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.

Approaches to Selecting "Time Zero" in External Control Arms with Multiple Potential Entry Points: A Simulation Study of 8 Approaches.

BACKGROUND: When including data from an external control arm to estimate comparative effectiveness, there is a methodological choice of when to set "time zero," the point at which a patient would be eligible/enrolled in a contemporary study. Where patients receive multiple lines of eligible therapy and thus alternative points could be selected, this issue is complex. METHODS: A simulation study was conducted in which patients received multiple prior lines of therapy before entering either cohort. The results from the control and intervention data sets are compared using 8 methods for selecting time zero. The base-case comparison was set up to be biased against the intervention (which is generally received later), with methods compared in their ability to estimate the true intervention effectiveness. We further investigate the impact of key study attributes (such as sample size) and degree of overlap in time-varying covariates (such as prior lines of therapy) on study results. RESULTS: Of the 8 methods, 5 (all lines, random line, systematically selecting groups based on mean absolute error, root mean square error, or propensity scores) showed good performance in accounting for differences between the line at which patients were included. The first eligible line can be statistically inefficient in some situations. All lines (with censoring) cannot be used for survival outcomes. The last eligible line cannot be recommended. CONCLUSIONS: Multiple methods are available for selecting the most appropriate time zero from an external control arm. Based on the simulation, we demonstrate that some methods frequently perform poorly, with several viable methods remaining. In selecting between the viable methods, analysts should consider the context of their analysis and justify the approach selected. HIGHLIGHTS: There are multiple methods available from which an analyst may select "time zero" in an external control cohort.This simulation study demonstrates that some methods perform poorly but most are viable options, depending on context and the degree of overlap in time zero across cohorts.Careful thought and clear justification should be used when selecting the strategy for a study.

Quantifying how small variations in design elements affect risk in an incident cohort study in claims.

BACKGROUND: Epidemiological study reporting is improving but is not transparent enough for easy evaluation or replication. One barrier is insufficient details about design elements in published studies. METHODS: Using a previously conducted drug safety evaluation in claims as a test case, we investigated the impact of small changes in five key design elements on risk estimation. These elements are index day of incident exposure's determination of look-back or follow-up periods, exposure duration algorithms, heparin exposure exclusion, propensity score model variables, and Cox proportional hazard model stratification. We covaried these elements using a fractional factorial design, resulting in 24 risk estimates for one outcome. We repeated eight of these combinations for two additional outcomes. We measured design effects on cohort sizes, follow-up time, and risk estimates. RESULTS: Small changes in specifications of index day and exposure algorithm affected the risk estimation process the most. They affected cohort size on average by 8 to 10%, follow-up time by up to 31%, and magnitude of log hazard ratios by up to 0.22. Other elements affected cohort before matching or risk estimate's precision but not its magnitude. Any change in design substantially altered the matched control-group subjects in 1:1 matching. CONCLUSIONS: Exposure-related design elements require attention from investigators initiating, evaluating, or wishing to replicate a study or from analysts standardizing definitions. The methods we developed, using factorial design and mapping design effect on causal estimation process, are applicable to planning of sensitivity analyses in similar studies.

Macrolides, Digoxin Toxicity and the Risk of Sudden Death: A Population-Based Study.

INTRODUCTION: Digoxin is commonly prescribed to elderly patients with heart failure and atrial fibrillation, and macrolide antibiotics markedly increase the risk of digoxin toxicity. OBJECTIVE: The aim was to determine whether, in older patients receiving digoxin, macrolide antibiotics are associated with sudden death. METHODS: We used a population-based, nested, case-control design from January 1, 1994 to December 31, 2012 in a cohort of Ontario residents aged 66 years or older prescribed digoxin. The primary outcome was the risk of sudden death within 14 days of exposure to one of three antibiotics (erythromycin, clarithromycin, or azithromycin), relative to cefuroxime. RESULTS: Among 39,072 Ontarians who died suddenly while receiving digoxin, 586 died within 14 days of receiving a study antibiotic. Relative to cefuroxime, we found no statistically significant increase in the risk of sudden death following treatment with erythromycin [adjusted odds ratio (aOR) 0.98; 95% confidence interval (CI) 0.65-1.48], clarithromycin (aOR 1.25; 95% CI 0.94-1.65), or azithromycin (aOR 1.07; 95% CI 0.75-1.53). CONCLUSION: This finding reinforces the cardiovascular safety of macrolide antibiotics in a high-risk population.