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A UK based Emergency Medicine podcast for anyone who works in emergency care. The St Emlyn ’s team are all passionate educators and clinicians who strive to bring you the best evidence based education. Our four pillars of learning are evidence-based medicine, clinical excellence, personal development and the philosophical overview of emergency care. We have a strong academic faculty and reputation for high quality education presented through multimedia platforms and articles. St Emlyn’s is a name given to a fictionalised emergency care system. This online clinical space is designed to allow clinical care to be discussed without compromising the safety or confidentiality of patients or clinicians.
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Wednesday Jan 30, 2019
Wednesday Jan 30, 2019
Optimizing Anti-Platelet Therapy in Acute Coronary Syndromes: Insights from St. Emlyn’s
Welcome back to the St. Emlyn’s blog! Today, we're delving into optimizing anti-platelet therapy for patients with suspected acute coronary syndromes (ACS), inspired by Dr. Charlie Raynard's research during his academic foundation program. His work provides valuable insights for managing ACS in emergency departments. Let's explore the study and its implications.
The Foundation of the Research
Dr. Charlie Raynard, working in Manchester, is pursuing a PhD focusing on innovations to manage patients with suspected ACS in emergency departments. His project, culminating in the paper titled "Optimizing Anti-Platelet Utilisation in Acute Care Settings," was accepted by the Emergency Medicine Journal (EMJ) and offers crucial findings that can impact clinical practice. You can find the full paper on our website. This research is particularly relevant for emergency medicine practitioners frequently encountering ACS patients.
Managing Uncertainty in Emergency Medicine
Emergency medicine often involves making decisions under uncertainty, treating conditions based on probable diagnoses rather than confirmed ones. This approach requires weighing the risks and benefits of treatment without complete information, crucial for improving patient outcomes.
Treating Sepsis Without Confirmation
When a patient presents with symptoms suggesting sepsis, we start treatment before confirming the diagnosis through blood cultures. The potential consequences of untreated sepsis—such as death—are so severe that we initiate antibiotics to mitigate the risk.
Deep Vein Thrombosis and Pulmonary Embolism
Similarly, in suspected deep vein thrombosis (DVT) or pulmonary embolism (PE), we may start treatment with low molecular weight heparin based on a high Wells score and a positive D-dimer test before imaging confirms the diagnosis. These examples highlight the necessity of making informed decisions despite the lack of definitive evidence.
Internal Modeling of Uncertainty
In these scenarios, we internally model the condition's risk against the treatment's benefits and risks. This process, though not always explicit, guides decision-making. However, this subjective approach can sometimes lead to risk-averse decisions where treatment may be initiated even when the objective benefits are unclear.
Objective Assessment of Risks and Benefits
Dr. Raynard's research aims to address this issue by objectively assessing the risks and benefits of different anti-platelet therapies for patients with suspected ACS. The goal is to develop a model that helps clinicians make more informed decisions under uncertainty, enhancing care quality and patient outcomes.
The Systematic Review Process
The first step was to collect data to inform the model. Dr. Raynard and his team conducted systematic reviews to evaluate the benefits of clopidogrel versus ticagrelor, both in combination with aspirin, in treating ACS. These reviews provided a foundation of evidence for the decision-making model.
Understanding Patient Utility
The team also sought to understand patient preferences and outcomes, a concept known as utility. They reviewed research that quantified patient utility for various ACS-related outcomes. This patient-centered measure is crucial for developing a model that accurately reflects real-world clinical decisions, aligning with patient values and needs.
Building the Decision Tree Model
The next phase involved building a decision tree model, a simple yet effective tool for modeling different clinical outcomes. The model included various branches representing possible outcomes, such as whether a patient had ACS, received anti-platelet therapy, experienced bleeding, or had a stroke.
Probabilities and Patient Preferences
Using data from the systematic reviews, the team calculated the probabilities of each outcome. They combined these probabilities with patient utility measures to assess the overall benefit of each treatment strategy. This approach allowed them to develop a measure of net expected utility for different ACS risk levels.
The decision tree model revealed critical insights into ACS management. One significant finding was the inflection point at which ticagrelor's benefits outweigh its risks compared to aspirin alone, identified at an 8% ACS probability.
Key Findings of the Study
Ticagrelor vs. Clopidogrel
The model showed that clopidogrel had no advantage over ticagrelor or aspirin in any scenario. Once the ACS probability exceeded 8%, ticagrelor was consistently the better option. This finding challenges current practices and suggests clopidogrel may not be necessary in many cases. For patients with a higher ACS probability, ticagrelor's benefits, particularly in preventing adverse cardiovascular events, outweigh the risks, mainly bleeding.
