Ep 7 - Delving into the Number Needed To Treat, RRR and ARR.

Understanding Relative Risk, Absolute Risk, and Number Needed to Treat: A Guide for Emergency Medicine Welcome back to the St. Emlyn’s podcast. I’m Iain Beardsell and joining me is Simon Carley. Today, we’re delving into the complex yet critical concepts of relative risk, absolute risk, and the number needed to treat (NNT) in the context of emergency medicine. These metrics are essential for understanding the effectiveness of treatments and making informed decisions in clinical practice. The Importance of Understanding Risk Metrics In emergency medicine, it’s vital to comprehend how different treatments impact patient outcomes. This understanding not only helps in communicating with patients but also aids in making better clinical decisions. Two key terms frequently encountered are relative risk reduction and absolute risk reduction. Relative Risk Reduction vs. Absolute Risk Reduction Imagine we are conducting a trial on a new drug for myocardial infarction (AMI) patients. Typically, 10% of AMI patients die within a month. If our new treatment claims a 50% relative risk reduction, it sounds impressive. However, understanding what this actually means is crucial. A 50% relative risk reduction translates to reducing the death rate from 10% to 5%. While this is significant, it's essential to recognize the difference between relative and absolute risk reduction. Calculating the Number Needed to Treat (NNT) The NNT is a valuable metric for understanding how many patients need to receive a particular treatment to prevent one additional adverse outcome. It’s derived from the absolute risk reduction. For instance, if a treatment reduces mortality from 10% to 5%, the absolute risk reduction is 5%. To calculate the NNT, divide 100 by the absolute risk reduction percentage. In this case, 100 divided by 5 equals an NNT of 20. This means we need to treat 20 patients to save one life. Examples of NNT in Practice Let’s consider some real-world examples. Tranexamic acid in trauma has an NNT of around 50, meaning we need to treat 50 patients to save one life. For aspirin in treating myocardial infarction, the NNT is also around 50. These figures highlight the effectiveness of these treatments in clinical practice. Balancing Benefits and Harms Understanding NNT is crucial, but it’s equally important to consider the number needed to harm (NNH). This metric indicates how many patients need to receive a treatment before one adverse effect occurs. For example, in trials involving starch solutions for sepsis, the NNH was found to be around 10-16. This means for every 10 to 16 patients treated, one additional death occurred. Balancing the benefits and harms is essential for making informed clinical decisions. Example: Stroke Thrombolysis In stroke thrombolysis, the NNT is around 8, meaning one in eight patients benefits from the treatment. However, the NNH is about 16, indicating one in 16 patients might experience a harmful outcome, such as intracerebral hemorrhage. Communicating these risks and benefits to patients is crucial for informed consent and shared decision-making. The Role of Natural Frequencies Using natural frequencies, such as “one in 100 people” or “one in 50 people,” helps in explaining risks and benefits in a more understandable way. For instance, saying “one in 100 people in your neighborhood” or “one person in a packed football stadium” can make the statistics more relatable. Misdiagnosis and Its Impact A key takeaway is that not every missed diagnosis leads to adverse outcomes. Often, treatments may have minimal benefit, and in some cases, they could cause harm. For example, the rush to administer clopidogrel in acute myocardial infarction might not always be necessary, given its relatively high NNT. Applying These Concepts in Clinical Practice Understanding and applying these concepts can change how we approach patient care. It allows us to prioritize interventions that provide th