The St.Emlyn’s Podcast

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 the most significant benefit while minimizing potential harm. It also highlights the importance of taking time to ensure the right diagnosis and treatment, rather than rushing into potentially harmful decisions.

The Number Needed to Educate (NNE)

A fun and thought-provoking concept introduced in our discussion is the Number Needed to Educate (NNE). How many blogs or articles do you need to read before it changes your clinical practice? This metric emphasizes the importance of continuous learning and staying updated with the latest evidence-based practices.

Conclusion

In emergency medicine, understanding relative risk, absolute risk, and NNT is vital for making informed treatment decisions. These metrics help in balancing the benefits and harms of treatments, leading to better patient outcomes. By effectively communicating these risks and benefits to patients, we can ensure shared decision-making and improve overall patient care.

 

Read more at St Emlyns and on the accompanying blogpost

 

What is a Diagnosis?

A diagnosis is essentially a label that we put on a patient to indicate what they have, which then guides our treatment decisions. In the ED, our primary focus is on identifying life-threatening conditions. This approach often involves working backwards by first ruling out serious conditions before considering what a patient might actually have.

Initial Diagnostic Approach

As emergency physicians, our initial approach is to use tests with high sensitivity. These tests are designed to pick up anyone who might have the disease. Once we rule out the serious conditions, we look at tests with high specificity to confirm the diagnosis, as treatments often carry risks. For example, therapies such as thrombolysis come with significant risks, so we need to be fairly certain before proceeding, unlike less consequential treatments like wrist splints.

Understanding Probabilities in Diagnoses

When we say a patient has a diagnosis, we’re essentially saying it’s likely enough to treat. Conversely, when we say a patient doesn’t have a diagnosis, we mean it’s unlikely enough to withhold treatment. This probabilistic approach is vital in the ED and can be surprising to many people.

Case Study: Cardiac Chest Pain

Let’s apply this to a patient with cardiac-sounding chest pain. Our goal is to either rule out or confirm the disease and start appropriate treatment. We start with specific tests to rule in a diagnosis, such as an ECG. A positive ECG with significant ST segment changes indicates a high likelihood of disease, warranting immediate treatment. This approach quickly sorts out high-risk patients.

For patients with normal or near-normal ECGs but still concerning symptoms, we need sensitive tests to ensure we don't miss anyone with myocardial disease. About 10% of these patients might have underlying issues, so we need to ensure our tests are sensitive enough to catch these cases.

Using Prevalence and Pre-test Probability

To decide if a patient has the disease, we must consider the prevalence or pre-test probability in our population. For example, in patients with normal ECGs and no alarming history, the pre-test probability might be around 10%. This isn’t low enough to rule out the disease but also not high enough to justify treatment without further testing.

Diagnostic Processes in the ED

We use a step-by-step diagnostic process. Starting with the most specific tests to rule in a diagnosis, we then use sensitive tests like high-sensitivity troponin to rule out diseases. High-sensitivity troponin tests are great for ruling out diseases due to their sensitivity. If the test is negative, we can be confident the patient doesn’t have myocardial damage. If the test is positive but not dramatically high, we may need additional tests to confirm the diagnosis.

Each diagnostic step adjusts our patient’s probability of having the disease. Our goal is to reach a probability low enough to safely rule out the disease or high enough to justify treatment. This process is continuous, and we apply it to every patient, whether they have chest pain or another symptom like a headache.

Understanding Likelihood Ratios

We often use likelihood ratios to interpret diagnostic tests. A positive likelihood ratio increases the probability of the disease, while a negative likelihood ratio decreases it. For example, a high-sensitivity troponin test is excellent at ruling out myocardial infarction because of its high sensitivity, though it’s not as good at ruling in due to lower specificity.

Optimising Diagnostic Tests

Diagnostic tests like troponin can be optimized by adjusting the threshold levels. For instance, a higher threshold might improve specificity and thus be better at ruling in the disease, while a lower threshold improves sensitivity, making it better at ruling out the disease. This principle applies to various tests, including white cell counts and amylase levels.

Continuous Assessment and Reassessment

In the ED, we continuously assess and reassess patients. Each diagnostic step, whether it’s asking a question about symptoms or ordering a lab test, adjusts our understanding of the patient’s condition. This iterative process helps us make informed decisions about treatment and ensures that we don’t miss critical diagnoses.

Applying the Approach to Different Symptoms

This diagnostic approach isn’t limited to chest pain. Whether a patient presents with a headache, abdominal pain, or any other symptom, we apply the same principles of sensitivity, specificity, and likelihood ratios. Each question we ask and each test we perform helps refine our assessment and move closer to a definitive diagnosis.

Conclusion

Mastering diagnostic skills in the ED involves understanding and applying probabilities, using specific and sensitive tests effectively, and continuously reassessing the patient’s condition. By focusing on these principles, we can make more accurate diagnoses, provide appropriate treatments, and ultimately improve patient outcomes.

More listening about diagnosis

Podcast – Diagnosis in Emergency Medicine Part 1 – SpIN and SnOUT

Podcast – Diagnosis in Emergency Medicine Part 2 – Beyond a simple yes or no

Podcast – Diagnosis in Emergency Medicine Part 3 – The importance of prevalence