Implementation of targeted screening for lung cancer in a high-risk population within routine NHS practice using low-dose computed tomography

Personalised lung cancer risk stratification and lung cancer screening: do general practice electronic medical records have a role?

BACKGROUND

In the United Kingdom (UK), cancer screening invitations are based on general practice (GP) registrations. We hypothesize that GP electronic medical records (EMR) can be utilised to calculate a lung cancer risk score with good accuracy/clinical utility.

METHODS

The development cohort was Secure Anonymised Information Linkage-SAIL (2.3 million GP EMR) and the validation cohort was UK Biobank-UKB (N = 211,597 with GP-EMR availability). Fast backward method was applied for variable selection and area under the curve (AUC) evaluated discrimination.

RESULTS

Age 55-75 were included (SAIL: N = 574,196; UKB: N = 137,918). Six-year lung cancer incidence was 1.1% (6430) in SAIL and 0.48% (656) in UKB. The final model included 17/56 variables in SAIL for the EMR-derived score: age, sex, socioeconomic status, smoking status, family history, body mass index (BMI), BMI:smoking interaction, alcohol misuse, chronic obstructive pulmonary disease, coronary heart disease, dementia, hypertension, painful condition, stroke, peripheral vascular disease and history of previous cancer and previous pneumonia. The GP-EMR-derived score had AUC of 80.4% in SAIL and 74.4% in UKB and outperformed ever-smoked criteria (currently the first step in UK lung cancer screening pilots).

DISCUSSION

A GP-EMR-derived score may have a role in UK lung cancer screening by accurately targeting high-risk individuals without requiring patient contact.

The value of blood-based measures of liver function and urate in lung cancer risk prediction: A cohort study and health economic analysis

BACKGROUND

Several studies have reported associations between low-cost blood-based measurements and lung cancer but their role in risk prediction is unclear. We examined the value of expanding lung cancer risk models for targeting low-dose computed tomography (LDCT), including blood measurements of liver function and urate.

METHODS

We analysed a cohort of 388,199 UK Biobank participants with 1873 events and calculated the c-index and fraction of new information (FNI) for models expanded to include combinations of blood measurements, lung function (forced expiratory volume in 1 s - FEV1), alcohol status and waist circumference. We calculated the hypothetical cost per lung cancer case detected by LDCT for different scenarios using a threshold of ≥ 1.51 % risk at 6 years.

RESULTS

The c-index was 0.805 (95 %CI:0.794-0.816) for the model containing conventional predictors. Expanding to include blood measurements increased the c-index to 0.815 (95 %CI: 0.804-0.826;p < 0.0001;FNI:0.06). Expanding to include FEV1, alcohol status, and waist circumference increased the c-index to 0.811 (95 %CI: 0.800-0.822;p < 0.0001;FNI: 0.04). The c-index for the fully expanded model containing all variables was 0.819 (95 %CI:0.808-0.830;p < 0.0001;FNI:0.09). Model expansion had a greater impact on the c-index and FNI for people with a history of smoking cigarettes relative to the full cohort. Compared with the conventional risk model, the expanded models reduced the number of participants meeting the criteria for LDCT screening by 15-21 %, and lung cancer cases detected by 7-8 %. The additional cost per lung cancer case detected relative to the conventional model was £ 1018 for adding blood tests and £ 9775 for the fully expanded model.

CONCLUSION

Blood measurements of liver function and urate made a modest improvement to lung cancer risk prediction compared with a model containing conventional risk factors. There was no evidence that model expansion would improve the cost per lung cancer case detected in UK healthcare settings.

Interventions Designed to Increase the Uptake of Lung Cancer Screening: An Equity-Oriented Scoping Review

Introduction

Participation in lung cancer screening (LCS) is lower in populations with the highest burden of lung cancer risk (through the social patterning of smoking behavior) and lowest levels of health care utilization (through structurally inaccessible care) leading to a widening of health inequities.

Methods

We conducted a scoping review using the Arksey and O'Malley methodological framework to inform equitable access to LCS by illuminating knowledge and implementation gaps in interventions designed to increase the uptake of LCS. We comprehensively searched for LCS interventions (Ovid Medline, Excerpta Medica database, the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and Scopus from 2000 to June 22, 2021) and included peer-reviewed articles and gray literature published in the English language that describe an intervention designed to increase the uptake of LCS, charted data using our previously published tool and conduced a health equity analysis to determine the intended-unintended and positive-negative outcomes of the interventions for populations experiencing the greatest inequities.

