utilisation and its associated costs are important for identifying areas of improvement regarding resource allocations. However, there is limited research exploring this issue in the setting of Brugada syndrome (BrS).This was a retrospective territory-wide study of BrS patients from Hong Kong. Healthcare resource utilisation for accident and emergency (A&E), inpatient and specialist outpatient attendances were analyzed over a 19-year period, with their associated costs presented in US dollars. A total of 507 BrS patients with a mean presentation age of 49.9 ± 16.3 years old were included. Of these, 384 patients displayed spontaneous type 1 electrocardiographic (ECG) Brugada pattern and 77 patients had presented with ventricular tachycardia/ventricular fibrillation (VT/VF). At the individual patient level, the median annualized costs were $110 (52-224) at the (A&E) setting, $6812 (1982-32414) at the inpatient setting and $557 (326-1001) for specialist outpatient attendances. Patients with initial VT/VF presentation had overall greater costs in inpatient ($20161 [9147-189215] vs $5290 [1613-24937],P < 0.0001) and specialist outpatient setting ($776 [438-1076] vs $542 [293-972],P = 0.015) compared to those who did not present VT. In addition, patients without Type 1 ECG pattern had greater median costs in the specialist outpatient setting ($7036 [3136-14378] vs $4895 [2409-10554],p=0.019). There is a greater health care demand in the inpatient and specialist outpatient settings for BrS patients. The most expensive attendance type was inpatient setting stay at $6812 per year. The total median annualized cost of BrS patients without VT/VF presentation was 78% lower compared to patients with VT/VF presentation.

The Role of Electrophysiological Study in the Risk Stratification of Brugada Syndrome.

Brugada syndrome (BrS) is a complex arrhythmogenic disease associated with an increased risk of sudden cardiac death (SCD). The role of electrophysiological study (EPS) for risk stratification purposes of asymptomatic BrS patients remains still controversial. This study aims to summarize the existing data about the role of electrophysiological study for arrhythmic risk stratification of BrS patients without a prior history of aborted SCD or fatal arrhythmic event. Two independent investigators (G.B. and G.T.) performed a systematic search in the MedLine database and Cochrane library from their inception until April 2022 without any limitations. The reference lists of the relevant research studies as well as the relevant review studies and meta-analyses were manually searched. Nineteen studies were included in the final analysis. The included studies enrolled 6218 BrS patients (mean age: 46.9 years old, males: 76%) while 4265 (68.6%) patients underwent an EPS. The quantitative synthesis showed that a positive EPS study was significantly associated with arrhythmic events in BrS patients (RR, 1.74 [1.23-2.45]; P = 0.002; I2 = 63%]. By including the studies that provided data on the association of EPS with arrhythmic events during follow-up in patients without a prior history of aborted SCD or fatal arrhythmic event, the association between positive EPS study and future arrhythmic events remained significant (RR, 1.60 [1.08-2.36]; P = 0.02; I2 = 19%). In conclusion, EPS is a useful invasive tool for the risk stratification of BrS patients and can be used to identify the population of BrS patients who may be candidates for primary prevention of SCD with implantable cardioverter-defibrillator (ICD) implantation.

Sodium-glucose cotransporter 2 inhibitors versus dipeptidyl peptidase 4 inhibitors on new-onset overall cancer in Type 2 diabetes mellitus: A population-based study.

BACKGROUND: Cancer is currently the second leading cause of death globally. There is much uncertainty regarding the comparative risks of new-onset overall cancer and pre-specified cancer for Type 2 diabetes mellitus (T2DM) patients on sodium-glucose cotransporter 2 inhibitors (SGLT2I) versus DPP4I. METHODS: This population-based cohort study patients included patients who were diagnosed with T2DM and administered either SGLT2 or DPP4 inhibitors between 1 January 2015 and 31 December 2020 in public hospitals of Hong Kong. RESULTS: This study included 60,112 T2DM patients (mean baseline age: 62.1 ± 12.4 years, male: 56.36%), of which 18,167 patients were SGLT2I users and 41,945 patients were dipeptidyl peptidase 4 inhibitor (DPP4I) users. Multivariable Cox regression found that SGLT2I use was associated with lower risks of all-cause mortality (HR: 0.92; 95% CI: 0.84-0.99; p= 0.04), cancer-related mortality (HR: 0.58; 95% CI: 0.42-0.80; p ≤ 0.001) and new diagnoses of any cancer (HR: 0.70; 95% CI: 0.59-0.84; p ≤ 0.001). SGLT2I use was associated with a lower risk of new-onset breast cancer (HR: 0.51; 95% CI: 0.32-0.80; p ≤ 0.001), but not of other malignancies. Subgroup analysis on the type of SGLT2I, dapagliflozin (HR: 0.78; 95% CI: 0.64-0.95; p = 0.01) and ertugliflozin (HR: 0.65; 95% CI: 0.43-0.98; p = 0.04) use was associated with lower risks of new cancer diagnosis. Dapagliflozin use was also linked to lower risks of breast cancer (HR: 0.48; 95% CI: 0.27-0.83; p = 0.001). CONCLUSION: Sodium-glucose cotransporter 2 inhibitor use was associated with lower risks of all-cause mortality, cancer-related mortality and new-onset overall cancer compared to DPP4I use after propensity score matching and multivariable adjustment.

