Improving Home COPD Monitoring Through Statistical Process Control (SPC)

Article By: Alex Stenzler

Respiratory Therapy Vol. 14 No. 4 n Fall 2019


Chronic Obstructive Pulmonary Disease (COPD) is a group of progressive lung diseases that obstruct airflow. It is the third leading cause of death in the United States, affecting 16 million Americans and millions more who are not aware they have the disease. The American Lung Association (ALA) states that there may be as many as 24 million American adults living with COPD. Over 800,000 hospitalizations each year in the U.S. are related to COPD. In 2015, 3.2 million people died from COPD worldwide, an increase of 11.6 % compared with 1990. During that same time period, the global prevalence of COPD increased by 44.2 % to 174.5 million individuals.

Integral to the management of patients with COPD is the development of new and effective treatments, as well as the monitoring of these patients to identify exacerbations and enable early intervention. The burden of COPD on US healthcare systems has been moving these patients back to their homes earlier, which increases the need for technology that is able to monitor these patients at home. This home monitoring has increased the need for hospital laboratory quality lung function monitoring collection from patients without onsite technical assistance, so that meaningful and actionable data can be used to intercede early when pulmonary degradation is identified.

Forced Vital Capacity measurements (Forced Spirometry) are the standard for evaluating dynamic lung function in COPD. It requires that patients take a maximum inhalation to Total Lung Capacity and then forcefully blow out all the air in their lungs as hard and as fast as they can, for at least six seconds and frequently longer. To perform this correctly, all of their respiratory muscles, including their diaphragm, abdominal and internal intercostals muscles must provide a maximum level of sustained contraction. Patients are expected to meet the ATS/ ERS criteria for reproducibility, including three measurements that meet ATS/ERS standards for acceptability and two measurements where the FEV1 and FVC are within 150 mL of each other. To meet these criteria, a patient may be requested to perform up to 8 measurements. Understandably, for most COPD patients the extended contraction of these muscles can be fatiguing.

The potential for fatigue during Forced Spirometry is well known and is usually occur whenever patients are required to perform serial spirometry measurements either within a short time frame, or very frequently. Fatigue is usually associated with bronchial provocation challenges, serial measurements in response to a drug dose, as well as when daily monitoring patients at home with respiratory dysfunction is required.

The major risks associated with fatiguing patients with COPD are that they either become unwilling to perform additional measurements to attain repeatability or that the fatigue of their muscles reduces their ability to perform maximum-effort measurements correctly when required to perform multiple measurement. In the worst situation the patient refuses to perform any measurements in anticipation of the fatigue from the FVC measurement. With the ability to monitor COPD patients being so important, adherence to frequent testing is the rate critical step to collecting the data and providing effective care.

Monitoring Patients through Statistical Process Control

Statistical Process Control (SPC) is a set of statistical methods based on the theory of variation that enables the detection of changes in measurements following an extended period of collecting baseline data. It can be used to detect, early on, whether any changes have occurred, long before results from larger evaluations are available and the modeling fits well with patient-centered medical home programs.

In 2013, Sirichana, Patel, Taylor, et al, presented work on a Statistical Process Control (SPC) algorithm approach for monitoring patients with COPD at home. This SPC process enables patients to perform tests on a “daily” basis and not be either fatigued or annoyed at having to perform multiple measurements at each session.

The statistical process control (SPC) algorithm maintains a short history of past spirometry data (typically a seven-day rolling average from sessions of home monitoring or data from baseline testing). When a new measurement is collected, it is compared to the past data rolling average. If the new test data is within a Z-Score of 1.645 below to 1.96 above the previous data, the patient is done with a single measurement for that day or that session. Only if the data falls outside those limits is the patient required to perform additional test maneuvers. The higher cut‐off value identifies the highest 2.5% of normally distributed values (P=0.975). If the measured values are higher than this, subjects are asked to repeat the maneuver to assure that there was not a technical error. On the repeat measurement, the lowest of the two measures are accepted as the daily “Best” for inclusion in the rolling average (technical acceptability).

