Using an asthma control questionnaire and administrative data to predict health-care utilization

Patient-Reported Outcomes in Asthma

Food and Drug Administration's Center for Drug Evaluation and Research defines patient-reported outcomes as "any report of the status of a patient's health condition, health behavior, or experience with healthcare that comes directly form the patient, without interpretation of the patient's response by a clinician or anyone else." Validated patient-reported outcome measures are used extensively in pediatric and adult asthma across clinical and research settings to assess the impact of treatments on patient outcomes over time. This work aims to review some of the most commonly used asthma patient-reported outcomes across the following criteria: validity, reliability, responsiveness, time to complete, ease of administration, target population, recall period, scoring method, availability in different languages, use in clinical practice or research settings, licensing requirements, and cost of use.

Primary Care Asthma Attack Prediction Models for Adults: A Systematic Review of Reported Methodologies and Outcomes

Prognostic models hold great potential for predicting asthma exacerbations, providing opportunities for early intervention, and are a popular area of current research. However, it is unclear how models should be compared and contrasted, given their differences in both design and performance, particularly with a view to potential implementation in routine practice. This systematic review aimed to identify novel predictive models of asthma attacks in adults and compare differences in construction related to populations, outcome definitions, prediction time horizons, algorithms, validation, and performance estimation. Twenty-five studies were identified for comparison, with varying definitions of asthma attacks and prediction event time horizons ranging from 15 days to 30 months. The most commonly used algorithm was logistic regression (20/25 studies); however, none of the six which tested multiple algorithms identified it as highest performing algorithm. The effect of various study design characteristics on performance was evaluated in order to provide context to the limitations of highly performing models. Models used a variety of constructs, which affected both their performance and their viability for implementation in routine practice. Consultation with stakeholders is necessary to identify priorities for model refinement and to create a benchmark of acceptable performance for implementation in clinical practice.

Error and Timeliness Analysis for Using Machine Learning to Predict Asthma Hospital Visits: Retrospective Cohort Study

Background

Asthma hospital visits, including emergency department visits and inpatient stays, are a significant burden on health care. To leverage preventive care more effectively in managing asthma, we previously employed machine learning and data from the University of Washington Medicine (UWM) to build the world’s most accurate model to forecast which asthma patients will have asthma hospital visits during the following 12 months.

Objective

Currently, two questions remain regarding our model’s performance. First, for a patient who will have asthma hospital visits in the future, how far in advance can our model make an initial identification of risk? Second, if our model erroneously predicts a patient to have asthma hospital visits at the UWM during the following 12 months, how likely will the patient have ≥1 asthma hospital visit somewhere else or ≥1 surrogate indicator of a poor outcome? This work aims to answer these two questions.

Methods

Our patient cohort included every adult asthma patient who received care at the UWM between 2011 and 2018. Using the UWM data, our model made predictions on the asthma patients in 2018. For every such patient with ≥1 asthma hospital visit at the UWM in 2019, we computed the number of days in advance that our model gave an initial warning. For every such patient erroneously predicted to have ≥1 asthma hospital visit at the UWM in 2019, we used PreManage and the UWM data to check whether the patient had ≥1 asthma hospital visit outside of the UWM in 2019 or any surrogate indicators of poor outcomes. Such surrogate indicators included a prescription for systemic corticosteroids during the following 12 months, any type of visit for asthma exacerbation during the following 12 months, and asthma hospital visits between 13 and 24 months later.

Results

Among the 218 asthma patients in 2018 with asthma hospital visits at the UWM in 2019, 61.9% (135/218) were given initial warnings of such visits ≥3 months ahead by our model and 84.4% (184/218) were given initial warnings ≥1 day ahead. Among the 1310 asthma patients in 2018 who were erroneously predicted to have asthma hospital visits at the UWM in 2019, 29.01% (380/1310) had asthma hospital visits outside of the UWM in 2019 or surrogate indicators of poor outcomes.

Conclusions

Our model gave timely risk warnings for most asthma patients with poor outcomes. We found that 29.01% (380/1310) of asthma patients for whom our model gave false-positive predictions had asthma hospital visits somewhere else during the following 12 months or surrogate indicators of poor outcomes, and thus were reasonable candidates for preventive interventions. There is still significant room for improving our model to give more accurate and more timely risk warnings.

International Registered Report Identifier (IRRID)

RR2-10.2196/5039

Blood eosinophils, fractional exhaled nitric oxide and the risk of asthma attacks in randomised controlled trials: protocol for a systemic review and control arm patient-level meta-analysis for clinical prediction modelling

INTRODUCTION

The reduction of the risk of asthma attacks is a major goal of guidelines. The fact that type-2 inflammatory biomarkers identify a higher risk, anti-inflammatory responsive phenotype is potentially relevant to this goal. We aim to quantify the relation between blood eosinophils, exhaled nitric oxide (FeNO) and the risk of severe asthma attacks.

METHODS AND ANALYSIS

A systematic review of randomised controlled trials (RCTs) will be conducted by searching MEDLINE from January 1993 to April 2021. We will include RCTs that investigated the effect of fixed treatment(s) regimen(s) on severe asthma exacerbation rates over at least 24 weeks and reported a baseline value for blood eosinophils and FeNO. Study selection will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, and the methodological appraisal of the studies will be assessed by the Cochrane Risk-of-Bias Tool for RCTs. Study authors will be contacted to request anonymised individual participant data (IPD) for patients randomised to the trial's control arm. An IPD meta-analysis will be performed for multivariable prognostic modelling with performance assessment (calibration plots and the c-statistic) in a cross-validation by study procedure. The outcome to predict is the absolute number of severe asthma attacks to occur in the following 12 months if anti-inflammatory therapy is not changed (ie, annualised number of attacks requiring ≥3 days of systemic corticosteroids and/or hospitalisation if the patient was randomised to the control arm of an RCT). A summary prognostic equation and risk stratification chart will be reported as a basis for further analyses of individualised treatment benefit.

ETHICS AND DISSEMINATION

The protocol has been reviewed by the relevant Oxford academic ethics committee and found to comprise fully anonymised data not requiring further ethical approbation. Results will be communicated in an international meeting and submitted to a peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42021245337.

Predicting Continuity of Asthma Care Using a Machine Learning Model: Retrospective Cohort Study

Continuity of care (COC) has been shown to possess numerous health benefits for chronic diseases. Specifically, the establishment of its level can facilitate clinical decision-making and enhanced allocation of healthcare resources. However, the use of a generalizable predictive methodology to determine the COC in patients has been underinvestigated. To fill this research gap, this study aimed to develop a machine learning model to predict the future COC of asthma patients and explore the associated factors. We included 31,724 adult outpatients with asthma who received care from the University of Washington Medicine between 2011 and 2018, and examined 138 features to build the machine learning model. Following the 10-fold cross-validations, the proposed model yielded an accuracy of 88.20%, an average area under the receiver operating characteristic curve of 0.96, and an average F1 score of 0.86. Further analysis revealed that the severity of asthma, comorbidities, insurance, and age were highly correlated with the COC of patients with asthma. This study used predictive methods to obtain the COC of patients, and our excellent modeling strategy achieved high performance. After further optimization, the model could facilitate future clinical decisions, hospital management, and improve outcomes.

Ranking Rule-Based Automatic Explanations for Machine Learning Predictions on Asthma Hospital Encounters in Patients With Asthma: Retrospective Cohort Study

Background

Asthma hospital encounters impose a heavy burden on the health care system. To improve preventive care and outcomes for patients with asthma, we recently developed a black-box machine learning model to predict whether a patient with asthma will have one or more asthma hospital encounters in the succeeding 12 months. Our model is more accurate than previous models. However, black-box machine learning models do not explain their predictions, which forms a barrier to widespread clinical adoption. To solve this issue, we previously developed a method to automatically provide rule-based explanations for the model’s predictions and to suggest tailored interventions without sacrificing model performance. For an average patient correctly predicted by our model to have future asthma hospital encounters, our explanation method generated over 5000 rule-based explanations, if any. However, the user of the automated explanation function, often a busy clinician, will want to quickly obtain the most useful information for a patient by viewing only the top few explanations. Therefore, a methodology is required to appropriately rank the explanations generated for a patient. However, this is currently an open problem.

Objective

The aim of this study is to develop a method to appropriately rank the rule-based explanations that our automated explanation method generates for a patient.

Methods

We developed a ranking method that struck a balance among multiple factors. Through a secondary analysis of 82,888 data instances of adults with asthma from the University of Washington Medicine between 2011 and 2018, we demonstrated our ranking method on the test case of predicting asthma hospital encounters in patients with asthma.

Results

For each patient predicted to have asthma hospital encounters in the succeeding 12 months, the top few explanations returned by our ranking method typically have high quality and low redundancy. Many top-ranked explanations provide useful insights on the various aspects of the patient’s situation, which cannot be easily obtained by viewing the patient’s data in the current electronic health record system.

Conclusions

The explanation ranking module is an essential component of the automated explanation function, and it addresses the interpretability issue that deters the widespread adoption of machine learning predictive models in clinical practice. In the next few years, we plan to test our explanation ranking method on predictive modeling problems addressing other diseases as well as on data from other health care systems.