Results

Our search yielded 3572 peer-reviewed articles and 54,292 pieces of gray literature. Ultimately, we included 35 peer-reviewed articles and one gray literature. The interventions occurred in the United States, United Kingdom, Japan, and Italy, focusing on shared decision-making, the use of electronic health records as reminders, patient navigation, community-based campaigns, and mobile computed tomography scanners. We developed an equity-oriented LCS framework and mapped the dimensions and outcomes of the interventions on access to LCS on the basis of approachability, acceptability, availability, affordability, and appropriateness of the intervention. No intervention was mapped across all five dimensions. Most notably, knowledge and implementation gaps were identified in dimensions of acceptability, availability, and affordability.

Conclusions

Interventions that were most effective in improving access to LCS targeted priority populations, raised community-level awareness, tailored materials for sociocultural acceptability, did not depend on prior patient engagement/registration with the health care system, proactively considered costs related to participation, and enhanced utilization through informed decision-making.

Socio-economic inequalities in cancer survival: how do they translate into Number of Life-Years Lost?

BACKGROUND

We aimed to investigate the impact of socio-economic inequalities in cancer survival in England on the Number of Life-Years Lost (NLYL) due to cancer.

METHODS

We analysed 1.2 million patients diagnosed with one of the 23 most common cancers (92.3% of all incident cancers in England) between 2010 and 2014. Socio-economic deprivation of patients was based on the income domain of the English Index of Deprivation. We estimated the NLYL due to cancer within 3 years since diagnosis for each cancer and stratified by sex, age and deprivation, using a non-parametric approach. The relative survival framework enables us to disentangle death from cancer and death from other causes without the information on the cause of death.

RESULTS

The largest socio-economic inequalities were seen mostly in adults <45 years with poor-prognosis cancers. In this age group, the most deprived patients with lung, pancreatic and oesophageal cancer lost up to 6 additional months within 3 years since diagnosis than the least deprived. For most moderate/good prognosis cancers, the socio-economic inequalities widened with age.

CONCLUSIONS

More deprived patients and particularly the young with more lethal cancers, lose systematically more life-years than the less deprived. To reduce these inequalities, cancer policies should systematically encompass the inequities component.

Earlier diagnosis of lung cancer

The purpose of this article is to review options for more rapid diagnosis of lung cancer at an earlier stage, thereby improving survival. These options include screening, allowing general practitioners to refer patients directly to low-dose computed tomography scan instead of a chest X-ray and the abolition of the "visitation filter", i.e. hospital doctors' ability to reject referrals from general practitioners without prior discussion with the referring doctor.

Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom

BACKGROUND

The National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two risk prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no study has compared the performance of lung cancer risk models in the UK.

METHODS

We analysed current and former smokers aged 40-80 years in the UK Biobank (N = 217,199), EPIC-UK (N = 30,813), and Generations Study (N = 25,777). We quantified model calibration (ratio of expected to observed cases, E/O) and discrimination (AUC).

RESULTS

Risk discrimination in UK Biobank was best for the Lung Cancer Death Risk Assessment Tool (LCDRAT, AUC = 0.82, 95% CI = 0.81-0.84), followed by the LCRAT (AUC = 0.81, 95% CI = 0.79-0.82) and the Bach model (AUC = 0.80, 95% CI = 0.79-0.81). Results were similar in EPIC-UK and the Generations Study. All models overestimated risk in all cohorts, with E/O in UK Biobank ranging from 1.20 for LLPv3 (95% CI = 1.14-1.27) to 2.16 for LLPv2 (95% CI = 2.05-2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF 2013 criteria classified 50.7% of future cases as screening eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.0%), LLPv3 (56.6%), and LLPv2 (53.7%).

CONCLUSION

In UK cohorts, the ability of risk prediction models to classify future lung cancer cases as eligible for screening was best for LCDRAT/LCRAT, very good for PLCOm2012, and lowest for LLPv2. Our results highlight the importance of validating prediction tools in specific countries.