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

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

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

Clinical characteristics, genetic basis and healthcare resource utilisation and costs in patients with catecholaminergic polymorphic ventricular tachycardia: A retrospective cohort study

Background: This study examined the clinical characteristics, genetic basis, healthcare utilisation and costs of catecholaminergic ventricular tachycardia (CPVT) patients from a Chinese city. Methods: This was a territory-wide retrospective cohort study of consecutive CPVT patients at public hospitals or clinics in Hong Kong. Healthcare resource utilisation for accident and emergency (A&E), inpatient and outpatient attendances were analysed over 19 years (2001–2019) followed by calculations of annualised costs (in USD). Results: Sixteen patients with a median presentation age (interquartile range (IQR) of 11 (9–14) years old) were included. Fifteen patients (93.8%) were initially symptomatic. Ten patients had both premature ventricular complexes (PVCs) and ventricular tachycardia/fibrillation (VT/VF). One patient had PVCs without VT/VF. Genetic tests were performed on 14 patients (87.5%). Eight (57.1%) tested positive for the ryanodine receptor 2 (RyR2) gene. Seven variants have been described elsewhere (c.14848G>A, c.12475C>A, c.7420A>G, c.11836G>A, c.14159T>C, c.10046C>T and c.7202G>A).c.14861C>G is a novel RyR2 variant not been reported outside this cohort. Patients were treated with beta-blockers (n = 16), amiodarone (n = 3) and verapamil (n = 2). Sympathectomy (n = 8) and implantable-cardioverter defibrillator implantation (n = 3) were performed. Over a median follow-up of 13.3 years (IQR: 8.4–18.1) years, six patients exhibited incident VT/VF. At the patient level, the median (IQR) annualised costs for A&E, inpatient and outpatient attendances were $66 (40–95),$10521 (5240–66887) and $791 (546–1105), respectively. Conclusions: All patients presented before the age of 19. The yield of genetic testing was 57%. The most expensive attendance type was inpatient stays, followed by outpatients and A&E attendances

Attendance-related healthcare resource utilisation and costs in patients with Brugada Syndrome in Hong Kong: A retrospective cohort study.

Understanding healthcare 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 analysed 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 annualised 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 healthcare 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 annualised cost of BrS patients without VT/VF presentation was 78% lower compared to patients with VT/VF presentation. [Abstract copyright: Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.

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 seven 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 (ROC) 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. Abstract: The management of Brugada Syndrome (BrS) patients at intermediate risk of arrhythmic events remains controversial. This study evaluated the predictive performance of published risk scores in a cohort of BrS patients from Hong Kong (N=548) and its intermediate risk subgroup (N=274). A novel risk score developed by modifying the best performing existing score (by. Sieira et al.) showed an area under the curve of 0.855 and 0.760 for the whole BrS cohort and the intermediate risk subgroup, respectively. The performance of the different scores was significantly improved machine learning-based methods, such as random survival forests and gradient boosting classifier. [Abstract copyright: Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.

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

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

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. 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. 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], &lt; 0.001) or HF (HR: 3.84 [3.47-4.24], &lt; 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], &lt; 0.001; AF-HF-CHD: HR: 3.71, [2.66-5.16], &lt; 0.001). 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