International Registered Report Identifier (IRRID)

RR2-10.2196/5039

[Strategies for prescription of inhaled corticosteroids in mild-to-moderate asthma]

Asthma is a common respiratory condition characterized by chronic inflammation of the airways. Most asthmatics have a mild-to-moderate form of the disease, but are still at risk of severe exacerbations and significantly impaired quality of life. This article reviews the strategies for prescription of inhaled corticosteroids in patients with mild-to-moderate asthma. The definition of asthma severity, the goals of asthma management and the adjustment of therapeutics are successively addressed. The major changes proposed by the GINA group in 2019 are also discussed.

Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study

Background

Asthma affects a large proportion of the population and leads to many hospital encounters involving both hospitalizations and emergency department visits every year. To lower the number of such encounters, many health care systems and health plans deploy predictive models to prospectively identify patients at high risk and offer them care management services for preventive care. However, the previous models do not have sufficient accuracy for serving this purpose well. Embracing the modeling strategy of examining many candidate features, we built a new machine learning model to forecast future asthma hospital encounters of patients with asthma at Intermountain Healthcare, a nonacademic health care system. This model is more accurate than the previously published models. However, it is unclear how well our modeling strategy generalizes to academic health care systems, whose patient composition differs from that of Intermountain Healthcare.

Objective

This study aims to evaluate the generalizability of our modeling strategy to the University of Washington Medicine (UWM), an academic health care system.

Methods

All adult patients with asthma who visited UWM facilities between 2011 and 2018 served as the patient cohort. We considered 234 candidate features. Through a secondary analysis of 82,888 UWM data instances from 2011 to 2018, we built a machine learning model to forecast asthma hospital encounters of patients with asthma in the subsequent 12 months.

Results

Our UWM model yielded an area under the receiver operating characteristic curve (AUC) of 0.902. When placing the cutoff point for making binary classification at the top 10% (1464/14,644) of patients with asthma with the largest forecasted risk, our UWM model yielded an accuracy of 90.6% (13,268/14,644), a sensitivity of 70.2% (153/218), and a specificity of 90.91% (13,115/14,426).

Conclusions

Our modeling strategy showed excellent generalizability to the UWM, leading to a model with an AUC that is higher than all of the AUCs previously reported in the literature for forecasting asthma hospital encounters. After further optimization, our model could be used to facilitate the efficient and effective allocation of asthma care management resources to improve outcomes.

International Registered Report Identifier (IRRID)

RR2-10.2196/resprot.5039

Generalizability of an Automatic Explanation Method for Machine Learning Prediction Results on Asthma-Related Hospital Visits in Patients With Asthma: Quantitative Analysis

Background

Asthma exerts a substantial burden on patients and health care systems. To facilitate preventive care for asthma management and improve patient outcomes, we recently developed two machine learning models, one on Intermountain Healthcare data and the other on Kaiser Permanente Southern California (KPSC) data, to forecast asthma-related hospital visits, including emergency department visits and hospitalizations, in the succeeding 12 months among patients with asthma. As is typical for machine learning approaches, these two models do not explain their forecasting results. To address the interpretability issue of black-box models, we designed an automatic method to offer rule format explanations for the forecasting results of any machine learning model on imbalanced tabular data and to suggest customized interventions with no accuracy loss. Our method worked well for explaining the forecasting results of our Intermountain Healthcare model, but its generalizability to other health care systems remains unknown.

Objective

The objective of this study is to evaluate the generalizability of our automatic explanation method to KPSC for forecasting asthma-related hospital visits.

Methods

Through a secondary analysis of 987,506 data instances from 2012 to 2017 at KPSC, we used our method to explain the forecasting results of our KPSC model and to suggest customized interventions. The patient cohort covered a random sample of 70% of patients with asthma who had a KPSC health plan for any period between 2015 and 2018.

Results

Our method explained the forecasting results for 97.57% (2204/2259) of the patients with asthma who were correctly forecasted to undergo asthma-related hospital visits in the succeeding 12 months.

Conclusions

For forecasting asthma-related hospital visits, our automatic explanation method exhibited an acceptable generalizability to KPSC.

International Registered Report Identifier (IRRID)

RR2-10.2196/resprot.5039

Automatically Explaining Machine Learning Prediction Results on Asthma Hospital Visits in Patients With Asthma: Secondary Analysis

Background

Asthma is a major chronic disease that poses a heavy burden on health care. To facilitate the allocation of care management resources aimed at improving outcomes for high-risk patients with asthma, we recently built a machine learning model to predict asthma hospital visits in the subsequent year in patients with asthma. Our model is more accurate than previous models. However, like most machine learning models, it offers no explanation of its prediction results. This creates a barrier for use in care management, where interpretability is desired.

Objective

This study aims to develop a method to automatically explain the prediction results of the model and recommend tailored interventions without lowering the performance measures of the model.

Methods

Our data were imbalanced, with only a small portion of data instances linking to future asthma hospital visits. To handle imbalanced data, we extended our previous method of automatically offering rule-formed explanations for the prediction results of any machine learning model on tabular data without lowering the model’s performance measures. In a secondary analysis of the 334,564 data instances from Intermountain Healthcare between 2005 and 2018 used to form our model, we employed the extended method to automatically explain the prediction results of our model and recommend tailored interventions. The patient cohort consisted of all patients with asthma who received care at Intermountain Healthcare between 2005 and 2018, and resided in Utah or Idaho as recorded at the visit.

Results

Our method explained the prediction results for 89.7% (391/436) of the patients with asthma who, per our model’s correct prediction, were likely to incur asthma hospital visits in the subsequent year.

Conclusions

This study is the first to demonstrate the feasibility of automatically offering rule-formed explanations for the prediction results of any machine learning model on imbalanced tabular data without lowering the performance measures of the model. After further improvement, our asthma outcome prediction model coupled with the automatic explanation function could be used by clinicians to guide the allocation of limited asthma care management resources and the identification of appropriate interventions.

Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: Secondary Analysis

Background

Asthma causes numerous hospital encounters annually, including emergency department visits and hospitalizations. To improve patient outcomes and reduce the number of these encounters, predictive models are widely used to prospectively pinpoint high-risk patients with asthma for preventive care via care management. However, previous models do not have adequate accuracy to achieve this goal well. Adopting the modeling guideline for checking extensive candidate features, we recently constructed a machine learning model on Intermountain Healthcare data to predict asthma-related hospital encounters in patients with asthma. Although this model is more accurate than the previous models, whether our modeling guideline is generalizable to other health care systems remains unknown.

Objective

This study aims to assess the generalizability of our modeling guideline to Kaiser Permanente Southern California (KPSC).

Methods

The patient cohort included a random sample of 70.00% (397,858/568,369) of patients with asthma who were enrolled in a KPSC health plan for any duration between 2015 and 2018. We produced a machine learning model via a secondary analysis of 987,506 KPSC data instances from 2012 to 2017 and by checking 337 candidate features to project asthma-related hospital encounters in the following 12-month period in patients with asthma.

Results

Our model reached an area under the receiver operating characteristic curve of 0.820. When the cutoff point for binary classification was placed at the top 10.00% (20,474/204,744) of patients with asthma having the largest predicted risk, our model achieved an accuracy of 90.08% (184,435/204,744), a sensitivity of 51.90% (2259/4353), and a specificity of 90.91% (182,176/200,391).

Conclusions

Our modeling guideline exhibited acceptable generalizability to KPSC and resulted in a model that is more accurate than those formerly built by others. After further enhancement, our model could be used to guide asthma care management.

International Registered Report Identifier (IRRID)

RR2-10.2196/resprot.5039

Testing the Generalizability of an Automated Method for Explaining Machine Learning Predictions on Asthma Patients' Asthma Hospital Visits to an Academic Healthcare System

Asthma puts a tremendous overhead on healthcare. To enable effective preventive care to improve outcomes in managing asthma, we recently created two machine learning models, one using University of Washington Medicine data and the other using Intermountain Healthcare data, to predict asthma hospital visits in the next 12 months in asthma patients. As is common in machine learning, neither model supplies explanations for its predictions. To tackle this interpretability issue of black-box models, we developed an automated method to produce rule-style explanations for any machine learning model's predictions made on imbalanced tabular data and to recommend customized interventions without lowering the prediction accuracy. Our method exhibited good performance in explaining our Intermountain Healthcare model's predictions. Yet, it stays unknown how well our method generalizes to academic healthcare systems, whose patient composition differs from that of Intermountain Healthcare. This study evaluates our automated explaining method's generalizability to the academic healthcare system University of Washington Medicine on predicting asthma hospital visits. We did a secondary analysis on 82,888 University of Washington Medicine data instances of asthmatic adults between 2011 and 2018, using our method to explain our University of Washington Medicine model's predictions and to recommend customized interventions. Our results showed that for predicting asthma hospital visits, our automated explaining method had satisfactory generalizability to University of Washington Medicine. In particular, our method explained the predictions for 87.6% of the asthma patients whom our University of Washington Medicine model accurately predicted to experience asthma hospital visits in the next 12 months.

Real-World Patterns of Pharmacotherapeutic Management of Asthma Patients With Exacerbations in the Spanish National Health System

Background

Little is known about the real1world characteristics of asthma patients with exacerbations or their pharmacotherapeutic management. We described the sociodemographic and clinical characteristics, and the patterns of short and long-term management of asthma attacks, in a population-wide cohort of exacerbators in the region of Valencia, Spain.

Methods

We selected asthma patients with at least one exacerbation in 2015 and 2016, we classified them according to their patterns of exacerbations in the 4 years previous to the index exacerbation and their therapeutic step at baseline based on medication received in the previous year. We described the short and long-term pharmacological management of the index exacerbation.

