Predicting asthma control and exacerbations: chronic asthma as a complex dynamic model

Multivariate analysis and data mining help predict asthma exacerbations

BACKGROUND

Work-related asthma has become a highly prevalent occupational lung disorder.

OBJECTIVE

Our study aims to evaluate occupational exposure as a predictor for asthma exacerbation.

METHOD

We performed a retrospective evaluation of 584 consecutive patients diagnosed and treated for asthma between October 2017 and December 2019 in four clinics from Western Romania. We evaluated the enrolled patients for their asthma control level by employing the Asthma Control Test (ACT < 20 represents uncontrolled asthma), the medical record of asthma exacerbations, occupational exposure, and lung function (i.e. spirometry). Then, we used statistical and data mining methods to explore the most important predictors for asthma exacerbations.

RESULTS

We identified essential predictors by calculating the odds ratios (OR) for the exacerbation in a logistic regression model. The average age was 45.42 ± 11.74 years (19-85 years), and 422 (72.26%) participants were females. 42.97% of participants had exacerbations in the past year, and 31.16% had a history of occupational exposure. In a multivariate model analysis adjusted for age and gender, the most important predictors for exacerbation were uncontrolled asthma (OR 4.79, p < .001), occupational exposure (OR 4.65, p < .001), and lung function impairment (FEV1 < 80%) (OR 1.15, p = .011). The ensemble machine learning experiments on combined patient features harnessed by our data mining approach reveal that the best predictor is professional exposure, followed by ACT.

CONCLUSIONS

Machine learning ensemble methods and statistical analysis concordantly indicate that occupational exposure and ACT < 20 are strong predictors for asthma exacerbation.

A stochastic mixed effects model to assess treatment effects and fluctuations in home‐measured peak expiratory flow and the association with exacerbation risk in asthma

Home‐based measures of lung function, inflammation, symptoms, and medication use are frequently collected in respiratory clinical trials. However, new statistical approaches are needed to make better use of the information contained in these data‐rich variables. In this work, we use data from two phase III asthma clinical trials demonstrating the benefit of benralizumab treatment to develop a novel longitudinal mixed effects model of peak expiratory flow (PEF), a lung function measure easily captured at home using a hand‐held device. The model is based on an extension of the mixed effects modeling framework to incorporate stochastic differential equations and allows for quantification of several statistical properties of a patient's PEF data: the longitudinal trend, long‐term fluctuations, and day‐to‐day variability. These properties are compared between treatment groups and related to a patient's exacerbation risk using a repeated time‐to‐event model. The mixed effects model adequately described the observed data from the two clinical trials, and model parameters were accurately estimated. Benralizumab treatment was shown to improve a patient's average PEF level and reduce long‐term fluctuations. Both of these effects were shown to be associated with a lower exacerbation risk. The day‐to‐day variability was neither significantly affected by treatment nor associated with exacerbation risk. Our work shows the potential of a stochastic model‐based analysis of home‐based lung function measures to support better estimation and understanding of treatment effects and disease stability. The proposed analysis can serve as a complement to descriptive statistics of home‐based measures in the reporting of respiratory clinical trials.

Does the Use of Asthma-Controller Medication in Accordance with Guidelines Reduce the Incidence of Acute Exacerbations and Healthcare Costs?

Background

In asthma, consistent control of chronic airway inflammation is crucial, and the use of asthma-controller medication has been emphasized. Our purpose in this study is to compare the incidence of acute exacerbation and healthcare costs related to the use of asthma-controller medication.

Methods

By using data collected by the National Health Insurance Review and Assessment Service, we compared one-year clinical outcomes and medical costs from July 2014 to June 2015 (follow-up period) between two groups of patients with asthma who received different prescriptions for recommended asthma-controller medication (inhaled corticosteroids or leukotriene receptor antagonists) at least once from July 2013 to June 2014 (assessment period).

Results

There were 51,757 patients who satisfied our inclusion criteria. Among them, 13,702 patients (26.5%) were prescribed a recommended asthma-controller medication during the assessment period. In patients using a recommended asthma-controller medication, the frequency of acute exacerbations decreased in the follow-up period, from 2.7% to 1.1%. The total medical costs of the controller group decreased during the follow-up period compared to the assessment period, from $3,772,692 to $1,985,475. Only 50.9% of patients in the controller group used healthcare services in the follow-up period, and the use of asthma-controller medication decreased in the follow-up period.

Conclusion

Overall, patients using a recommended asthma-controller medication showed decreased acute exacerbation and reduced total healthcare cost by half.

