Stratification of COPD patients by previous admission for targeting of preventative care

Lifestyle practices that reduce seasonal PM2.5 exposure and their impact on COPD

Particulate matter (PM) is a major air pollutant that has led to global health concerns and can cause and exacerbate chronic obstructive pulmonary disease (COPD). We asked patients with COPD to complete a detailed questionnaire about their lifestyle practices to reduce PM_2.5 exposure and analyzed the relationship between ambient PM_2.5 concentrations and lifestyle practices. We prospectively enrolled 104 COPD patients from four hospitals in different areas of Korea. They completed detailed questionnaires twice (at enrollment and the end of the study) and Internet of Things-based sensors were installed in their homes to continuously measure PM_2.5 for 1 year. The relationship between PM_2.5 concentrations, lifestyle practices, and COPD exacerbations were analyzed in each season. The PM_2.5 concentration was higher outdoors than indoors in all seasons except summer, and the difference was largest in winter. The six lifestyle practices that significantly lowered the annual indoor PM_2.5 concentration compared with the outdoors. The higher the economic status and educational level of patients, the lower the indoor PM_2.5 concentration. Some lifestyle practices were associated with reduced small airway resistance, presented as R5-R20 determined by impulse oscillometry, and scores of the St. George's Respiratory Questionnaire. Some lifestyle practices are associated with reduced indoor PM_2.5 concentrations and can even affect clinical outcomes, including small airway resistance and quality of life of COPD patients.

Applicability of internet search index for asthma admission forecast using machine learning

OBJECTIVE

This study aimed to determine whether a search index could provide insight into trends in asthma admission in China. An Internet search index is a powerful tool to monitor and predict epidemic outbreaks. However, whether using an internet search index can significantly improve asthma admissions forecasts remains unknown. The long-term goal is to develop a surveillance system to help early detection and interventions for asthma and to avoid asthma health care resource shortages in advance.

METHODS

In this study, we used a search index combined with air pollution data, weather data, and historical admissions data to forecast asthma admissions using machine learning.

RESULTS

Results demonstrated that the best area under the curve in the test set that can be achieved is 0.832, using all predictors mentioned earlier.

CONCLUSION

A search index is a powerful predictor in asthma admissions forecast, and a recent search index can reflect current asthma admissions with a lag-effect to a certain extent. The addition of a real-time, easily accessible search index improves forecasting capabilities and demonstrates the predictive potential of search index.

Case finding for chronic obstructive pulmonary disease in people attending long-term condition clinics in primary care

Despite increased interest and awareness of chronic obstructive pulmonary disease (COPD), nearly half of the people with COPD remain undiagnosed. Inviting people at risk for screening is unlikely to be effective as many will not attend. Co-morbidities are common in people with COPD but COPD is also a comorbidity of other long-term conditions and people with these conditions are under regular review in primary care clinics. This study aimed to develop a pilot programme to case find people with COPD among patients attending other long-term clinics in primary care. Twenty-three general practices were recruited to participate in South West England. All current or ex-smokers aged ≥35 attending a long-term condition clinic who were not known to have COPD were asked to complete a questionnaire designed to help identify people with COPD and to perform microspirometry. Practices were asked to collect data on up to 100 patients. One thousand three hundred and thirty-three patients were assessed. Four hundred and ten people (31%) were current smokers. Six hundred and thirteen (46%) had high questionnaire scores and 287 (22%) of these also had a forced expiratory volume in 1 second (FEV1) below the lower limit of normal (LLN). The mean FEV1 in these patients was 59.0% of predicted (range 22-79.0%). Two hundred and twenty-four had an FEV1 between 50% and 80% of predicted, 50 had an FEV1 between 30% and 50% of predicted. One hundred and sixteen (40%) of the people with an FEV1 below the LLN were still smoking and 55 accepted referral to cessation services. A total of 56% of the other smokers assessed but not thought to have COPD also accepted referral. Assessing symptoms and performing microspirometry in people attending long-term condition clinics in primary care is feasible and has a high yield of identifying people likely to have previously undiagnosed COPD.

Multi-component assessment of chronic obstructive pulmonary disease: an evaluation of the ADO and DOSE indices and the global obstructive lung disease categories in international primary care data sets

