Every healthcare professional involved in the care of patients with chronic obstructive pulmonary disease understands the challenge. COPD is one of the world’s leading causes of illness and death, affecting more than 300 million people and placing an enormous burden on healthcare systems. Although treatment options have improved significantly over recent decades, clinicians continue to face a fundamental problem. They mostly rely on diagnostic measurements that do not fully capture the disease’s complexity. For many years, a simple form of spirometry has been the cornerstone of COPD diagnosis and management. The ratio of forced expiratory volume in one second and forced vital capacity is relatively easy and inexpensive to measure. It is therefore widely available and is supported by decades of clinical experience. This ratio has become so firmly established as a measure of the disease that it is virtually impossible to imagine COPD assessment without it.

Imagine sitting across the kitchen table from someone you have known for decades. They begin telling you about their day, but something has changed. Their words are slower than they once were. Some sounds are blurred together, others seem to disappear entirely. You know the person well. You know the stories they are likely to tell and the expressions they often use. Yet despite all of that familiarity, you find yourself asking them to repeat themselves again and again.

When a new medical test reaches a clinic or laboratory, most people focus on the technology itself. They think about the machine, the test kit, or the scientific breakthrough that makes diagnosis possible. Yet behind every successful diagnostic test lies something far less visible but equally important: the biological specimens used to develop and validate it. Without the right samples, even the most promising diagnostic innovation can fail. A test may appear accurate in the laboratory but perform poorly in real-world settings. It may work well for one patient population but struggle in another. It may detect a disease in some regions of the world while missing important variations elsewhere.