A systematic review of methodology used in the development of prediction models for future asthma exacerbation

Background

Clinical prediction models are widely used to guide medical advice and therapeutic interventions. Asthma is one of the most common chronic diseases globally and is characterised by acute deteriorations. These exacerbations are largely preventable, so there is interest in using clinical prediction models in this area. The objective of this review was to identify studies which have developed such models, determine whether consistent and appropriate methodology was used and whether statistically reliable prognostic models exist.

Methods

We searched online databases MEDLINE (1948 onwards), CINAHL Plus (1937 onwards), The Cochrane Library, Web of Science (1898 onwards) and ClinicalTrials.gov, using index terms relating to asthma and prognosis. Data was extracted and assessment of quality was based on GRADE and an early version of PROBAST (Prediction study Risk of Bias Assessment Tool). A meta-analysis of the discrimination and calibration measures was carried out to determine overall performance across models.

Results

Ten unique prognostic models were identified. GRADE identified moderate risk of bias in two of the studies, but more detailed quality assessment via PROBAST highlighted that most models were developed using highly selected and small datasets, incompletely recorded predictors and outcomes, and incomplete methodology. None of the identified models modelled recurrent exacerbations, instead favouring either presence/absence of an event, or time to first or specified event. Preferred methodologies were logistic regression and Cox proportional hazards regression.

The overall pooled c-statistic was 0.77 (95% confidence interval 0.73 to 0.80), though individually some models performed no better than chance. The meta-analysis had an I² value of 99.75% indicating a high amount of heterogeneity between studies. The majority of studies were small and did not include internal or external validation, therefore the individual performance measures are likely to be optimistic.

Conclusions

Current prognostic models for asthma exacerbations are heterogeneous in methodology, but reported c-statistics suggest a clinically useful model could be created. Studies were consistent in lacking robust validation and in not modelling serial events. Further research is required with respect to incorporating recurrent events, and to externally validate tools in large representative populations to demonstrate the generalizability of published results.

High ACT score is not sufficient to reduce the risk of asthma exacerbations in asthma with low lung function

BACKGROUND

Low forced expiratory volume in 1 s (FEV₁) is a risk factor for asthma exacerbations (AEs). We aimed to determine if asthma control could reduce the future risk of AEs in patients with low FEV₁. This study was conducted to evaluate the future risks of AEs within six months according to Asthma Control Test™ (ACT) score and FEV₁.

METHODS

A total of 565 patients with asthma were enrolled from the COREA cohort. The ACT score, lung function test, and number of AEs were assessed at baseline, three-month follow-up, and six-month follow-up with conventional asthma treatments by asthma specialists in real clinical settings.

RESULTS

Female sex, low ACT score, low FEV₁, low FVC, and AE history in the previous three months were related with increased AEs within six months. AEs during six-month follow-up occurred in 24% of patients with ACT <20 and FEV₁ < 60% at baseline. Among patients with an ACT score ≥20, 3.4% of patients with an FEV₁ < 2.16 L and 9.8% of patients with FEV₁ ≥ 2.16 L had experienced AEs (P = 0.01), although no differences were observed in the presence of AEs within six months according to the predicted FEV₁ (FEV₁ ≥ 60% vs. FEV₁ < 60%, 5.66% vs. 8.51%, P = 0.65).

CONCLUSION

Patient with low FEV₁ seemed to show higher risk of AEs than those with near-normal FEV₁ despite ACT score ≥20 and asthma treatments. Therefore, treatment strategies that prevent AEs are needed in high-risk asthmatic patients.

Predicting asthma control deterioration in children

Background

Pediatric asthma affects 7.1 million American children incurring an annual total direct healthcare cost around 9.3 billion dollars. Asthma control in children is suboptimal, leading to frequent asthma exacerbations, excess costs, and decreased quality of life. Successful prediction of risk for asthma control deterioration at the individual patient level would enhance self-management and enable early interventions to reduce asthma exacerbations. We developed and tested the first set of models for predicting a child’s asthma control deterioration one week prior to occurrence.

Methods

We previously reported validation of the Asthma Symptom Tracker, a weekly asthma self-monitoring tool. Over a period of two years, we used this tool to collect a total of 2912 weekly assessments of asthma control on 210 children. We combined the asthma control data set with patient attributes and environmental variables to develop machine learning models to predict a child’s asthma control deterioration one week ahead.