Results

18,714 patients experienced at least one exacerbation. The majority had no previous exacerbation (46.5%), or exacerbated in only one of the years (26.8%). 2.9% had attacks every single year, 25.7% of whom only received rescue medication at baseline. 29.5% of patients without previous exacerbation received maintenance therapy at baseline. Shortly following the index exacerbation, 2,461 patients (13.1%) did not receive any asthma prescription. Among those treated, 70.3% were prescribed a maintenance therapy, 62.4% received a rescue medication, and 30.5% received an oral corticoid. Throughout the year following the index exacerbation, most patients remained in their baseline therapeutic step.

Conclusions

Most patients that exacerbate present very mild to mild forms of the disease or low levels of treatment and most exacerbations are managed in primary care. These insights may help to refine strategies for improving asthma control in the population.

Validity and Reliability of the Assessment Tool for Asthma (ATA) Questionnaire: the ATA Study

OBJECTIVES

A multicenter trial was designed to validate the "Assessment Tools for Asthma (ATA)" questionnaire, a newly developed questionnaire, which evaluates both asthma control and risk factors associated with asthma control with a single instrument.

MATERIALS AND METHODS

This cross-sectional study involved 810 cases from 14 clinics in 9 Turkish cities. The ATA questionnaire and Asthma Control Test (ACT) were administered. The Visual Analog Scale (VAS) was used to evaluate the control status of 100 randomized cases. ATA is an eight-item physician-administered questionnaire. It comprises the following two sections-ATA1, assesses symptomatic control criteria, and the remaining section, queries the flare-up of asthma, control of comorbidities, treatment adherence, and inhaler technique.

RESULTS

The mean scores for ATA1, ATA total, VAS, and ACT were 24.7±14.8, 53.8±19, 7.1±3, and 18.8±5.5, respectively. According to the ATA questionnaire, among all patients, 34.3% had controlled, 18.8% had partly controlled, and 46.9% had uncontrolled asthma. Furthermore, 16.6% patients had flare-ups between visits, 96.4% patients had uncontrolled comorbidity, 17% patients had irregular asthma treatment, and only 8.4% patients used the incorrect inhaler technique. The ATA questionnaire showed internal consistency (Cronbach's alpha coefficient=0.683). ACT, ATA1, and two specialists' evaluations using VAS correlated strongly with the ATA total scores (Spearman correlation coefficient (r) values: 0.776, 0.783, and 0.909, respectively; p-values: p<0.001, p<0.001, and p<0.001, respectively). According to Receiver Operating Characteristic analysis, the cut-off value of ATA was 50 (sensitivity=84.4%, specificity=82.40%).

CONCLUSION

The validated ATA questionnaire may be a practical tool for physicians in asthma management.

Developing a Model to Predict Hospital Encounters for Asthma in Asthmatic Patients: Secondary Analysis

Background

As a major chronic disease, asthma causes many emergency department (ED) visits and hospitalizations each year. Predictive modeling is a key technology to prospectively identify high-risk asthmatic patients and enroll them in care management for preventive care to reduce future hospital encounters, including inpatient stays and ED visits. However, existing models for predicting hospital encounters in asthmatic patients are inaccurate. Usually, they miss over half of the patients who will incur future hospital encounters and incorrectly classify many others who will not. This makes it difficult to match the limited resources of care management to the patients who will incur future hospital encounters, increasing health care costs and degrading patient outcomes.

Objective

The goal of this study was to develop a more accurate model for predicting hospital encounters in asthmatic patients.

Methods

Secondary analysis of 334,564 data instances from Intermountain Healthcare from 2005 to 2018 was conducted to build a machine learning classification model to predict the hospital encounters for asthma in the following year in asthmatic patients. The patient cohort included all asthmatic patients who resided in Utah or Idaho and visited Intermountain Healthcare facilities during 2005 to 2018. A total of 235 candidate features were considered for model building.

Results

The model achieved an area under the receiver operating characteristic curve of 0.859 (95% CI 0.846-0.871). When the cutoff threshold for conducting binary classification was set at the top 10.00% (1926/19,256) of asthmatic patients with the highest predicted risk, the model reached an accuracy of 90.31% (17,391/19,256; 95% CI 89.86-90.70), a sensitivity of 53.7% (436/812; 95% CI 50.12-57.18), and a specificity of 91.93% (16,955/18,444; 95% CI 91.54-92.31). To steer future research on this topic, we pinpointed several potential improvements to our model.

Conclusions

Our model improves the state of the art for predicting hospital encounters for asthma in asthmatic patients. After further refinement, the model could be integrated into a decision support tool to guide asthma care management allocation.

International Registered Report Identifier (IRRID)

RR2-10.2196/resprot.5039

Fractional exhaled nitric oxide was not associated with the future risk of exacerbations in Chinese asthmatics: a non-interventional 1-year real-world study

Background

Exacerbations are recognized as the most relevant predictor of future risk in asthmatics. We aimed to evaluate the association between asthma exacerbations, fractional exhaled nitric oxide (FENO), spirometry indices, and other potential risk factors in a non-interventional, real-world study performed in Guangzhou, China.

Methods

We performed a prospective 12 months follow-up of Chinese asthmatics. Spirometry and FENO measurements were performed at baseline. Adherence to inhaled corticosteroids (ICS) use was divided into two categories (>80% and <80%). Patients were seen 4 times after the initial baseline visit.

Results

A total of 222 patients with asthma (49.1% males) completed the study, of which 51 (23.0%) experienced exacerbations during the study period. Of the patients, 117 (52.7%) had good compliance. We compared lung function indices between the patients with and without exacerbations. There was no difference of forced expiratory volume in 1 s (FEV1) predicted, forced vital capacity (FVC) predicted, and FEVI/FVC (all, P>0.05) between the groups. There was also no significant difference in FENO level between the two groups. Compared to those that had exacerbations, patients without exacerbations had better treatment compliance (P<0.001). Logistic regression analysis identified an association between asthma exacerbations, poor control of symptom [odds ratio (OR) =2.295; 95% confidence interval (CI): 1.130-4.663; P=0.022], and nonadherence to asthma medications (OR =4.718; 95% CI: 2.149-10.359; P<0.001).

Conclusions

Poor adherence rather than baseline FENO and FEV1% predicted was associated with the future risk of exacerbations in Chinese asthmatics in real world.

Identifying Population Groups with Low Palliative Care Program Enrolment Using Classification and Regression Tree Analysis

Classification and regression tree (CART) analysis was used to identify subpopulations with lower palliative care program (PCP) enrolment rates. CART analysis uses recursive partitioning to group predictors. The PCP enrolment rate was 72 percent for the 6,892 adults who died of cancer from 2000 and 2005 in two counties in Nova Scotia, Canada. The lowest PCP enrolment rates were for nursing home residents over 82 years (27 percent), a group residing more than 43 kilometres from the PCP (31 percent), and another group living less than two weeks after their cancer diagnosis (37 percent). The highest rate (86 percent) was for the 2,118 persons who received palliative radiation. Findings from multiple logistic regression (MLR) were provided for comparison. CART findings identified low PCP enrolment subpopulations that were defined by interactions among demographic, social, medical, and health system predictors.

Optimizing community-level surveillance data for pediatric asthma management

Community-level approaches for pediatric asthma management rely on locally collected information derived primarily from two sources: claims records and school-based surveys. We combined claims and school-based surveillance data, and examined the asthma-related risk patterns among adolescent students. Symptom data collected from school-based asthma surveys conducted in Oakland, CA were used for case identification and determination of severity levels for students (high and low). Survey data were matched to Medicaid claims data for all asthma-related health care encounters for the year prior to the survey. We then employed recursive partitioning to develop classification trees that identified patterns of demographics and healthcare utilization associated with severity. A total of 561 students had complete matched data; 86.1% were classified as high-severity, and 13.9% as low-severity asthma. The classification tree consisted of eight subsets: three indicating high severity and five indicating low severity. The risk subsets highlighted varying combinations of non-specific demographic and socioeconomic predictors of asthma prevalence, morbidity and severity. For example, the subset with the highest class-prior probability (92.1%) predicted high-severity asthma and consisted of students without prescribed rescue medication, but with at least one in-clinic nebulizer treatment. The predictive accuracy of the tree-based model was approximately 66.7%, with an estimated 91.1% of high-severity cases and 42.3% of low-severity cases correctly predicted. Our analysis draws on the strengths of two complementary datasets to provide community-level information on children with asthma, and demonstrates the utility of recursive partitioning methods to explore a combination of features that convey asthma severity.

Exacerbations in Adults with Asthma: A Systematic Review and External Validation of Prediction Models

BACKGROUND

Several prediction models assessing future risk of exacerbations in adult patients with asthma have been published. Applicability of these models is uncertain because their predictive performance has often not been assessed beyond the population in which they were derived.

OBJECTIVE

This study aimed to identify and critically appraise prediction models for asthma exacerbations and validate them in 2 clinically distinct populations.

METHODS

PubMed and EMBASE were searched to April 2017 for reports describing adult asthma populations in which multivariable models were constructed to predict exacerbations during any time frame. After critical appraisal, the models' predictive performances were assessed in a primary and a secondary care population for author-defined exacerbations and for American Thoracic Society/European Respiratory Society-defined severe exacerbations.

