What Is Sample Metadata in Clinical Trials? All Your Questions Answered.

Whether you enjoy cooking or not, we can all agree that you can’t make a recipe without an ingredients list. This list is crucial, because it outlines exactly what you need to create a dish, including the specific items and their quantities. Without it, you wouldn’t know what components are required or in what proportions, making it difficult to accurately replicate the recipe. 

Ingredients lists also offer us insights into the flavor profiles, textures, smells, and physical appearances of our foods. Have you ever eaten something at a restaurant that was so good that you wished you could replicate the recipe at home? Conversely, have you ever tried a new dish, knowing there was something about the dish you didn’t like, but you couldn’t quite put your finger on what it was because you didn’t know what all the ingredients were?

This analogy perfectly illustrates the role of metadata in the world of clinical research. Sample metadata is essentially an ingredients list for a biospecimen, providing essential information about the variables that directly influence the lab data that we derive from patient samples. Examples include collection dates and times, patient demographics, pre-analytical variables, and so much more.

So why is this metadata important to clinical research? What are some of the common challenges with sharing this data from one stakeholder to another? And why do these challenges contribute to the need for reconciliation? To answer these questions, let’s continue exploring sample metadata through the lens of making and sharing a recipe.

Why is sample metadata important?

If patient samples are the primary source of the data that demonstrates the safety and efficacy of a treatment, then we also need to know all of the variables that influence the makeup of that sample. The metadata for patient samples details crucial elements, such as the patient’s demographic information, the type of sample collected, the collection date, the processing methods, and other relevant details. This data is essential for both sponsors and labs, as it is necessary for accurately tracking, comparing, and analyzing samples. 

Trying to run a trial without this metadata would be like trying to cook a complex dish without knowing the specific ingredients or their quantities, leading to confusion and potentially flawed results. Therefore, the metadata acts as a guide, helping researchers manage and interpret the data from patient samples correctly and efficiently, while ensuring the integrity and replicability of study outcomes.

What are the challenges with receiving sample metadata from sites?

Let’s imagine for a moment that you want to cook a recipe that you recently tried at a friend’s house. You call up your friend, and they send you a photo of a tattered, smudged notecard with scribbled handwriting. Some of the ingredients are missing quantities, and you notice that at least one of the ingredients you know is in the recipe isn’t even listed on the recipe card at all. How can you be expected to replicate the recipe based on this information alone? 

As ridiculous as this example may sound, it’s actually incredibly common in the world of clinical trials when we talk about sample metadata. Paper requisition forms, which are often filled out by hand, must capture essential metadata. However, the manual nature of these forms often leads to incomplete or illegible entries. These challenges can lead to lab queries; delays in processing samples or reporting lab results; errors in data analysis; and potential issues with regulatory compliance.

Why do we need to reconcile sample metadata?

Now imagine you receive multiple versions of a recipe for the same dish from different sources. Each version has slight variations: one might list an ingredient that another omits, or the quantities might differ slightly. To ensure the dish turns out correctly, you must reconcile these different versions, verifying and combining the information to create a consistent, accurate recipe that will produce the flavor profile, texture, smell, and physical appearance you expect.

In clinical trials, sample metadata reconciliation often must occur across various sites and labs — simply because each source may have slightly different information about the same sample. For example, a research site might record the sample collection date differently than the lab, or sample processing history for a tissue sample might get lost in translation. Like a chef who needs to reconcile multiple versions of a recipe, sponsors need to reconcile these disparate pieces of metadata to ensure they have a complete and accurate picture of the samples that are driving their study outcomes.

Reconciliation of sample metadata involves cross-checking and verifying the information from all sources to resolve any discrepancies and fill in any gaps. This process is crucial because accurate metadata is essential for correctly identifying and analyzing patient samples. Without reconciliation, errors or inconsistencies in the metadata could lead to incorrect data interpretation or data exclusion, affecting the reliability of the trial results. 

What are the consequences of not having access to clean sample metadata?

If you don’t have all of the correct ingredients for a recipe, either you can’t cook the dish at all or your dish won’t turn out right. The same can be said about patient samples in clinical trials. If you don’t know the correct collection date and time for a sample, how can you demonstrate that the sample was tested within the stability window? If you don’t have patient demographic data, how can you come to conclusions about a treatment’s effectiveness or safety based on the patient’s eligibility for the trial or based on factors like sex, age, and ethnicity? 

Ultimately, these issues can hinder translational medicine and biospecimen operations teams from taking action on samples in their trials or making study decisions that rely on clean, complete sample metadata. Moreover, gaps or errors in sample metadata can complicate sample tracking and analysis, leading to delays and increased costs as researchers spend additional time verifying and correcting information. In some cases, metadata issues could even result in data exclusion, sample destruction, or sample recollection events, further delaying the trial and increasing expenses. 

Just as a flawed recipe can ruin a dish, flawed metadata can undermine the reliability and validity of a clinical trial, potentially jeopardizing patient safety and wasting valuable resources. Therefore, ensuring clean, complete metadata is essential for the success and credibility of clinical trials.

Click here to read our comprehensive guide on sample metadata management and data reconciliation

Slope has perfected the recipe to clean, accessible sample metadata

Slope understands the importance of sample metadata to your study outcomes. That’s why our biospecimen lifecycle software, Biospecimen360™, leverages guided workflows for sites to support the automated capture of critical sample metadata during sample collection, processing, storage, and shipping. 

Biospecimen360™ is also vendor-agnostic. It facilitates the use of e-requisition forms, in tandem with integrations with EDC, LIMS, IRT/RTSM, and more, ensuring that requisitions are filled out completely and more accurately, while also transmitting this data to study stakeholders in real-time. This approach streamlines data flow among all study stakeholders, while maximizing the accuracy of your sample metadata and reducing the need for reconciliation. 

To learn more, click here to speak with one of our experts.