Rate of progression of CT-quantified emphysema in male current and ex-smokers: a follow-up study.

BACKGROUND: Little is known about the factors associated with CT-quantified emphysema progression in heavy smokers. The objective of this study was to investigate the effect of length of smoking cessation and clinical / demographical factors on the rate of emphysema progression and FEV1-decline in male heavy smokers. METHODS: 3,670 male smokers with mean (SD) 40.8 (17.9) packyears underwent chest CT scans and pulmonary function tests at baseline and after 1 and 3 years follow-up. Smoking status (quitted ≥5, ≥1-<5, <1 years or current smoker) was noted. Rate of progression of emphysema and FEV1-decline after follow-up were assessed by analysis of variance adjusting for age, height, baseline pulmonary function and emphysema severity, packyears, years in study and respiratory symptoms. The quitted ≥5 group was used as reference. RESULTS: Median (Q1-Q3) emphysema severity,<-950 HU, was 8.8 (5.1 - 14.1) and mean (SD) FEV1 was 3.4 (0.73) L or 98.5 (18.5) % of predicted. The group quitted '>5 years' showed significantly lower rates of progression of emphysema compared to current smokers, 1.07% and 1.12% per year, respectively (p<0.001). Current smokers had a yearly FEV1-decline of 69 ml, while subjects quit smoking >5 years had a yearly decline of 57.5 ml (p<0.001). CONCLUSION: Quit smoking >5 years significantly slows the rate of emphysema progression and lung function decline. TRIAL REGISTRATION: Registered at http://www.trialregister.nl with trial number ISRCTN63545820.

Baseline Characteristics and Mortality Outcomes of Control Group Participants and Eligible Non-Responders in the NELSON Lung Cancer Screening Study.

INTRODUCTION: Individuals who are younger, have a high socioeconomic background and/or have a healthy lifestyle are more inclined to participate in screening trials. This form of bias may affect the generalizability of study results to the target population. This study aimed to investigate the generalizability of the NELSON lung cancer screening trial to the Dutch population. METHODS: People at high risk for developing lung cancer were identified by sending a health questionnaire to 606,409 persons aged 50-74 years, based on population registries. Eligible subjects received an invitation to participate (n = 30,051). 15,822 subjects agreed to participate and were randomized, whereas 15,137 did not respond (so-called eligible nonresponders). Baseline characteristics and mortality profiles were compared between control group participants and eligible nonresponders. RESULTS: Participants had better self-reported health (p = 0.02), were younger, more physically active, higher educated, and more often former smokers compared with eligible nonresponders (all p < 0.001). No differences were seen in self-reported outcomes of pulmonary tests, history of lung cancer, and smoked pack-years. Mortality due to all-causes (p < 0.001) and mortality classification separately was lower among participants. However, the proportion of subjects death due to cancer was higher among participants (62.4% vs. 54.9%). CONCLUSION: Modest differences in baseline characteristics between participants and eligible nonresponders, led to minor differences in mortality profiles. However, group sizes were large and therefore it seems unlikely that these small differences will influence the generalizability of the NELSON trial. Results of the NELSON trial can roughly be used to predict the effect of population-based lung cancer screening.

Risk-based selection from the general population in a screening trial: selection criteria, recruitment and power for the Dutch-Belgian randomised lung cancer multi-slice CT screening trial (NELSON).

A method to obtain the optimal selection criteria, taking into account available resources and capacity and the impact on power, is presented for the Dutch-Belgian randomised lung cancer screening trial (NELSON). NELSON investigates whether 16-detector multi-slice computed tomography screening will decrease lung cancer mortality compared to no screening. A questionnaire was sent to 335,441 (mainly) men, aged 50-75. Smoking exposure (years smoked, cigarettes/day, years quit) was determined, and expected lung cancer mortality was estimated for different selection scenarios for the 106,931 respondents, using lung cancer mortality data by level of smoking exposure (US Cancer Prevention Study I and II). Selection criteria were chosen so that the required response among eligible subjects to reach sufficient sample size was minimised and the required sample size was within our capacity. Inviting current and former smokers (quit 15 cigarettes/day during >25 years or >10 cigarettes/day during >30 years was most optimal. With a power of 80%, 17,300-27,900 participants are needed to show a 20-25% lung cancer mortality reduction 10 years after randomisation. Until October 18, 2005 11,103 (first recruitment round) and 4,325 (second recruitment round) (total = 15,428) participants have been randomised. Selecting participants for lung cancer screening trials based on risk estimates is feasible and helpful to minimize sample size and costs. When pooling with Danish trial data (n = +/-4,000) NELSON is the only trial without screening in controls that is expected to have 80% power to show a lung cancer mortality reduction of at least 25% 10 years after randomisation.

Prediction of torsional failure in 22 cadaver femora with and without simulated subtrochanteric metastatic defects: a CT scan-based finite element analysis.

BACKGROUND: In metastatic bone disease, prophylactic fixation of impending long bone fracture is preferred over surgical treatment of a manifest fracture. There are no reliable guidelines for prediction of pathological fracture risk, however. We aimed to determine whether finite element (FE) models constructed from quantitative CT scans could be used for predicting pathological fracture load and location in a cadaver model of metastatic bone disease. MATERIAL AND METHODS: Subject-specific FE models were constructed from quantitative CT scans of 11 pairs of human femora. To simulate a metastatic defect, a transcortical hole was made in the subtrochanteric region in one femur of each pair. All femora were experimentally loaded in torsion until fracture. FE simulations of the experimental set-up were performed and torsional stiffness and strain energy density (SED) distribution were determined. RESULTS: In 15 of the 22 cases, locations of maximal SED fitted with the actual fracture locations. The calculated torsional stiffness of the entire femur combined with a criterion based on the local SED distribution in the FE model predicted 82% of the variance of the experimental torsional failure load. INTERPRETATION: In the future, CT scan-based FE analysis may provide a useful tool for identification of impending pathological fractures requiring prophylactic stabilization.