Selection of eligible participants for screening for lung cancer using primary care data

Efficacy of Digital Outreach Strategies for Collecting Smoking Data: Pragmatic Randomized Trial

BACKGROUND

Tobacco smoking is an important risk factor for disease, but inaccurate smoking history data in the electronic medical record (EMR) limits the reach of lung cancer screening (LCS) and tobacco cessation interventions. Patient-generated health data is a novel approach to documenting smoking history; however, the comparative effectiveness of different approaches is unclear.

OBJECTIVE

We designed a quality improvement intervention to evaluate the effectiveness of portal questionnaires compared to SMS text message-based surveys, to compare message frames, and to evaluate the completeness of patient-generated smoking histories.

METHODS

We randomly assigned patients aged between 50 and 80 years with a history of tobacco use who identified English as a preferred language and have never undergone LCS to receive an EMR portal questionnaire or a text survey. The portal questionnaire used a "helpfulness" message, while the text survey tested frame types informed by behavior economics ("gain," "loss," and "helpfulness") and nudge messaging. The primary outcome was the response rate for each modality and framing type. Completeness and consistency with documented structured smoking data were also evaluated.

RESULTS

Participants were more likely to respond to the text survey (191/1000, 19.1%) compared to the portal questionnaire (35/504, 6.9%). Across all text survey rounds, patients were less responsive to the "helpfulness" frame compared with the "gain" frame (odds ratio [OR] 0.29, 95% CI 0.09-0.91; P<.05) and "loss" frame (OR 0.32, 95% CI 11.8-99.4; P<.05). Compared to the structured data in the EMR, the patient-generated data were significantly more likely to be complete enough to determine LCS eligibility both compared to the portal questionnaire (OR 34.2, 95% CI 3.8-11.1; P<.05) and to the text survey (OR 6.8, 95% CI 3.8-11.1; P<.05).

CONCLUSIONS

We found that an approach using patient-generated data is a feasible way to engage patients and collect complete smoking histories. Patients are likely to respond to a text survey using "gain" or "loss" framing to report detailed smoking histories. Optimizing an SMS text message approach to collect medical information has implications for preventative and follow-up clinical care beyond smoking histories, LCS, and smoking cessation therapy.

Invitation strategies and participation in a community-based lung cancer screening programme located in areas of high socioeconomic deprivation

INTRODUCTION

Although lung cancer screening is being implemented in the UK, there is uncertainty about the optimal invitation strategy. Here, we report participation in a community screening programme following a population-based invitation approach, examine factors associated with participation, and compare outcomes with hypothetical targeted invitations.

METHODS

Letters were sent to all individuals (age 55-80) registered with a general practice (n=35 practices) in North and East Manchester, inviting ever-smokers to attend a Lung Health Check (LHC). Attendees at higher risk (PLCOm2012NoRace score≥1.5%) were offered two rounds of annual low-dose CT screening. Primary care recorded smoking codes (live and historical) were used to model hypothetical targeted invitation approaches for comparison.

RESULTS

Letters were sent to 35 899 individuals, 71% from the most socioeconomically deprived quintile. Estimated response rate in ever-smokers was 49%; a lower response rate was associated with younger age, male sex, and primary care recorded current smoking status (adjOR 0.55 (95% CI 0.52 to 0.58), p<0.001). 83% of eligible respondents attended an LHC (n=8887/10 708). 51% were eligible for screening (n=4540/8887) of whom 98% had a baseline scan (n=4468/4540). Screening adherence was 83% (n=3488/4199) and lung cancer detection 3.2% (n=144) over 2 rounds. Modelled targeted approaches required 32%-48% fewer invitations, identified 94.6%-99.3% individuals eligible for screening, and included 97.1%-98.6% of screen-detected lung cancers.

DISCUSSION

Using a population-based invitation strategy, in an area of high socioeconomic deprivation, is effective and may increase screening accessibility. Due to limitations in primary care records, targeted approaches should incorporate historical smoking codes and individuals with absent smoking records.

