Relationship between concavity of the flow-volume loop and small airway measures in smokers with normal spirometry.

BACKGROUND: There is increasing evidence of small airway abnormalities in smokers despite normal spirometry. The concavity in the descending limb of the maximum expiratory flow curve (MEFV) is a recognised feature of obstruction and can provide information beyond FEV1, and potentially early smoking-related damage. We aimed to evaluate concavity measures compared to known small airway measurements. METHODS: Eighty smokers with normal spirometry had small airway function assessed: multiple breath nitrogen washout (MBNW) from which ventilation heterogeneity in the diffusion-dependent acinar (Sacin) and convection-dependent conductive (Scond) airways were assessed, and impulse oscillometry system (IOS) from which respiratory resistance and reactance at 5 Hz (R5 and X5) were measured. Concavity measures were calculated from the MEFV, partitioned into global and peripheral concavity. RESULTS: We found abnormal peripheral and global concavity as well as acinar ventilation heterogeneity are common in "normal" smokers. Concavity measures were not related to either MBNW or IOS measurements. CONCLUSION: Abnormalities in concavity indices and MBNW or oscillometry parameters are common in smokers despite normal spirometry. However, these measures likely reflect different mechanisms of peripheral airway dysfunction.

Bronchodilator Responses in Respiratory Impedance, Hyperinflation and Gas Trapping in COPD.

Hyperinflation, gas trapping and their responses to long-acting bronchodilator are clinically important in COPD. The forced oscillation technique (FOT) measures of respiratory system resistance and reactance are sensitive markers of bronchodilator response in COPD. The relationships between changes in resistance and reactance, and changes in hyperinflation and gas trapping, following long-acting bronchodilator (LA-BD) have not been studied. 15 subjects with mild-moderate COPD underwent FOT, spirometry then body plethysmography, before and 2 hours after a single 150 microg dose of the LA-BD indacaterol. Hyperinflation was quantified as the inspiratory capacity to total lung capacity ratio (IC/TLC), and gas trapping as residual volume to TLC ratio (RV/TLC). At baseline, FOT parameters were moderately correlated with IC/TLC (|r| 0.53-0.73, p < 0.05). At 2 hours post-LA-BD, there were moderate correlations between change in FOT and change in RV/TLC (|r| 0.60-0.82, p < 0.05). Baseline FOT parameters also correlated with the subsequent post-LA-BD change in both IC/TLC (|r| 0.54-0.62, p < 0.05) and RV/TLC (|r| 0.57-0.76, p < 0.05). FOT impedance reflects hyperinflation and gas trapping in COPD, and the potential for long-acting bronchodilator responsiveness. These results provide us with further insight into the physiological mechanisms of action of long-acting bronchodilator treatment, and may be clinically useful for predicting treatment responses.

A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States.

This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast's construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011-2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year's regional prevalence. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011-2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases.

A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States.

This paper models the prevalence of antibodies to Borrelia burgdorferi in domestic dogs in the United States using climate, geographic, and societal factors. We then use this model to forecast the prevalence of antibodies to B. burgdorferi in dogs for 2016. The data available for this study consists of 11,937,925 B. burgdorferi serologic test results collected at the county level within the 48 contiguous United States from 2011-2015. Using the serologic data, a baseline B. burgdorferi antibody prevalence map was constructed through the use of spatial smoothing techniques after temporal aggregation; i.e., head-banging and Kriging. In addition, several covariates purported to be associated with B. burgdorferi prevalence were collected on the same spatio-temporal granularity, and include forestation, elevation, water coverage, temperature, relative humidity, precipitation, population density, and median household income. A Bayesian spatio-temporal conditional autoregressive (CAR) model was used to analyze these data, for the purposes of identifying significant risk factors and for constructing disease forecasts. The fidelity of the forecasting technique was assessed using historical data, and a Lyme disease forecast for dogs in 2016 was constructed. The correlation between the county level model and baseline B. burgdorferi antibody prevalence estimates from 2011 to 2015 is 0.894, illustrating that the Bayesian spatio-temporal CAR model provides a good fit to these data. The fidelity of the forecasting technique was assessed in the usual fashion; i.e., the 2011-2014 data was used to forecast the 2015 county level prevalence, with comparisons between observed and predicted being made. The weighted (to acknowledge sample size) correlation between 2015 county level observed prevalence and 2015 forecasted prevalence is 0.978. A forecast for the prevalence of B. burgdorferi antibodies in domestic dogs in 2016 is also provided. The forecast presented from this model can be used to alert veterinarians in areas likely to see above average B. burgdorferi antibody prevalence in dogs in the upcoming year. In addition, because dogs and humans can be exposed to ticks in similar habitats, these data may ultimately prove useful in predicting areas where human Lyme disease risk may emerge.