Predicting Mortality in COPD with Validated and Sensitive Biomarkers; Fibrinogen and Mid-Range-Proadrenomedullin (MR-proADM).

Although fibrinogen is a FDA qualified prognostic biomarker in COPD, it still lacks sufficient resolution to be clinically useful. Next to replication of findings in different cohorts also the combination with other validated biomarkers should be investigated. Therefore, the aim of this study was to confirm in a large well-defined population of COPD patients whether fibrinogen can predict mortality and whether a combination with the biomarker MR-proADM can increase prognostic accuracy. From the COMIC cohort study we included COPD patients with a blood sample obtained in stable state (n = 640) and/or at hospitalization for an acute exacerbation of COPD (n = 262). Risk of death during 3 years of follow up for the separate and combined biomarker models was analyzed with Cox regression. Furthermore, logistic regression models for death after one year were constructed. When both fibrinogen and MR-proADM were included in the survival model, a doubling in fibrinogen and MR-proADM levels gave a 2.2 (95% CI 1.3-3.7) and 2.1 (95% CI 1.5-3.0) fold increased risk of dying, respectively. The prediction model for death after 1 year improved significantly when MR-proADM was added to the model with fibrinogen (AUC increased from 0.78 to 0.83; p = 0.02). However, the combined model was not significantly more adequate than the model with solely MR-proADM (AUC 0.83 vs 0.82; p = 0.34). The study suggests that MR-proADM is more promising than fibrinogen in prediciting mortality. Adding fibrinogen to a model containing MR-proADM does not significantly increase the predictive capacity of the model.

Diagnosing Non-Small Cell Lung Cancer by Exhaled Breath Profiling Using an Electronic Nose: A Multicenter Validation Study.

BACKGROUND: Despite the potential of exhaled breath analysis of volatile organic compounds to diagnose lung cancer, clinical implementation has not been realized, partly due to the lack of validation studies. RESEARCH QUESTION: This study addressed two questions. First, can we simultaneously train and validate a prediction model to distinguish patients with non-small cell lung cancer from non-lung cancer subjects based on exhaled breath patterns? Second, does addition of clinical variables to exhaled breath data improve the diagnosis of lung cancer? STUDY DESIGN AND METHODS: In this multicenter study, subjects with non-small cell lung cancer and control subjects performed 5 min of tidal breathing through the aeoNose, a handheld electronic nose device. A training cohort was used for developing a prediction model based on breath data, and a blinded cohort was used for validation. Multivariable logistic regression analysis was performed, including breath data and clinical variables, in which the formula and cutoff value for the probability of lung cancer were applied to the validation data. RESULTS: A total of 376 subjects formed the training set, and 199 subjects formed the validation set. The full training model (including exhaled breath data and clinical parameters from the training set) were combined in a multivariable logistic regression analysis, maintaining a cut off of 16% probability of lung cancer, resulting in a sensitivity of 95%, a specificity of 51%, and a negative predictive value of 94%; the area under the receiver-operating characteristic curve was 0.87. Performance of the prediction model on the validation cohort showed corresponding results with a sensitivity of 95%, a specificity of 49%, a negative predictive value of 94%, and an area under the receiver-operating characteristic curve of 0.86. INTERPRETATION: Combining exhaled breath data and clinical variables in a multicenter, multi-device validation study can adequately distinguish patients with lung cancer from subjects without lung cancer in a noninvasive manner. This study paves the way to implement exhaled breath analysis in the daily practice of diagnosing lung cancer. CLINICAL TRIAL REGISTRATION: The Netherlands Trial Register; No.: NL7025; URL: https://trialregister.nl/trial/7025.

Stability in eosinophil categorisation during subsequent severe exacerbations of COPD.

BACKGROUND: The blood eosinophil count has been shown to be a promising biomarker for establishing personalised treatment strategies to reduce corticosteroid use, either inhaled or systemic, in chronic obstructive pulmonary disease (COPD). Eosinophil levels seem relatively stable over time in stable state, but little is known whether this is also true in subsequent severe acute exacerbations of COPD (AECOPD). AIMS AND OBJECTIVES: To determine the stability in eosinophil categorisation between two subsequent severe AECOPDs employing frequently used cut-off levels. METHODS: During two subsequent severe AECOPDs, blood eosinophil counts were determined at admission to the hospital in 237 patients in the Cohort of Mortality and Inflammation in COPD Study. The following four cut-off levels were analysed: absolute counts of eosinophils ≥0.2×109/L (200 cells/µL) and ≥0.3×109/L (300 cells/µL) and relative eosinophil percentage of ≥2% and ≥3% of total leucocyte count. Categorisations were considered stable if during the second AECOPD their blood eosinophil status led to the same classification: eosinophilic or not. RESULTS: Depending on the used cut-off, the overall stability in eosinophil categorisation varied between 70% and 85% during two subsequent AECOPDs. From patients who were eosinophilic at the first AECOPD, 34%-45% remained eosinophilic at the subsequent AECOPD, while 9%-21% of patients being non-eosinophilic at the first AECOPD became eosinophilic at the subsequent AECOPD. CONCLUSIONS: The eosinophil variability leads to category changes in subsequent AECOPDs, which limits the eosinophil categorisation stability. Therefore, measurement of eosinophils at each new exacerbation seems warranted.