Assessing, updating and utilising primary care smoking records for lung cancer screening

BACKGROUND

Lung cancer screening with low-dose computed tomography for high-risk populations is being implemented in the UK. However, inclusive identification and invitation of the high-risk population is a major challenge for equitable lung screening implementation. Primary care electronic health records (EHRs) can be used to identify lung screening-eligible individuals based on age and smoking history, but the quality of EHR smoking data is limited. This study piloted a novel strategy for ascertaining smoking status in primary care and tested EHR search combinations to identify those potentially eligible for lung cancer screening.

METHODS

Seven primary care General Practices in South Wales, UK were included. Practice-level data on missing tobacco codes in EHRs were obtained. To update patient EHRs with no tobacco code, we developed and tested an algorithm that sent a text message request to patients via their GP practice to update their smoking status. The patient's response automatically updated their EHR with the relevant tobacco code. Four search strategies using different combinations of tobacco codes for the age range 55-74+ 364 were tested to estimate the likely impact on the potential lung screening-eligible population in Wales. Search strategies included: BROAD (wide range of ever smoking codes); VOLUME (wide range of ever-smoking codes excluding "trivial" former smoking); FOCUSED (cigarette-related tobacco codes only), and RECENT (current smoking within the last 20 years).

RESULTS

Tobacco codes were not recorded for 3.3% of patients (n = 724/21,956). Of those with no tobacco code and a validated mobile telephone number (n = 333), 55% (n = 183) responded via text message with their smoking status. Of the 183 patients who responded, 43.2% (n = 79) had a history of smoking and were potentially eligible for lung cancer screening. Applying the BROAD search strategy was projected to result in an additional 148,522 patients eligible to receive an invitation for lung cancer screening when compared to the RECENT strategy.

CONCLUSION

An automated text message system could be used to improve the completeness of primary care EHR smoking data in preparation for rolling out a national lung cancer screening programme. Varying the search strategy for tobacco codes may have profound implications for the size of the population eligible for lung-screening invitation.

ERS International Congress 2022: highlights from the Thoracic Oncology Assembly

Thoracic malignancies are associated with a substantial public health burden. Lung cancer is the leading cause of cancer-related mortality worldwide, with significant impact on patients' quality of life. Following 2 years of virtual European Respiratory Society (ERS) Congresses due to the COVID-19 pandemic, the 2022 hybrid ERS Congress in Barcelona, Spain allowed peers from all over the world to meet again and present their work. Thoracic oncology experts presented best practices and latest developments in lung cancer screening, lung cancer diagnosis and management. Early lung cancer diagnosis, subsequent pros and cons of aggressive management, identification and management of systemic treatments' side-effects, and the application of artificial intelligence and biomarkers across all aspects of the thoracic oncology pathway were among the areas that triggered specific interest and will be summarised here.

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.

Defining the road map to a UK national lung cancer screening programme

Lung cancer screening with low-dose CT was recommended by the UK National Screening Committee (UKNSC) in September, 2022, on the basis of data from trials showing a reduction in lung cancer mortality. These trials provide sufficient evidence to show clinical efficacy, but further work is needed to prove deliverability in preparation for a national roll-out of the first major targeted screening programme. The UK has been world leading in addressing logistical issues with lung cancer screening through clinical trials, implementation pilots, and the National Health Service (NHS) England Targeted Lung Health Check Programme. In this Policy Review, we describe the consensus reached by a multiprofessional group of experts in lung cancer screening on the key requirements and priorities for effective implementation of a programme. We summarise the output from a round-table meeting of clinicians, behavioural scientists, stakeholder organisations, and representatives from NHS England, the UKNSC, and the four UK nations. This Policy Review will be an important tool in the ongoing expansion and evolution of an already successful programme, and provides a summary of UK expert opinion for consideration by those organising and delivering lung cancer screenings in other countries.

Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models

BACKGROUND

Lung cancer is the second most common cancer in incidence and the leading cause of cancer deaths worldwide. Meanwhile, lung cancer screening with low-dose CT can reduce mortality. The UK National Screening Committee recommended targeted lung cancer screening on Sept 29, 2022, and asked for more modelling work to be done to help refine the recommendation. This study aims to develop and validate a risk prediction model-the CanPredict (lung) model-for lung cancer screening in the UK and compare the model performance against seven other risk prediction models.