Secular trends of health care resource utilization and costs between Brugada syndrome and congenital long QT syndrome: A territory-wide study.

BACKGROUND: Health care resource utilization (HCRU) and costs are important metrics of health care burden, but they have rarely been explored in the setting of cardiac ion channelopathies. HYPOTHESIS: This study tested the hypothesis that attendance-related HCRUs and costs differed between patients with Brugada syndrome (BrS) and congenital long QT syndrome (LQTS). METHODS: This was a retrospective cohort study of consecutive BrS and LQTS patients at public hospitals or clinics in Hong Kong, China. HCRUs and costs (in USD) for Accident and Emergency (A&E), inpatient, general outpatient and specialist outpatient attendances were analyzed between 2001 and 2019 at the cohort level. Comparisons were made using incidence rate ratios (IRRs [95% confidence intervals]). RESULTS: Over the 19-year period, 516 BrS (median age of initial presentation: 51 [interquartile range: 38-61] years, 92% male) and 134 LQTS (median age of initial presentation: 21 [9-44] years, 32% male) patients were included. Compared to LQTS patients, BrS patients had lower total costs (2 008 126 [2 007 622-2 008 629] vs. 2 343 864 [2 342 828-2 344 900]; IRR: 0.857 [0.855-0.858]), higher costs for A&E attendances (83 113 [83 048-83 177] vs. 70 604 [70 487-70 721]; IRR: 1.177 [1.165-1.189]) and general outpatient services (2,176 [2,166-2,187] vs. 921 [908-935]; IRR: 2.363 [2.187-2.552]), but lower costs for inpatient stay (1 391 624 [1 391 359-1 391 889] vs. 1 713 742 [1 713 166-1 714 319]; IRR: 0.812 [0.810-0.814]) and lower costs for specialist outpatient services (531 213 [531 049-531 376] vs. 558 597 [558268-558926]; IRR: 0.951 [0.947-0.9550]). CONCLUSIONS: Overall, BrS patients consume 14% less health care resources compared to LQTS patients in terms of attendance costs. BrS patients require more A&E and general outpatient services, but less inpatient and specialist outpatient services than LQTS patients.

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 current 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.

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.

Risk Factors of Pancreatic Cancer in Patients With Type 2 Diabetes Mellitus: The Hong Kong Diabetes Study.

Context: Diabetes mellitus (DM) is associated with the development of pancreatic cancer (PaC), but few large-scale studies have examined its predictive risk factors. Objective: The present study aims to examine the predictors for PaC in patients with type 2 diabetes mellitus (T2DM) in a territory-wide, retrospective cohort study. Methods: This was a territory-wide, retrospective cohort study of patients with T2DM mellitus older than 40 years with no prior history of PaC. Baseline demographics, use of antidiabetic medications, comorbidities, and biochemical parameters were extracted. Cox regression was used to calculate hazard ratios (HR) with 95% CI. Subgroup analyses based on chronic kidney disease (CKD) stages were performed. Results: This study consisted of 273 738 patients (age = 65.4 ± 12.7 years, male = 48.2%, follow-up duration = 3547 ± 1207 days, disease duration = 4.8 ± 2.3 years), of whom 1148 developed PaC. The number of antidiabetic medications prescribed (HR: 1.20; 95% CI, 1.01-1.42; P = .040), diabetic microvascular complications (HR: 1.91; 95% CI, 1.30-2.81; P < .001), chronic kidney disease (HR: 1.81; 95% CI, 1.25-2.64; P = .002), use of acarbose (HR: 2.24; 95% CI, 1.35-3.74; P = .002), and use of glucagon-like peptide-1 receptor agonist (HR: 4.00; 95% CI: 1.28-12.53, P = .017) were associated with PaC development on multivariable Cox regression adjusting for the duration of DM, mean glycated hemoglobin A1c, and history of pancreatic diseases. Stage 3A CKD or below was associated with PaC but not stage 3B or beyond. Conclusion: Diabetic microvascular complications, especially stage 1, 2, and 3A CKD, were associated with PaCs.

Comparing the Performance of Published Risk Scores in Brugada Syndrome: A Multi-center Cohort Study.

