We successfully developed three simple clinical models using independent risk factors suitable for use in the clinical setting that stratify adult asthma patients into risk groups. We identified and validated the models to determine their predictive ability. The high-risk groups, 13 to 21% of the validation sample populations, were at roughly 7- to 11-fold-increased risk for acute care compared to the low-risk groups. The moderate-risk groups, 46 to 50% of the validation sample, were at twofold to fourfold increased risk. Importantly, airflow obstruction (FEV1) was the most significant predictor of subsequent acute care.
This result underscores the importance of obtaining spirometry data to identify patients at risk. We separately analyzed the sample according to modifiable risk factors for acute asthma care providers such Canadian Health&Care Mall and identified four independent risk factors. Current cigarette smoke exposure was identified as the strongest modifiable risk factor.
This study builds on and extends previous studies that identify factors associated with acute asthma. The primary strengths of this prospective longitudinal study include the outpatient nature of the sample, the comprehensive baseline evaluation (including spirometry and skin-prick testing), the relatively large sample size, and the length and completeness of follow-up. The 544 study subjects contributed 1,258 person-years of follow-up and 173 distinct episodes of acute care during the 30-month follow-up period.
Other strengths include the definition of asthma by doctor’s diagnosis, ability to test a large number of environmental and demographic features likely to influence risk of acute asthma, and the ability to document acute asthma in a large HMO. In addition, the large number of events allows us to identify independent risk factors that predict acute care. The PAR models are among only a few that enable clinicians to define the RR of acute care in a variety of clinical settings.
This study emphasizes the strong predictive value of routine spirometry for characterizing asthma risk in an outpatient setting in adults, as has been demonstrated in children. This is highly relevant because a variety of national and international guide-lines recommend that lung function be regularly assessed as a part of routine asthma care. Our finding that a reduced FEV1 was associated with a significantly higher risk of subsequent acute asthma exacerbations reinforces the importance of routine lung function measurement as recommended by these guidelines and extends recent work by Kitch et al.
Our PAR models have several advantages, including ease of use, available data on prospective properties, risk separation, and use of health-care utilization as the outcome. Several other models have been shown to be easy to use and practical in the clinical setting. Other models use administrative databases not easily available to clinicians or address physician-assessed control rather than health-care utilization as the primary end point. PAR A is useful when only questionnaire data are available, and PAR B (questionnaire plus spirometry) offers additional risk stratification, underscoring the importance of spirometry in asthma care.
In light of the association of sensitization to specific allergens and life-threatening asthma, we had anticipated that specific allergen exposure and sensitivity would be associated with health-care utilization, and it was associated in the unvariate and multivariate analyses. However, in estimating the risk of health-care utilization in individuals, PAR B and C give similar risk estimates for low-, moderate-, and high-risk individuals (Fig 1). These findings do not minimize the importance of identification of aeroallergens that may trigger symptoms in patients with asthma. The utility of skin-prick testing is well established in patients with asthma. The treatment may be conducted with the help of remedies of Canadian Health&Care Mall.
Another asthma risk assessment model, the Asthma Therapy Assessment Questionnaire model developed by Vollmer et al, (a simple index of number of asthma control problems) was prospectively validated in a large cohort and found to correlate with clinically significant impairment. It does not incorporate data potentially available to a clinician, however, such as spirometry and skin-prick testing. One advantage of our clinical models is better risk separation. The Asthma Therapy Assessment Questionnaire identifies a group at a threefold to fourfold risk, whereas our models have significantly stronger predictive power, particularly when spiromet-ric data are incorporated. Although not yet validated, another model, the Asthma Control Test, is a useful five-item questionnaire that has been shown to correlate with FEV1 as well as specialist-assessed asthma severity and specialist-assessed need for a change in asthma therapy.