METHODS

For this retrospective, population-based, cohort study, we used linked electronic health records from two English primary care databases: QResearch (Jan 1, 2005-March 31, 2020) and Clinical Practice Research Datalink (CPRD) Gold (Jan 1, 2004-Jan 1, 2015). The primary study outcome was an incident diagnosis of lung cancer. We used a Cox proportional-hazards model in the derivation cohort (12·99 million individuals aged 25-84 years from the QResearch database) to develop the CanPredict (lung) model in men and women. We used discrimination measures (Harrell's C statistic, D statistic, and the explained variation in time to diagnosis of lung cancer [R²_D]) and calibration plots to evaluate model performance by sex and ethnicity, using data from QResearch (4·14 million people for internal validation) and CPRD (2·54 million for external validation). Seven models for predicting lung cancer risk (Liverpool Lung Project [LLP]_v2, LLP_v3, Lung Cancer Risk Assessment Tool [LCRAT], Prostate, Lung, Colorectal, and Ovarian [PLCO]_M2012, PLCO_M2014, Pittsburgh, and Bach) were selected to compare their model performance with the CanPredict (lung) model using two approaches: (1) in ever-smokers aged 55-74 years (the population recommended for lung cancer screening in the UK), and (2) in the populations for each model determined by that model's eligibility criteria.

FINDINGS

There were 73 380 incident lung cancer cases in the QResearch derivation cohort, 22 838 cases in the QResearch internal validation cohort, and 16 145 cases in the CPRD external validation cohort during follow-up. The predictors in the final model included sociodemographic characteristics (age, sex, ethnicity, Townsend score), lifestyle factors (BMI, smoking and alcohol status), comorbidities, family history of lung cancer, and personal history of other cancers. Some predictors were different between the models for women and men, but model performance was similar between sexes. The CanPredict (lung) model showed excellent discrimination and calibration in both internal and external validation of the full model, by sex and ethnicity. The model explained 65% of the variation in time to diagnosis of lung cancer R²_D in both sexes in the QResearch validation cohort and 59% of the R²_D in both sexes in the CPRD validation cohort. Harrell's C statistics were 0·90 in the QResearch (validation) cohort and 0·87 in the CPRD cohort, and the D statistics were 2·8 in the QResearch (validation) cohort and 2·4 in the CPRD cohort. Compared with seven other lung cancer prediction models, the CanPredict (lung) model had the best performance in discrimination, calibration, and net benefit across three prediction horizons (5, 6, and 10 years) in the two approaches. The CanPredict (lung) model also had higher sensitivity than the current UK recommended models (LLP_v2 and PLCO_M2012), as it identified more lung cancer cases than those models by screening the same amount of individuals at high risk.

INTERPRETATION

The CanPredict (lung) model was developed, and internally and externally validated, using data from 19·67 million people from two English primary care databases. Our model has potential utility for risk stratification of the UK primary care population and selection of individuals at high risk of lung cancer for targeted screening. If our model is recommended to be implemented in primary care, each individual's risk can be calculated using information in the primary care electronic health records, and people at high risk can be identified for the lung cancer screening programme.

FUNDING

Innovate UK (UK Research and Innovation).

TRANSLATION

For the Chinese translation of the abstract see Supplementary Materials section.

Utilisation of primary care electronic patient records for identification and targeted invitation of individuals to a lung cancer screening programme

Lung cancer screening (LCS) eligibility is largely determined by tobacco consumption. Primary care smoking data could guide LCS invitation and eligibility assessment. We present observational data from the SUMMIT Study, where individual self-reported smoking status was concordant with primary care records in 75.3%. However, 10.3% demonstrated inconsistencies between historic and most recent smoking status documentation. Quantified tobacco consumption was frequently missing, precluding direct LCS eligibility assessment. Primary care recorded "ever-smoker" status, encompassing both recent and historic documentation, can be used to target LCS invitation. Identifying those with missing or erroneous "never-smoker" smoking status is crucial for equitable invitation to LCS.