Improving lung cancer diagnosis by combining exhaled-breath data and clinical parameters.

INTRODUCTION: Exhaled-breath analysis of volatile organic compounds could detect lung cancer earlier, possibly leading to improved outcomes. Combining exhaled-breath data with clinical parameters may improve lung cancer diagnosis. METHODS: Based on data from a previous multi-centre study, this article reports additional analyses. 138 subjects with non-small cell lung cancer (NSCLC) and 143 controls without NSCLC breathed into the Aeonose. The diagnostic accuracy, presented as area under the receiver operating characteristic curve (AUC-ROC), of the Aeonose itself was compared with 1) performing a multivariate logistic regression analysis of the distinct clinical parameters obtained, and 2) using this clinical information beforehand in the training process of the artificial neural network (ANN) for the breath analysis. RESULTS: NSCLC patients (mean±sd age 67.1±9.1 years, 58% male) were compared with controls (62.1±7.0 years, 40.6% male). The AUC-ROC of the classification value of the Aeonose itself was 0.75 (95% CI 0.69-0.81). Adding age, number of pack-years and presence of COPD to this value in a multivariate regression analysis resulted in an improved performance with an AUC-ROC of 0.86 (95% CI 0.81-0.90). Adding these clinical variables beforehand to the ANN for classifying the breath print also led to an improved performance with an AUC-ROC of 0.84 (95% CI 0.79-0.89). CONCLUSIONS: Adding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose, either post hoc in a multivariate regression analysis or a priori to the ANN, significantly improves the diagnostic accuracy to detect the presence or absence of lung cancer.

Stable-State Midrange Proadrenomedullin Is Associated With Severe Exacerbations in COPD.

BACKGROUND: Elevated levels of midrange proadrenomedullin (MR-proADM) are associated with worse outcome in different diseases, including COPD. The association of stable-state MR-proADM with severe acute exacerbations of COPD (AECOPDs) requiring hospitalization, or with community-acquired pneumonia (CAP) in patients with COPD, has not been studied yet. The aim of this study was to evaluate the association of stable-state MR-proADM with severe AECOPD and CAP in patients with COPD. METHODS: This study pooled data of 1,285 patients from the Cohort of Mortality and Inflammation in COPD (COMIC) and PRedicting Outcome using systemic Markers In Severe Exacerbations of Chronic Obstructive Pulmonary Disease (PROMISE-COPD) cohort studies. Time until first severe AECOPD was compared between patients with high (≥ 0.87 nmol/L) or low (< 0.87 nmol/L) levels of plasma MR-proADM in stable state as previously defined. For time until first CAP, only COMIC data (n = 795) were available. RESULTS: Patients with COPD with high-level stable-state MR-proADM have a significantly higher risk for severe AECOPD compared with those with low-level MR-proADM with a corrected hazard ratio (HR) of 1.30 (95% CI, 1.01-1.68). Patients with high-level stable-state MR-proADM had a significantly higher risk for CAP compared with patients with COPD with low-level MR-proADM in univariate analysis (HR, 1.93; 95% CI, 1.24-3.01), but after correction for age, lung function, and previous AECOPD, the association was no longer significant (corrected HR, 1.10; 95% CI, 0.68-1.80). CONCLUSIONS: Stable-state high-level MR-proADM in patients with COPD is associated with severe AECOPD but not with CAP.

Statins and morbidity and mortality in COPD in the COMIC study: a prospective COPD cohort study.

BACKGROUND: Both chronic inflammation and cardiovascular comorbidity play an important role in the morbidity and mortality of patients with chronic obstructive pulmonary disease (COPD). Statins could be a potential adjunct therapy. The additional effects of statins in COPD are, however, still under discussion. The aim of this study is to further investigate the association of statin use with clinical outcomes in a well-described COPD cohort. METHODS: 795 patients of the Cohort of Mortality and Inflammation in COPD (COMIC) study were divided into statin users or not. Statin use was defined as having a statin for at least 90 consecutive days after inclusion. Outcome parameters were 3-year survival, based on all-cause mortality, time until first hospitalisation for an acute exacerbation of COPD (AECOPD) and time until first community-acquired pneumonia (CAP). A sensitivity analysis was performed without patients who started a statin 3 months or more after inclusion to exclude immortal time bias. RESULTS: Statin use resulted in a better overall survival (corrected HR 0.70 (95% CI 0.51 to 0.96) in multivariate analysis), but in the sensitivity analysis this association disappeared. Statin use was not associated with time until first hospitalisation for an AECOPD (cHR 0.95, 95% CI 0.74 to 1.22) or time until first CAP (cHR 1.1, 95% CI 0.83 to 1.47). CONCLUSIONS: In the COMIC study, statin use is not associated with a reduced risk of all-cause mortality, time until first hospitalisation for an AECOPD or time until first CAP in patients with COPD.