RESULTS

We found 12 reports from which 24 prediction models were evaluated. Three predictors (previous health care utilization, symptoms, and spirometry values) were retained in most models. Assessment was hampered by suboptimal methodology and reporting, and by differences in exacerbation outcomes. Discrimination (area under the receiver-operating characteristic curve [c-statistic]) of models for author-defined exacerbations was better in the primary care population (mean, 0.71) than in the secondary care population (mean, 0.60) and similar (0.65 and 0.62, respectively) for American Thoracic Society/European Respiratory Society-defined severe exacerbations. Model calibration was generally poor, but consistent between the 2 populations.

CONCLUSIONS

The preservation of 3 predictors in models derived from variable populations and the fairly consistent predictive properties of most models in 2 distinct validation populations suggest the feasibility of a generalizable model predicting severe exacerbations. Nevertheless, improvement of the models is warranted because predictive performances are below the desired level.

Predicting future risk of exacerbations in Japanese patients with adult asthma: A prospective 1-year follow up study

BACKGROUND

To avoid future risk is a definitive goal of long-term asthma management. Exacerbations are considered to be the most relevant future risk in real life asthma management. Few comparative studies have evaluated the risk factors associated with exacerbations in Japanese patients with asthma.

METHODS

We performed the prospective 1-year follow up study in Japanese patients with adult asthma. A total of 189 patients with asthma were enrolled and followed up for 1 year. Finally, 181 patients completed the study protocol.

RESULTS

Of 181 patients, 43 patients (23.8%) had exacerbations during the follow-up period. Among the 45 patients who had exacerbations during the preceding year, 32 patients (71.1%) had exacerbations. Prevalence of patients with previous exacerbations and those with previous admissions were significantly higher in patients with exacerbations than those with no exacerbation. Logistic regression analysis also identified a significant association between exacerbations during the follow-up period and exacerbations during the preceding year, admissions during the preceding 3 years, ACT score below 20, low %FVC (<80%), or low FEV₁ (<70%), respectively. Of the 55 patients with severe asthma, 29 patients (52.7%) had exacerbations. Among the 36 patients with severe asthma with previous exacerbations, 26 patients (72.2%) had exacerbations. The history of exacerbations during the preceding year was associated with a significantly increased risk of exacerbations both among the patients with severe asthma and those with non-severe asthma.

CONCLUSIONS

This study implicated that exacerbations during the preceding year reliably predict future risk of exacerbations in Japanese patients with asthma.

CHOICE: Choosing Health Options In Chronic Care Emergencies

Background

Over 70% of the health-care budget in England is spent on the care of people with long-term conditions (LTCs), and a major cost component is unscheduled health care. Psychological morbidity is high in people with LTCs and is associated with a range of adverse outcomes, including increased mortality, poorer physical health outcomes, increased health costs and service utilisation.

Objectives

The aim of this programme of research was to examine the relationship between psychological morbidity and use of unscheduled care in people with LTCs, and to develop a psychosocial intervention that would have the potential to reduce unscheduled care use. We focused largely on emergency hospital admissions (EHAs) and attendances at emergency departments (EDs).

Design

A three-phase mixed-methods study. Research methods included systematic reviews; a longitudinal prospective cohort study in primary care to identify people with LTCs at risk of EHA or ED admission; a replication study in primary care using routinely collected data; an exploratory and feasibility cluster randomised controlled trial in primary care; and qualitative studies to identify personal reasons for the use of unscheduled care and factors in routine consultations in primary care that may influence health-care use. People with lived experience of LTCs worked closely with the research team.

Setting

Primary care. Manchester and London.

Participants

People aged ≥ 18 years with at least one of four common LTCs: asthma, coronary heart disease, chronic obstructive pulmonary disease (COPD) and diabetes. Participants also included health-care staff.

Results

Evidence synthesis suggested that depression, but not anxiety, is a predictor of use of unscheduled care in patients with LTCs, and low-intensity complex interventions reduce unscheduled care use in people with asthma and COPD. The results of the prospective study were that depression, not having a partner and life stressors, in addition to prior use of unscheduled care, severity of illness and multimorbidity, were independent predictors of EHA and ED admission. Approximately half of the cost of health care for people with LTCs was accounted for by use of unscheduled care. The results of the replication study, carried out in London, broadly supported our findings for risk of ED attendances, but not EHAs. This was most likely due to low rates of detection of depression in general practitioner (GP) data sets. Qualitative work showed that patients were reluctant to use unscheduled care, deciding to do so when they perceived a serious and urgent need for care, and following previous experience that unscheduled care had successfully and unquestioningly met similar needs in the past. In general, emergency and primary care doctors did not regard unscheduled care as problematic. We found there are missed opportunities to identify and discuss psychosocial issues during routine consultations in primary care due to the ‘overmechanisation’ of routine health-care reviews. The feasibility trial examined two levels of an intervention for people with COPD: we tried to improve the way in which practices manage patients with COPD and developed a targeted psychosocial treatment for patients at risk of using unscheduled care. The former had low acceptability, whereas the latter had high acceptability. Exploratory health economic analyses suggested that the practice-level intervention would be unlikely to be cost-effective, limiting the value of detailed health economic modelling.

Limitations

The findings of this programme may not apply to all people with LTCs. It was conducted in an area of high social deprivation, which may limit the generalisability to more affluent areas. The response rate to the prospective longitudinal study was low. The feasibility trial focused solely on people with COPD.

Conclusions

Prior use of unscheduled care is the most powerful predictor of unscheduled care use in people with LTCs. However, psychosocial factors, particularly depression, are important additional predictors of use of unscheduled care in patients with LTCs, independent of severity and multimorbidity. Patients and health-care practitioners are unaware that psychosocial factors influence health-care use, and such factors are rarely acknowledged or addressed in consultations or discussions about use of unscheduled care. A targeted patient intervention for people with LTCs and comorbid depression has shown high levels of acceptability when delivered in a primary care context. An intervention at the level of the GP practice showed little evidence of acceptability or cost-effectiveness.

Future work

The potential benefits of case-finding for depression in patients with LTCs in primary care need to be evaluated, in addition to further evaluation of the targeted patient intervention.

Funding

The National Institute for Health Research Programme Grants for Applied Research programme.

Predictive factors for moderate or severe exacerbations in asthma patients receiving outpatient care

BACKGROUND

Asthma exacerbations are important events that affect disease control, but predictive factors for severe or moderate exacerbations are not known. The objective was to study the predictive factors for moderate (ME) and severe (SE) exacerbations in asthma patients receiving outpatient care.

METHODS

Patients aged > 12 years with asthma were included in the study and followed-up at 4-monthly intervals over a 12-month period. Clinical (severity, level of control, asthma control test [ACT]), atopic, functional, inflammatory, SE and ME parameters were recorded. Univariate analysis was used to compare data from patients presenting at least 1 SE or ME during the follow-up period vs no exacerbations. Statistically significant (p <0.1) factors were then subjected to multiple analysis by binary logistic regression.

RESULTS

A total of 330 patients completed the study, most of whom were atopic (76%), women (nearly 70%), with moderate and mild persistent asthma (>80%). Twenty-seven patients (8%) had a SE and 183 had a ME (58.5%) during follow-up. In the case of SEs, the only predictive factor identified in the multiple analysis was previous SE (baseline visit OR 4.218 95% CI 1.53-11.58, 4-month follow-up OR 6.88 95% CI 2.018-23.51) and inhalation technique (OR 3.572 95% CI 1.324-9.638). In the case of MEs, the only predictive factor found in the multiple analysis were previous ME (baseline visit OR 2.90 95% CI 1.54-5.48, 4-month follow- up OR 1.702 95% CI 1.146-2.529).

CONCLUSIONS

The primary predictive factor for SE or ME is prior SE or ME, respectively. SEs seem to constitute a specific patient "phenotype", in which the sole predictive factor is prior SEs.

Determinants and impact of suboptimal asthma control in Europe: The INTERNATIONAL CROSS-SECTIONAL AND LONGITUDINAL ASSESSMENT ON ASTHMA CONTROL (LIAISON) study

Background

According to the Global Initiative of Asthma, the aim of asthma treatment is to gain and maintain control. In the INTERNATIONAL CROSS-SECTIONAL AND LONGITUDINAL ASSESSMENT ON ASTHMA CONTROL (LIAISON) study, we evaluated the level of asthma control and quality of life (QoL), as well as their determinants and impact in a population consulting specialist settings.

Methods

LIAISON is a prospective, multicentre, observational study with a cross-sectional and a 12-month longitudinal phase. Adults with an asthma diagnosis since at least 6 months, receiving the same asthma treatment in the 4 weeks before enrolment were included. Asthma control was assessed with the 6-item Asthma Control Questionnaire (ACQ) and QoL with the MiniAsthma Quality of Life Questionnaire (MiniAQLQ).

Results

Overall, 8111 asthmatic patients were enrolled in 12 European countries. Asthma control was suboptimal in 56.5 % of patients and it was associated with poorer asthma-related QoL, higher risk of exacerbations and greater consumption of healthcare resources.

Variables associated with suboptimal control were age, gender, obesity, smoking and comorbidities. Major determinants of poor asthma control were seasonal worsening and persisting exposure to allergens/irritants/triggers, followed by treatment-related issues.

Conclusions

The cross-sectional phase results confirm that suboptimal control is frequent and has a high individual and economic impact.

Trial registration

The clinicaltrials.gov identifier is NCT01567280.

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-016-0374-z) contains supplementary material, which is available to authorized users.

Phase-specific healthcare costs of cervical cancer: estimates from a population-based study

BACKGROUND

There is a lack of evidence on the economic burden of managing cervical cancer in the public payer Canadian setting.