Suitable tools for assessing the severity of chronic obstructive pulmonary disease (COPD) include multi-component indices and the global initiative for chronic obstructive lung disease (GOLD) categories. The aim of this study was to evaluate the dyspnoea, obstruction, smoking, exacerbation (DOSE) and the age, dyspnoea, obstruction (ADO) indices and GOLD categories as measures of current health status and future outcomes in COPD patients. This was an observational cohort study comprising 5,114 primary care COPD patients across three databases from UK, Sweden and Holland. The associations of DOSE and ADO indices with (i) health status using the Clinical COPD Questionnaire (CCQ) and St George's Respiratory Questionnaire (SGRQ) and COPD Assessment test (CAT) and with (ii) current and future exacerbations, admissions and mortality were assessed in GOLD categories and DOSE and ADO indices. DOSE and ADO indices were significant predictors of future exacerbations: incident rate ratio was 1.52 (95% confidence intervals 1.46-1.57) for DOSE, 1.16 (1.12-1.20) for ADO index and 1.50 (1.33-1.68) and 1.23 (1.10-1.39), respectively, for hospitalisations. Negative binomial regression showed that the DOSE index was a better predictor of future admissions than were its component items. The hazard ratios for mortality were generally higher for ADO index groups than for DOSE index groups. The GOLD categories produced widely differing assessments for future exacerbation risk or for hospitalisation depending on the methods used to calculate them. None of the assessment systems were excellent at predicting future risk in COPD; the DOSE index appears better than the ADO index for predicting many outcomes, but not mortality. The GOLD categories predict future risk inconsistently. The DOSE index and the GOLD categories using exacerbation frequency may be used to identify those at high risk for exacerbations and admissions.

The causes and consequences of seasonal variation in COPD exacerbations

The time of year when patients experience exacerbations of chronic obstructive pulmonary disease is a much-overlooked feature of the disease. The higher incidence of exacerbations in winter has important consequences for patients in terms of increased morbidity and mortality. The seasonality also imposes a considerable burden on already-overloaded health care services, with both primary care consultations and hospital admissions increasing in number. The seasonality of exacerbations varies with latitude, and is greater in more temperate climates, where there may be less protection from outdoor and indoor cold exposure. The precise causes of the seasonality are unknown, but thought to be partly due to the increased prevalence of respiratory viral infections circulating in cold, damp conditions. Increased susceptibility to viral infection may also be a mechanism mediated through increased airway inflammation or possibly reduced vitamin D levels. The seasonality of exacerbations informs us about the triggers of exacerbations and suggests possible strategies to reduce their number.

Effect of a telephonic alert system (Healthy outlook) for patients with chronic obstructive pulmonary disease: a cohort study with matched controls

BACKGROUND

Healthy Outlook was a telephonic alert system for patients with chronic obstructive pulmonary disease (COPD) in the UK. It used routine meteorological and communicable disease reports to identify times of increased risk to health. We tested its effect on hospital use and mortality.

METHODS

Enrolees with a history of hospital admissions were linked to hospital administrative data. They were compared with control patients from local general practices, matched for demographic characteristics, health conditions, previous hospital use and predictive risk scores. We compared unplanned hospital admissions, admissions for COPD, outpatient attendances, planned admissions and mortality, over 12 months following enrolment.

RESULTS

Intervention and matched control groups appeared similar at baseline (n = 1413 in each group). Over the 12 months following enrolment, Healthy Outlook enrolees experienced more COPD admissions than matched controls (adjusted rate ratio 1.26, 95% confidence interval (CI), 1.05-1.52) and more outpatient attendances (adjusted rate ratio 1.08, 95% CI, 1.03-1.12). Enrolees also had lower mortality rates over 12 months (adjusted odds ratio 0.61, 95% CI, 0.45-0.84).

CONCLUSION

Healthy Outlook did not reduce admission rates, though mortality rates were lower. Findings for hospital utilization were unlikely to have been affected by confounding.

Forecasting asthma-related hospital admissions in London using negative binomial models

Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.

A randomised controlled trial of the effect of automated interactive calling combined with a health risk forecast on frequency and severity of exacerbations of COPD assessed clinically and using EXACT PRO

BACKGROUND

We have developed a winter forecasting service to predict when patients with COPD are at higher risk of an exacerbation and alert them via an automated telephone call.

AIMS

To assess the effect of the service and its ability to predict periods of increased risk.

METHODS

A 4-month prospective randomised controlled trial using clinical criteria and the EXACT PRO questionnaire to identify exacerbations. Patients were randomly allocated to receive alert calls. All patients completed a diary including the EXACT PRO questionnaire on a BlackBerry Smartphone each day. They were contacted and assessed if they appeared to be exacerbating.

RESULTS

79 patients participated, 40 received alert calls. The exacerbation frequency per patient per week was significantly greater during periods of predicted high risk (0.086 ± 0.010 v 0.055 ± 0.010). The exacerbation frequency (± standard error of the mean, SEM) in patients receiving alert calls was lower (0.95 ± 0.27 v 1.17 ± 0.29) but this was not statistically significant. Fewer patients receiving alert calls had one or more EXACT event compared to the controls (34% v 53%, p=0.11), their duration was shorter (8.2 ± 2.0 v10.1 ± 1.9 days, p=0.481) and they were less severe (AUC 65 ± 21 v 115 ± 22, p=0.118). There were no significant differences in the mean change (± SEM) in SGRQ scores between the groups.

CONCLUSIONS

The ability of the forecast to predict high risk periods was confirmed unequivocally. Alert calls appeared to reduce the frequency and severity of exacerbations but these effects did not reach statistical significance, perhaps because of the number of participants, lower than expected exacerbation rates, and the fact that there was contact with patients in both groups whenever they appeared to be exacerbating.