Single-station N2 (ssN2) versus multi-station N2 has been used as a selection criterion for treatment recommendations between surgical versus non-surgical multimodality treatment in stage III-N2 NSCLC. We hypothesized that clinical staging would be susceptible to upstaging on pathologic staging and, therefore, challenge this practice.
Objective Risk prediction models are used to determine eligibility for targeted lung cancer screening. However, prospective data regarding model performance in this setting are limited. Here we report the performance of the PLCO m2012 risk model, which calculates 6 year lung cancer risk, in a cohort invited for lung cancer screening in a socioeconomically deprived area. Methods and analysis Calibration (expected/observed (E/O) lung cancer diagnoses over 6 years) and discrimination (area under the receiver operating characteristic curve) of PLCO m2012 and other models was performed in Manchester Lung Health Check (M-LHC) participants, where PLCO m2012 ≥1.51% was used prospectively to determine screening eligibility. Lung cancers diagnosed by any route were captured within 6 years of risk assessment, for both screened and non-screened participants. Performance of a range of models was evaluated. Results Out of 2541 attendees, 56% were high-risk (n=1430/2541) and offered screening; 44% were low-risk (n=1111/2541) and not screened. Over 6 years, 7.3% (n=105/1430) and 0.9% (n=10/1111) were diagnosed with lung cancer in the high and low-risk cohorts, respectively (p<0.0001). Risk was underestimated in both high-risk, screened (E/O 0.68 (0.57–0.82)) and low-risk, unscreened groups (E/O 0.61 (0.33–1.14)). Most other models also underestimated risk. Conclusion Risk-based eligibility using PLCO m2012 successfully classified most eventual lung cancer cases in the high-risk, screened group. Prediction models generally underestimated risk in this socioeconomically deprived cohort, irrespective of screening status. The effect of screening on increasing the probability of lung cancer diagnosis should be considered when interpreting measures of prediction model performance.
Introduction. Integrating smoking cessation support into lung cancer screening can improve abstinence rates. However, healthcare decision makers need evidence of cost effectiveness to understand the cost/benefit of adopting this approach. Methods. To evaluate the cost-effectiveness of different smoking cessation interventions, and service delivery, we used a Markov model, adapted from previous National Institute for Health and Care Excellence guidelines on smoking cessation. This uses long-term epidemiological data to capture the prevalence of the smoking-related illnesses, where prevalence is estimated based on age, sex, and smoking status. Probabilistic sensitivity analysis was conducted to capture joint parameter uncertainty. Results. All smoking cessation interventions appeared cost-effective at a threshold of 20,000 pounds per quality-adjusted life year, compared to no intervention or behavioural support alone. Offering immediate smoking cessation as part of lung cancer screening appointments, compared with usual care (onward referral to stop smoking services) was also estimated to be cost-effective with a net monetary benefit of 2,198 pounds per person, and a saving of between 34 and 79 pounds per person in reduced workplace absenteeism among working age attendees. Estimated healthcare cost savings were more than four times greater in the most deprived quintile compared to the least deprived, alongside a fivefold increase in QALYs accrued. Conclusions. Smoking cessation interventions within lung cancer screening are cost-effective and should be integrated so that treatment is initiated during screening visits. This is likely to reduce overall costs to the health service, and wider integrated care systems, improve quality and length of life, and may lessen health inequalities.
Clinical prognostic models help inform decision-making by estimating a patient's risk of experiencing an outcome in the future. The net benefit is increasingly being used to assess the clinical utility of models. By calculating an appropriately weighted average of the true and false positives of a model, the net benefit assesses the value added by a binary decision policy obtained when thresholding a model. Although such 'treat or not' decisions are common, prognostic models are also often used to tailor and personalise the care of patients, which implicitly involves the consideration of multiple interventions at different risk thresholds. We extend the net benefit to consider multiple decision thresholds simultaneously, by taking a weighted area under a rescaled version of the net benefit curve, deriving the continuous net benefit. In addition to the consideration of a continuum of interventions, we also show how the continuous net benefit can be used for populations with a range of optimal thresholds for a single treatment, due to individual variations in expected treatment benefit or harm, highlighting limitations of current proposed methods that calculate the area under the decision curve. We showcase the continuous net benefit through two examples of cardiovascular preventive care, comparing two modelling choices using the continuous net benefit. The continuous net benefit informs researchers of the clinical utility of models during selection, development, and validation, and helps decision makers understand their usefulness, improving their viability towards implementation.
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