OBJECTIVE

We used individual patient-level data to obtain a comprehensive estimate of the cost of managing cervical cancer in the province of Ontario, identifying main cost drivers and predictors of increased costs.

STUDY DESIGN

The cost-of-illness technique was used to estimate the incremental costs associated with cervical cancer in 4 phases: prediagnosis, initial care, continuing care, and terminal care. All patients with cervical cancer diagnosed between 2005 and 2009 in the province of Ontario were propensity-score matched to 5 noncancer controls on birth year, income quintile, rurality, comorbidities, and patterns of healthcare utilization pattern during the 2 years before cancer diagnosis. Both cases and the noncancer comparison group were followed to death or March 31, 2013. Costs for all healthcare services paid for by the Ontario Ministry of Health and Long-term Care during the follow-up period were estimated by the use of linked administrative data. Incremental costs for managing cervical cancer were calculated through generalized estimating equations. Predictors of greater health costs were explored using multivariate quantile regression models.

RESULTS

All costs were presented in 2012 Canadian dollars ($1.00CDN = $1.00USD). The total incremental costs for managing cervical cancer were $362 in the pre-diagnosis phase, $15,722 in the initial phase, $3924 per year in the continuing phase, and $52,539 in the terminal phase. Inpatient care accounted for 34%, 28%, and 52% of total healthcare cost in the initial, continuing, and terminal phase, respectively. Physician services ranked first in the continuing phase (30%) and second in the initial (26%) and terminal (13%) phases. Advanced age, advanced cancer stage at diagnosis, and comorbidities were significant predictors of greater costs in most care phases.

CONCLUSION

Aggregate costs of care for cervical cancer are substantial and vary by cancer stage, phase of care, patient age at diagnosis, and comorbidities before diagnosis. These estimates can serve as baseline data in economic analyses that aim to evaluate interventions for managing cervical cancer.

Asthma control and exacerbations: two different sides of the same coin

PURPOSE OF REVIEW

Optimal asthma management includes both the control of asthma symptoms and reducing the risk of future asthma exacerbations. Traditionally, treatment has been adjusted largely on the basis of symptoms and lung function and for many patients, this approach delivers both excellent symptom control and reduced risk. However, the relationship between these two key components of the disease may vary between different asthmatic phenotypes and disease severities and there is increasing recognition of the need for more individualized treatment approaches.

RECENT FINDINGS

A number of factors which predict exacerbation risk have been identified including demographic and behavioural features and specific inflammatory biomarkers. Type-2 cytokine-driven eosinophilic airways inflammation predisposes to frequent exacerbations and predicts response to corticosteroids, and the usefulness of sputum eosinophilia as both a marker of exacerbation risk and biomarker for adjustment of corticosteroid treatment has been established for some time. However, attempts to develop surrogate markers, which would be more straightforward to deliver in the clinic, have been challenging.

SUMMARY

Some patients with asthma have persistent symptoms in the absence of type-2 cytokine driven-eosinophilic airways inflammation due to noncorticosteroid responsive mechanisms (T2-low disease). Composite biomarker strategies using easily measured surrogate indicators of type-2 inflammation (such as fractional exhaled nitric oxide, blood eosinophil count and serum periostin levels) may predict exacerbation risk better but it is unclear if they can be used to adjust corticosteroid treatment. Biomarkers will be used to target novel biologic treatments but additionally may be used to optimize corticosteroid treatment dose and act as prognostics for exacerbation risk and potentially other important longer term asthma outcomes.

Predicting future emergency department visits and hospitalizations for asthma using the Pediatric Asthma Control and Communication Instrument - Emergency Department version (PACCI-ED)

OBJECTIVE

Emergency departments (EDs) are potential settings for interventions to improve asthma outcomes. Screening tools can identify children at risk of future morbidity. Our objective was to determine the predictive validity of the Pediatric Asthma Control and Communication Instrument - Emergency Department version (PACCI-ED) for future asthma-related ED visits and hospitalizations.

METHODS

This was a retrospective cohort study of 108 children 1-17 years old who visited an ED for asthma and completed the PACCI-ED. The PACCI-ED queries parents about prior 12-month ED visits, hospitalizations, steroid use, perceived asthma morbidity and burden, and asthma control (over prior 1-2 weeks). The primary outcome was subsequent ED visits and hospitalizations within 1 year of enrollment. Poisson regression was used to model PACCI-ED questions for future ED visits controlling for age and socioeconomic status.

RESULTS

Reported ED visits predicted future ED visits (adjusted incidence rate ratio (aIRR) 3.1, 95% confidence interval (CI): 1.3-7.2) but not future hospitalizations. Reported hospitalizations predicted future ED visits (aIRR 3.3; 95% CI 1.7-6.3) and hospitalizations (aIRR 6.4; 95% CI 2.3-17.6). The remaining PACCI-ED questions did not predict future ED visits or hospitalizations.

CONCLUSIONS

The PACCI-ED risk domain was the only domain that predicted future asthma ED visits and hospitalizations. Questions about previous ED visits and hospitalizations are the most effective questions when screening children with asthma in EDs for the risk of future health-care use.

Novel pulmonary rehabilitation approach in a 46-year-old female with significant decline in lung and functional status following H1N1

INTRODUCTION

Pulmonary rehabilitation (PR) is an established therapeutic intervention for improving limb muscle dysfunction, reducing morbidity and mortality in a variety of chronic lung conditions. Providers are instrumental in improving success by optimizing disease management, minimizing barriers and tailoring a program to meet the patient's goals and functional needs.

CASE REPORT

We present a case of a young woman with mild asthma who developed severe chronic obstructive pulmonary disease following H1N1. She remained limited in instrumental activities of daily living following traditional PR but participated in therapeutic horseback riding with notable improvement in functional capacity and emotional well-being.

CONCLUSIONS

There is a growing body of knowledge on the benefits of PR but little is known about the physiologic and psychological benefits of other forms of exercise such as horseback riding. This case highlights the importance of individualizing care and identifies a novel area of research to be explored.

Tailoring interventions: identifying predictors of poor asthma control

BACKGROUND

Determining the factors that will predict long-term asthma control is essential for improving health outcomes and decreasing the burden on the health care system. Patient-reported outcomes (PROs) on health behaviors can provide valuable information about future asthma control but have rarely been considered in previous analyses.

OBJECTIVE

To develop statistical models for evaluating the predictors of long-term asthma control using PROs such as scores of the Asthma Control Test and the Asthma Self-Efficacy Scale.

METHODS

Of 1,437 individuals contacted, 566 (39%) at baseline and 486 (34%) at follow-up completed the questionnaires, including 4 PROs (Asthma Control Test, Asthma Self-Efficacy Scale, Mini-Asthma Quality of Life Questionnaire, and Beliefs about Medication Questionnaire). Long-term asthma control was evaluated by assessing overuse of rescue medication and emergency department visits. A multivariate logistic generalized estimating equation model was fitted to evaluate the possible effect of the studied factors on asthma control.

RESULTS

The complete case generalized estimating equation analysis included 286 participants who had complete PROs at the 2 evaluation times. After adjusting for socioeconomic status and smoking status, the Mini-Asthma Quality of Life Questionnaire was a significant predictor of asthma exacerbation. For each 1-point increase on the Mini-Asthma Quality of Life Questionnaire, there was a 0.25 decrease in the odds of a patient's asthma getting out of control.

CONCLUSION

These findings suggest opportunities to decrease the burden on health care by tailoring interventions that combine PROs with other clinical and sociodemographic variables.

Defining and managing risk in asthma

Asthma attacks are a major global source of morbidity and cost. The incidence and impact of asthma attacks have not improved despite widespread adoption of effective universal treatment guidelines. Consequently, there is increasing interest in managing asthma based on specific assessments of both current symptoms and future risk. In this review, we consider 'risk' in asthma, and how it might be assessed from the patient's history and objective measurements. We also discuss the potential for encouraging shared decision-making and improving medical consensus through explicit communication of risk and highlight the potential opportunities and challenges in risk assessment to improve asthma management through individualised treatment strategies.

Patterns of health care utilization preceding a colorectal cancer diagnosis are strong predictors of dying quickly following diagnosis

Background

Understanding the predictors of a quick death following diagnosis may improve timely access to palliative care. The objective of this study was to explore whether factors in the 24 months prior to a colorectal cancer (CRC) diagnosis predict a quick death post-diagnosis.

Methods

Data were from a longitudinal study of all adult persons diagnosed with CRC in Nova Scotia, Canada, from 01Jan2001-31Dec2005. This study included all persons who died of any cause by 31Dec2010, except those who died within 30 days of CRC surgery (n = 1885 decedents). Classification and regression tree models were used to explore predictors of time from diagnosis to death for the following time intervals: 2, 4, 6, 8, 12, and 26 weeks from diagnosis to death. All models were performed with and without stage at diagnosis as a predictor variable. Clinico-demographic and health service utilization data in the 24 months pre-diagnosis were provided via linked administrative databases.

Results

The strongest, most consistent predictors of dying within 2, 4, 6, and 8 weeks of CRC diagnosis were related to health services utilization in the 24 months prior to diagnosis: i.e., number of specialist visits, number of days spent in hospital, and number of family physician visits. Stage at diagnosis was the strongest predictor of dying within 12 and 26 weeks of diagnosis.

Conclusions

Identifying potential predictors of a short timeframe between cancer diagnosis and death may aid in the development of strategies to facilitate timely and appropriate referral to palliative care upon a cancer diagnosis.