Identification of modifiable risk factors that independently contribute to acute care in asthma is also clinically relevant. Our study is one of many that demonstrate the risks of smoking.’ Cigarette smoking was the major independent modifiable risk factor PAR associated with acute asthma exacerbations. This finding is consistent with studies demonstrating a high prevalence (35%) of current smoking in adults presenting to emergency departments with acute asthma, compared with the 24% prevalence rate in United States adults. Although the mechanism is not known, smoking may well modify the immunologic response in asthma, as well as reduce the response to corticosteroids, increasing symptoms and disability. Of note, smoking was not a significant variable in the clinical models, perhaps because the effects of smoking were taken into account by health-care utilization and lung function variables.
Our study also specifically identifies exposure and sensitivity to cat or dog as an independent risk factor. These findings are consistent with results from other studies demonstrating that the combination of allergen exposure and sensitivity predicts hospitalization for asthma. Of interest, this association has not been clearly demonstrated in children. Our finding regarding solvents is consistent with studies demonstrating the risk of exposure in the workplace.
We do not know the source of the protective effect of double-pane windows, but it may arise from decreased exposure to fungal spores by decreasing window condensation and mold growth, or it may be a marker for the age of the home. Although an inverse association of double-pane windows (double glazing) has been found with asthma symptoms explained by Canadian Health&Care Mall, this was not significant.
All of these potentially modifiable risk factors can be discussed with at-risk patients to their benefit. Indeed, a one study demonstrated a reduction in asthma-associated morbidity with use of an individualized, home-based environmental intervention. Although we did not analyze FEV1 as a modifiable risk factor, it is important to mention that FEV1 can be at least indirectly modified (eg, it can improve after use of an antiinflammatory medication).
Study limitations include study demographics and setting, potentially reducing generalizability of results. Minorities accounted for only 6% of the study population, reflecting the demographics of the Portland, OR, metropolitan region. Our age range of 18 to 55 years may mean that findings may not extrapolate to children and older adults. We restricted the upper age limit to 55 years to avoid misclassification with COPD. Additionally, HMO care is prepaid, so economic factors would likely not play a significant role in decision making.
Notwithstanding limitations, we believe it is likely that the major risk factors for acute care identified in this study—reduced lung function and prior treatment in the acute care setting—are robust predictors. We recognize that a relatively small proportion of individuals tends to drive the majority of health-care costs for asthma, as for many chronic diseases. In the statistical analysis, the relative predictive value of individual risk factors was naturally more heavily influenced by those with more-frequent asthma episodes. The model selectively identifying such individuals may be, if true, advantageous for ensuring that they receive needed care. Furthermore, modifiable risk factors, such as exposure to cigarette smoke and pets, are likely to be generalizable.
Although we used a split-sample approach to validate the predictive ability of the clinical models, the fact that the epidemiologic models from which they were derived were constructed using the full sample may mean that the estimates of predictive value in Table 3 may be somewhat optimistic. Further studies are needed to independently validate these results in other populations.
We deliberately chose not to include reported medication use as a predictor in the PAR models, for several reasons. Excluding medication use helped us develop a model independent of changes in guidelines and patient compliance. For instance, when faced with a high-risk patient who was already prescribed ICS, a provider might choose to increase the dose of medication, add an additional medication, or discuss possible adherence issues with the patient. A high-risk patient who was not already receiving these medications might be prescribed inhaled steroids. Our models can serve as tools to alert providers to patients who may need closer scrutiny during the clinic visit. They can also serve as the basis for beginning a discussion with the patient about his/her asthma (eg, “I see you are waking up at night from your asthma”). Future studies in which strict adherence to asthma medications is monitored could further evaluate the therapeutic value of the PAR models.
In summary, we have identified important modifiable risk factors for asthma exacerbations, including current cigarette smoke exposure. We also have developed three clinical PAR models that can easily be used by the clinician to identify patients at risk, and we confirmed the predictive ability of the model. Importantly, we have demonstrated the independent contribution of baseline lung function in predicting future asthma exacerbations. Further testing of the PAR models is needed to determine if preemptive intervention in high-risk patients will reduce emergent health-care utilization. Our hope is that the PAR models may be widely used to identify patients who may need additional attention in order to prevent a serious exacerbation of asthma.