Results

Our best model achieved an accuracy of 71.8 %, a sensitivity of 73.8 %, a specificity of 71.4 %, and an area under the receiver operating characteristic curve of 0.757. We also identified potential improvements to our models to stimulate future research on this topic.

Conclusions

Our best model successfully predicted a child’s asthma control level one week ahead. With adequate accuracy, the model could be integrated into electronic asthma self-monitoring systems to provide real-time decision support and personalized early warnings of potential asthma control deteriorations.

A simulation model of building intervention impacts on indoor environmental quality, pediatric asthma, and costs

BACKGROUND

Although indoor environmental conditions can affect pediatric asthmatic patients, few studies have characterized the effect of building interventions on asthma-related outcomes. Simulation models can evaluate such complex systems but have not been applied in this context.

OBJECTIVE

We sought to evaluate the impact of building interventions on indoor environmental quality and pediatric asthma health care use, and to conduct cost comparisons between intervention and health care costs and energy savings.

METHODS

We applied our previously developed discrete event simulation model (DEM) to simulate the effect of environmental factors, medication compliance, seasonality, and medical history on (1) pollutant concentrations indoors and (2) asthma outcomes in low-income multifamily housing. We estimated health care use and costs at baseline and subsequent to interventions, and then compared health care costs with energy savings and intervention costs.

RESULTS

Interventions, such as integrated pest management and repairing kitchen exhaust fans, led to 7% to 12% reductions in serious asthma events with 1- to 3-year payback periods. Weatherization efforts targeted solely toward tightening a building envelope led to 20% more serious asthma events, but bundling with repairing kitchen exhaust fans and eliminating indoor sources (eg, gas stoves or smokers) mitigated this effect.

CONCLUSION

Our pediatric asthma model provides a tool to prioritize individual and bundled building interventions based on their effects on health and costs, highlighting the tradeoffs between weatherization, indoor air quality, and health. Our work bridges the gap between clinical and environmental health sciences by increasing physicians' understanding of the effect that home environmental changes can have on their patients' asthma.

Air pollution indicators predict outbreaks of asthma exacerbations among elementary school children: integration of daily environmental and school health surveillance systems in Pennsylvania

Objectives of this study are to determine if a relationship exists between asthma exacerbations among elementary school children in industrialized countries (with climatic seasons) and exposure to daily air pollution with particulate matter, sulfur dioxide, nitrogen dioxide, nitrogen oxides, carbon monoxide, and ozone, when controlled for potential confounders; and, if so, to derive a statistical model that predicts variation of asthma exacerbations among elementary school children. Using an ecological study design, health records of 168,25 students from elementary schools in 49 Pennsylvania counties employing "Health eTools for Schools" were analyzed. Asthma exacerbations were recorded by nurses as treatment given during clinic visits each day. Daily air pollution measurements were obtained from the EPA's air quality monitoring sites. The distribution of asthmatic grouping for pollen and calendar seasons was developed. A Poisson regression model was used to predict the number of asthma exacerbations. The greatest occurrence of asthma exacerbations was in autumn, followed by summer, spring and winter. If the number of asthma exacerbations on a day is N and the daily mean of asthma exacerbations for the three-year period is 48, the probabilities of N > 48 in tree pollen and grass pollen seasons were 56.5% and 40.8%, respectively (p < 0.001). According to the Poisson regression, the week number and prior day CO, SO₂, NO₂, NOx, PM₂.₅, and O₃ had significant effects on asthma exacerbations among students. Monitoring of air pollutants over time could be a reliable new means for predicting asthma exacerbations among elementary school children. Such predictions could help parents and school nurses implement effective precautionary measures.

The effects of indoor environmental exposures on pediatric asthma: a discrete event simulation model

BACKGROUND

In the United States, asthma is the most common chronic disease of childhood across all socioeconomic classes and is the most frequent cause of hospitalization among children. Asthma exacerbations have been associated with exposure to residential indoor environmental stressors such as allergens and air pollutants as well as numerous additional factors. Simulation modeling is a valuable tool that can be used to evaluate interventions for complex multifactorial diseases such as asthma but in spite of its flexibility and applicability, modeling applications in either environmental exposures or asthma have been limited to date.

METHODS

We designed a discrete event simulation model to study the effect of environmental factors on asthma exacerbations in school-age children living in low-income multi-family housing. Model outcomes include asthma symptoms, medication use, hospitalizations, and emergency room visits. Environmental factors were linked to percent predicted forced expiratory volume in 1 second (FEV1%), which in turn was linked to risk equations for each outcome. Exposures affecting FEV1% included indoor and outdoor sources of NO2 and PM2.5, cockroach allergen, and dampness as a proxy for mold.

