Why Better Spirometry Data Takes More Than Better Technology

A clinical trial is only as good as the quality of its poorest data. While advances in study design, technology and statistical analysis have transformed clinical research, the generation of high-quality endpoint data remains dependent on consistent execution at the point of testing. Few endpoints illustrate this more clearly than spirometry, where participant effort, technician expertise and procedural consistency all play a critical role in the reliability of the results.

From bell spirometers to AI: what hasn't changed

Since John Hutchinson first developed the spirometer and coined the term vital capacity more than 180 years ago, spirometry has evolved into one of the most widely used and trusted tools in respiratory medicine. Modern spirometers bear little resemblance to the original bell spirometers, benefiting from major technological advances including automated quality checks, digital connectivity and artificial intelligence-assisted interpretation. Yet despite these advances, one fundamental aspect of spirometry remains unchanged: the participant must still perform the test with maximal effort.

This reliance on human performance introduces a significant source of variability. Achieving optimal spirometry requires participants to understand and correctly execute complex breathing manoeuvres, often repeatedly, while technicians must coach, assess and determine whether those efforts are acceptable and reproducible. Even with sophisticated equipment, poor technique or inconsistent application of quality standards can compromise data integrity.

The role of the ATS/ERS standards

Recognising these challenges, the 2019 ATS/ERS Spirometry Standards built upon earlier guidance to further standardise testing procedures and reporting requirements. The standards introduced a structured grading system and refined definitions of manoeuvre acceptability and session repeatability, providing a globally recognised framework for assessing spirometry quality. These recommendations have helped establish a gold-standard testing process while defining the minimum functional requirements expected of spirometry equipment.

Beyond the checklist: the need for expert judgement

Importantly, standardisation is not simply about following a checklist. Clinical trials often involve diverse participant populations, ranging from young children to elderly patients and individuals with advanced respiratory disease. Applying guideline-based acceptability criteria requires expert judgement to ensure that quality expectations are appropriate for the characteristics and capabilities of the study population. A rigid interpretation of standards may unnecessarily exclude valuable data, while overly permissive criteria can introduce variability and bias.

Building an expert-led quality programme

For this reason, successful spirometry endpoint programmes require more than compliant equipment and published standards. They demand an expert-led approach that combines scientific understanding, operational consistency and continuous quality oversight. Standardising equipment across sites helps minimise device-related variability, while comprehensive protocol-specific training ensures that site personnel apply testing procedures consistently and understand the nuances of endpoint collection. Equally important is an effective quality assurance programme that reviews data centrally, identifies trends and provides timely, constructive feedback to sites. This feedback loop enables corrective actions before quality issues become widespread, helping to maintain consistency throughout the trial.

The payoff: reliable, reproducible data

When these elements work together -appropriate application of standards, protocol-specific training, equipment standardisation and proactive quality assurance -the result is more reliable, reproducible and clinically meaningful spirometry data. Site staff are better equipped to coach participants effectively, participants are more likely to achieve high-quality manoeuvres, and sponsors gain greater confidence that observed treatment effects reflect genuine clinical outcomes rather than testing variability.

Conclusion

Ultimately, the goal of spirometry quality assurance is not merely to achieve compliance with a standard. It is to maximise the value of every participant effort and ensure that the endpoint data used to evaluate therapies are as accurate and representative as possible. In an era of increasingly complex clinical trials, expert oversight remains essential to translating spirometry standards into consistently high-quality endpoint data.

This article touches on just a few of the factors that influence spirometry quality in clinical trials. From common testing challenges and participant coaching strategies to troubleshooting techniques, quality assurance processes and study design considerations, there is much more to successful spirometry endpoint collection than the device itself.

Download the full ebook, "How to Achieve Quality Spirometry Endpoint Data", to gain a deeper understanding of the practical steps that can improve spirometry quality, reduce variability and help maximise the value of respiratory endpoint data.

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