Time to intervention during cardiac interventions. Are we forgetting a confounder?

Area of Science:

• Cardiothoracic Surgery
• Biostatistics
• Public Health Policy

Background:

• Modern cardiothoracic surgery increasingly uses new techniques and technologies.
• Statistical methods in publications must be rigorously executed to prevent biased conclusions, especially in meta-analyses.
• Public health policies rely on unbiased data for accurate inference and optimal patient safety.

Discussion:

• Hidden mediators, such as patient waiting times, can significantly bias study conclusions if not adequately addressed.
• Careful analysis of these mediators is essential when comparing the effectiveness of surgical procedures and medical devices.
• Identifying and accounting for mediators like waiting time is critical for designing effective interventions for future randomized controlled trials (RCTs).

Key Insights:

• Randomized controlled trials (RCTs) for devices and procedures must conduct and report intention-to-treat (ITT) analysis.
• ITT analysis captures all events from randomization, effectively controlling for differential waiting times.
• Observational registries and databases should define 'time zero' as the referral time, not hospital admission, to include pre-treatment waiting periods.

Outlook:

• Accurate statistical analysis in cardiothoracic surgery research is paramount for evidence-based decision-making.
• Future research should prioritize the identification and control of hidden mediators to enhance the reliability of findings.
• Improved statistical practices will lead to more effective interventions, better patient outcomes, and enhanced patient safety.