Peak expiratory flow rate control chart in asthma care: chart construction and use in asthma care

Internal Audit to Monitor the Injected Activity in PET/CT Using Control Charts

BACKGROUND AND OBJECTIVE

In an effort to limit the risks associated with medical radiation exposure, the last century witnessed the development of dose control mechanisms, recommended by the International Commission on Radiological Protection. This organization recommends the optimization of radiation protection to provide the highest level of safety that may reasonably be achievable. Adhering to the "as low as reasonably achievable" principle, the purpose of this study was to monitor the 18F-FDG injected activity in PET and optimize the radiation protection through an internal audit process. This monitoring allows the identification of opportunities for improvement in patient care and safety, as well as to establish a periodic review of the medical unit reference levels.

METHODS

The methodology is based on short run Quesenberry (Q) statistics and normalized nonconstant sample size (Z-chart) control charts. Anonymized data from 512 patients were selected from a set of 18F-FDG PET/CT (Siemens, Biograph 6) examinations performed during 10 months. The analyzed variable was the ratio between the 18F-FDG injected activity (MBq) and patient weight (kg).

RESULTS

Mean injected 18F-FDG activity was 347.811 ± 64.967 MBq corresponding to a mean effective dose of 6.608 ± 1.234 mSv. The ratio between the 18F-FDG injected activity and the body mass of patients was reduced from 5.243 ± 0.716 to 5.171 ± 0.672 MBq/kg during the statistical data analysis. The study demonstrates that control charts can be a useful tool to signal situations where patients receive an activity significantly different from the standard practice in a medical unit.

CONCLUSION

The use of joint control charts is a suitable tool for detecting nonoptimized radiopharmaceutical administration. This analysis provides opportunities to evaluate and improve the quality of practice in nuclear medicine. This methodology constitutes an internal audit that may help health care professionals to make appropriate decisions to ensure all patients receive the safest and most appropriate care.

Departmental action limits for TQA energy variations defined by means of statistical process control methods

The purpose of this study is to define departmental action limits for energy percentage variation measured by means of step-wedge helical Tomotherapy quality assurance module. Individual charts using the Statistical Process Control techniques have been used to identify retrospectively out-of-control situations ascribable to documented actions performed on the Tomotherapy system. Using the in-control data of our analysis process capability indices (cp, cpk, cpm and cpmk) are calculated in order to document the real working condition of the Tomotherapy system. Our findings indicate use of an action limit of 1.0% for energy percentage variation difference between the measured and reference output is a good working condition of a Tomotherapy system. cp and cpk indices are suggested as good indices that correctly report the system capability. A method for calculating and reporting Tomotherapy action limits for the integrated self-checking TQA energy check was shown in this study. SPC technique has proven to be efficient in defining departmental action limits from retrospective data for TQA energy measurements, hence optimally enabling corrective improvements in the process of quality assurance.

Correlated gamma-based hidden Markov model for the smart asthma management based on rescue inhaler usage

Asthma is a very common chronic disease that affects a large portion of population in many nations. Driven by the fast development in sensor and mobile communication technology, a smart asthma management system has become available to continuously monitor the key health indicators of asthma patients. Such data provides opportunities for healthcare practitioners to examine patients not only in the clinic (on-site) but also outside of the clinic (off-site) in their daily life. In this paper, taking advantage from this data availability, we propose a correlated gamma-based hidden Markov model framework, which can reveal and highlight useful information from the rescue inhaler-usage profiles of individual patients for practitioners. The proposed method can provide diagnostic information about the asthma control status of individual patients and can help practitioners to make more informed therapeutic decisions accordingly. The proposed method is validated through both numerical study and case study based on real world data. Copyright © 2017 John Wiley & Sons, Ltd.

