Safety net hospital risk model demonstrates stronger, population-specific applicability in characterizing lung cancer risk

Adriana A Rodriguez Alvarez; Benjamin Crosby; Sarah Singh; Janice Weinberg; Nicole Byrne; Aniket Vazirani; Kei Suzuki

doi:10.21037/tcr-23-2304

Safety net hospital risk model demonstrates stronger, population-specific applicability in characterizing lung cancer risk

Transl Cancer Res. 2024 Apr 30;13(4):1596-1605. doi: 10.21037/tcr-23-2304. Epub 2024 Apr 12.

Authors

Adriana A Rodriguez Alvarez¹, Benjamin Crosby¹, Sarah Singh², Janice Weinberg³, Nicole Byrne¹, Aniket Vazirani⁴, Kei Suzuki^{5

6}

Affiliations

¹ Department of Clinical Research, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA.
² Department of Surgery, University of California Davis, Sacramento, CA, USA.
³ Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
⁴ Department of Surgery, Boston Medical Center, Boston, MA, USA.
⁵ Department of Thoracic Surgery, Inova Fairfax Medical Campus, Falls Church, VA, USA.
⁶ Clinical Administration, Inova Fairfax Medical Campus, Falls Church, VA, USA.

Abstract

Background: Determining lung cancer (LC) risk using personalized risk stratification may improve screening effectiveness. While the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) is a well-established stratification model for LC screening, it was derived from a predominantly Caucasian population and its effectiveness in a safety net hospital (SNH) population is unknown. We have developed a model more tailored to the SNH population and compared its performance to the PLCO model in a SNH setting.

Methods: Retrospective dataset was compiled from patients screened for LC at SNH from 2015 to 2019. Descriptive statistics were calculated using the following variables: age, sex, race, education, body mass index (BMI), smoking history, personal cancer history, family LC history, chronic obstructive pulmonary disease (COPD), and emphysema. Variables distribution was compared using t- and chi-square tests. LC risk scores were calculated using SNH and PLCO models and categorized as low (scores <0.65%), moderate (0.65-1.49%), and high (>1.5%). Linear regression was applied to evaluate the relationship between models and covariates.

Results: Of 896 individuals, 38 were diagnosed with LC. Data reflected the SNH patient demographics, which predominantly were African American (53.5%), current smokers (69.9%), and with emphysema (70.1%). Among the non-LC cohort, SNH model most frequently categorized patients as low risk, while PLCO model most frequently classified patients as moderate risk. Among the LC cohort, there was no significant difference between mean scores or risk stratification. SNH model showed 92.1% sensitivity and 96.8% specificity while PLCO model showed 89.4% sensitivity and 26.1% specificity. Emphysema demonstrated a strong association in SNH model (P<0.001) while race showed no relation.

Conclusions: SNH model demonstrated greater specificity for characterizing LC risk in a SNH population. The results demonstrated the importance of study sample representation when identifying risk factors in a stratification model.

Keywords: Diversity population; Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial model (PLCO model); emphysema; low-dose computed tomography (LDCT); safety net hospital model (SNH model).