Search

Predictive models play a pivotal role in the provision of accurate and useful probabilistic assessments of clinical outcomes in chronic diseases. This study was aimed to develop a dedicated prognostic index for quantifying progression risk in chronic obstructive pulmonary disease (COPD). Data were collected prospectively from 75 COPD patients during a three years period. A predictive model of progression risk of COPD was developed using Bayesian logistic regression analysis by Markov chain Monte Carlo method. One-year cycles were used for the disease progression in this model. Primary end points for progression were impairment in basal dyspne index (BDI) score, FEV(1) decline, and exacerbation frequency in last three years. Time-varying covariates age, smoking, body mass index (BMI), severity of disease according to GOLD, PaO2, PaCO(2), IC, RV/TLC, DLCO were used under the study. The mean age was 57.1 + or - 8.1. BDI were strongly correlated with exacerbation frequency (p= 0.001) but not with FEV(1) decline. BMI was found to be a predictor factor for impairment in BDI (p= 0.03). The following independent risk factors were significant to predict exacerbation frequency: GOLD staging (OR for GOLD I vs. II and III = 2.3 and 4.0), hypoxemia (OR for mild vs moderate and severe = 2.1 and 5.1) and hyperinflation (OR= 1.6). PaO2 (p= 0.026), IC (p= 0.02) and RV/TLC (p= 0.03) were found to be predictive factors for FEV(1) decline. The model estimated BDI, lung function and exacerbation frequency at the last time point by testing initial data of three years with 95% reliability (p0.001). Accordingly, this model was evaluated as confident of 95% for assessing the future status of COPD patients. Using Bayesian predictive models, it was possible to develop a risk-stratification index that accurately predicted progression of COPD. This model can provide decision-making about future in COPD patients with high reliability looking clinical data of beginning.

Several factors have been accused for asthma exacerbations, however, very few studies have evaluated whether different factors predict severity of asthma exacerbation. We aimed to determine the predictive factors for severity of asthma exacerbation. Retrospective analysis of data on 93 patients visited our emergency-department because of asthma exacerbation was reviewed. Hospitalization in intensive care unit and/or intubation because of asthma was accepted as the criteria for severe exacerbation. Logistic regression analysis estimated the strength of association of each variable, potentially related to severe asthmatic exacerbation, with severe/very severe as compared to mild/moderate asthmatic exacerbation. Independent variables included in the analysis were age, sex, smoking history, inhaler steroid using, compliance with medication, chronic asthma severity, presence of additional atopic diseases, prick test positivity, provocative factors, number of short-acting beta(2)-agonist using, number of visits to emergency department for asthma over one year period, previous severe exacerbation, pulmonary functions, and blood eosinophil count. 20 were severe/very severe and 73 mild/moderate asthmatic exacerbation. Frequent using of short-acting beta(2)-agonist (OR= 1.5, 95% CI= 1.08-5.3, p= 0.003), noncompliance with medication (OR= 3.6, 95% CI= 1.3-9.9, p= 0.013), previous severe asthmatic exacerbation (OR= 3.8, 95% CI= 1.48-10.01, p= 0.005) and recent admission to hospital (OR= 2.9, 95% CI= 1.07-8.09, p= 0.037) were found to be predictive factors for severe asthmatic exacerbation. Different predictive factors, in particular frequent using of short-acting beta(2)-agonist and noncompliance with medication may be associated with severe asthma exacerbations compared to milder exacerbations. This suggests different mechanisms are responsible for severity of asthma exacerbation.