Retrospective analysis of linear accelerator output constancy checks using process control techniques

Shewhart control charts have previously been suggested as a process control tool for use in routine linear accelerator (linac) output verifications. However, a comprehensive approach to process control has not been investigated for linac output verifications. The purpose of this work is to investigate a comprehensive process control approach to linac output constancy quality assurance (QA). The RBA‐3 dose constancy check was used to verify outputs of photon beams and electron beams delivered by a Varian Clinac 21EX linac. The data were collected during 2009 to 2010. Shewhart‐type control charts, exponentially weighted moving average (EWMA) charts, and capability indices were applied to these processes. The Shewhart‐type individuals chart (X‐chart) was used and the number of data points used to calculate the control limits was varied. The parameters tested for the EWMA charts (smoothing parameter (λ) and the control limit width (L)) were λ=0.05, L=2.492; λ=0.10, L=2.703; and λ=0.20, L=2.860, as well as the number of points used to estimate the initial process mean and variation. Lastly, the number of in‐control data points used to determine process capability (Cp) and acceptability (Cpk) were investigated, comparing the first in‐control run to the longest in‐control run of the process data. Cp and Cpk values greater than 1.0 were considered acceptable. The 95% confidence intervals were reported. The X‐charts detected systematic errors (e.g., device setup errors). In‐control run lengths on the X‐charts varied from 5 to 30 output measurements (about one to seven months). EWMA charts showed in‐control runs ranging from 9 to 33 output measurements (about two to eight months). The Cp and Cpk ratios are higher than 1.0 for all energies, except 12 and 20 MeV. However, 10 MV and 6, 9, and 16 MeV were in question when considering the 95% confidence limits. The X‐chart should be calculated using 8–12 data points. For EWMA chart, using 4 data points is sufficient to calculate the initial mean and variance of the process. The EWMA limits should be calculated with λ=0.10, L=2.703. At least 25–30 in‐control data points should be used to calculate the Cp and Cpk indices.

PACS number: 89

Control charts demonstrated limited utility for the monitoring of lung function in asthma

OBJECTIVE

Statistical process control charts have been advocated for use in monitoring of lung function in asthma. We aimed to evaluate their application in asthma using existing data from a randomized trial.

STUDY DESIGN AND SETTING

Patients on optimal inhaled corticosteroid/bronchodilator therapy (n=81) were randomized to continue the same or change to corticosteroid alone. Baseline statistical control was assessed from 20 days of electronically recorded lung function (peak expiratory flow [PEF], forced expiratory volume in one second [FEV(1)]). The ability to detect lung function changes was assessed during 10 days after randomization.

RESULTS

PEF measurements were in statistical control during baseline for only 59-79% of patients for different combinations of five control chart rules (e.g., Rule 1: >3 standard deviations outside mean and 95% expected to be in control), with similar proportions for FEV(1). After randomization, among those previously in statistical control, Rule 1 signaled lower FEV(1) for 35% of patients randomized to treatment change compared with 6% continuing baseline treatment (P=0.004). Control charts performed poorly for PEF, signaling a decrease for 8% of patients randomized to treatment change compared with 11% continuing treatment (P=0.7). A paradoxical increase was signaled for many patients continuing treatment.

CONCLUSION

Control charts may not be suitable for use in many patients with asthma.

Statistical models for the control phase of clinical monitoring

The rise in the prevalence of chronic conditions means that these are now the leading causes of death and disability worldwide, accounting for almost 60% of all deaths and 43% of the global burden of disease. Management of chronic conditions requires both effective treatment and ongoing monitoring. Although costs related to monitoring are substantial, there is relatively little evidence on its effectiveness. Monitoring is inherently different to diagnosis in its use of regularly repeated tests, and increasing frequency can result in poorer rather than better statistical properties because of multiple testing in the presence of high variability. We present here a general framework for modelling the control phase of a monitoring programme, and for the estimation of quantities of potential clinical interest such as the ratio of false to true positive tests. We show how four recent clinical studies of monitoring cardiovascular disease, hypertension, diabetes and HIV infection can be thought as special cases of this framework; as well as using this framework to clarify the choice of estimation and calculation methods available. Noticeably, in each of the presented examples over-frequent monitoring appears to be a greater problem than under-frequent monitoring. We also present recalculations of results under alternative conditions, illustrating conceptual decisions about modelling the true or observed value of a clinical measure.

Quality control chart method for analyzing PEF variability in occupational asthma

BACKGROUND

Asthma is the most common occupational respiratory disorder in the industrialized world. In this research we investigated the effectiveness of quality control chart as a method to explore the work-relatedness of asthma using peak expiratory flow (PEF) as the variable of interest.

