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10.2147/COPD.S153426 http://scihub22266oqcxt.onion/10.2147/COPD.S153426 C5856300!5856300!29559773     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Chron+Obstruct+Pulmon+Dis 2018 ; 13 (ä): 889-902 Nephropedia Template TP {{tp|p=29559773|t=2018. Use of clinical characteristics to predict spirometric classification of obstructive lung disease |pdf=|usr=}}{{29559773}} gab.com Text Use of clinical characteristics to predict spirometric classification of obstructive lung disease JO: Int J Chron Obstruct Pulmon Dis http://www.kidney.de/pget.php?&tw=1&pmid=29559773 #FOAvip Background: There is no consensus on how to define patients with symptoms of asthma and chronic obstructive pulmonary disease (COPD). A diagnosis of asthma?COPD overlap (ACO) syndrome has been proposed, but its value is debated. This study (GSK Study 201703 [NCT02302417]) investigated the ability of statistical modeling approaches to define distinct disease groups in patients with obstructive lung disease (OLD) using medical history and spirometric data. Methods: Patients aged ?18 years with diagnoses of asthma and/or COPD were categorized into three groups: 1) asthma (nonobstructive; reversible), 2) ACO (obstructive; reversible), and 3) COPD (obstructive; nonreversible). Obstruction was defined as a post-bronchodilator forced expiratory volume in 1 second (FEV1)/forced vital capacity <0.7, and reversibility as a post-albuterol increase in FEV1 ?200 mL and ?12%. A primary model (PM), based on patients? responses to a health care practitioner-administered questionnaire, was developed using multinomial logistic regression modeling. Other multivariate statistical analysis models for identifying asthma and COPD as distinct entities were developed and assessed using receiver operating characteristic (ROC) analysis. Partial least squares discriminant analysis (PLS-DA) assessed the degree of overlap between groups. Results: The PM predicted spirometric classifications with modest sensitivity. Other analysis models performed with high discrimination (area under the ROC curve: asthma model, 0.94; COPD model, 0.87). PLS-DA identified distinct phenotypic groups corresponding to asthma and COPD. Conclusion: Within the OLD spectrum, patients with asthma or COPD can be identified as two distinct groups with a high degree of precision. Patients outside these classifications do not constitute a homogeneous group. Twit Text FOAVip Use of clinical characteristics to predict spirometric classification of obstructive lung disease ????Int J Chron Obstruct Pulmon Dis http://www.kidney.de/pget.php?&tw=1&pmid=29559773 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29559773 #FOAvip English Wikipedia <ref>{{Cite pmid|29559773}}</ref> Use of clinical characteristics to predict spirometric classification of obstructive lung disease #MMPMID29559773 Pascoe SJ; Wu W; Collison KA; Nelsen LM; Wurst KE; Lee LA Int J Chron Obstruct Pulmon Dis 2018[]; 13 (ä): 889-902 PMID29559773show ga Background: There is no consensus on how to define patients with symptoms of asthma and chronic obstructive pulmonary disease (COPD). A diagnosis of asthma?COPD overlap (ACO) syndrome has been proposed, but its value is debated. This study (GSK Study 201703 [NCT02302417]) investigated the ability of statistical modeling approaches to define distinct disease groups in patients with obstructive lung disease (OLD) using medical history and spirometric data. Methods: Patients aged ?18 years with diagnoses of asthma and/or COPD were categorized into three groups: 1) asthma (nonobstructive; reversible), 2) ACO (obstructive; reversible), and 3) COPD (obstructive; nonreversible). Obstruction was defined as a post-bronchodilator forced expiratory volume in 1 second (FEV1)/forced vital capacity <0.7, and reversibility as a post-albuterol increase in FEV1 ?200 mL and ?12%. A primary model (PM), based on patients? responses to a health care practitioner-administered questionnaire, was developed using multinomial logistic regression modeling. Other multivariate statistical analysis models for identifying asthma and COPD as distinct entities were developed and assessed using receiver operating characteristic (ROC) analysis. Partial least squares discriminant analysis (PLS-DA) assessed the degree of overlap between groups. Results: The PM predicted spirometric classifications with modest sensitivity. Other analysis models performed with high discrimination (area under the ROC curve: asthma model, 0.94; COPD model, 0.87). PLS-DA identified distinct phenotypic groups corresponding to asthma and COPD. Conclusion: Within the OLD spectrum, patients with asthma or COPD can be identified as two distinct groups with a high degree of precision. Patients outside these classifications do not constitute a homogeneous group. äUse of clinical characteristics to predict spirometric classification (epmc).pdf DeepDyve Pubget Overpricing