Frailty in lung cancer hospitalizations: Identifying critical predictors for improved patient outcomes.

11170 Background: Frailty is a condition primarily characterized by deficits in multiple health-related factors, which, when combined with the presence of disease, lead to poor outcomes. Lung cancer patients are no exception; those who are frail tend to experience worse clinical outcomes and increased healthcare resource utilization. Identifying predictors of frailty can help guide targeted interventions and improve outcomes for this patient subset, which is the primary aim of our study. Methods: We conducted a cross-sectional analysis of the National Inpatient Sample database (2016-2020) to evaluate predictors of frailty in lung cancer hospitalizations. Frailty was defined using ICD-10 codes. Chi-square test was used to compare categorical variables and all weighted analyses were performed to adjust for the overall population and the complexity of the dataset, with significance set at p<0.05. Results: 1,120,440 lung cancer hospitalizations were identified and 0.27% (N=3,025) met criteria for frailty. Frail patients were older than non-frail patients (mean age: 79.6 years vs 64.4 years). 71% of frail patients were White, 15% were Black, and 8% were Hispanic, compared to 62% White, 19% Black, and 10% Hispanic in the non-frail group (p=0.001). Majority of frail patients were covered by Medicare (84%) and Medicaid (5%), compared to 59% on Medicare, 14% on Medicaid, and 18% on private insurance in the non-frail group (p<0.001). Geographically, 50% of frail patients were from the South and 20% from the Midwest (p=0.03). Chi-square identified coronary artery disease (CAD), congestive heart failure (CHF), chronic kidney disease (CKD), underweight, sarcopenia, and dyslipidemia as predictors of frailty. However, a negative association was observed between obesity and frailty (Table). Conclusions: Chronic comorbidities and decreased muscle mass remain significant predictors of frailty in lung cancer, with regional and socio-economic variations highlighting potential care disparities. The lower incidence of frailty in obese patients raises the question of the ‘obesity paradox’ in frailty, which warrants further investigation to explore possible causation. Identifying and targeting early predictors of frailty could lead to improved patient outcomes and more efficient healthcare utilization. Predictors of frailty in lung cancer. Predictors Frailty Present (%) Frailty Absent (%) p-value CAD 42.09 34.24 <0.001 CHF 47.14 36.22 <0.001 BMI < 18.5 12.96 3.57 <0.001 Obesity 7.74 15.89 <0.001 Dyslipidemia 41.08 35.18 0.002 CKD 38.55 30.56 <0.001 Sarcopenia 0.17 0.02 0.006