METHOD

PEF measurements were recorded from several workers suspected of having occupational asthma. The measurements were recorded for a period of 2 weeks at work and two away from work. For each subject, average [symbol: see text] quality control chart was developed and analyzed using Shewhart methods.

RESULTS

Using the ratio of average daily PEF diurnal variation at work to the baseline average diurnal variation, quality control chart detected and characterized increased variation in PEF between the periods at workplace and away from workplace. This ratio was evaluated at 15% and 20% and compared to the results of the gold standard. A ratio of greater than 15% produced a sensitivity of 94% and specificity of 61%. These results are as good as and in some cases better than the previously published work.

CONCLUSIONS

Quality control chart is an effective and inexpensive method for early intervention to detect workers suspected for occupational asthma. Also, the findings in this article are an invitation for further research in occupational asthma using quality control chart methods for other indices of airways obstruction (FEV-1, RAW) or inflammation (NO).

Application of statistical process control in healthcare improvement: systematic review

OBJECTIVE

To systematically review the literature regarding how statistical process control--with control charts as a core tool--has been applied to healthcare quality improvement, and to examine the benefits, limitations, barriers and facilitating factors related to such application.

DATA SOURCES

Original articles found in relevant databases, including Web of Science and Medline, covering the period 1966 to June 2004.

STUDY SELECTION

From 311 articles, 57 empirical studies, published between 1990 and 2004, met the inclusion criteria.

METHODS

A standardised data abstraction form was used for extracting data relevant to the review questions, and the data were analysed thematically.

RESULTS

Statistical process control was applied in a wide range of settings and specialties, at diverse levels of organisation and directly by patients, using 97 different variables. The review revealed 12 categories of benefits, 6 categories of limitations, 10 categories of barriers, and 23 factors that facilitate its application and all are fully referenced in this report. Statistical process control helped different actors manage change and improve healthcare processes. It also enabled patients with, for example asthma or diabetes mellitus, to manage their own health, and thus has therapeutic qualities. Its power hinges on correct and smart application, which is not necessarily a trivial task. This review catalogs 11 approaches to such smart application, including risk adjustment and data stratification.

CONCLUSION

Statistical process control is a versatile tool which can help diverse stakeholders to manage change in healthcare and improve patients' health.

Monitoring patients using control charts: a systematic review

OBJECTIVE

To systematically review the uses control charts to monitor clinical variables in individual patients.

DATA SOURCES

Systematic searches of MEDLINE, CINAHL, Embase and five other databases yielded 74 studies, of which seven met our inclusion criteria of using control charts to monitor clinical variables for disease at an individual patient level.

REVIEW METHODS

Included articles were reviewed independently by two reviewers. Data were extracted on study design clinical condition or disease being monitored, clinical variable or marker, measurement method, outcome measure and any changes in clinical indicator identified in the articles.

RESULTS

Control charts were applied to four conditions--hypertension, asthma, renal function post-transplant and diabetes. Studies fell into two categories. Three studies sought to determine the 'performance' of control charts in comparison with existing 'gold standard methods' in terms of sensitivity and specificity based on moderate sample sizes (n = 35-45). This category of studies found control charts to be simple, low-cost, effective tools with good sensitivity and specificity characteristics and concluded in favour of control charts. The other four studies were individual patient case-studies in which the use of control charts to monitor clinical variables was associated with a positive impact on patient and carer experience albeit anecdotally and with varying degrees of attention.

CONCLUSIONS

Control charts appear to have a promising but largely under-researched role in monitoring clinical variables in individual patients. Furthermore, rigorous evaluation of control charts is required.

Effects of self-efficacy, social support and knowledge on adherence to PEFR self-monitoring among adults with asthma: a prospective repeated measures study

BACKGROUND

Long-term adherence to self-monitoring of peak expiratory flow rate (PEFR) is essential for early detection of declining lung function in individuals with asthma. Psychosocial and cognitive factors can influence adherence to PEFR self-monitoring behaviors.

OBJECTIVES

The specific aims of this prospective, repeated measures study were to: (1) determine the effects of asthma self-efficacy, perceived satisfaction with social support and asthma knowledge on adherence to PEFR self-monitoring behavior; and (2) examine whether adherence to PEFR self-monitoring mediates the effects of psychosocial/cognitive factors on lung function and asthma symptoms in adults with asthma.

