Secondary prevention of cardiovascular diseases in India: Findings from registries and large cohorts

Evaluating LDL-C control in Indian acute coronary syndrome (ACS) patients- A retrospective real-world study LDL-C control in ACS

Background

Low-density lipoprotein-cholesterol (LDL-C) is an independent risk factor for atherosclerotic cardiovascular disease (ASCVD) progression. Although lipid lowering therapies remain the cornerstone of secondary ACSVD prevention, there exists residual dyslipidemia. The current study aimed to evaluate the real-world experience related to the treatment patterns and LDL-C control in Indian Acute Coronary Syndrome (ACS) patients.

Methods

This was a real-world, descriptive, retrospective, observational, and multicentric study conducted across India. The data was collected for 1 year following the ACS event. The change in the levels of LDL-C from the baseline to the follow-up visits and the control of LDL-C, the change in lipid profile, lipoprotein levels, treatment patterns for lipid-lowering, and tolerability of existing treatments were evaluated.

Results

Overall, 575 patients were included from 11 centers across India. The mean age of the patients was 52.92 years, with male predominance (76.35%). Although there was a significant reduction in the mean levels of LDL-C from the baseline [(122.64 ± 42.01 mg/dl to 74.41 ± 26.45 mg/dl (p < 0.001)], it was observed that despite high-intensity statin therapy, only 20.87% patients managed to achieve target LDL-C of <55 mg/dL and 55.65% were unable to reach LDL-C levels of <70 mg/dl one year after the event. Six patients reported adverse events without treatment discontinuation.

Conclusion

The majority of the patients received high-intensity statins and did not attain target LDL-C levels, suggesting LDL-C control after an ACS event requires management with novel therapies having better efficacy as recommended by international and national guidelines.

A Delphi Study to Prioritize Evidence-Based Strategies for Cardiovascular Disease Care in India

Providing quality cardiovascular disease (CVD) care in low resource setting requires understanding of priority and effective interventions. This study aimed to identify and prioritize evidence-based quality improvement strategies for CVD care in India using a modified two-round Delphi process in which, we asked 46 experts (clinicians, researchers, program implementers and policy makers) to rate 25 proven CVD care strategies grouped into: (1) patient support, (2) information communication technology (ICT) for health, (3) group problem solving, (4) training, and (5) multicomponent strategy on a scale of 1 (highest/best)-5 (lowest/worst) on priority, relative advantage, and feasibility. Subsequently, we convened an expert consensus panel of 32 members to deliberate and achieve consensus regarding the prioritized set of strategies for CVD care. The Delphi study found that group problem solving strategies achieved the best score for priority (1.80) but fared poorly on feasibility (2.88). Compared to others, multicomponent strategies were rated favorably across all domains (priority = 1.84, relative advantage = 1.94, and feasibility = 2.40). The ICT for health strategies achieved the worst scores for priority = 2.01, relative advantage = 2.31, and feasibility = 2.85. Training and patient support strategies scored moderately across all domains. The expert panel narrowed the selection of a multicomponent strategy consisting of (1) electronic health records with clinical decision-support system, (2) non-physician health worker facilitated care, (3) patient education materials, (4) text-message based reminders for healthy lifestyle, and (5) audit and feedback report for providers. Future research will evaluate the real-world feasibility and effectiveness of the multicomponent strategy in patients with CVD in a low- and middle-income country setting.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43477-023-00087-2.

Automatic Classification of 15 Leads ECG Signal of Myocardial Infarction Using One Dimension Convolutional Neural Network

Impaired blood flow caused by coronary artery occlusion due to thrombus can cause damage to the heart muscle which is often called Myocardial Infarction (MI). To avoid the complexity of MI diseases such as heart failure or arrhythmias that can cause death, it is necessary to diagnose and detect them early. An electrocardiogram (ECG) signal is a diagnostic medium that can be used to detect acute MI. Diagnostics with the help of data science is very useful in detecting MI in ECG signals. The purpose of study is to propose an automatic classification framework for Myocardial Infarction (MI) with 15 lead ECG signals consisting of 12 standard leads and 3 Frank leads. This research contributes to the improvement of classification performance for 10 MI classes and normal classes. The PTB dataset trained with the proposed 1D-CNN architecture was able to produce average accuracy, sensitivity, specificity, precision and F1-score of 99.98%, 99.91%, 99.99%, 99.91, and 99.91%. From the evaluation results, it can be concluded that the proposed 1D-CNN architecture is able to provide excellent performance in detecting MI attacks.