RESULTS

Model design parameters and equations are described in detail. We evaluated the model by simulating 50,000 children over 10 years and showed that pollutant concentrations and health outcome rates are comparable to values reported in the literature. In an application example, we simulated what would happen if the kitchen and bathroom exhaust fans were improved for the entire cohort, and showed reductions in pollutant concentrations and healthcare utilization rates.

CONCLUSIONS

We describe the design and evaluation of a discrete event simulation model of pediatric asthma for children living in low-income multi-family housing. Our model simulates the effect of environmental factors (combustion pollutants and allergens), medication compliance, seasonality, and medical history on asthma outcomes (symptom-days, medication use, hospitalizations, and emergency room visits). The model can be used to evaluate building interventions and green building construction practices on pollutant concentrations, energy savings, and asthma healthcare utilization costs, and demonstrates the value of a simulation approach for studying complex diseases such as asthma.

Fluctuation analysis-based risk assessment for respiratory virus activity and air pollution associated asthma incidence

Asthma is a growing epidemic worldwide. Exacerbations of asthma have been associated with bacterial and viral respiratory tract infections and air pollution. We correlated the asthma admission rates with fluctuations in respiratory virus activity and traffic-related air pollution, namely particulate matter with an aerodynamic diameter ≤ 10 μm (PM₁₀), nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂), and ozone (O₃). A probabilistic risk assessment framework was developed based on a detrended fluctuation analysis to predict future respiratory virus and air pollutant associated asthma incidence. Results indicated a strong association between asthma admission rate and influenza (r=0.80, p<0.05) and SO₂ level (r=0.73, p<0.05) in Taiwan in the period 2001-2008. No significant correlation was found for asthma admission and PM₁₀, O₃, NO₂, and CO. The proposed fluctuation analysis provides a simple correlation exponent describing the complex interactions of respiratory viruses and air pollutants with asthma. This study revealed that there was a 95% probability of having exceeded 2987 asthma admissions per 100,000 population. It was unlikely (30% probability) that the asthma admission rate exceeded 3492 per 100,000 population. The probability of asthma admission risk can be limited to below 50% by keeping the correlation exponent of influenza to below 0.9. We concluded that fluctuation analysis based risk assessment provides a novel predictor of asthma incidence.

Assessing the exacerbations risk of influenza-associated chronic occupational asthma

The purpose of this article was to conduct a risk-based study based on a linkage of experimental human influenza infections and fluctuation analysis of airway function to assess whether influenza viral infection was risk factor for exacerbations of chronic occupational asthma. Here we provided a comprehensive probabilistic analysis aimed at quantifying influenza-associated exacerbations risk for occupational asthmatics, based on a combination of published distributions of viral shedding and symptoms scores and lung respiratory system properties characterized by long-range peak expiratory flow (PEF) dynamics. Using a coupled detrended fluctuation analysis-experimental human influenza approach, we estimated the conditional probability of moderate or severe lung airway obstruction and hence the exacerbations risk of influenza-associated occupational asthma in individuals. The long-range correlation exponent (alpha) was used as a predictor of future exacerbations risk of influenza-associated asthma. For our illustrative distribution of PEF fluctuations and influenza-induced asthma exacerbations risk relations, we found that the probability of exacerbations risk can be limited to below 50% by keeping alpha to below 0.53. This study also found that limiting wheeze scores to 0.56 yields a 75% probability of influenza-associated asthma exacerbations risk and a limit of 0.34 yields a 50% probability that may give a representative estimate of the distribution of chronic respiratory system properties. This study implicates that influenza viral infection is an important risk factor for exacerbations of chronic occupational asthma.

Factors associated with the control of severe asthma

INTRODUCTION

Control is a priority treatment objective in asthma, and classification based on control is recommended in the follow-up of asthmatic patients. Different factors affect this control, and there are several regional differences, both in terms of prevalence and in terms of management and degree of control.

OBJECTIVE

To evaluate the factors associated with control of severe asthma in routine clinical practice.

MATERIAL AND METHODS

This was a prospective, cross-sectional, observational study of patients with severe asthma who were receiving treatment with a fixed combination of a corticosteroid (at least 800 microg/day of budesonide or equivalent) and an inhaled beta(2)-adrenergic agonist in respiratory medicine and allergology clinics throughout Spain. The authors collected demographic and socioeconomic data, as well as clinical data on asthma. The patients also completed a self-administered validated questionnaire-the Asthma Control Questionnaire (ACQ)-about the control of their asthma.

