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Participant selection for lung cancer screening using primary care electronic medical records: The Catalan scenario
Mercè Marzo-Castillejo1, Juanjo Mascort Roca2, Albert Brau Tarrida3
1Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; Unitat de Suport a la Recerca, Àmbits d'Atenció Primària Metropolitana Sud i Penedès, Institut Català de la Salut, L'Hospitalet de Llobregat, Spain; Grupo de investigación Càncer y Atención Primaria de la IDIAPJGol, Spain.
Objective:
To assess the feasibility of using primary care electronic health records (EHRs) and the PLCOm2012noRace lung cancer (LC) risk prediction model to identify high-risk individuals in the Catalan population.
Design:
Population-based cohort study. SITE: Catalonia, using data from the Information System for the Improvement of Research in Primary Care (SIDIAP), which covers approximately 80% of the population.
Participants:
A total of 1,998,282 individuals aged 55-79 years were initially considered, with data spanning from 2012 to 2023. After applying inclusion and exclusion criteria based on smoking status, 24,294 individuals with complete smoking history were included.
Interventions:
Estimation of LC risk using the PLCOm2012noRace model.
Main Measurements:
Variables: age, smoking history, body mass index, educational level, chronic obstructive pulmonary disease, personal history of cancer, and family history of LC. A 6-year risk threshold of ≥2.6% was used to define eligibility for LC screening.
Results:
Overall, 18.6% of individuals exceeded the risk threshold, with higher prevalence in men (21.4%) and those aged 60-79 years (23.8%). Current smokers had the highest risk (25.7%), which decreased with time since quitting. On average, high-risk individuals could have been identified 4.29 years before.
Conclusions:
The use of EHRs and the PLCOm2012noRace model is a feasible approach to identify individuals at high risk of LC in the Catalan population. However, missing or outdated data, especially regarding smoking intensity, may limit the predictive performance. These findings highlight the need for systematic and timely data collection to support effective risk-based screening strategies.
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