The management of Brugada Syndrome (BrS) patients at intermediate risk of arrhythmic events remains controversial. The present study evaluated the predictive performance of different risk scores in an Asian BrS population and its intermediate risk subgroup. This retrospective cohort study included consecutive patients diagnosed with BrS from January 1, 1997 to June 20, 2020 from Hong Kong. The primary outcome is sustained ventricular tachyarrhythmias. Two novel risk risk scores and 7 machine learning-based models (random survival forest, Ada boost classifier, Gaussian naïve Bayes, light gradient boosting machine, random forest classifier, gradient boosting classifier and decision tree classifier) were developed. The area under the receiver operator characteristic curve (AUC) [95% confidence intervals] was compared between the different models. This study included 548 consecutive BrS patients (7% female, age at diagnosis: 50 ± 16 years, follow-up: 84 ± 55 months). For the whole cohort, the score developed by Sieira et al showed the best performance (AUC: 0.806 [0.747-0.865]). A novel risk score was developed using the Sieira score and additional variables significant on univariable Cox regression (AUC: 0.855 [0.808-0.901]). A simpler score based on non-invasive results only showed a statistically comparable AUC (0.784 [0.724-0.845]), improved using random survival forests (AUC: 0.942 [0.913-0.964]). For the intermediate risk subgroup (N = 274), a gradient boosting classifier model showed the best performance (AUC: 0.814 [0.791-0.832]). A simple risk score based on clinical and electrocardiographic variables showed a good performance for predicting VT/VF, improved using machine learning.

The Impact of Cardiac Comorbidity Sequence at Baseline and Mortality Risk in Type 2 Diabetes Mellitus: A Retrospective Population-Based Cohort Study.

Introduction: The presence of multiple comorbidities increases the risk of all-cause mortality, but the effects of the comorbidity sequence before the baseline date on mortality remain unexplored. This study investigated the relationship between coronary heart disease (CHD), atrial fibrillation (AF) and heart failure (HF) through their sequence of development and the effect on all-cause mortality risk in type 2 diabetes mellitus. Methods: This study included patients with type 2 diabetes mellitus prescribed antidiabetic/cardiovascular medications in public hospitals of Hong Kong between 1 January 2009 and 31 December 2009, with follow-up until death or 31 December 2019. The Cox regression was used to identify comorbidity sequences predicting all-cause mortality in patients with different medication subgroups. Results: A total of 249,291 patients (age: 66.0 ± 12.4 years, 47.4% male) were included. At baseline, 7564, 10,900 and 25,589 patients had AF, HF and CHD, respectively. Over follow-up (3524 ± 1218 days), 85,870 patients died (mortality rate: 35.7 per 1000 person-years). Sulphonylurea users with CHD developing later and insulin users with CHD developing earlier in the disease course had lower mortality risks. Amongst insulin users with two of the three comorbidities, those with CHD with preceding AF (hazard ratio (HR): 3.06, 95% CI: [2.60−3.61], p < 0.001) or HF (HR: 3.84 [3.47−4.24], p < 0.001) had a higher mortality. In users of lipid-lowering agents with all three comorbidities, those with preceding AF had a higher risk of mortality (AF-CHD-HF: HR: 3.22, [2.24−4.61], p < 0.001; AF-HF-CHD: HR: 3.71, [2.66−5.16], p < 0.001). Conclusions: The sequence of comorbidity development affects the risk of all-cause mortality to varying degrees in diabetic patients on different antidiabetic/cardiovascular medications.

COVID-19 vaccination and carditis in children and adolescents: a systematic review and meta-analysis.

BACKGROUND: Coronavirus Disease-2019 (COVID-19) vaccination has been associated with the development of carditis, especially in children and adolescent males. However, the rates of these events in the global setting have not been explored in a systematic manner. The aim of this systematic review and meta-analysis is to investigate the rates of carditis in children and adolescents receiving COVID-19 vaccines. METHODS: PubMed, Embase and several Latin American databases were searched for studies. The number of events, and where available, at-risk populations were extracted. Rate ratios were calculated and expressed as a rate per million doses received. Subgroup analysis based on the dose administered was performed. Subjects ≤ 19 years old who developed pericarditis or myocarditis following COVID-19 vaccination were included. RESULTS: A total of 369 entries were retrieved. After screening, 39 articles were included. Our meta-analysis found that 343 patients developed carditis after the administration of 12,602,625 COVID-19 vaccination doses (pooled rate per million: 37.76; 95% confidence interval [CI] 23.57, 59.19). The rate of carditis was higher amongst male patients (pooled rate ratio: 5.04; 95% CI 1.40, 18.19) and after the second vaccination dose (pooled rate ratio: 5.60; 95% CI 1.97, 15.89). In 301 cases of carditis (281 male; mean age: 15.90 (standard deviation [SD] 1.52) years old) reported amongst the case series/reports, 261 patients were reported to have received treatment. 97.34% of the patients presented with chest pain. The common findings include ST elevation and T wave abnormalities on electrocardiography. Oedema and late gadolinium enhancement in the myocardium were frequently observed in cardiac magnetic resonance imaging (CMR). The mean length of hospital stay was 3.91 days (SD 1.75). In 298 out of 299 patients (99.67%) the carditis resolved with or without treatment. CONCLUSIONS: Carditis is a rare complication after COVID-19 vaccination across the globe, but the vast majority of episodes are self-limiting with rapid resolution of symptoms within days. Central illustration. Balancing the benefits of vaccines on COVID-19-caused carditis and post-vaccination carditis.