Association Between the Number of Prescription Medications and Postoperative Outcomes of Veterans with Clinical Stage I Non-small Cell Lung Cancer

S. Tohmasi¹, M. W. Schoen², D. B.. Eaton³, M. P. Subramanian¹, B. Heiden¹, N. E. Rossetti¹, S. Chang¹, Y. Yan¹, A. A. Baumann¹, M. R.. Patel², D. Gopukumar⁴, D. Kreisel¹, R. G.. Nava¹, P. Samson¹, B. Meyers¹, B. D. Kozower⁵, V. Puri^6
1Washington University in St. Louis, St. Louis, Missouri ²VA St. Louis Health Care System, St. Louis, Missouri ³VA St. Louis Health Care System, St Louis, Missouri ⁴Saint Louis University School of Medicine, St. Louis, Missouri ⁵Washington University School of Medicine, St. Louis, Missouri ⁶Washington University School of Medicine, Saint Louis, Missouri

Purpose: Comorbidity assessment is critical when evaluating surgical patients with non-small cell lung cancer (NSCLC). Unfortunately, coding inaccuracies may limit the applicability of comorbidity indices for perioperative risk stratification. We evaluated the relationship between prescription medications, a potentially more accurate comorbidity measure, and outcomes in veterans with early-stage NSCLC undergoing resection.

Methods: Using a unique Veterans Health Administration dataset including prospectively maintained pharmacy records and additional treatment-related variables that were manually abstracted through extensive chart reviews, we compiled a cohort of veterans with clinical stage I NSCLC receiving definitive surgical treatment between 10/01/2006 and 09/30/2016. Using multivariable regression models, we assessed the relationship between the number of unique medications prescribed in the year prior to surgery and composite outcomes including mortality and major complications (pneumonia, empyema, myocardial infarction, respiratory failure, renal failure, or stroke) at 30- and 90-days as well as overall survival (OS). The top 225 most frequently prescribed medications were categorized using the Anatomical Therapeutic Chemical (ATC) Level 1 classification system. Multivariable regression modeling was used to assess the relationship between each ATC Level 1 medication class and composite outcomes as well as the number of prescription medications within each ATC Level 1 class and composite outcomes.

Results: Among 9,741 veterans, a median (IQR) of 11 (7, 17) unique medication prescriptions were filled in the year prior to surgery. Higher number of medications was associated with increased age, higher Charlson Comorbidity Index (CCI), increased body mass index, female sex, black race, living closer to the hospital, undergoing a sublobar resection, and more recent year of surgery. After controlling for relevant patient, tumor, and treatment factors, including CCI, number of medications was independently and strongly associated with 30- and 90-day composite outcomes [adjusted OR (95% CI): 1.016 (1.007, 1.024); 1.016 (1.008, 1.024), respectively]. Number of medications was also associated with worse OS [adjusted HR: 1.017 (1.013, 1.021)]. Within a subgroup of patients with a high comorbidity burden (CCI of 6-8), higher number of prescription medications was associated with increased risk of death (Figure 1). Patients prescribed medications from the ATC Respiratory System class had significantly worse 30- and 90-day composite outcomes compared to patients not prescribed these medications [adjusted OR (95% CI): 1.347 (1.160, 1.565); 1.362 (1.177, 1.577), respectively]. There was a trend towards worse 30- and 90-day composite outcomes and OS with each additional medication prescribed from the ATC Respiratory and Nervous System classes (Table 1).

Conclusion: The number of prescription medications is independently associated with short- and long-term outcomes in patients undergoing surgical resection for NSCLC, even when accounting for covariates including comorbidities. Medications may be a simple and reliable tool to assess comorbidities and surgical risk in the perioperative period for patients with early-stage NSCLC.

Identify the source of the funding for this research project: This research was funded in part by US Department of Veterans Affairs Merit Award 1I01HX002475-01A2.