The lower cut‐off value identifies the lowest 5% of normally distributed values (P=0.050). If the measured values are lower than this, subjects are asked to repeat once. If the repeated value is above the threshold, then that value is accepted as the daily “Best” for inclusion in the rolling average. If lower again, a clinical event is identified. Lower limit events on two consecutive days can be considered an as a marker of a potential exacerbation requiring further investigation by a healthcare professional (clinical event detection).

Sirichana enrolled 13 COPD patients in this pilot study with a mean (SD) age of 70.4 (7.3) years. Baseline FEV1 was 52.0% predicted (15.5%). The patients were initially monitored using ATS/ERS spirometry criteria, followed by a change to the SPC algorithm criteria. They collected a total of 1,999 days of monitoring including 1,358 days of conventional monitoring using ATS/ERS spirometry criteria and 641 days of monitoring using the SPC algorithm. During the conventional monitoring (ATS) period, patients performed an average of 4.5 (0.5) maneuvers per day (range 3-5) as compared to 1-2 maneuvers per day during the SPC algorithm period. The time required of the patients for performing the spirometry tests was 13:05 (5:23) minutes per day with a 55.1 % adherence during the ATS monitoring period as compared with only 6:37 (2:82) minutes per day but with an 85.6% adherence during the SPC algorithm period.

The group mean (SD) for FEV1 from the ATS/ERS maneuver period was 1.00 (0.45) L, which was comparable to that from SPC maneuver period, 0.99 (0.47) L (P = 0.995). They detected 0.16/patient-year exacerbations during ATS/ERS monitoring compared to 1.01/patient-year during SPC monitoring.

In 2017, Taylor incorporated statistical process control into the spirometry monitoring platform of Monitored Therapeutics, Inc. (MTI) as part of the introduction of Avatar-Assisted-Technology to collect hospital laboratory quality spirometry from patients at home ( A review of 30,451 forced spirometry measurements collected by MTI from patients at home with an analysis of the best measurement of each day, demonstrated that 82% met ATS/ERS criteria for start of effort including time to peak flow and back extrapolated volume and 99.5% of the measurements met end-of-test plateau. A secondary analysis was performed of weeks where patients performed at least 3 measurements. There were 2,705 weeks when more than three measurements were performed (30,272 total measurements). In 92% of the weeks, the ATS/ERS 150 mL reproducibility requirements were met for FEV1 and 88% for FVC.


The use of a Statistical Process Control approach for monitoring COPD patients has been shown to significantly shorten the time each day for testing in patients at home, reduce patient fatigue, and increase adherence with testing. For patients undergoing serial measurements, it assures that measurements that don’t meet ATS/ERS quality criteria are not accepted for a single measurement requirement and yet minimizes the number of attempts to assure meaningful data. It should be recommended by the ATS Pulmonary Function steering committee that Statistical Process Control should be the standard for home monitoring of patients with COPD if an effective monitoring program is the intended use of spirometers.


  1. lookup/copd/learn-about-copd/how-serious-is-copd.html
  2. infographic#1
  3. obstructive-pulmonary-disease-(copd)
  4. American Thoracic Society and European Respiratory Society (Eur Respir J, 2005, Vol 26, pp. 948-968. No. 5 in SERIES “ATS/ERS TASK FORCE: STANDARDISATION OF LUNG FUNCTION TESTING”)
  5. Marsteller JA, et. al. Statistical process control: Possible
    use to monitor and evaluate patient-centered medical
    home models. Agency for Healthcare Research and Quality 2013 controlpossible-uses-monitor-and-evaluate-patient-centered- medical-home
  6. Sirichana W, Patel MH, Taylor M, Tseng CH, Barjakterevic
    I, DLeerup EC, Cooper CB. Justification for statistical process control in daily home spirometry for COPD patients. European Respiratory Society (Poster) 2013