How to improve respiratory data quality in non-respiratory clinical trials

Bettering the lives of patients through better respiratory data endpoints

Kevin McCarthy, RPFT – Director, Scientific Affairs at Clario

Clinical trials that measure lung function ─ whether for respiratory or non-respiratory diseases ─ are among the most expensive to conduct. In such a competitive environment, it’s critical that trial sponsors capture the highest quality data from all patients in order to drive the success of the study and the overall development program.

But the process of evaluating lung function via standard spirometry tests is fraught with challenges, causing the respiratory data derived from these tests to be inherently variable. In fact, the American Thoracic Society (ATS) states, “The largest single source of within-subject variability is improper performance of the test.”

Variability in respiratory data can impact go/no-go development decisions or result in the need for pivotal programs to be repeated.

High data variability can result in the need for pivotal programs to be repeated, and at earlier development phases, can impact go/no-go development decisions. Trial sponsors can avoid these consequences by taking steps to address the challenges of pulmonary function testing (PFT) – whether they are developing treatments for respiratory or non-respiratory diseases.

View the webinar replay, “Integrating the 2019 ATS/ERS Standards Into Your Trial“, for more information on how to improve the lives of patients through better respiratory data regardless of their disease.

Lung function testing in non-respiratory trials

When you think of conducting PFTs, you’re probably thinking about a trial in respiratory disease, like asthma or COPD, right? But there are several scenarios in which sponsors developing non-respiratory treatments need to conduct PFTs in order to evaluate their compound’s effect on lung safety and/or disease progression. Let’s review a few here:

• Delivery route for inhaled drugs: The lung can serve as the entry organ for drugs treating non-pulmonary conditions, e.g., delivering insulin through the lung in inhaled formulations of diabetes other systemic disease treatments, or as an alternate route for administering vaccinations. Lung function testing is warranted to look for changes at the alveolar-capillary interface, where the drug passes from the lung to the blood.
• Systemic toxicity of drugs / interventions: Because the lung is one of the few organs through which all of the circulating blood passes through, it can be exposed to systemic drug toxicity. Certain oncology drugs can also produce a toxic reaction in the lung which can be detected with PFTs. Likewise, lung tissue in close proximity to irradiated tissue can be (see note) damaged by radiation.
• Disease progression via the lung: There are a number of non-respiratory diseases in which the lung is a very important target organ to track disease progression. Several auto-immune diseases such as systemic sclerosis, rheumatoid arthritis and Sjogren’s syndrome are known to have pulmonary manifestations which can cause fibrotic changes in the lungs that can be detected and monitored with lung function testing. Neuromuscular diseases like Amyotrophic Lateral Sclerosis (ALS), and Multiple Sclerosis (MS) do not affect lung tissue directly but impact respiratory muscle function. In these diseases, respiratory muscle function and response to therapy can be monitored with lung function testing.

Improve respiratory data quality through centralized data collection

There are many obstacles that get in the way of capturing high quality respiratory data in clinical trials, including:

• Protocol complexity
• Suboptimal patient coaching and maneuver performance
• Varied investigative site experience levels
• Inconsistent PFT data collection / review among investigative sites

All of these are even harder to overcome when sponsors rely on investigative sites to use their own, non-standardized equipment and don’t leverage centralized, expert over read for the review of this important data. One review showed more than 50% of spirometry measurements collected from sites using their own equipment were unacceptable. Centralized review can also improve the quality of the collected data.

Centralized respiratory data collection includes OverRead by qualified Respiratory Specialists with real-time data availability; sponsors and CROs can quickly identify low performing sites and subject data outliers so they can intervene to address the causes of data variability and meet their clinical objectives faster. A recent review of approximately 3.3 million spirometry measurements that were conducted by sites not using standardized equipment, showed approximately 11% of the tests submitted by these sites had errors (Figure 1). After a centralized review of these measurements, the rate of acceptability improved to 98%. 

In order to reduce risks and uncertainties about the safety and/or efficacy of their compounds, many sponsors adopt a standardized and centralized approach to spirometry and other PFTs to yield high quality lung function data that meet ATS/ERS standards. With centralized respiratory data collection, sponsors enable their investigative sites to reduce errors that contribute to overall variability and capture the highest quality respiratory data for any clinical trial. Standardized equipment ─ including software workflows that guide the patient through proper testing maneuvers ─ and customized site training on devices and indication-specific nuances minimize risks in respiratory data collection procedures and reduce uncertainties about the accuracy and acceptability of the collected data.

Improving data quality with the new spirometry standards

The current set of spirometry standards (ATS/ERS 2019 Update) has added a 4th phase to the forced expiratory maneuver in which the subject rapidly inhales to full inflation again upon completing the forced exhalation. This inhaled volume, the forced inspiratory vital capacity (FIVC), should agree with the forced vital capacity (FVC) the subject just exhaled. If the FIVC is more than 5% larger than the FVC, the forced exhalation started before the subject completed inhaling to full inflation. Clario has carefully incorporated the 2019 spirometry standards in the software of their testing devices and already have clinical trials underway using the 2019 standards. Our initial findings suggest verification that the forced exhalation started from full inflation will reduce the within-subject variability.

Because the 4th phase of forced spirometry is new, many spirometry operators are unfamiliar with the new process. It is important that all operators in a clinical trial be properly trained on how to coach to full inflation both before and after the forced exhalation and use the software to evaluate the objective evidence that an effort started from full inflation. The software of Clario’s spirometers have been programmed to facilitate the evaluation of each effort and present this information to the operators upon completion of each effort.

After training, each operator in a clinical trial must demonstrate proficiency with the spirometry testing process suggested by the new 2019 ATS/ERS spirometry standards by submitting 2 proficiency tests showing 3 acceptable FVCs and 3 acceptable FEV1s from efforts that demonstrated they started from full inflation before testing study subjects. When forced exhalations start from full inflation, the variability of the spirometry measurements declines.

Conclusion

Whether you’re conducting PFT in a clinical trial to evaluate the efficacy of a respiratory compound, or to determine a non-respiratory compound’s effect on lung safety or disease progression, centralizing data collection and analysis processes ensures the highest quality data to drive the success of your study and overall development program.

Visit our respiratory page for more information on how to improve respiratory data quality in clinical trials.

References

¹https://aspe.hhs.gov/report/examination-clinical-trial-costs-and-barriers-drug-development

²Data on file, Clario