Performance of a brief asthma control screening tool in community pharmacy: a cross-sectional and prospective longitudinal analysis

BACKGROUND

Guidelines recommend basing asthma management on assessment of asthma control. Validated control tools, while suitable for clinical research, may not be feasible for routine use in primary care.

AIMS

To describe the performance of the Pharmacy Asthma Control Screening tool (PACS) compared with the Asthma Control Questionnaire (ACQ-6).

METHODS

Data were obtained from a multicentre study of a community pharmacy asthma management programme in Australia, with three or four visits over six months. Eligible participants had suboptimal asthma control or no recent visit to their doctor for asthma. Asthma control was assessed at baseline and at six months with the PACS tool and ACQ-6.

RESULTS

A total of 570 patients were enrolled and 398 (70%) completed the programme. The average ACQ-6 score was 1.58±1.05 at baseline and 0.96±0.88 (n=392) after six months. Sensitivity and specificity of PACS 'poor control' for not well-controlled asthma (ACQ- 6 >1.0) were 0.92 and 0.66, respectively, at baseline and 0.76 and 0.83 at six months. Agreement between the two tools at six months was moderate (κ=0.54). Both tools showed highly significant change during the study (p<0.0001 for each), but agreement between the change in the two tools was only fair (κ=0.31).

CONCLUSIONS

This study shows that a simple asthma control screening tool is feasible for use in community pharmacies and has good sensitivity for identifying patients with not well-controlled asthma. Screening tools are useful in primary care to identify patients who require more detailed assessment of their asthma status, whereas for monitoring asthma control over time, a continuous control measure is more appropriate.

Assessing the risk of attack in the management of asthma: a review and proposal for revision of the current control-centred paradigm

Asthma guidelines focus on day-to-day control of symptoms. However, asthma attacks remain common. They continue to cause mortality and considerable morbidity, and are a major financial burden to the UK National Health Service (NHS) and the wider community. Asthma attacks have chronic consequences, being associated with loss of lung function and significant psychological morbidity. In this article we argue that addressing daily symptom control is only one aspect of asthma treatment, and that there should be a more explicit focus on reducing the risk of asthma attacks. Management of future risk by general practitioners is already central to other conditions such as ischaemic heart disease and chronic renal impairment. We therefore propose a revised approach that separately considers the related domains of daily control and future risk of asthma attack. We believe this approach will have advantages over the current 'stepwise' approach to asthma management. It should encourage individualised treatment, including non-pharmacological measures, and thus may lead to more efficacious and less harmful management strategies. We speculate that this type of approach has the potential to reduce morbidity and healthcare costs related to asthma attacks.

Two short interventions to reduce health care requirements in asthma patients. A multicentre controlled study (ASTHMACAP II)

BACKGROUND AND OBJECTIVE

Asthma control is suboptimal. The objective of this study was to reduce health care requirements and work absenteeism.

MATERIAL AND METHODS

Multicenter randomized controlled study investigating asthma control, educational parameters, health service use, and absenteeism. After adjusting treatment according to GINA recommendations, control group patients (CG) followed their physician's recommendations, while intervention group (IG) patients additionally underwent a 5-minute educational intervention. This protocol was repeated at 3 months, and a final assessment was carried out at 6 months.

RESULTS

479 patients (mean age 40 (SD 17) years) were recruited from primary care, and 334 completed the study. Comparatively, IG patients showed an improvement at the 3- and 6-month evaluations in the six educational parameters (P<0.001) and required fewer urgent visits to the GP for exacerbations [RR=0.49 (95% CI 0.26-0.90); P<0.04], and before the third evaluation, also in urgent GP visits [RR=0.25 (95% CI 0.12-0.52); P<0.001]. Before this third evaluation, IG had fewer scheduled visits to the GP [RR=0.48 (95% CI 0.28-0.82); P<0.003], and fewer visits to the primary care [RR=0.40 (95% CI 0.18-0.87); P<0.05], and to hospital emergency rooms [RR=0.13 (95% CI 0.04-0.42); P<0.001]. In addition, before the third evaluation, IG patients were less often absent from work [RR=0.22 (95% CI 0.05-0.98); P<0.03] or unable to work at home [RR=0.31 (95% CI 0.12-0.82); P<0.02].

CONCLUSIONS

Two short educational interventions improved asthma education and decreased the use of health resources and work absenteeism.

Why do patients with long-term conditions use unscheduled care? A qualitative literature review

Unscheduled care (UC) refers to non-routine face-to-face care, such as accident and emergency care, out-of-hours care, or walk-in centres. Current health service policy aims to reduce its use. Unscheduled care is common in people with long-term conditions such as diabetes, asthma, chronic obstructive pulmonary disease and coronary heart disease. By reviewing qualitative research literature, we aimed to understand the breadth of psychosocial and other influences on UC use in people with long-term conditions. Few qualitative papers specifically address UC in patients in these disease groups. Therefore, our literature search also included qualitative research that explored factors potentially relevant to UC use, including attitudes to healthcare use in general. By searching Medline, Embase, Psycinfo and Cinahl from inception to 2011, we identified 42 papers, published since 1984, describing relevant original research and took a meta-ethnographic approach in reviewing them. The review was conducted between Spring 2009 and April 2011, with a further search in December 2011. Most papers reported on asthma (n = 13) or on multiple or unspecified conditions (n = 12). The most common methods reported were interviews (n = 33) and focus groups (n = 13), and analyses were generally descriptive. Theoretical and ethical background was rarely explicit, but the implicit starting point was generally the 'problem' of UC, and health-care, use in general, decontextualised from the lives of the patients using it. Patients' use of UC emerged as understandable, rational responses to pressing clinical need in situations in which patients thought it the only option. This belief reflected the value that they had learned to attach to UC versus routine care through previous experiences. For socially or economically marginalised patients, UC offered access to clinical or social care that was otherwise unavailable to them.

4-1BB ligand-mediated imbalance of helper 17 T cells and regulatory T cells in patients with allergic asthma

OBJECTIVES

To investigate the presence of 4-1BB ligand (4-1BBL) in the peripheral blood of patients with allergic asthma and evaluate its role in controlling the balance between helper 17 T (T(h)17) and regulatory T (T(reg)) cells.

METHODS

Soluble 4-1BBL (s4-1BBL) was quantified by enzyme-linked immunosorbent assay in plasma from patients with asthma (n = 45) and from healthy control subjects (n = 35). The proportion of monocytes positive for membrane-bound 4-1BBL (m4-1BBL) was determined by flow cytometry. Peripheral blood mononuclear cells from patients with asthma were incubated with anti-4-1BB monoclonal antibody in vitro. Concentrations of interleukin (IL)-17 and transforming growth factor (TGF)-β(1) in the culture supernatant were analysed.

RESULTS

Plasma s4-1BBL concentrations and the proportion of m4-1BBL-positive monocytes were significantly lower in patients with asthma than in control subjects. The culture supernatant concentration of TGF-β(1) was increased and that of IL-17 was decreased by incubation with anti-4-1BB monoclonal antibody.

CONCLUSIONS

Both soluble and membrane-bound 4-1BBL were reduced in patients with allergic asthma compared with control subjects. 4-1BBL/4-1BB signalling may play an important role in allergic asthma by regulating the T(h)17/T(reg) balance.

Physical activity and other health-related factors predict health care utilisation in older adults: the ActiFE Ulm study

BACKGROUND

Health care utilisation (HCU) can be a useful outcome for estimating costs and patient needs. It can also be used as a surrogate parameter for healthy ageing. The aim of this study was to analyse the associations of formerly described and potentially new parameters influencing health care utilisation in older adults in Germany.

PATIENTS AND METHODS

The ActiFE Ulm (Activity and Function in the Elderly in Ulm) study is a population-based study in 1,506 community dwelling older adults aged 65-90 years in Ulm and surrounding areas in southwestern Germany. Between March 2009 and April 2010 a full geriatric assessment was performed including accelerometer-based average daily walking duration, comorbidity, medication, physical and psychological functioning, health care utilisation, sociodemographic factors etc. The association between above named measures and health care utilisation, represented by the number of drugs, the days in hospital and the number of physician contacts over one year was calculated in multiple regression models. Analysis was conducted among subjects with complete information (n = 1,059, mean age 76 years, 55% male).

RESULTS

The average number of drugs was 4.5 and over 95% of participants visited a physician at least once a year while still more than 65% contacted their physician more than twice a year. Reduced physical activity, BMI, self-rated health and/or comorbidity and male sex were the best predictors of health care utilisation in community dwelling older adults when looking at both the number of drugs and the number of physician contacts over 12 months together. With regard to single diseases entities the best predictors of both the number of drugs and the number of physician contacts were asthma, chronic obstructive pulmonary disease (COPD)/chronic bronchitis and chronic neurological diseases (mostly Parkinson's disease). The number of drugs was most strongly associated with coronary heart disease, diabetes, and high blood pressure.

CONCLUSION

Reduced walking activity, self-rated health and/or comorbidity and male sex are the best predictors of health care utilisation as measured by the number of drugs and number of physician contacts over 12 months. Walking activity could be regarded as the most promising modifiable predictor of HCU in older adults.

Validation of administrative data sources for endoscopy utilization in colorectal cancer diagnosis

Background

Validation of administrative data is important to assess potential sources of bias in outcome evaluation and to prevent dissemination of misleading or inaccurate information. The purpose of the study was to determine the completeness and accuracy of endoscopy data in several administrative data sources in the year prior to colorectal cancer diagnosis as part of a larger project focused on evaluating the quality of pre-diagnostic care.

