Impact of incorporating echocardiographic screening into a clinical prediction model to optimise utilisation of echocardiography in primary care

Combination of Tele-Cardiology Tools for Cardiovascular Risk Stratification in Primary Care: Data from the PROVAR+ Study

BACKGROUND

Tele-cardiology tools are valuable strategies to improve risk stratification.

OBJECTIVE

We aimed to evaluate the accuracy of tele-electrocardiography (ECG) to predict abnormalities in screening echocardiography (echo) in primary care (PC).

METHODS

In 17 months, 6 health providers at 16 PC units were trained on simplified handheld echo protocols. Tele-ECGs were recorded for final diagnosis by a cardiologist. Consented patients with major ECG abnormalities by the Minnesota code, and a 1:5 sample of normal individuals underwent clinical questionnaire and screening echo interpreted remotely. Major heart disease was defined as moderate/severe valve disease, ventricular dysfunction/hypertrophy, pericardial effusion, or wall-motion abnormalities. Association between major ECG and echo abnormalities was assessed by logistic regression as follows: 1) unadjusted model; 2) model 1 adjusted for age/sex; 3) model 2 plus risk factors (hypertension/diabetes); 4) model 3 plus history of cardiovascular disease (Chagas/rheumatic heart disease/ischemic heart disease/stroke/heart failure). P-values < 0.05 were considered significant.

RESULTS

A total 1,411 patients underwent echo; 1,149 (81%) had major ECG abnormalities. Median age was 67 (IQR 60 to 74) years, and 51.4% were male. Major ECG abnormalities were associated with a 2.4-fold chance of major heart disease on echo in bivariate analysis (OR = 2.42 [95% CI 1.76 to 3.39]), and remained significant after adjustments in models (p < 0.001) 2 (OR = 2.57 [95% CI 1.84 to 3.65]), model 3 (OR = 2.52 [95% CI 1.80 to3.58]), and model 4 (OR = 2.23 [95%CI 1.59 to 3.19]). Age, male sex, heart failure, and ischemic heart disease were also independent predictors of major heart disease on echo.

CONCLUSIONS

Tele-ECG abnormalities increased the likelihood of major heart disease on screening echo, even after adjustments for demographic and clinical variables.

Pericardial Diseases Mortality Trends in Brazil From 2000 to 2022

Background Pericardial diseases manifest in various clinical forms, including acute pericarditis, constrictive pericarditis, pericardial effusion, and cardiac tamponade, with acute pericarditis being the most prevalent. These conditions significantly contribute to mortality rates. Therefore, this article aimed to analyze mortality trends in the Brazilian population based on age and sex, shedding light on the impact of pericardial diseases on public health outcomes. Methods  This is a retrospective time-series analysis of pericardial disease mortality rates in Brazil (2000-2022). Data was obtained from the Department of Informatics of the Unified Health System (DATASUS), and the 10th edition of the International Classification of Diseases (ICD-10) codes: I30, I31, and I32 were included for analysis. We gathered population and demographic data categorized by age range and sex from the Brazilian Institute of Geography and Statistics (IBGE). Subsequently, we computed the age-standardized mortality rate per 100,000 individuals and assessed the annual percentage changes (APCs) and average annual percentage changes (AAPCs) using joinpoint regression, along with their corresponding 95% confidence intervals (CIs). Results  In terms of mortality trends based on sex, overall mortality rates remained stable for males and combined sexes over the study period. However, there was a notable increase in mortality rates among females (AAPC=1.18), particularly between 2020 and 2022, with a significant APC of 27.55. Analyzing pericardial diseases across different age groups (20 to 80 years and above), it wasobserved that mortality rates significantly increased in the 70-79 and 80 years and above age groups throughout the study period (AAPC=1.0339 and AAPC=3.4587, respectively). These two age groups experienced the highest significant rise in mortality between 2020 and 2022. Other age groups did not exhibit a significant change in AAPC. Conclusions  This comprehensive analysis spanning two decades (2000-2022), examined the mortality trends of pericardial diseases in Brazil and revealed relative stability overall. Males exhibited an overall higher mortality number due to pericardial diseases; however, females showed the most significant increase in mortality trend throughout the whole period. In the first segment (2000-2015), mortality rose across all cohorts, which was attributed to substandard healthcare facilities and infectious diseases like tuberculosis. The second segment (2016-2020) saw a decline in mortality, likely due to improved healthcare, particularly the increased availability of echocardiograms. However, the third segment (2020-2022) witnessed a sharp rise in mortality, coinciding with the COVID-19 pandemic, with post-COVID-19 symptoms, particularly pericarditis. Pericarditis-related death rates declined compared to pericardial effusion, and mortality rates correlated directly with age, with older cohorts experiencing higher mortality due to increased comorbidities, and decline in health and immunocompetency.

Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients-The SaMi-Trop cohort

Background

Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively common and its treatment using low-cost drugs can improve symptoms and reduce mortality. Recently, an artificial intelligence (AI)-enabled ECG algorithm showed excellent accuracy to detect LVSD in a general population, but its accuracy in ChD has not been tested.

Objective

To analyze the ability of AI to recognize LVSD in patients with ChD, defined as a left ventricular ejection fraction determined by the Echocardiogram ≤ 40%.

Methodology/principal findings

This is a cross-sectional study of ECG obtained from a large cohort of patients with ChD named São Paulo-Minas Gerais Tropical Medicine Research Center (SaMi-Trop) Study. The digital ECGs of the participants were submitted to the analysis of the trained machine to detect LVSD. The diagnostic performance of the AI-enabled ECG to detect LVSD was tested using an echocardiogram as the gold standard to detect LVSD, defined as an ejection fraction <40%. The model was enriched with NT-proBNP plasma levels, male sex, and QRS ≥ 120ms.

Among the 1,304 participants of this study, 67% were women, median age of 60; there were 93 (7.1%) individuals with LVSD. Most patients had major ECG abnormalities (59.5%). The AI algorithm identified LVSD among ChD patients with an odds ratio of 63.3 (95% CI 32.3–128.9), a sensitivity of 73%, a specificity of 83%, an overall accuracy of 83%, and a negative predictive value of 97%; the AUC was 0.839. The model adjusted for the male sex and QRS ≥ 120ms improved the AUC to 0.859. The model adjusted for the male sex and elevated NT-proBNP had a higher accuracy of 0.89 and an AUC of 0.874.

Conclusion

The AI analysis of the ECG of Chagas disease patients can be transformed into a powerful tool for the recognition of LVSD.

Chagas disease (ChD) is caused by the protozoan parasite Trypanosoma cruzi and continues to be a health problem despite the control of its transmission. ChD is a heterogeneous condition with a wide variation in its clinical course and prognosis. The majority (60%–70%) of infected individuals remain asymptomatic throughout life. Although some develop only conduction defects and mild segmental wall motion abnormalities, others develop severe symptoms of heart failure (HF), thromboembolic phenomena, and life threatening ventricular arrhythmias. HF is one of major causes of the death of patients with ChD. There is some evidence on effective drugs against the parasite in the chronic form of the disease capable of preventing long-term adverse outcomes, but it is still limited. However low-cost medications are able to reduce mortality and improve the quality of life of patients with HF. Because of the lack of tertiary care facilities outside urban centers, an automatic diagnostic tool based on the ECG, which is a relatively simple exam without requiring human interpretation, would improve the capacity to recognize HF. Recently, digital signals of the electrocardiogram were recognized by Artificial Intelligence (AI) and associated with an excellent accuracy for HF in the general population. Our results demonstrate that AI-ECG could ensure a rapid recognition of HF in patients who require a referral to a cardiologist and the use of disease-modifying drugs. AI can be used as a powerful public heath tool, it can transform the lives of 6 million patients with ChD worldwide, and it may well have a formidable impact on patient management and prognosis.