RESULTS

The authors included 1471 patients, of whom 1224 (83%) were valid for the final analysis. Women accounted for 61%. Mean age was 51 +/- 16 years. The mean number of exacerbations during the previous year was 2.0 +/- 2.0. The global score on the ACQ was 1.8 +/- 1.1 (0 = no symptoms; 6 = maximum number of symptoms). Only 20.4% of patients were well controlled (ACQ < 0.75), and 55.7% of patients were poorly controlled (ACQ > 1.5). The multivariate analysis revealed that the variable with the greatest effect on control of asthma was the number of exacerbations during the previous year: when the number of exacerbations increased from 0 to 1 or more, the ACQ score increased by 0.56 points. Employed patients had a mean of 0.23 points less (better control) than unemployed and retired patients. Control of asthma was also significantly affected by adherence to treatment, patient knowledge of the disease, body mass index, gender, and number of visits to a physician in the previous 3 months.

CONCLUSIONS

Many patients with severe asthma have poor control of their disease. The number of exacerbations is the variable with the greatest effect on control of asthma. Knowledge of the disease and adherence to treatment are associated with better control.

Reduced complexity of activity patterns in patients with chronic fatigue syndrome: a case control study

Background

Chronic fatigue syndrome (CFS) is an illness characterised by pervasive physical and mental fatigue without specific identified pathological changes. Many patients with CFS show reduced physical activity which, though quantifiable, has yielded little information to date. Nonlinear dynamic analysis of physiological data can be used to measure complexity in terms of dissimilarity within timescales and similarity across timescales. A reduction in these objective measures has been associated with disease and ageing. We aimed to test the hypothesis that activity patterns of patients with CFS would show reduced complexity compared to healthy controls.

Methods

We analysed continuous activity data over 12 days from 42 patients with CFS and 21 matched healthy controls. We estimated complexity in two ways, measuring dissimilarity within timescales by calculating entropy after a symbolic dynamic transformation of the data and similarity across timescales by calculating the fractal dimension using allometric aggregation.

Results

CFS cases showed reduced complexity compared to controls, as evidenced by reduced dissimilarity within timescales (mean (SD) Renyi₍₃₎ entropy 4.05 (0.21) vs. 4.30 (0.09), t = -6.6, p < 0.001) and reduced similarity across timescales (fractal dimension 1.19 (0.04) vs. 1.14 (0.04), t = 4.2, p < 0.001). This reduction in complexity persisted after adjustment for total activity.

Conclusion

Patients with CFS show evidence of reduced complexity of activity patterns. Measures of complexity applied to activity have potential value as objective indicators for CFS.

Complexity of chronic asthma and chronic obstructive pulmonary disease: implications for risk assessment, and disease progression and control

Although assessment of asthma control is important to guide treatment, it is difficult since the temporal pattern and risk of exacerbations are often unpredictable. In this Review, we summarise the classic methods to assess control with unidimensional and multidimensional approaches. Next, we show how ideas from the science of complexity can explain the seemingly unpredictable nature of bronchial asthma and emphysema, with implications for chronic obstructive pulmonary disease. We show that fluctuation analysis, a method used in statistical physics, can be used to gain insight into asthma as a dynamic disease of the respiratory system, viewed as a set of interacting subsystems (eg, inflammatory, immunological, and mechanical). The basis of the fluctuation analysis methods is the quantification of the long-term temporal history of lung function parameters. We summarise how this analysis can be used to assess the risk of future asthma episodes, with implications for asthma severity and control both in children and adults.

New visions in respiratory allergy (asthma and allergic rhinitis): an iPAC summary and future trends

In many aspects, respiratory allergies, i.e., allergic asthma and rhinitis, represent the hallmarks of allergy. Epidemiologic data highlight their large prevalence of most parts of the world, socioeconomic analysis reveal their large impact on global health and the large number of scientific publications in this field regularly brings to light many new aspects of these diseases. However, the current understanding of respiratory allergies, in particular in children remains scarce. How can we efficiently prevent respiratory allergies in allergy-prone infants? How can we prevent the progression of the disease? What therapeutic strategies could efficiently address efficient immunomodulation? the international Pediatric Allergy and Asthma Consortium, addressed these issues by a thorough review of the literature providing a state-of-the-art current knowledge in respiratory allergy, and identified a series of needs to be addressed in future studies.