Tobacco use and motivation to stop smoking among long-term smokers who are ineligible for lung cancer screening

Efficacy of Risk Prediction Models and Thresholds to Select Patients for Lung Cancer Screening

PurposeScreening for lung cancer is recommended to reduce lung cancer mortality, but there is no consensus on patient selection for screening in Canada. Risk prediction models are more efficacious than the screening recommendations of the Canadian Task Force on Preventive Health Care (CTFPHC), but it remains to be determined which model and threshold are optimal. MethodsWe retrospectively applied the PLCO_m2012, PLCO_all2014 and LLPv2 risk prediction models to 120 lung cancer patients from a Canadian province, at risk thresholds of ≥ 1.51% and ≥ 2.00%, to determine screening eligibility at time of diagnosis. OncoSim modelling was used to compare these risk thresholds. ResultsSensitivities of the risk prediction models at a threshold of ≥ 1.51% were similar with 93 (77.5%), 96 (80.0%), and 97 (80.8%) patients selected for screening, respectively. The PLCO_m2012 and PLCO_all2014 models selected significantly more patients for screening at a ≥ 1.51% threshold. The OncoSim simulation model estimated that the ≥ 1.51% threshold would detect 4 more cancers per 100 000 people than the ≥ 2.00% threshold. All risk prediction models, at both thresholds, achieved greater sensitivity than CTFPHC recommendations, which selected 56 (46.7%) patients for screening. ConclusionCommonly considered lung cancer screening risk thresholds (≥1.51% and ≥2.00%) are more sensitive than the CTFPHC 30-pack-years criterion to detect lung cancer. A lower risk threshold would achieve a larger population impact of lung cancer screening but would require more resources. Patients with limited or no smoking history, young patients, and patients with no history of COPD may be missed regardless of the model chosen.

Chronic obstructive pulmonary disease prevalence and prediction in a high-risk lung cancer screening population

Background

Chronic obstructive pulmonary disease (COPD) is an underdiagnosed condition sharing risk factors with lung cancer. Lung cancer screening may provide an opportunity to improve COPD diagnosis. Using Pan-Canadian Early Detection of Lung Cancer (PanCan) study data, the present study sought to determine the following: 1) What is the prevalence of COPD in a lung cancer screening population? 2) Can a model based on clinical and screening low-dose CT scan data predict the likelihood of COPD?

Methods

The single arm PanCan study recruited current or former smokers age 50–75 who had a calculated risk of lung cancer of at least 2% over 6 years. A baseline health questionnaire, spirometry, and low-dose CT scan were performed. CT scans were assessed by a radiologist for extent and distribution of emphysema. With spirometry as the gold standard, logistic regression was used to assess factors associated with COPD.

Results

Among 2514 recruited subjects, 1136 (45.2%) met spirometry criteria for COPD, including 833 of 1987 (41.9%) of those with no prior diagnosis, 53.8% of whom had moderate or worse disease. In a multivariate model, age, current smoking status, number of pack-years, presence of dyspnea, wheeze, participation in a high-risk occupation, and emphysema extent on LDCT were all statistically associated with COPD, while the overall model had poor discrimination (c-statistic = 0.627 (95% CI of 0.607 to 0.650). The lowest and the highest risk decile in the model predicted COPD risk of 27.4 and 65.3%.

Conclusions

COPD had a high prevalence in a lung cancer screening population. While a risk model had poor discrimination, all deciles of risk had a high prevalence of COPD, and spirometry could be considered as an additional test in lung cancer screening programs.

Trial registration

(Clinical Trial Registration: ClinicalTrials.gov, number NCT00751660, registered September 12, 2008)

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-020-01344-y.

Effect of nicotine dependence on quality of life and sleep quality in patients with lung cancer who continue to smoke after diagnosis

Background

Hundreds of millions of Chinese individuals continue to smoke and rates of lung cancer still continue to rise. However, there were few studies that examined the effects of nicotine dependence on quality of life (QOL) and sleep quality in lung cancer patients. This study aimed to investigate the effect of nicotine dependence on QOL and sleep quality in lung cancer patients who continue to smoke after diagnosis.

Methods

This cross-sectional survey study included 202 patients with lung cancer. Smokers were separated into two groups based on the Fagerstrom Test for Nicotine dependence: the low dependence (LD) (<4 score) group (n=59) and the high dependence (HD) (≥4 score) group (n=143). Both Chinese version of the European Organization for Research and Treatment of Cancer Quality of Life Core Questionnaire 30 (EORTC QLQ-C30) and Chinese version of Pittsburgh Sleep Quality Index (PSQI) were used to evaluate the two groups of lung cancer patients. Then we analyzed the difference of QOL and sleep quality between two distinct nicotine dependence groups.

Results

Physical functioning, role functioning, emotional functioning, cognitive functioning, global health status and social functioning items in the LD group were significantly higher than the HD group (P<0.001). Fatigue, nausea/vomiting, pain, dyspnea, insomnia, appetite loss, diarrhea and financial problems in the LD group were significantly lower than those in the HD group (P<0.001). Significantly higher scores in the HD group were found concerning the three sleep components including sleep duration, sleep efficiency and daytime function. The mean global PSQI score in the HD group was significantly higher than the LD group (P=0.014).

Conclusions

These findings suggest that lung cancer patients who continue to smoke after diagnosis should receive health education in order to improve their QOL and quality of sleep after the word education. This can be useful for clinicians and nurses who are trying to motivate smokers to quit smoking.