Lung cancer screening in clinical practice: identification of high-risk chronic obstructive pulmonary disease patients.

OBJECTIVE: The NELSON study demonstrated a positive association between computed tomography scanning and reduced mortality associated with lung cancer. The COPD-LUCSS-DLCO is a tool designed to improve screening selection criteria of lung cancer for chronic obstructive pulmonary disease patients. The aim of this study was to examine and compare the discriminating value of both scores in a community-based cohort of chronic obstructive pulmonary disease patients. METHODS: A retrospective study of chronic obstructive pulmonary disease patients followed in pulmonology consultation for a period of 10 years (2009-2019) was conducted. The NELSON criteria and COPD-LUCSS-DLCO score were calculated for each patient at the time of the study inclusion. The lung cancer incidence was calculated for each of the subgroups during the follow-up period. RESULTS: A total of 103 patients were included in the study (mean age 64.7±9.2 years, 88.3% male). Applying the COPD-LUCSS-DLCO score, high-risk patients have a 5.9-fold greater risk of developing lung cancer versus the low risk. In contrast, there was no significant association between NELSON selection criteria and lung cancer incidence. The area under the curve was 0.69 for COPD-LUCSS-DLCO and 0.59 for NELSON criteria. Comparing test results showed no differences. CONCLUSIONS: The use of the COPD-LUCSS-DLCO score in clinical practice can help to detect chronic obstructive pulmonary disease patients in greater risk of developing lung cancer with better performance than NELSON criteria. Therefore, models that include a risk biomarker strategy can improve selection criteria and consequently can enhance a better lung cancer prediction.

Lung cancer screening in clinical practice: identification of high-risk chronic obstructive pulmonary disease patients

SUMMARY OBJECTIVE: The NELSON study demonstrated a positive association between computed tomography scanning and reduced mortality associated with lung cancer. The COPD-LUCSS-DLCO is a tool designed to improve screening selection criteria of lung cancer for chronic obstructive pulmonary disease patients. The aim of this study was to examine and compare the discriminating value of both scores in a community-based cohort of chronic obstructive pulmonary disease patients. METHODS: A retrospective study of chronic obstructive pulmonary disease patients followed in pulmonology consultation for a period of 10 years (2009-2019) was conducted. The NELSON criteria and COPD-LUCSS-DLCO score were calculated for each patient at the time of the study inclusion. The lung cancer incidence was calculated for each of the subgroups during the follow-up period. RESULTS: A total of 103 patients were included in the study (mean age 64.7±9.2 years, 88.3% male). Applying the COPD-LUCSS-DLCO score, high-risk patients have a 5.9-fold greater risk of developing lung cancer versus the low risk. In contrast, there was no significant association between NELSON selection criteria and lung cancer incidence. The area under the curve was 0.69 for COPD-LUCSS-DLCO and 0.59 for NELSON criteria. Comparing test results showed no differences. CONCLUSIONS: The use of the COPD-LUCSS-DLCO score in clinical practice can help to detect chronic obstructive pulmonary disease patients in greater risk of developing lung cancer with better performance than NELSON criteria. Therefore, models that include a risk biomarker strategy can improve selection criteria and consequently can enhance a better lung cancer prediction.

Beyond Apnea-Hypopnea Index: how clinical and comorbidity are important in obstructive sleep apnea.

INTRODUCTION: The classification of the severity of obstructive sleep apnea (OSA) based on the Apnea/Hypopnea Index (AHI) does not reflect the heterogeneity and prognosis of the disease. The Baveno classification proposes a new assessment system that includes symptoms and comorbidities. The aim of our study was to evaluate the application of the Baveno classification in clinical practice and to explore its association with sleep indices, adherence to therapy and symptoms over a 6-months period. MATERIAL AND METHODS: Prospective study including patients diagnosed with OSA between January and July 2021 was conducted. Patients were divided into 4 groups (A-D) according to the Baveno classification. The adherence to PAP treatment and Epworth Sleepiness Scale (ESS) values were obtained 6 months after initiation of therapy. RESULTS: A total of 91 patients (84% male, 58 ± 13 years) were included in the study. The median ESS score was 10 (6-15), mean AHI was 28.4 ± 22.2 events/hour and the time with SpO2 < 90% (T90) was 9.7 ± 14.9%. At diagnosis, patients were classified into Baveno groups: A: 30%; B: 35%; C: 17%, D: 19%. There were no statistical differences in AHI between the different groups. On the other hand, T90 had higher values in patients with comorbidities (C, D). Regarding the treatment, the prescription of PAP was higher in patients with comorbidities (C, D), and adherence to this treatment at 6 months was higher in group D. Among patients under PAP therapy, there was a statistically significant decrease in daytime sleepiness at 6 months in groups B and D. CONCLUSIONS: The Baveno classification distributes patients with OSA evenly across the different phenotypes, regardless of the AHI value. The treatment decision was linked to the comorbidities (C, D) were the ones who had the greatest adherence to treatment at 6 months were in group D. ESS improved with greater emphasis in the most symptomatic (B, D), while the AHI is essential for the diagnosis of OSA, the Baveno classification may guide physicians better in their treatment decision.