Integrating tobacco treatment into lung cancer screening practices: Study protocol for the Screen ASSIST randomized clinical trial

Neil, Jordan M.; Marotta, Caylin; Gonzalez, Irina; Chang, Yuchiao; Levy, Douglas E.; Wint, Amy J.; Harris, K. A.; Hawari, Saif; Noonan, Elise; Styklunas, Grace M; Crute, Sydney; Howard, Sydney; Sheppard, Joanne; Lennes, Inga T.; Jacobson, Francine L.; Flores, Efrén J.; Haas, Jennifer S.; Park, Elyse R.; Rigotti, Nancy A.

doi:10.1016/j.cct.2021.106586

Cited by 6 publications

(2 citation statements)

References 27 publications

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“…To determine how best to integrate smoking cessation treatment in the lung screening setting, the National Cancer Institute initiated the Smoking Cessation at Lung Examination (SCALE) collaboration [7] that is comprised of eight clinical trials, including Georgetown's Lung Screening, Tobacco, and Health (LSTH) trial (NCT03200236) [8][9][10][11][12]. To date, several SCALE-related papers have assessed predictors of enrollment and retention [7,10,13].…”

Section: Predictors Of Enrollment and Retention In Smoking Cessation ...mentioning

confidence: 99%

Engaging Patients in Smoking Cessation Treatment within the Lung Cancer Screening Setting: Lessons Learned from an NCI SCALE Trial

et al. 2022

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Offering smoking cessation treatment at lung cancer screening (LCS) will maximize mortality reduction associated with screening, but predictors of treatment engagement are not well understood. We examined participant characteristics of engagement in an NCI SCALE cessation trial. Eligible LCS patients (N = 818) were randomized to the Intensive arm (8 phone counseling sessions +8 weeks of nicotine replacement therapy (NRT)) vs. Minimal arm (3 sessions + 2 weeks of NRT). Engagement was measured by number of sessions completed (none, some, or all) and NRT mailed (none vs. any) in each arm. In the Intensive arm, those with ≥some college (OR = 2.1, 95% CI = 1.1, 4.0) and undergoing an annual scan (OR = 2.1, 95% CI = 1.1, 4.2) engaged in some counseling vs. none. Individuals with higher nicotine dependence were more likely (OR = 2.8, 95% CI = 1.3, 6.2) to request NRT. In the Minimal arm, those with higher education (OR = 2.1, 95% CI = 1.1, 3.9) and undergoing an annual scan (OR = 2.0, 95% CI = 1.04, 3.8) completed some sessions vs. none. Requesting NRT was associated with more pack-years (OR = 1.9, 95% CI = 1.1, 3.5). Regardless of treatment intensity, additional strategies are needed to engage those with lower education, less intensive smoking histories, and undergoing a first scan. These efforts will be important given the broader 2021 LCS guidelines.

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Section: Predictors Of Enrollment and Retention In Smoking Cessation ...mentioning

confidence: 99%

Engaging Patients in Smoking Cessation Treatment within the Lung Cancer Screening Setting: Lessons Learned from an NCI SCALE Trial

et al. 2022

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“…We focus on the integration of the NLP to extract smoking behavior into the extract, transform, load (ETL) process in the local EDW4R at the University of Florida Health (UF Health). These data are not reliably collected in structured form, however, their availability as structured data allows computation of clinical and research-relevant information such as identifying patients who fit guidelines for lung cancer screening [28][29][30][31][32][33] and recruitment for cancer-related clinical trials [34][35][36][37]. We explain how this architecture can be generalized for future NLP models that extract different concepts.…”

Section: Introductionmentioning

confidence: 99%

Enhancing an enterprise data warehouse for research with data extracted using natural language processing

Magoc,

Everson,

Harle

2023

J. Clin. Trans. Sci.

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Objective: This study aims to develop a generalizable architecture for enhancing an enterprise data warehouse for research (EDW4R) with results from a natural language processing (NLP) model, which allows discrete data derived from clinical notes to be made broadly available for research use without need for NLP expertise. The study also quantifies the additional value that information extracted from clinical narratives brings to EDW4R. Materials and methods: Clinical notes written during one month at an academic health center were used to evaluate the performance of an existing NLP model and to quantify its value added to the structured data. Manual review was utilized for performance analysis. The architecture for enhancing the EDW4R is described in detail to enable reproducibility. Results: Two weeks were needed to enhance EDW4R with data from 250 million clinical notes. NLP generated 16 and 39% increase in data availability for two variables. Discussion: Our architecture is highly generalizable to a new NLP model. The positive predictive value obtained by an independent team showed only slightly lower NLP performance than the values reported by the NLP developers. The NLP showed significant value added to data already available in structured format. Conclusion: Given the value added by data extracted using NLP, it is important to enhance EDW4R with these data to enable research teams without NLP expertise to benefit from value added by NLP models.

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Update on the National Cancer Institute’s Smoking Cessation at Lung Examination Collaboration Trials

Lang

2024

CHEST

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Integrating tobacco treatment into lung cancer screening practices: Study protocol for the Screen ASSIST randomized clinical trial

Cited by 6 publications

References 27 publications

Engaging Patients in Smoking Cessation Treatment within the Lung Cancer Screening Setting: Lessons Learned from an NCI SCALE Trial

Engaging Patients in Smoking Cessation Treatment within the Lung Cancer Screening Setting: Lessons Learned from an NCI SCALE Trial

Enhancing an enterprise data warehouse for research with data extracted using natural language processing

Update on the National Cancer Institute’s Smoking Cessation at Lung Examination Collaboration Trials

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