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A predictive model for the development of chronic obstructive pulmonary disease.

ABSTRACT: The screening of a person at risk for chronic obstructive pulmonary disease (COPD) and timely treatment may provide opportunities to delay the progressive destruction of lung function. Therefore, a model to predict the disease is required. We hypothesized that demographic and clinical information in combination with genetic markers would aid in the prediction of COPD development, prior to its onset. The aim of the present study was to create a predictive model for COPD development. Demographic, clinical presentation and genetic polymorphisms were recorded in COPD patients and control subjects. Nighty-six single-nucleotide polymorphisms of 46 genes were selected for genotyping in the case-control study. A predictive model was produced using logistic regression with a stepwise model-building approach and was validated. A total of 331 patients and 351 control subjects were included. The logistic regression identified the following predictors: Gender, respiratory infection in early life, low birth weight, smoking history and genotype polymorphisms (rs2070600, rs10947233, rs1800629, rs2241712 and rs1205). The model was established using the following formula: COPD = 1/[1 + exp (-2.4933-1.2197 gender + 1.1842 respiratory infection in early life + 2.4350 low birth weight + 1.8524 smoking - 1.1978 rs2070600 + 2.0270 rs10947233 + 1.1913 rs10947233 + 0.6468 rs1800629 + 0.5272 rs2241712 + 0.4024 rs1205)] (when the value is >0.5). The Hosmer-Lemeshow test showed no significant deviations between the observed and predicted events. Validation of the model in 50 patients showed a modest sensitivity and specificity. Therefore, a predictive model based on demographic, clinical and genetic information may identify COPD prior to its onset.

SUBMITTER: Guo YI

PROVIDER: S-EPMC4660625 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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A predictive model for the development of chronic obstructive pulmonary disease.

Guo Y I YI Qian Yanrong Y Gong Y I YI Pan Chunming C Shi Guochao G Wan Huanying H

Biomedical reports 20150805 6

The screening of a person at risk for chronic obstructive pulmonary disease (COPD) and timely treatment may provide opportunities to delay the progressive destruction of lung function. Therefore, a model to predict the disease is required. We hypothesized that demographic and clinical information in combination with genetic markers would aid in the prediction of COPD development, prior to its onset. The aim of the present study was to create a predictive model for COPD development. Demographic, ...[more]

PMID: 26623030

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Project description:BackgroundThe frequent exacerbator phenotype of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) is characterized by experiencing at least two exacerbations per year, leading to a significant economic burden on healthcare systems worldwide. Although several biomarkers have been shown to be effective in assessing AECOPD severity in recent years, there is a lack of studies on markers to predict the frequent exacerbator phenotype of AECOPD. The current study aimed to develop a new predictive model for the frequent exacerbator phenotype of AECOPD based on rapid, inexpensive, and easily obtained routine markers.MethodsThis was a single-center, retrospective study that enrolled a total of 2,236 AECOPD patients. The participants were divided into two groups based on the frequency of exacerbations: infrequent group (n=1,827) and frequent group (n=409). They underwent a complete blood count, as well as blood biochemistry, blood lipid and coagulation testing, and general characteristics were also recorded. Univariate analysis and binary multivariate logistic regression analyses were used to explore independent risk factors for the frequent exacerbator phenotype of AECOPD, which could be used as components of a new predictive model. The receiver operator characteristic (ROC) curve was used to assess the predictive value of the new model, which consisted of all significant risk factors predicting the primary outcome. The nomogram risk prediction model was established using R software.ResultsAge, gender, length of stay (LOS), neutrophils, monocytes, eosinophils, direct bilirubin (DBil), gamma-glutamyl transferase (GGT), and the glucose-to-lymphocyte ratio (GLR) were independent risk factors for the frequent exacerbator phenotype of AECOPD. The area under the curve (AUC) of the new predictive model was 0.681 [95% confidence interval (CI): 0.653-0.708], and the sensitivity was 63.6% (95% CI: 58.9-68.2%) and the specificity was 65.0% (95% CI: 60.3-69.6%).ConclusionsA new predictive model based on demographic characteristics and blood parameters can be used to predict the frequency of acute exacerbations in the management of chronic obstructive pulmonary disease (COPD).

Project description:The prevalence of chronic obstructive pulmonary disease (COPD) and its associated comorbidities increase with age. However, little is understood about differences in the disease in patients over 65 years of age compared with younger patients.To determine disease characteristics of COPD and its impact in older patients compared with younger patients.We examined baseline characteristics of patients with COPD (global obstructive lung disease stage II-IV) in 2 large cohorts: Genetic Epidemiology of COPD Study (COPDGene) and Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE). We compared demographics, indices of disease severity, prevalence of comorbidities, exacerbation frequency, and quality of life scores in patients ?65 years of age vs patients <65 years of age. We also tested for associations of age with disease characteristics and health outcomes.In the COPDGene cohort, older patients (n = 1663) had more severe disease as measured by forced expiratory volume in 1 second (1.22 vs 1.52 L, P < .001), use of long-term oxygen therapy (35% vs 22%, P < .001), 6-minute walk distance (355 vs 375 m, P < .001), and radiographic evidence of emphysema (14% vs 8%, P < .001) and air trapping (47% vs 36%, P < .001) and were more likely to have comorbidities compared with younger patients (n = 2027). Similarly, in the ECLIPSE cohort, older patients (n = 1030) had lower forced expiratory volume in 1 second (1.22 vs 1.34 L, P < .001), greater use of long-term oxygen therapy (7% vs 5%, P = .02), shorter 6- minute walk distance (360 vs 389 m, P < .001), and more radiographic evidence of emphysema (17% vs 14%, P = .009) than younger patients (n = 1131). In adjusted analyses of both cohorts, older age was associated with decreased frequency of exacerbations [odds ratio = 0.52, 95% confidence interval (CI) = 0.43-0.64 in COPDGene, odds ratio = 0.79, 95% CI = 0.64-0.99 in ECLIPSE] and a better quality of life (lower St. Georges respiratory questionnaire score) (? = -8.7, 95% CI = -10.0 to -7.4 in COPDGene, ? = -4.4, 95% CI = -6.1 to -3.2 in ECLIPSE).Despite greater severity of illness, older patients with COPD had better quality of life and reported fewer exacerbations than younger patients. Although this observation needs to be explored further, it may be related to the fact that older patients change their expectations and learn to adapt to their disease.

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A predictive model for the development of chronic obstructive pulmonary disease.

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A predictive model for the development of chronic obstructive pulmonary disease.

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