Clinical Decision Instruments - Episode I (DERIVATION)

Summary

In this episode of Sketchy EVM, Anthony Croco explores the development of Clinical Decision Instruments (CDIs), focusing on the derivation phase. CDIs, such as the Ottawa Ankle Rule, are tools that assist in emergency medicine decision-making, helping to reduce unnecessary tests and minimize adverse effects. The derivation phase involves identifying key variables that predict outcomes, followed by statistical analysis to create a useful tool. The episode highlights the importance of validation before clinical use, illustrating the challenge with the Catch Rule, which was abandoned after failing validation. Future episodes will delve into the validation and impact analysis phases.

Takeaways

• 😀 Clinical Decision Instruments (CDIs) are tools used in emergency medicine to aid in diagnosis and prognosis by minimizing unnecessary tests and treatments.
• 😀 The term 'Clinical Decision Instruments' is preferred over 'rules' to avoid implying rigid guidelines that do not account for exceptions.
• 😀 CDIs can help save time, money, and reduce the risk of adverse effects by streamlining decision-making in clinical settings.
• 😀 The process of developing a CDI involves three phases: derivation, validation, and impact analysis.
• 😀 A derivation study is the first step in CDI development and involves identifying candidate variables that predict diagnostic or prognostic outcomes.
• 😀 The success of a CDI relies on selecting candidate variables with high inter-rater reliability, meaning findings are consistent across different clinicians.
• 😀 The complexity of a CDI must be carefully considered: simple instruments are easy to remember but may be less accurate, while complex instruments may be difficult to use.
• 😀 Statistical methods play a crucial role in selecting the most important variables from a list of candidate variables in the derivation study.
• 😀 Derivation studies often yield promising results, but these results must be validated in larger, diverse populations to ensure their generalizability and effectiveness.
• 😀 A real-world example is the CATCH rule for children with head injuries. Although the derivation study showed promise, subsequent validation studies revealed its limitations, leading to its abandonment.

Q & A

What are Clinical Decision Instruments (CDIs) and why are they used in emergency medicine?

CDIs are tools designed to assist healthcare professionals in making decisions related to diagnosis and treatment. In emergency medicine, they are used to streamline decision-making, reduce unnecessary tests and investigations, minimize risks to patients, and standardize practice to limit variation in care.

Why does the speaker prefer the term 'Clinical Decision Instrument' over 'Clinical Decision Rule'?

The speaker prefers the term 'Clinical Decision Instrument' because the word 'rule' suggests something that must be strictly followed, which can imply a lack of flexibility. Instruments, on the other hand, allow for a more nuanced approach, acknowledging that there may be exceptions to any given 'rule'.

What are the three main phases involved in the development of a Clinical Decision Instrument?

The three main phases are: 1) Derivation study, where the potential variables and structure of the instrument are identified. 2) Validation studies, which assess the accuracy and reliability of the instrument in real-world settings. 3) Impact analysis, which evaluates how the CDI affects clinical practice and patient outcomes.

What is the primary goal of a derivation study in CDI development?

The primary goal of a derivation study is to identify relevant candidate variables—factors like patient history, physical exam findings, or test results—that can predict the outcome being studied. It helps determine which variables should be included in the clinical decision instrument.

What is meant by 'inter-rater reliability' when selecting candidate variables for a CDI?

Inter-rater reliability refers to the consistency of results when different clinicians assess the same candidate variables. If two different clinicians obtain similar results when measuring a variable, that variable is considered reliable and can be included in the CDI.

What role do statistical methods play in a derivation study?

Statistical methods are used to analyze the data collected from the study and identify which candidate variables are most strongly associated with the outcome being studied. This helps determine which factors should be included in the final clinical decision instrument.

What is the potential issue with relying solely on derivation studies when implementing a CDI?

The issue is that results from a derivation study might occur by chance or may not be replicable in different populations or settings. This is why a CDI must undergo validation studies to ensure its accuracy and applicability before it can be widely used in clinical practice.

Can you give an example of a CDI that was initially successful in a derivation study but failed in validation?

The 'Catch rule' for identifying children with closed head injuries who need a CT scan is a prime example. The derivation study showed promising results, but subsequent validation studies revealed that it was not as effective as initially thought, and the rule was eventually abandoned.

What are the challenges in balancing the simplicity and complexity of a CDI?

A simple CDI is easy to remember and implement but may not be as accurate or comprehensive as a complex one. On the other hand, a more complex CDI may be more accurate but could be difficult for clinicians to remember or use effectively in fast-paced environments like emergency departments.

What does the speaker mean when they say, 'draw your own conclusions' in relation to CDIs?

'Draw your own conclusions' refers to the idea that clinicians should critically evaluate the evidence behind a CDI and make informed decisions based on the data and their own clinical judgment, rather than blindly following a tool or rule.