Methods

Primary and secondary data sources for endoscopy were collected from the Alberta Cancer Registry, cancer medical charts and three different administrative data sources. 1672 randomly sampled patients diagnosed with invasive colorectal cancer in years 2000–2005 in Alberta, Canada were included. A retrospective validation study of administrative data for endoscopy in the year prior to colorectal cancer diagnosis was conducted. A gold standard dataset was created by combining all the datasets. Number and percent identified, agreement and percent unique to a given data source were calculated and compared across each dataset and to the gold standard with respect to identifying all patients who underwent endoscopy and all endoscopies received by those patients.

Results

The combined administrative data and physician billing data identified as high or higher percentage of patients who had one or more endoscopy (84% and 78%, respectively) and total endoscopy procedures (89% and 81%, respectively) than the chart review (78% for both).

Conclusions

Endoscopy data has a high level of completeness and accuracy in physician billing data alone. Combined with hospital in/outpatient data it is more complete than chart review alone.

Using the pediatric asthma therapy assessment questionnaire to measure asthma control and healthcare utilization in children

BACKGROUND

: The usefulness of questionnaires to assess asthma control in clinical practice is recognized in recent international guidelines. While several questionnaires have been developed to measure asthma control in adults, there has been little study of the performance of such instruments in children.

OBJECTIVE

: To determine whether there is an association between asthma-related healthcare use and poor asthma control, as determined by categorical score on the control domain of the Asthma Therapy Assessment Questionnaire for children and adolescents (the pediatric ATAQ).

METHODS

: An analysis of a 1998 mailed survey of parents or caregivers of children aged 5-17 years with asthma enrolled in three large managed-care organizations in the Northeast and Midwest US was conducted. Pediatric ATAQ control domain score (reported for the past 4 weeks) was the main outcome measure. The pediatric ATAQ control domain was scored from 0 to 7, with 0 indicating no asthma control problems as measured by the questionnaire, and higher scores indicating increasing asthma problems. The hypothesis of an association between pediatric ATAQ control domain score and asthma-related healthcare use (hospitalizations, ER or urgent care facility visits, and doctor visits for worsening asthma in the past 12 months) was examined.

RESULTS

: 406 completed surveys were received. Asthma-related hospitalizations, ER/urgent care visits, and doctor visits were reported for 38, 173, and 319 children, respectively. Of the three control score categories (0, 1-3, and 4-7), children with a control score of 4-7 were more likely to have been hospitalized (p = 0.01), to have visited the ER or urgent care facility (p < 0.0001), or to have visited a doctor (p = 0.0001) because of asthma managed care.In multivariate models including demographic variables and a measure of general health status, higher odds of ER/urgent care visits (odds ratio [OR] 3.47, 95% CI 1.92, 6.26) and doctor visits (OR 7.14; 95% CI 2.40, 21.2) was observed for children with an asthma control score of 4-7 than for children with no identified asthma control problems (score of 0). An asthma control score of 4-7 was significantly associated with hospitalization in a multivariate model including only demographic variables (OR 3.06; 95% CI 1.28, 7.33) but not in a model that included general health status (OR 2.44; 95% CI 0.96, 6.16). Relative to an excellent health status, a fair or poor health status was significantly associated with asthma-related hospitalization (OR 7.03; 95% CI 1.71, 28.87). Compared with White race, Black race was significantly associated with hospitalization (OR 2.30; 95% CI 1.05, 5.04) and ER/urgent care visits (OR 2.89; 95% CI 1.67, 5.01).

CONCLUSIONS

: Children identified as having poor asthma control using the pediatric ATAQ instrument had significantly higher rates of asthma-related hospitalizations, ER or urgent care visits, and doctor visits than those with good control. This asthma control measure may be useful in identifying children in need of more intensive asthma management.

Measurement of asthma control according to Global Initiative for Asthma guidelines: a comparison with the Asthma Control Questionnaire

Introduction

Asthma Control Questionnaire (ACQ) is a validated tool to measure asthma control. Cut-off points that best discriminate “well-controlled” or “not well-controlled” asthma have been suggested from the analysis of a large randomized clinical trial but they may not be adequate for daily clinical practice.

Aims

To establish cut-off points of the ACQ that best discriminate the level of control according to Global Initiative for Asthma (GINA) 2006 guidelines in patients with asthma managed at Allergology and Pulmonology Departments as well as Primary Care Centers in Spain.

Patients and methods

An epidemiological descriptive study, with prospective data collection. Asthma control following GINA-2006 classification and 7-item ACQ was assessed. The study population was split in two parts: 2/3 for finding the cut-off points (development population) and 1/3 for validating the results (validation population).

Results

A total of 1,363 stable asthmatic patients were included (mean age 38 ± 14 years, 60.3% women; 69.1% non-smokers). Patient classification according to GINA-defined asthma control was: controlled 13.6%, partially controlled 34.2%, and uncontrolled 52.3%. The ACQ cut-off points that better agreed with GINA-defined asthma control categories were calculated using receiver operating curves (ROC). The analysis showed that ACQ < 0.5 was the optimal cut-off point for “controlled asthma” (sensitivity 74.1%, specificity 77.5%) and 1.00 for “uncontrolled asthma” (sensitivity 73%, specificity 88.2%). Kappa index between GINA categories and ACQ was 0.62 (p < 0.001).

Conclusion

The ACQ cut-off points associated with GINA-defined asthma control in a real-life setting were <0.5 for controlled asthma and ≥1 for uncontrolled asthma.

Asthma outcomes: composite scores of asthma control

BACKGROUND

Current asthma guidelines recommend assessing the level of a patient's asthma control. Consequently, there is increasing use of asthma control as an outcome measure in clinical research studies. Several composite assessment instruments have been developed to measure asthma control.

OBJECTIVE

National Institutes of Health institutes and federal agencies convened an expert group to propose the most appropriate standardized composite score of asthma control instruments to be used in future asthma studies.

METHODS

We conducted a comprehensive search of PubMed using both the National Library of Medicine's Medical Subject Headings and key terms to identify studies that attempted to develop and/or test composite score instruments for asthma control. We classified instruments as core (required in future studies), supplemental (used according to study aims and standardized), or emerging (requiring validation and standardization). This work was discussed at a National Institutes of Health-organized workshop convened in March 2010 and finalized in September 2011.

RESULTS

We identified 17 composite score instruments with published validation information; all had comparable content. Eight instruments demonstrated responsiveness over time; 3 demonstrated responsiveness to treatment. A minimal clinically important difference has been established for 3 instruments. The instruments have demographic limitations; some are proprietary, and their use could be limited by cost.

CONCLUSION

Two asthma composite score instruments are sufficiently validated for use in adult populations, but additional research is necessary to validate their use in nonwhite populations. Gaps also exist in validating instruments for pediatric populations.

Current control and future risk in asthma management

Despite international and national guidelines, poor asthma control remains an issue. Asthma exacerbations are costly to both the individual, and the healthcare provider. Improvements in our understanding of the therapeutic benefit of asthma therapies suggest that, in general, while long-acting bronchodilator therapy improves asthma symptoms, the anti-inflammatory activity of inhaled corticosteroids reduces acute asthma exacerbations. Studies have explored factors which could be predictive of exacerbations. A history of previous exacerbations, poor asthma control, poor inhaler technique, a history of lower respiratory tract infections, poor adherence to medication, the presence of allergic rhinitis, gastro-oesophageal reflux disease, psychological dysfunction, smoking and obesity have all been implicated as having a predictive role in the future risk of asthma exacerbation. Here we review the current literature and discuss this in the context of primary care management of asthma.

Relationship between asthma control status, the Asthma Control Test™ and urgent health-care utilization in Asia

BACKGROUND AND OBJECTIVE

Accurate assessment of control is an integral part of asthma management. We investigated the relationship between control status derived from the Global Initiative for Asthma (GINA), the Asthma Control Test (ACT) and urgent health-care utilization.

METHODS

Asthma Insights and Reality in Asia-Pacific Phase 2 (AIRIAP 2) was a cross-sectional, community-based survey of 4805 subjects with asthma from urban centres across Asia. A symptom control index was derived from the AIRIAP 2 questionnaire using the GINA control criteria for day- and night-time symptoms, need for rescue medication, activity limitation and exacerbations; lung function was excluded. The main outcomes were asthma control, based on these GINA criteria and the ACT, and the relationship between control and self-reported urgent health-care utilization (hospitalization, emergency room visits or other unscheduled urgent visits) related to asthma over the previous 12months.

RESULTS

Each of the symptom criteria was significantly associated with urgent health-care utilization, with odds ratios (ORs) ranging from 2.25 (95% confidence interval (CI): 1.94-2.61) for daytime symptoms to 2.57 (95% CI: 2.29-2.90) for nocturnal awakening. Similarly, control status was significantly associated with urgent health-care utilization, with ORs of 0.19 (95% CI: 0.13-0.28), 0.70 (95% CI: 0.65-0.76) and 1.00 for controlled, partly controlled and uncontrolled, respectively. The optimal ACT cut-off score for identifying uncontrolled asthma was ≤19 for subjects aged ≥12years. Urgent health-care utilization was reported by 57.2% versus 28.7% of patients scoring ≤19 versus >19 (P<0.001).