DESIGN

Sixty-eight participants completed standardized questionnaires three times at baseline, 1 month, and 3 months and kept the records of PEFR self-monitoring behaviors twice a day. Data were analyzed using multiple linear regressions. Adherence rates to PEFR self-monitoring were 93.5% and 74.9% at 1 and 3 month, representing those who remained in the study (n=39). Other participants withdrew from the study.

FINDINGS

Both at 1 and 3 month, psychosocial/cognitive factors as a whole did not account for a significant variance in adherence to PEFR self-monitoring. Univariate analyses, however, indicated that baseline asthma self-efficacy and asthma knowledge at 1 month were significant independent predictors for adherence to PEFR self-monitoring at 3 month.

CONCLUSION

Adherence to PEFR self-monitoring did not mediate the effects of asthma self-efficacy, perceived satisfaction with social support, and asthma knowledge on lung function and asthma symptoms. Lung function was low, but participants reported low asthma symptoms, both of which remained stable over time. Because of a small sample size and high attrition, the findings of the study need to be interpreted with caution. Given the importance of long-term adherence to self-management in asthma and other chronic illnesses, factors influencing adherence need to be further investigated to set a basis for future interventions.

Control chart for monitoring occupational asthma

INTRODUCTION

Work-related asthma has become the most prevalent occupational respiratory disease in the developed world. Occupational asthma is thought to affect 5%-10% of people worldwide. The first step in the diagnosis of occupational asthma is to establish work-relatedness. Although considerable research has been conducted in the area of occupational asthma, no simple, effective, and statistically sound method has been developed that can be used as an initial step to effectively identify the workers at risk for occupational asthma. The purpose of this research was to investigate whether Shewhart control chart method can be used as an effective method to detect occupational asthma.

METHOD

Forty-five workers who completed the study and provided usable peak expiratory flow (a lung function marker) recordings while at work and away from work were included in this study. Control charts were developed using Shewhart's Method. The lower control limit of at work control chart (LCL(W)) was compared to each subject's Personal Best (PB) value.

RESULTS

Reviewing the results of this comparison showed LCL(W)<60% PB to have a sensitivity of 85.71%, specificity of 87.50%, and an error rate of 13.33%. When the subjects suspected for false positive and false negative diagnoses were identified, the test produced a sensitivity of 95.24%, a specificity of 95.83% and an error rate of 4.44%.

CONCLUSIONS

Our results were as good as, and in some cases better than, published clinical guidelines.

IMPACT ON INDUSTRY

Our research showed that the control chart method is an effective, simple, and inexpensive tool for early intervention in workers suspected for occupational asthma.

Rethinking asthma from business to bedside

OBJECTIVE

To explore the possible application of concepts, methods, and tools validated in the worlds of statistics, business, engineering, and system dynamics to improve insight and outcomes in asthma care.

DATA SOURCES

Peer-reviewed articles, books, and other publications on the topic of asthma care, dating back to 1931, were retrieved using MEDLINE searches and other external sources. Searches were restricted to include only studies with human subjects and the English language as descriptors, and the terms used included the following: system dynamics, statistical process control charts, continual improvement, mental models, personal mastery, systems thinking, team learning, and shared vision. Personal author observations and experiences were also included.

STUDY SELECTION

Review and original research studies that reported on these concepts were evaluated.

RESULTS

Results show that the concepts behind these models may be transferred to the system of asthma care. Indeed, case studies have shown that control charts can be used to detect signals of improved or declined function in asymptomatic patients, monitor medication adverse effects and the effects of adding a new medication, and evaluate the effectiveness of different medication delivery systems.

CONCLUSIONS

These concepts and tools can provide immeasurable value to the process of rethinking and redesigning asthma care. They have the potential to help physicians and patients alike in designing and executing systems of care that are capable of not only delivering better outcomes but also inviting commitment to the long-term care many require.