Practical Implications for Emergency Medicine
For clinicians, these findings have immediate implications. In a first-world healthcare system where ticagrelor is available, it should be the preferred choice for higher-risk ACS patients. According to the model, clopidogrel has limited utility.
Cost Considerations
In settings where ticagrelor's cost is a concern, clopidogrel might still be used. Future research should explore ticagrelor's cost-effectiveness to provide a comprehensive understanding of its benefits relative to its cost. Balancing clinical effectiveness with economic considerations ensures the best possible care is accessible and sustainable.
Dynamic Risk Prediction
Another exciting aspect of this research is the potential for dynamic risk prediction. The initial model used a single troponin level to calculate the ACS probability upon the patient's emergency department arrival. However, as more data becomes available—such as a second troponin test or imaging results—the model can be updated to provide more accurate risk predictions.
Implementing TMAX
The TMAX decision aid, which calculates the ACS probability based on patient data, is valuable here. By incorporating dynamic updates, clinicians can refine their treatment decisions as new information emerges, ensuring patients receive the most appropriate care throughout their emergency department journey. This dynamic approach ensures treatment decisions remain aligned with the most current and comprehensive data.
Future Directions
This research opens several avenues for future investigation. One key area is developing models that can dynamically update risk predictions and treatment recommendations as new data becomes available. This approach could revolutionize how we manage patients with suspected ACS, making decision-making processes more accurate and patient-centered.
Ongoing Research
Continued research is essential to validate these findings and explore their application in different clinical settings. By refining these models and incorporating them into clinical practice, we can improve outcomes for patients with ACS and other conditions requiring prompt and accurate decision-making. Integrating real-time data and patient-specific information will enhance the precision and effectiveness of clinical interventions.
A Deeper Dive into the Decision Tree Model
How Decision Trees Work
A decision tree model is a graphical representation of possible solutions to a decision based on certain conditions. It helps in making decisions by laying out various possible outcomes, including the chance of occurrence and the associated risks and benefits. In this context, the decision tree model built by Dr. Raynard’s team mapped out different scenarios for patients with suspected ACS.
Branches of the Decision Tree
The branches represent different paths a patient’s treatment could take. For instance, one branch might represent a patient who has ACS and receives ticagrelor and aspirin, while another might represent a patient who has ACS but only receives aspirin. Each branch has further sub-branches to account for potential outcomes like bleeding, stroke, or recovery without complications.
Calculating Probabilities and Utilities
Each path down these branches has an associated probability and utility value. The probability represents the likelihood of that specific outcome occurring, while the utility value reflects the patient's preference for that outcome. For example, avoiding a stroke would have a high utility value because it is a highly preferred outcome. By combining these probabilities and utilities, the model can calculate the net expected utility for each treatment strategy.
Interpreting the Results
The decision tree model's results provided a clear picture of which treatment strategies offered the most significant benefit. The model showed that ticagrelor, in combination with aspirin, offered a higher net expected utility compared to clopidogrel and aspirin alone, especially for patients with a higher probability of ACS. This insight is crucial for making evidence-based treatment decisions in the emergency department.
Challenges and Considerations
Subjectivity in Clinical Decision-Making
One main challenge in emergency medicine is the subjectivity involved in clinical decision-making. Physicians often have to make quick decisions based on incomplete information, leading to variability in treatment approaches. Providing an objective framework through a decision tree model helps standardize care and reduce variability.
Balancing Risks and Benefits
Another critical consideration is balancing the risks and benefits of treatment. While ticagrelor offers significant benefits in preventing adverse cardiovascular events, it also carries a risk of bleeding. The decision tree model helps clinicians weigh these risks and benefits objectively, ensuring treatment decisions are based on a comprehensive assessment of patient-specific factors.
Implementing the Findings in Clinical Practice
Educating Clinicians
To implement these findings effectively, it’s essential to educate clinicians about the benefits of using decision tree models and dynamic risk prediction tools like TMAX. Training programs and workshops can help clinicians understand how to incorporate these tools into their daily practice, enhancing their decision-making processes.
Integrating Decision Support Tools
Integrating decision support tools into electronic health records (EHR) can also facilitate using these models in clinical practice. By embedding these tools within EHR systems, clinicians can access real-time risk assessments and treatment recommendations at the point of care, making it easier to apply evidence-based guidelines.
Monitoring and Feedback
Ongoing monitoring and feedback are crucial for successful implementation. By tracking patient outcomes and gathering feedback from clinicians, healthcare institutions can refine and improve the use of decision tree models and risk prediction tools, ensuring they remain relevant and effective.
Conclusion
Dr. Charlie Raynard's research provides valuable insights into optimizing antiplatelet therapy for patients with suspected acute coronary syndromes. By objectively assessing the risks and benefits of different treatment strategies, this research offers a roadmap for more informed decision-making in emergency medicine.
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