OBJECTIVES: Smoking status is known to be a significant risk factor and shortening the survival in NSCLC, yet it is commonly registered as unstructured data in the medical records, complicating its use in real-world evidence (RWE) studies. As part of a larger data collection, we identified smoking status of NSCLC patients from patient texts, analyzed the overall survival (OS) and healthcare resource utilization (HCRU) of NSCLC patients by smoking status.

METHODS: In the study, we included electronic health records of patients diagnosed with NSCLC between January 2013 to August 2023 in Helsinki University Hospital, Finland. Smoking status was identified from patient texts using a pretrained machine learning (ML) classification algorithm. All-cause specialized care resource use and costs (outpatient contacts, ER visits, and hospital admissions) and OS were analyzed by smoking status during the NSCLC follow-up.

RESULTS: Out of the 6 248 identified NSCLC patients, 93% had a known smoking status. Smokers (N= 2 369, 41%) had first-year follow-up costs of 26 146.63€ (confidence interval, CI 95% 25 261.66- 27 031.60), ex-smokers (N=2 721, 47%) 25 904.96 € (CI95% 25 066.16-26 743.76), and nonsmokers (N=720, 12%) 25 220.73 € (CI95% 23 277.51- 27 163.95), respectively. OS was 11.0 months for the smokers (CI95% 10.0-12.2), 12.9 months for the ex-smokers (CI95% 11.8-14.2), and 21.7 months for the nonsmokers (CI95% 18.8-26.7).

CONCLUSIONS: Smoking status was successfully identified from patient texts for a vast majority of the patients using the ML algorithm. All-cause HCRU was nearly the same regardless of the smoking status whereas the nonsmokers had increased survival compared to the ex-smokers and smokers.