The need for standardisation of peak flow charts

Peak expiratory flow (PEF) monitoring is recommended in asthma guidelines as a tool for assessing severity, monitoring response to treatment, detecting exacerbations, identifying triggers, and providing objective justification for treatment to the patient, but some clinicians have expressed concerns about its relevance in the management of asthma. We have identified a sevenfold variation in the scale of existing PEF charts, with resulting wide variation in the appearance of the same PEF date on different charts. There is an obvious need for standardisation of PEF charts to avoid confusion for patients and to allow development of pattern recognition skills by clinicians. Evidence is provided from visual perception studies to suggest that preference should be given to a horizontally compressed PEF chart to facilitate identification of exacerbations and of overall trends, but this needs to be formally evaluated by retrospective and prospective studies. It is hoped that clinical expertise in PEF pattern recognition can eventually be incorporated into electronic decision making algorithms, as has occurred in occupational asthma, but, in the meantime, the ideal PEF chart for asthma management will represent a compromise between ease of manual data entry and ease of interpretation.

Time-between control charts for monitoring asthma attacks

BACKGROUND

The monitoring of peak expiratory flow rate (PEFR) is crucial for effective management of asthma. Daily PEFR monitoring is recommended, yet the data are rarely used by patients to help them understand their progress or by clinicians to modify treatment plans. Time-between control charts, which have been shown to be specially suited for monitoring rare events, can be used to monitor asthma attacks.

METHODS

Each patient is asked to record his or her PEFR value once a day, and these data are used to construct the control chart. PEFR data for three previously reported cases are presented and used to illustrate the control chart methodology. If duration of consecutive attacks is plotted and the observed duration exceeds the upper control limit (UCL), the patient is getting worse. If length of consecutive symptom-free days is plotted and the observed duration exceeds the UCL, the patient is getting better. In both circumstances, the clinician and the patient explore what brought about the prolonged recovery or periods of deterioration. The object is to increase time until the next attack.

DISCUSSION

Using time-between control charts in monitoring asthma attacks has the advantage of providing a visual display of data that, unlike eyeballing of trends, clarifies when patients should seek additional clinical advice. The control limit allows clinicians and patients to ignore random variations and focus on real changes in underlying patterns of asthma attacks.

Using statistical process control charts for the continual improvement of asthma care

BACKGROUND

Home monitoring of lung function using simple, inexpensive tools to measure peak expiratory flow rate (PEFR) has been possible since the 1970s. Yet although current national and international guidelines recommend monitoring of PEFRs via traditional run charts, their use by both patients and physicians remains low. The role of statistical process control (SPC) theory and charts in the serial monitoring of lung function at home were explored and applied to the direct care of patients with asthma. The method represents an integration of collective professional and improvement knowledge with the related disciplines of continual improvement, SPC, system thinking/system dynamics, paradigms, and the learning community/organization.

CASE STUDIES

Use of PEFR control charts for four patients cared for at the Asthma-Allergy Clinic and Research Center (Shreveport, La) is described. The key to good asthma control is the ability to optimize lung function by reducing the variation between serial lung function measurements and thereby generate a safe range of function. Knowledge of the type of variation (special cause or common cause) in the system helps in focusing clinical decision making. Case 4, an 11-year-old boy, for example, shows how control charts were used to learn the effects of a new inhaled corticosteroid. Comparison of the last 14 days of baseline and the last 14 days of open label use of the inhaled corticosteroid showed an obvious improvement in actual PEFR values--which a run chart or comparison of means would have easily demonstrated. The control chart showed that this child's care process at baseline was functionally at risk for severe asthma (46% personal best) and that the effect of the new medication not only elevated the mean function but shifted the range of function from 46%-72% personal best to 78%-102% personal best. At this new range of function the patient's system of care was not capable of delivering values that are at risk for severe asthma. Unless the range of function the change in care is capable of producing is specifically quantitated, misinterpretation of improvement data can occur.

DISCUSSION

Developing the concept of the PEFR control chart involved examining and challenging traditional mental models for monitoring PEFR at home in the care of asthma, acquiring a better understanding of the workings of dynamic systems and with system thinking, and sharing what was learned with patients and seeking their input.

CONCLUSIONS

The PEFR control chart employs an interesting statistical platform that enables the integration of knowledge of serial measurements and knowledge of the variation between those measurements into a tool with which to better assess the asthma care process being followed. This tool provides clinical insights, practical knowledge, and opportunities unavailable to patients and physicians via traditional PEFR charting.