CONCLUSIONS

The GINA control classification and the ACT are valid symptom-based measures that are significantly associated with urgent health-care utilization.

Predictors of symptoms are different from predictors of severe exacerbations from asthma in children

BACKGROUND

Asthma therapy is typically prescribed and titrated based on patient or parent self-report of symptoms. No longitudinal studies have assessed the relationship between symptoms and severe asthma exacerbations in children. The goal of our study was (1) to assess the association of asthma symptoms with severe asthma exacerbations and (2) to compare predictors of persistent asthma symptoms and predictors of severe asthma exacerbations.

METHODS

The Childhood Asthma Management Program was a multicenter clinical trial of 1,041 children randomized to receive budesonide, nedocromil, or placebo (as-needed β-agonist). We conducted a post hoc analysis of diary cards that were completed by subjects on a daily basis to categorize subjects as having persistent vs intermittent symptoms. We defined a severe asthma exacerbation as an episode requiring ≥ 3 days use of oral corticosteroids, hospitalization, or ED visit due to asthma based on self-report at study visits every 4 months.

RESULTS

While accounting for longitudinal measures, having persistent symptoms from asthma was significantly associated with having severe asthma exacerbations. Predictors of having persistent symptoms compared with intermittent symptoms included not being treated with inhaled corticosteroids, lower FEV(1)/FVC ratio, and a lower natural logarithm of provocative concentration of methacholine producing a 20% decline in FEV(1) (lnPC(20)). Predictors of having one or more severe asthma exacerbations included younger age, history of hospitalization or ED visit in the prior year, ≥ 3 days use of oral corticosteroids in the prior 3 months, lower FEV(1)/FVC ratio, lower lnPC(20), and higher logarithm to the base 10 eosinophil count; treatment with inhaled corticosteroids was predictive of having no severe asthma exacerbations.

CONCLUSIONS

Patients with persistent symptoms from asthma were more likely to experience severe asthma exacerbations. Nevertheless, demographic and laboratory predictors of having persistent symptoms are different from predictors of severe asthma exacerbations. Although symptoms and exacerbations are closely related, their predictors are different. The current focus of the National Asthma Education and Prevention Program guidelines on the two separate domains of asthma control, impairment and risk, are supported by our analysis.

Asthma Severity in patients initiating controller monotherapy versus combination therapy

BACKGROUND

Asthma treatment guidelines recommend medications based on the level of asthma control.

OBJECTIVE

To evaluate differences in asthma control between patients who initiated asthma controller monotherapy versus combination therapy.

PATIENTS AND METHODS

Children (5-16 years; n = 488) and adults (17-80 years; n = 530) with asthma and no controller therapy in the prior 6 months were included. Telephone surveys were conducted within 5 days of filling a new asthma controller prescription with either the caregiver of children or the adult patient. Demographics, asthma control before therapy, and asthma-related resource use were assessed for patients initiating monotherapy (filling one asthma controller prescription) and combination therapy (filling more than one controller medication or a fixed-dose combination).

RESULTS

Mean pediatric age was 10 years; 53% were male. Mean adult age was 47 years; 25% were male. There were no significant differences in asthma control score between patients receiving monotherapy and combination therapy. Children on combination therapy did not have more nighttime awakening or short-acting β-agonist use but were more likely to have been hospitalized due to asthma attack (p = .05) and have more unscheduled (p = .0374) and scheduled (p = .009) physician visits. Adults on combination therapy were more likely to have been hospitalized due to asthma attack (p < .05) and have regular doctor visits for asthma (p < .01).

CONCLUSIONS

Assessment of asthma control scores in the 4 weeks before index medication suggests that asthma severity during a treatment-free period did not differ significantly for patients initiating controller monotherapy versus combination therapy. From these findings, it appears that although physicians may not focus on asthma control when choosing the intensity of initial controller therapy, the intensity of health-care encounters may be an influence.

Uniform definition of asthma severity, control, and exacerbations: document presented for the World Health Organization Consultation on Severe Asthma

Asthma is a global health problem affecting around 300 million individuals of all ages, ethnic groups and countries. It is estimated that around 250,000 people die prematurely each year as a result of asthma. Concepts of asthma severity and control are important in evaluating patients and their response to treatment, as well as for public health, registries, and research (clinical trials, epidemiologic, genetic, and mechanistic studies), but the terminology applied is not standardized, and terms are often used interchangeably. A common international approach is favored to define severe asthma, uncontrolled asthma, and when the 2 coincide, although adaptation may be required in accordance with local conditions. A World Health Organization meeting was convened April 5-6, 2009, to propose a uniform definition of severe asthma. An article was written by a group of experts and reviewed by the Global Alliance against Chronic Respiratory Diseases review group. Severe asthma is defined by the level of current clinical control and risks as "Uncontrolled asthma which can result in risk of frequent severe exacerbations (or death) and/or adverse reactions to medications and/or chronic morbidity (including impaired lung function or reduced lung growth in children)." Severe asthma includes 3 groups, each carrying different public health messages and challenges: (1) untreated severe asthma, (2) difficult-to-treat severe asthma, and (3) treatment-resistant severe asthma. The last group includes asthma for which control is not achieved despite the highest level of recommended treatment and asthma for which control can be maintained only with the highest level of recommended treatment.

Insights into severe asthma control as assessed by guidelines, pulmonologist, patient, and partner

INTRODUCTION

The most recent guidelines on asthma management advocate a treatment strategy based on control of the disease rather than severity, a switch based on reported evidence.

AIMS

This observational, questionnaire-based study set out to investigate how control of the disease is assessed by the physician as well as the patient and his/her live-in partner and to compare these assessments with an assessment made according to the guidelines.

METHODS

In 169 patients with severe, persistent asthma on at least a high-dose inhaled corticosteroid plus an inhaled long-acting β2-agonist, control of the disease was assessed by the pulmonologist, the patient, and the patient's live-in partner. These assessments were compared with an assessment based on the guidelines. Results. Both patients and partners tended to judge disease control as better than their pulmonologists who, in turn, estimated control as acceptable in 58% of their patients in whom the guidelines would advocate more aggressive treatment. The most common guidelines criteria defining inadequate control in the "uncontrolled" 87.4% of this population were "limitation of physical activity" (72.3%) and "FEV₁" ≤ 85% of personal best" (63.3%).

CONCLUSIONS

To assess control in severe asthma, the patient's opinion is of limited value, as is that of their partners. Although a guidelines-based strategy has been shown to be effective in clinical trials conducted on large-scale populations in which mild or moderate disease is predominant, more aggressive treatment to achieve definitive control may not be appropriate in the 10% of asthma sufferers with severe disease; in everyday practice, lung specialists appear to implement such a strategy.

Asthma: are we monitoring the correct measures?

The prevalence of asthma, a common chronic inflammatory disease of the airways, has risen sharply over the past 25-30 years, with the biggest increase found in children. Currently, more than 22 million Americans have asthma. Asthma also is associated with significant morbidity and mortality worldwide. Each year, asthma is responsible for $16 billion in direct and indirect costs due to health care utilization and loss of productivity, with over 14 million missed workdays. Asthma also accounts for almost 1.8 million emergency room visits and almost 500,000 hospitalizations annually. Therefore, assessment and monitoring of disease activity is critical to improve clinical and economic outcomes for patients with asthma. To help in this endeavor, practitioners and payers rely on evidence-based guidelines to classify disease severity, to guide treatment decisions, and to assess the degree of asthma control. In August 2007, the National Asthma Education and Prevention Program (NAEPP) updated its guidelines based on greater knowledge of disease pathophysiology and the development of newer therapeutic agents. This includes an increased emphasis on the need to establish disease severity, including the components of impairment and risk, as well as on the level of asthma control. Despite the availability of the NAEPP and other guidelines, asthma control often remains suboptimal. While numerous clinical and patient-reported measures are available, it is clear that the optimal monitoring schema for patients with asthma remains undefined. To clearly establish whether asthma control is attained, multiple measures are required and should include clinical and patient-reported assessments.

Risk stratification for hospitalization in acute asthma: the CHOP classification tree

OBJECTIVE

Simple risk stratification rules are limited in acute asthma. We developed and externally validated a classification tree for asthma hospitalization.

METHODS

Data were obtained from 2 large, multicenter studies on acute asthma, the National Emergency Department Safety Study and the Multicenter Airway Research Collaboration cohorts. Both studies involved emergency department (ED) patients aged 18 to 54 years presenting to the ED with acute asthma. Clinical information was obtained from medical record review. The Classification and Regression Tree method was used to generate a simple decision tree. The tree was derived in the National Emergency Department Safety Study cohort and then was validated in the Multicenter Airway Research Collaboration cohort.

RESULTS

There were 1825 patients in the derivation cohort and 1335 in the validation cohort. Admission rates were 18% and 21% in the derivation and validation cohorts, respectively. The Classification and Regression Tree method identified 4 important variables (CHOP): change [C] in peak expiratory flow severity category, ever hospitalization [H] for asthma, oxygen [O] saturation on room air, and initial peak expiratory flow [P]. In a simple 3-step process, the decision rule risk-stratified patients into 7 groups, with a risk of admission ranging from 9% to 48%. The classification tree performed satisfactorily on discrimination in both the derivation and validation cohorts, with an area under the receiver operating characteristic curve of 0.72 and 0.65, respectively.

CONCLUSIONS

We developed and externally validated a novel classification tree for hospitalization among ED patients with acute asthma. Use of this explicit risk stratification rule may aid decision making in the emergency care of acute asthma.